Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)  

Energy usage in robotic applications is rapidly increasing as industrial robot installations grow. This research introduces a novel approach, using the rapidly exploring random tree (RRT)-based scheme for optimizing the robot’s motion planning and minimizing energy consumption. Sampling-based algorithms for path planning, such as RRT and its many other variants, are widely used in robotic motion planning due to their efficiency in solving complex high-dimensional problems efficiently. However, standard versions of these algorithms cannot guarantee that the generated trajectories are always optimum and mostly ignore the energy consumption in robotic applications. This paper proposes an energy-efficient industrial robotics motion planning approach using the novel flight cost-based RRT (FC-RRT*) algorithm in pick-and-place operation to generate nodes in a predetermined direction and then calculate energy consumption using the circle point method. After optimizing the motion trajectory, power consumption is computed for the rotary axes of a six degree of freedom (6DOF) serial type of industrial robot using the work–energy hypothesis for the rotational motion of a rigid body. The results are compared to the traditional RRT and RRT* (RRT-star) algorithm as well as the kinematic solutions. The experimental results of axis indexing tests indicate that by employing the sampling-based FC-RRT* algorithm, the robot joints consume less energy (1.6% to 16.5% less) compared to both the kinematic solution and the conventional RRT* algorithm.

DOI： 10.3390/en16196910

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)  

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/docs60977.2023.10294547

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Authorship：Last author,　Corresponding author   Publishing type：Research paper (scientific journal)   Publisher：Institute of Electrical and Electronics Engineers (IEEE)  

This paper studies the solution procedure to the problem of conflict-free route planning for automated guided vehicles for just-in-time delivery to minimize the earliness and tardiness of a specified delivery time while minimizing the total completion time. The characteristic of the problem is that a vehicle can have an idle time at any nodes considering dispatching of dynamic task arrivals. This problem is modeled by a time-space network where the pickup and delivery tasks are dynamically scheduled. A heuristic solution procedure is developed to derive a conflict-free routing problem such that earliness and tardiness penalties are minimized while the total completion time of each task is also minimized under dynamic task arrivals. The effectiveness of the proposed method is demonstrated by comparing the performance with Gurobi from computational results. The results show that the proposed method can obtain a better solution that is close to an optimal solution with significantly shorter computational time than that derived from Gurobi. The trade-off relationship between earliness/tardiness penalties and the total completion time is investigated in various situations. The effects of the interval of task assignment on the performance of earliness/tardiness and the total completion time are studied. The computational results show the conditions when the trade-off relationship can be obtained. <italic>Note to Practitioners</italic>&#x2014;The multiple AGVs are used in public transportation and social systems with rapid development of artificial intelligence and optimization methods. The benefits of AGV affect the reduction of congestion, traffic police, legal services, and economic savings in the manufacturing and logistics industries. In order to satisfy the customer&#x2019;s demands, it is requested to deliver the products just-in-time for the desired time without decreasing the total system performance. This paper presents the dispatching and conflict-free routing of AGVs for just-in-time transportation while minimizing the total completion time of the tasks. In many real factories with multiple production processes, the shortest time transportation may sometimes cause the congestions of AGVs waiting for handling or overstocked. Just-in-time transportation is required not only to reduce inventories but also to supply only when it is needed. Computational results show the situations when the just-in-time delivery and the minimization of the total completion time can be achieved.

DOI： 10.1109/tase.2022.3194082

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)  

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)  

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Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (other academic)  

DOI： 10.11509/isciesci.67.3_116

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

The use of multiple mobile robots has grown significantly over the past few years in logistics, manufacturing and public services. Conflict–free route planning is one of the major research challenges for such mobile robots. Optimization methods such as graph search algorithms are used extensively to solve route planning problems. Those methods can assure the quality of solutions, however, they are not flexible to deal with unexpected situations. In this article, we propose a flexible route planning method that combines the reinforcement learning algorithm and a graph search algorithm for conflict–free route planning problems for multiple robots. In the proposed method, Q–learning, a reinforcement algorithm, is applied to avoid collisions using off–line learning with a limited state space to reduce the total learning time. Each vehicle independently finds the shortest route using the A* algorithm, and Q–learning is used to avoid collisions. The effectiveness of the proposed method is examined by comparing it with conventional methods in terms of computation time and the quality of solutions. Computational results show that for dynamic transportation problems, the proposed method can generate the solutions with approximately 10% of the computation time compared to the conventional Q–learning approach. We found that the required computation time is linearly increased with respect to the number of vehicles and nodes in the problems.

DOI： 10.3390/app13031879

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Language：Japanese   Publisher：The Japan Society of Mechanical Engineers  

The objective of this study is to use the open-source robot control software ROS, which can reuse code among multiple types of robots, to automatically generate the motions that substitute the robot arm for stocking works in convenience stores, etc. . Currently, it is common to use a single planner when generating actions, however, by switching between planners at waypoints provided by mapping the surrounding environment, we confirm that it is possible to reduce the time to complete actions. The experiment has been conducted with VS-060 from DENSO to verify the generated motions.

DOI： 10.1299/jsmemsd.2023.104

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Language：Japanese   Publisher：The Japan Society of Mechanical Engineers  

Production scheduling problem is one of the typical optimization problems and it has been utilized as a practical problem to be solved in the manufacturing industry. We consider a simultaneous optimization of product input sequence and workforce scheduling for multi-stage, multi-item cell production systems. The optimization problem is formulated as a Resource Constrained Project Scheduling Problem (RCPSP). RCPSP has been studied as a project scheduling problem considering resources such as workers. However, the traveling time of workers to their work stations and the travel time of parts to each work station have not been taken into account in the conventional study. We propose a RCPSP formulation for the scheduling problem for multi-stage, multi-product cell production lines with workforce scheduling considering the travel time of operations and parts. The exact solution of the RCPSP is based on Gurobi, a mathematical programming solver. The effectiveness of the derived solution is confirmed via simulation software (plant simulation).

DOI： 10.1299/jsmemsd.2023.212

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)  

In recent years, automated multiple mobile robots are introduced for transporting loads and inspecting final products in factories to reduce the burden of human labor shortage. Mobile robots are required to develop automated systems that can make decisions as flexibly like human operators. Most conventional route planning problems for mobile robots have been utilizing either, optimization methods or learning methods. However, those conventional methods have a difficulty in applying it to the conflict-free route planning problems with a large number of states with three dimensional environment. We propose a method that combines deep reinforcement learning and graph search methods. In the proposed method, the routing is firstly determined by a graph search algorithm, and Deep Q-Network (DQN). A deep reinforcement learning method is used to avoid collisions. A route planning problem in a three dimensional environment is successfully solved by using DQN that can process multi dimensional states. The proposed method is also applied to the multiple drones route planning problem. The performance of the proposed method is compared with that of the optimization methods. As a result, it was found that a near optimal route planning was obtained in approximately 6% of the computation time required to find the optimal solution.

DOI： 10.1109/CASE56687.2023.10260638

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Supply chain digital twin has emerged as a powerful tool in studying the behavior of an actual supply chain. However, most studies in the field of supply chain digital twin have only focused on what-if analysis that compares several different scenarios. This study proposes a data-driven evolutionary algorithm to efficiently solve the service constrained inventory optimization problem using historical data that generated by supply chain digital twins. The objective is to minimize the total costs while satisfying the required service level for a supply chain. The random forest algorithm is used to build surrogate models which can be used to estimate the total costs and service level in a supply chain. The surrogate models are optimized by an ensemble approach-based differential evolution algorithm which can adaptively use different search strategies to improve the performance during the computation process. A three-echelon supply chain digital twin on the geographic information system (GIS) map in real-time is used to examine the efficiency of the proposed method. The experimental results indicate that the data-driven evolutionary algorithm can reduce the total costs and maintain the required service level. The finding suggests that our proposed method can learn from the historical data and generate better inventory policies for a supply chain digital twin.

DOI： 10.1007/s40747-023-01179-0

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)  

In recent years, multiple industrial robots have been introduced into factories, and they are required to perform complex tasks. In order to improve work efficiency, it is also important to generate a collision-free trajectory in a limited space where the robots interfere with each other. However, in the actual collision avoidance motion, the robot may collide due to motion delay or disturbances. In particular, collisions between industrial robots and humans have caused fatal accidents in the past, and a collision avoidance motion planning method for multiple robot arms is required to avoid such accidents. Previous research has achieved a real time avoidance method for multiple robot arms using a graph search algorithm and Q-learning which is one of the reinforcement learning method. In this paper, we propose a collision avoidance motion planning method using DQN (Deep Q-Network) instead of Q-learning. By using DQN, it is possible to deal with the case where the input is an image and continuous state space. We compare the performance of the proposed method and the conventional method by simulation experiments. As a result, the proposed method shows its usefulness in the cases where the conventional method could not avoid collisions.

DOI： 10.1109/CASE56687.2023.10260329

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/CASE56687.2023.10260452

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Other Link： https://dblp.uni-trier.de/db/conf/case/case2023.html#KawabeNLF23

Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Elsevier BV  

DOI： 10.1016/j.ifacol.2023.10.1145

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)  

A multi-objective optimization algorithm is proposed in this paper for motion planning and layout design in robotic cellular manufacturing systems. The sequence-pair is used to represent the layout of a robotic cell, which can avoid the overlapping of modules. For each layout, the robot motion planning using Rapidly exploring Random Trees (RRT) is conducted to compute the total operation time. A non-dominated Sorting Genetic Algorithm II (NSGA-II) is used to minimize the layout area and operation time. The proposed method is applied to a 6-DOF (Degree of Freedom) robot manipulator, Niryo Ned. In the experiments, a Pareto set is obtained. The experimental results suggest the tradeoff relationship between the operation time and layout area. The findings show that the proposed method can efficiently solve the optimal motion planning and layout design problem in robotic cellular manufacturing systems.

DOI： 10.1109/IEEM55944.2022.9989566

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Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)  

With the prevalence of COVID-19 infection, the use of mathematical models for infectious diseases has attracted considerable attention. In a previous study, human behavioral strategies are represented using evolutionary game theory and integrated with the SIR model of the COVID-19 epidemic. However, actual COVID-19 infection has an incubation period. In addition, due to viral mutations, the number of infected people is higher in the second and subsequent epidemics than in the first one. In this study, the previous study that uses evolutionary game theory to represent human behavioral selection in the SIR model is extended to the SEIR model. Then, considering the viral mutations, the relationship between the number of infected people and the risk of infection is formulated. The simulation results indicate that, by increasing the infection rate as the infection spread, the maximum number of infected people at each infection peak continued to increase until the maximum number of simultaneously infected people is reached. This finding indicates that the number of infected people is affected by the higher infection rate caused by the virus mutation.

DOI： 10.1109/IEEM55944.2022.9989989

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)  

In recent years, robots have been introduced to production sites due to the shortage of manpower. As a result, there is a need to reduce the costs of robot systems. Conventional research has focused on the optimization of robot configuration and motion planning, In this study, we propose a decision support system for the selection of equipment for robots, hands, and workpieces in a transfer system. Given information on the length and payload of each element, the problem of selecting equipment that minimizes the sum of the costs of these three elements is formulated as an integer programming problem. The system considers various conditions necessary for equipment selection, such as physical constraints and reachability of the robot end-effector to the workpiece, and expresses the grasp availability of the workpiece, payload, and reachability of the robot as inequality constraints. Using the proposed system, the optimal solution was obtained in the computational experiments.

DOI： 10.1109/IEEM55944.2022.9989780

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)  

We propose an optimization system for motion planning of robot arms using Petri Nets. The proposed optimization system consists of four sub-systems consisting of automatic generation of Petri Nets from event log data, optimization system of firing sequence of derived Petri Net model, verification system using Petri Net simulation, and an automatic program generation system. The model generation system automatically generates the Petri Net model from the event logs using process mining. The Petri Net verification system is used to check the consistency of the generated Petri Nets to obtain the optimal firing sequence for robot motion. The motion planning algorithm generates motion programs for robots based on optimal firing sequences. The proposed optimization model is applied to a 6-DOF (Degree of Freedom) robot manipulator (Niryo Ned). Experimental results show that the proposed method achieves motion plan optimization for the pick-and-place operation with different robot configurations.

DOI： 10.1109/IROS47612.2022.9982201

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Authorship：Corresponding author   Publishing type：Research paper (scientific journal)  

In recent years, scheduling optimization has been utilized in production systems. To construct a suitable mathematical model of a production scheduling problem, modeling techniques that can automatically select an appropriate objective function from historical data are necessary. This paper presents two methods to estimate weighting factors of the objective function in the scheduling problem from historical data, given the information of operation time and setup costs. We propose a machine learning-based method, and an inverse optimization-based method using the input/output data of the scheduling problems when the weighting factors of the objective function are unknown. These two methods are applied to a multi-objective parallel machine scheduling problem and a real-world chemical batch plant scheduling problem. The results of the estimation accuracy evaluation show that the proposed methods for estimating the weighting factors of the objective function are effective.

DOI： 10.3390/app12199472

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Language：Japanese   Publisher：THE INSTITUTE OF SYSTEMS, CONTROL AND INFORMATION ENGINEERS  

DOI： 10.11509/isciesci.66.7_277

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Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：MDPI  

The implementation of Industry 5.0 necessitates a decrease in the energy consumption of industrial robots. This research investigates energy optimization for optimal motion planning for a dual-arm industrial robot. The objective function for the energy minimization problem is stated based on the execution time and total energy consumption of the robot arm configurations in its workspace for pick-and-place operation. Firstly, the PID controller is being used to achieve the optimal parameters. The parameters of PID are then fine-tuned using metaheuristic algorithms such as Genetic Algorithms and Particle Swarm Optimization methods to create a more precise robot motion trajectory, resulting in an energy-efficient robot configuration. The results for different robot configurations were compared with both motion planning algorithms, which shows better compatibility in terms of both execution time and energy efficiency. The feasibility of the algorithms is demonstrated by conducting experiments on a dual-arm robot, named as duAro. In terms of energy efficiency, the results show that dual-arm motions can save more energy than single-arm motions for an industrial robot. Furthermore, combining the robot configuration problem with metaheuristic approaches saves energy consumption and robot execution time when compared to motion planning with PID controllers alone.

DOI： 10.3390/en15062074

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Authorship：Corresponding author   Language：Japanese  

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Authorship：Last author,　Corresponding author   Publishing type：Part of collection (book)  

Scheduling problems are widely used in recent production systems. In order to create an appropriate modeling of a production scheduling problem more effectively, it is necessary to build a mathematical modeling technique that automatically generates an appropriate schedule instead of an actual human operator. This paper addresses two types of model estimation methods for weighting factors in the multi-objective scheduling problems from input-output data. The one is a machine learning-based method, and the other one is the parameter estimation method based on an inverse optimization. These methods are applied to multi-objectives parallel machine scheduling problems. The accuracy of the proposed machine learning and inverse optimization methods is evaluated. A surrogate model that learns input-output data is proposed to reduce the computational efforts. Computational results show the effectiveness of the proposed method for weighting factors in the objective function from the optimal solutions.

DOI： 10.1016/B978-0-323-85159-6.50285-2

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Authorship：Corresponding author   Publishing type：Research paper (scientific journal)  

DOI： 10.1007/s10479-021-04506-1

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Publishing type：Part of collection (book)   Publisher：Springer Nature Switzerland  

DOI： 10.1007/978-3-031-16411-8_27

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Authorship：Corresponding author   Publishing type：Research paper (international conference proceedings)  

It has been proposed a method of constructing a supply chain in the cyber space as a multi-agent system by linking enterprise agents which is generated from each model of the component enterprise in a supply chain, by authors. Based on the proposed method, a prototype of the CPS platform for smart supply chain configuration has been implemented for discrete manufacturing. By using this platform, it is possible to register models of retailer, manufacturer and supplier enterprises with different behavior types, as an enterprise e-catalog. Furthermore, it is also possible to configure various virtual supply chains by changing the combination of enterprises. On the configured supply chain, it can simulate the behavior from the viewpoints of each enterprise and an entire supply chain. Several use cases of the platform have been executed. These use cases and their considerations are provided useful findings towards construction of a further practical CPS platform for a smart supply chain configuration.

DOI： 10.1016/j.procir.2022.05.046

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Authorship：Last author   Publishing type：Research paper (scientific journal)   Publisher：Japan Society of Mechanical Engineers  

This paper proposes an adaptive heterogeneous particle swarm optimization with a comprehensive learning strategy for solving single-objective constrained optimization problems. In this algorithm, particles can use an exploration strategy and an exploitation strategy to update their positions. The historical success rates of the two strategies are used to adaptively control the adoption rates of strategies in the next iteration. The search strategy in the canonical particle swarm optimization algorithm is based on elite solutions. As a result, when no particles can discover better solutions for several generations, this algorithm is likely to fall into stagnation. To respond to this challenge, a new strategy is proposed to explore the neighbors of the elite solutions in this study. Finally, a constraint handling method is equipped to the proposed algorithm to make it be able to solve constrained optimization problems. The proposed algorithm is compared with the canonical particle swarm optimization, differential evolution, and several recently proposed algorithms on the benchmark test suite. The Wilcoxon signed-rank test results show that the proposed algorithm is significantly better on most of the benchmark problems compared with the competitors. The proposed algorithm is also applied to solve two real-world mechanical engineering problems. The experimental results show that the proposed algorithm performs consistently well on these problems.

DOI： 10.1299/jamdsm.2022jamdsm0035

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Authorship：Last author,　Corresponding author   Publishing type：Research paper (scientific journal)   Publisher：Japan Society of Mechanical Engineers  

DOI： 10.1299/jamdsm.2022jamdsm0037

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Authorship：Corresponding author   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/CASE49997.2022.9926603

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Other Link： https://dblp.uni-trier.de/db/conf/case/case2022.html#KawabeN22

Authorship：Lead author,　Corresponding author   Language：English  

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Elsevier BV  

DOI： 10.1016/j.ins.2021.10.028

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Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/SMC52423.2021.9658903

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Other Link： https://dblp.uni-trier.de/db/conf/smc/smc2021.html#KorekaneN21

Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/case49439.2021.9551508

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Language：English   Publishing type：Research paper (international conference proceedings)  

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)  

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Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)  

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Elsevier  

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：MDPI AG  

The paper aims to study a multi-period maximal covering location problem with the configuration of different types of facilities, as an extension of the classical maximal covering location problem (MCLP). The proposed model can have applications such as locating disaster relief facilities, hospitals, and chain supermarkets. The facilities are supposed to be comprised of various units, called the modules. The modules have different sizes and can transfer between facilities during the planning horizon according to demand variation. Both the facilities and modules are capacitated as a real-life fact. To solve the problem, two upper bounds—(LR1) and (LR2)—and Lagrangian decomposition (LD) are developed. Two lower bounds are computed from feasible solutions obtained from (LR1), (LR2), and (LD) and a novel heuristic algorithm. The results demonstrate that the LD method combined with the lower bound obtained from the developed heuristic method (LD-HLB) shows better performance and is preferred to solve both small- and large-scale problems in terms of bound tightness and efficiency especially for solving large-scale problems. The upper bounds and lower bounds generated by the solution procedures can be used as the profit approximation by the managerial executives in their decision-making process.

DOI： 10.3390/app11010397

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Language：English   Publishing type：Part of collection (book)   Publisher：Springer International Publishing  

DOI： 10.1007/978-3-030-85906-0_67

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Elsevier BV  

This research project aims to provide a common methodology for constructing a virtual supply chain as a field to determine more appropriate action for each component enterprise. Describing method for an enterprise model including behavior description and sharing them as an enterprise e-catalog have been proposed. In this paper, the agent program codes for each enterprise are automatically generated from the retailer, manufacturer and supplier models selected from the e-catalogs, and the virtual supply chain is configured as a multi-agent system by connecting them. Test simulations are performed to verify whether this virtual supply chain operates correctly and produces appropriate results.

DOI： 10.1016/j.procir.2021.11.059

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Authorship：Lead author,　Corresponding author   Language：Japanese   Publishing type：Research paper (scientific journal)  

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

Particle swarm optimization (PSO) is an efficient optimization algorithm and has been applied to solve various real-world problems. However, the performance of PSO on a specific problem highly depends on the velocity updating strategy. For a real-world engineering problem, the function landscapes are usually very complex and problem-specific knowledge is sometimes unavailable. To respond to this challenge, we propose a multipopulation ensemble particle swarm optimizer (MPEPSO). The proposed algorithm consists of three existing efficient and simple PSO searching strategies. The particles are divided into four subpopulations including three indicator subpopulations and one reward subpopulation. Particles in the three indicator subpopulations update their velocities by different strategies. During every learning period, the improved function values of the three strategies are recorded. At the end of a learning period, the reward subpopulation is allocated to the best-performed strategy. Therefore, the appropriate PSO searching strategy can have more computational expense. The performance of MPEPSO is evaluated by the CEC 2014 test suite and compared with six other efficient PSO variants. These results suggest that MPEPSO ranks the first among these algorithms. Moreover, MPEPSO is applied to solve four engineering design problems. The results show the advantages of MPEPSO. The MATLAB source codes of MPEPSO are available at https://github.com/zi-ang-liu/MPEPSO.

DOI： 10.1155/2020/1450985

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Fuji Technology Press Ltd.  

In Industry 4.0, a network of enterprises and factories is constructed collaboratively and dynamically according to the cyber physical system (CPS) paradigm. It is necessary to build smart supply chains according to this concept. A network of component enterprises in a supply chain would be modeled as a virtual supply chain in the cyber world. From the viewpoint of Industry 4.0, virtualizing a supply chain is the foundation for constructing a CPS for a supply chain. The virtualization of a supply chain makes it easier for companies to study their integrating and expanding opportunities. By using this CPS, comprehensive and autonomous optimization of the supply chain can be achieved. This virtual supply chain can be used to simulate the planning phase with negotiation, as well as the production phase. In this paper, instead of specific mathematical modeling for each supply chain, a general configuration method of a virtual supply chain is proposed. The configuration method of a supply chain model is proposed as a virtual supply chain using enterprise e-catalogues. A virtual supply chain is constructed as a multi-agent system, which is connections of software agents that are automatically created from each selected enterprise model in the e-catalogues. Three types of component enterprise models are provided: manufacturer model, part/material supplier model, and retailer model. Modeling templates for these three types of enterprises are prepared, and each template is a nominal model in terms of enterprise’s behavior. Specific component-enterprise models are prepared by filling the appropriate template. Each component enterprise agent is implemented using the enterprise model selected from the catalogues. Manufacturer, retailer, and supplier e-catalogues, as well as an automatic construction system of a virtual supply chain, are implemented. Methods for developing templates for the manufacturer, retailer and supplier were provided, and the construction system for specific enterprise models (as e-catalogues) is implemented as a trial.

DOI： 10.20965/ijat.2020.p0713

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：American Society of Mechanical Engineers  

<title>Abstract</title>
In a modern large-scale fabrication, hundreds of vehicles are used for transportation. Since traffic conditions are changing rapidly, the routing of automated guided vehicles (AGV) needs to be changed according to the change in traffic conditions. We propose a conflict-free routing method for AGVs using reinforcement learning in dynamic transportation. An advantage of the proposed method is that a change in the state can be obtained as an evaluation function. Therefore, the action can be selected according to the states. A deadlock avoidance method in bidirectional transport systems is developed using reinforcement learning. The effectiveness of the proposed method is demonstrated by comparing the performance with the conventional Q learning algorithm from computational results.

DOI： 10.1115/isfa2020-9620

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：JAPAN SOC MECHANICAL ENGINEERS  

Many researchers have focused on the comparison between the JIT model and the EOQ model. However, few of them studied this problem from an evolutionary perspective. In this paper, a JIT purchasing with the single-setup-multi-delivery model is introduced to compare the total costs of the JIT model and the EOQ model. Also, we extend the classical JIT-EOQ models to a two-echelon supply chain which consists of one manufacturer and one supplier. Considering the bounded rationality of players and the quickly changing market, an evolutionary game model is proposed to discuss how these factors impact the strategy selection of the companies. And the evolutionarily stable strategy of the proposed model is analyzed. Based on the analysis, we derive the conditions when the supply chain system will choose the JIT strategy and propose a contract method to ensure that the system converges to the JIT strategy. Several numerical experiments are provided to observe the JIT and EOQ purchasing strategy selection of the manufacturer and the supplier. The results suggest that, in most situations, the JIT strategy is preferred. However, the EOQ strategy remains competitive when the supplier's inventory cost level is high or the demand is low. Supply chain members can choose the EOQ strategy even when the JIT strategy is more profitable. In some situations, strategy selection also depends on the market situation. The JIT policy with low investment costs and high supply chain performance is preferred for the companies.

DOI： 10.1299/jamdsm.2020jamdsm0070

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Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1109/TASE.2019.2952920

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Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer New York LLC  

The increasing maritime traffic forces terminal operators to efficiently reduce the container ships’ service time in order to maintain or increase their market share. This situation gives rise to the well-known berth allocation problem. Its goal is to determine the allocation and the berthing time of container ships arriving to the port with the aim of minimizing the total service time. For tackling this problem, we propose a dynamic programming-based matheuristic that allows to derive lower and upper bounds, and therefore, evaluate the optimality of the provided solutions. Its behavior is assessed on realistic problem instances from the related literature as well as on a new set of larger instances with 150 ships and 15 berths. The results indicate that our proposed approach shows a competitive performance.

DOI： 10.1007/s10479-017-2715-9

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.compchemeng.2019.106705

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Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Elsevier BV  

DOI： 10.1016/j.procir.2020.04.093

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)  

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

An evolutionary game model is investigated to study the stability conditions for four different reverse channel structures in the closed-loop supply chain. The proper channel structures are analyzed for the given conditions. We consider one centrally coordinated model and three decentralized models that consist of manufacturer collection, retailer collection, and third-party collection model. The profit function is maximized for the centralized model and Stackelberg equilibriums are obtained for the other three decentralized models. Using the optimal profit functions, an evolutionary game model is proposed. On the basis of the stable conditions, we propose a profit sharing allocation method that can make the centralized supply chain model stable from a long-term view. Also, several numerical experiments are conducted. The results show that the coordinated channel structure is preferable over other structures with a proper profit sharing allocation method.

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)  

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Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：MDPI  

The government plays a critical role in the promotion of recycling strategy among supply chain members. The purpose of this study is to investigate the optimal government policies on closed-loop supply chains and how these policies impact the market demand and the returning strategies of manufacturers and retailers. This paper presents a design of closed-loop supply chains under government regulation by considering a novel three-stage game theoretic model. Firstly, Stackelberg models are adopted to describe the one-shot game between the manufacturer and the retailer in a local market. Secondly, based on the Stackelberg equilibriums, a repeated and dynamic population game is developed. Thirdly, the government analyzes the population game to find the optimal tax and subsidy policies in the whole market. To solve the proposed model, the idea of backward induction is adopted. The results suggest that, by collecting tax and allocating subsidy, the government can influence the market demands and return rates. The centralized supply chain structure is always preferred for the government and the market. The government prefers to allocate subsidy to low-pollution, low-profit remanufactured products. The environmental attention of the government affects the subsidy policy.

DOI： 10.3390/su11185030

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DOI： 10.1109/COASE.2019.8843054

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Other Link： https://dblp.uni-trier.de/db/conf/case/case2019.html#MatsuokaNT19

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Language：English   Publishing type：Research paper (international conference proceedings)  

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)  

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Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1177/1687814018822934

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Elsevier BV  

DOI： 10.1016/j.procir.2019.03.305

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Elsevier BV  

DOI： 10.1016/j.procir.2019.03.230

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/ieem.2018.8607741

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/ieem.2018.8607705

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/ieem.2018.8607814

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Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

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Authorship：Corresponding author   Language：English  

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Publishing type：Research paper (scientific journal)   Publisher：SAGE Publications  

DOI： 10.1177/1687814018779621

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Other Link： http://journals.sagepub.com/doi/full-xml/10.1177/1687814018779621

Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Elsevier BV  

DOI： 10.1016/j.procir.2018.03.248

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Japan Society of Mechanical Engineers  

DOI： 10.1299/jamdsm.2018jamdsm0064

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Language：English   Publisher：JAPAN SOC MECHANICAL ENGINEERS  

DOI： 10.1299/jamdsm.2018jamdsm0064

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Institute of Electrical and Electronics Engineers Inc.  

Supply chain risk management is one of significant issues in the design and operations of supply chains. It is wellknown that leader-follower relationship influences the decision making of decentralized supply chains. In this paper, we address a game theoretical analysis of the leadership structure in the decentralized supply chain with risky supplier under demand uncertainty. The risky supplier has a risk of unavailability of the supply product for the ordered product according to the probability of supplier availability. The equilibrium solution is analytically derived for a single supplier, a single risky supplier and a single retailer. The influence of the probability of risky supplier availability to the optimal leadership structure is investigated. Numerical examples are provided to show the validity of the proposed game theoretical analysis.

DOI： 10.1109/SMC.2017.8123068

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Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：ELSEVIER SCIENCE INC  

In this paper, we investigate a general algorithm for converting the 0-1 integer linear programming problem (0-1IP) into an optimal transition firing sequence problem (OFSP) of a Petri net (PN). The general 0-1IP can be visualized graphically and then analyzed using the PN theory after application of our proposed conversion algorithm. The proposed algorithm is applied to a traveling salesman problem, a vehicle routing problem and an automated guided vehicles (AGV) routing problem. A PN reduction technique is employed to reduce the size of the PN. Valid inequalities are derived using reachability analysis of the converted PN model. These inequalities are imposed on the original 0-1IP. Computational results show that the total computational time for solving an AGVRP with the valid inequalities derived using the reachability analysis is significantly reduced. (C) 2017 Elsevier Inc. All rights reserved.

DOI： 10.1016/j.ins.2017.03.014

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：The Japan Society of Mechanical Engineers  

Multi-cluster tools are widely used for semiconductor manufacturing. Multi-cluster tools can cause a deadlock due to resource conflicts. The deadlock may cause a serious decrease of throughput and a deterioration of quality of product. Therefore, the deadlock prevention method is necessary. In this paper, the optimal supervisor of multi-cluster tools is derived by reachability analysis of Petri nets for deadlock prevention. The effects of the optimal deadlock-free model is evaluated from the viewpoint of the scheduling performance.

DOI： 10.1299/jsmemsd.2017.307

CiNii Article

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Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：PERGAMON-ELSEVIER SCIENCE LTD  

The aim of railway rolling stock planning problem is to find an optimal allocation of train-sets for a given set of trips in the train timetable in order to minimize the total cost. We propose a column generation and Lagrangian relaxation heuristics for short-term rolling stock planning problems with regular inspection constraints. The problem is formulated as a subtour traveling salesman problem to find a set of elementary shortest cycles that cover all trips in the timetable. In the proposed method, a tight lower bound is obtained from the continuous relaxation of Dantzig Wolfe reformulation by column generation. The pricing problem can be formulated as an elementary shortest cycle problem with resource constraints. A labeling algorithm is applied to solve the pricing problem. In order to reduce the computational effort, we apply a general state space augmenting algorithm to solve the pricing problems. Computational results show that the proposed column generation and Lagrangian relaxation heuristics can find good lower and upper bounds for 300 trips within reasonable computing time. (C) 2016 Elsevier Ltd. All rights reserved.

DOI： 10.1016/j.cor.2016.11.025

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：PERGAMON-ELSEVIER SCIENCE LTD  

We study a real-world production warehousing case, where the company always faces the challenge to find available space for its products and to manage the items in the warehouse. To resolve the problem, an integrated strategy that combines warehouse layout with the capacitated lot-sizing problem is presented, which have been traditionally treated separately in the existing literature. We develop a mixed integer linear programming model to formulate the integrated optimization problem with the objective of minimizing the total cost of production and warehouse operations. The problem with real data is a large-scale instance that is beyond the capability of optimization solvers. A novel Lagrangian relax-and fix heuristic approach and its variants are proposed to solve the large-scale problem. The preliminary numerical results from the heuristic approaches are reported. (C) 2016 Elsevier Ltd. All rights reserved.

DOI： 10.1016/j.omega.2016.06.005

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Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：SAGE PUBLICATIONS LTD  

Cluster tools are used for semiconductor wafer fabrication. A cluster tool consists of several loadlock modules, processing modules, and material handling armed robots to transfer wafers between loadlocks and processing modules. Most conventional scheduling methods consider a cyclic schedule to minimize makespan. We consider noncyclic scheduling of dual-armed cluster tools. In practice, it is important to minimize wafer residency time in processing modules to maintain the surface quality of wafers. In the noncyclic operations, the cluster tool causes deadlock which may lead to excessive loss in the wafer fabrication process. We propose an efficient deadlock-free scheduling method for the noncyclic scheduling problem of a dual-armed cluster tool to minimize the residency time and makespan. The proposed method is applied to a dual-armed cluster tool with a non-revisiting process and a revisiting process. Computational results demonstrate the effectiveness of the proposed method.

DOI： 10.1177/1687814017693217

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DOI： 10.5687/iscie.30.73

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：JAPAN SOC MECHANICAL ENGINEERS  

In this paper, we consider the production planning problem for a single manufacturer with the investment to improve the market impact under demand uncertainty. In the mathematical model, the average demand increases if the investment of market impact is increased for each product. The objective is to maximize the total profit with a piecewise linear investment cost and a budget constraint. The problem is formulated as a mixed integer nonlinear programming problem. A solution procedure based on Lagrangian relaxation is developed to solve the problem efficiently. In the proposed method, an analytical solution of the newsboy problem is effectively used to derive the lower and upper bounds. The condition of the concavity of the profit function is derived. The effectiveness of the proposed method is confirmed through computational experiments.

DOI： 10.1299/jamdsm.2017jamdsm0019

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Elsevier BV  

DOI： 10.1016/j.procir.2017.02.043

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

In global supply chains, coordination of decision making in decentralized supply chain has received much attention. The total profit in the decentralized situation is inferior to the optimal profit in the centralized situation. In order to improve the total profit in the decentralized situation, it is required to optimize the coordination of the production planning for supplier and retailer under different leader-follower relation under demand uncertainty. In this paper, the multi-period production planning problem for the supplier and the retailer is formulated as the nonlinear bilevel programming problem under uncertain demand. We propose the replacement of leader-follower relation in multi-period supply chain planning. In the proposed model, the leader can be changed dynamically in the middle of periods. The effectiveness of the replacement of leader-follower relation to the total profit in the bilevel production planning is examined by some numerical experiments.

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Elsevier BV  

DOI： 10.1016/j.procir.2015.12.112

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：JAPAN SOC MECHANICAL ENGINEERS  

We propose an application of a two-phase decomposition algorithm for a practical airline crew rostering problem for fair working time. The problem is to find an optimal assignment of duties to individual crew members such that various hard constraints such as rest days, rules and regulations are satisfied. The objective is to minimize the total deviation of the average working time from the standard working time for crew members. A two-phase decomposition algorithm is successfully applied to solve the problem. The proposed method decomposes the original problem into the master problem and the subproblem. The master problem is solved by an exact algorithm with a generalized set partitioning formulation if all possible rosters are enumerated by an efficient enumeration algorithm. The upper bound is improved by a metaheuristic algorithm. The effectiveness of the proposed method for a large scale rostering problem is shown from computational experiments.

DOI： 10.1299/jamdsm.2016jamdsm0036

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

In this paper, the modeling and solution strategies of a practical airline crew scheduling problem are studied. The airline crew scheduling problem to achieve the equalization of working time taking into account for practical constraints such as international and domestic flights, holiday assignments, and grouping constraints is formulated as an integer programming problem. The problem is reformulated as a set partitioning problem by Dantzig-Wolfe decomposition. The column generation algorithm is applied to solve the linear relaxation of the original problem. In order to improve the performance of the algorithm, a column fixing strategy with backtracking is proposed. In the proposed method, the schedule for all crews is obtained efficiently by exploiting the fixing of columns and the execution of the column generation procedure. The backtracking is introduced to find a feasible solution efficiently. The computational results show that the proposed algorithm can find better solutions than greedy-heuristics and the branch and bound procedure solving the original problem by a commercial solver.

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

Given an initial marking and final marking for a Petri net model, an optimal firing sequence problem is defined as the problem to find an optimal transition firing sequence to minimize the objective function. For the purpose of analysis of general integer programming problems, we propose a Petri net representation and reachability analysis of integer programming problems. In the proposed method, an integer programming problem is converted into the optimal firing sequence problem of Petri nets. By utilizing the proposed algorithm, integer programming problems can be visualized and analyzed by the Petri net theory. We apply the proposed methodology to the scheduling problems of dual armed cluster tools. The valid inequalities are derived from the reachability analysis. Numerical results show that those valid inequalities can significantly reduce the computational time of the original integer programming problem.

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

In this paper, we propose a game theoretical analysis of supply chain configurations for two manufacturers and two retailers. The supply chain configuration is the structure of the leader-follower relationship in the decentralized decision making of pricing decisions for manufacturers and retailers. The effects of the supply chain configurations on the total profit are investigated. The closed-form equilibrium solutions are derived through the backward induction. The results demonstrate that the change of leader-follower relationship in the supply chain configuration according to the situations enable us to obtain higher total profit.

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

Dual-armed cluster tools are widely used for semiconductor manufacturing. The system consists of several loadlock modules, processing modules, and material handling robots for transferring wafers between them. Most of the conventional works focused on the scheduling cluster tools for the minimization of makespan. It is also important to minimize the residence time in the process module in order to prevent the deterioration of the quality of the wafer surface. Also, cluster tools have to avoid deadlock situations which may lead to excessive loss for wafer fabrication system. In this paper, we propose an efficient scheduling method for deadlock avoidance strategy and the bi-objective of the minimization of makespan and wafer residence time.

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)  

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：THE INSTITUTE OF SYSTEMS, CONTROL AND INFORMATION ENGINEERS (ISCIE)  

We consider an integrated optimization model for production planning and warehouse storage allocation under demand uncertainty. A deterministic problem of the integrated production planning and warehouse storage allocation problem is extended into a stochastic programming problem formulation. A Lagrangian relaxation and fix heuristic is proposed to solve the mixed integer nonlinear programming problem by decomposing the problem into single item capacitated lot sizing and warehouse storage allocation problems. The problem instances based on real data are solved in the numerical experiments. Computational results demonstrate the effectiveness of the proposed method.

DOI： 10.5687/iscie.28.91

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Other Link： https://jlc.jst.go.jp/DN/JLC/20008049245?from=CiNii

Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：SPRINGER LONDON LTD  

In this paper, a coordination strategy is developed to integrate business decisions and manufacturing planning in supply chain management. We consider one manufacturer and multiple suppliers to determine production, prices, and inventory simultaneously with uncertain demands. This paper aims at providing an optimal discount policy derived from Stackelberg equilibrium to coordinate a manufacturer and multiple suppliers. The optimal discount coordination mechanism helps the manufacturer to select suppliers in order to maintain long-term relationship with the contracted suppliers under demand uncertainty. Noncooperative game is applied in order to resolve decision-making in order to determine quantities of components, price, production, and selection of suppliers simultaneously. Computational experiments are conducted to demonstrate the effectiveness and efficiency of the proposed approach.

DOI： 10.1007/s00170-014-6298-1

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC  

Semiconductor cluster tools are the integrated equipment to process a variety of silicon wafers for the fabrication of microelectronic components. The cluster tool system consists of several loadlock modules, processing chambers, and material handling armed robots for transferring wafers between them. Most scheduling problems for dual-armed cluster tools adopt cyclic scheduling with the assumption of swap sequence of the robot arm. A deadlock-free and non-cyclic scheduling is highly required to improve total throughput of cluster tools for various types of wafer flow patterns and various changes of equipment configurations. In this paper, we propose a Petri net decomposition approach to derive a near-optimal solution of deadlock-free and non-cyclic scheduling of dual-armed cluster tools to reduce the computational complexity. A timed Petri net model is introduced to represent a non-cyclic scheduling model for dual-armed cluster tools. In order to obtain a deadlock-free and non-cyclic schedule efficiently, we propose a deadlock avoidance control policy that restricts the markings to prevent unmarked siphons. Deadlock condition for the dual-armed cluster tools is characterized by the structure of Petri net model. The performance of the non-cyclic scheduling model by the proposed method is compared with that of the cyclic scheduling model. Computational results show the effectiveness of the non-cyclic scheduling model compared with the cyclic scheduling model for multiple wafer flow patterns.
Note to Practitioners-The scheduling of dual-armed cluster tools has been attracted by many researchers recently with growing semiconductor manufacturing. Most of the conventional studies have been concentrated on cyclic scheduling to minimize the cycle time with the swap strategy for robot arm operations. The swap strategy has been widely used to analyze the steady-state operation with cyclic schedules in order to simplify the model with less computational complexity. However, dual-armed cluster tool operations for the swap strategy are restricted because possible states are limited. Non-swap strategy outperforms the swap strategy in the transport-bound region. In order to deal with lot switching, frequent wafer flow pattern changes, periodic maintenance, and processing time variations, a deadlock-free and non-cyclic scheduling without swap strategy is highly required. In this paper, we propose a Petri net decomposition approach with a deadlock avoidance policy that can derive a near-optimal solution for much larger size of non-cyclic scheduling problems of dual-armed cluster tools. A timed Petri net model for non-cyclic scheduling is developed for the dual-armed cluster tools with different flow pattern without assuming the swap strategy. Deadlock is analyzed with minimal siphons of the proposed model and the deadlock-free conditions are derived. Computational experiments show the effectiveness of the non-cyclic scheduling solution derived by the proposed method for multiple wafer flow patterns.

DOI： 10.1109/TASE.2013.2292572

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

In this paper, we propose a combinatorial auction algorithm for airline crew scheduling problems. The objective is to achieve the equalization of working time for crew members. In the combinatorial auction algorithm, each crew member is regarded as a bidder and the combination of the rosters is considered as the packages of items that a bid can be placed. A new combinatorial auction algorithm with price-adjustment mechanism is proposed. Two types of bidding algorithms are combined. One is the random bids with neighborhood search, and the second one is the solution derived from the Lagrangian relaxation with subgradient optimization, which is called as the price adjustment mechanism. The effectiveness of the proposed method is shown from the computational results.

DOI： 10.1109/IEEM.2015.7385834

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

In this paper, we consider a supply chain planning problem for a single manufacturer with corporate social responsibility (CSR) investment decision under uncertain demand. The activity of CSR is modeled as the investment to customers. In the mathematical model, we assume that the average demand increases if the investment of CSR is increased. The objective function is the total profit including the piecewise linear investment costs. The supply chain planning problem is formulated as a mixed integer nonlinear programming problem. An efficient solution procedure based on Lagrangian relaxation is developed. The effectiveness of the proposed method is confirmed from computational experiments.

DOI： 10.1109/IEEM.2015.7385918

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

In this paper, we study an optimization approach for congestion management for traffic signal control systems via first-order hybrid Petri nets. The traffic signal control problem is represented by the optimal firingsequence problem for the first-order hybrid Petri net (FOHPN). In order to solve large scaleproblems, the Petri net decomposition approach for FOHPN is developed. The computational results show thatthe computation time can be reduced by the proposed approach and the outcome of the FOHPN decomposition approach for congestion management.

DOI： 10.1109/IEEM.2015.7385848

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE Computer Society  

We present a real world production warehousing case, where the company always faces the challenges to find available space for their products and to manage the items in the warehouse. We develop a decomposition algorithm to solve a mixed integer programming formulation of warehouse layout with capacitated lot sizing problem. The problem with real data is a large scale instance that is beyond the capability of optimization solvers. We propose a Lagrangian relax and fix heuristics to solve the problem efficiently. Computational results demonstrate the effectiveness of the proposed method by comparing the performance of the proposed method and branch and bound method.

DOI： 10.1109/IEEM.2013.6962608

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE Computer Society  

We present a real world production warehousing case, where the company always faces the challenges to find available space for their products and to manage the items in the warehouse. We develop a decomposition algorithm to solve a mixed integer programming formulation of warehouse layout with capacitated lot sizing problem. The problem with real data is a large scale instance that is beyond the capability of optimization solvers. We propose a Lagrangian relax and fix heuristics to solve the problem efficiently. Computational results demonstrate the effectiveness of the proposed method by comparing the performance of the proposed method and branch and bound method.

DOI： 10.1109/IEEM.2013.6962608

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：ELSEVIER SCIENCE BV  

A typical railway crew scheduling problem consists of two phases: a crew pairing problem to determine a set of crew duties and a crew rostering problem. The crew rostering problem aims to find a set of rosters that forms workforce assignment of crew duties and rest periods satisfying several working regulations. In this paper, we present a two-level decomposition approach to solve railway crew rostering problem with the objective of fair working condition. To reduce computational efforts, the original problem is decomposed into the upper-level master problem and the lower-level subproblem. The subproblem can be further decomposed into several subproblems for each roster. These problems are iteratively solved by incorporating cuts into the master problem. We show that the relaxed problem of the master problem can be formulated as a uniform parallel machine scheduling problem to minimize makespan, which is NP-hard. An efficient branch-and-bound algorithm is applied to solve the master problem. Effective valid cuts are developed to reduce feasible search space to tighten the duality gap. Using data provided by the railway company, we demonstrate the effectiveness of the proposed method compared with that of constraint programming techniques for large-scale problems through computational experiments. (C) 2014 Elsevier B.V. All rights reserved.

DOI： 10.1016/j.ejor.2014.02.010

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：ELSEVIER SCIENCE BV  

A typical railway crew scheduling problem consists of two phases: a crew pairing problem to determine a set of crew duties and a crew rostering problem. The crew rostering problem aims to find a set of rosters that forms workforce assignment of crew duties and rest periods satisfying several working regulations. In this paper, we present a two-level decomposition approach to solve railway crew rostering problem with the objective of fair working condition. To reduce computational efforts, the original problem is decomposed into the upper-level master problem and the lower-level subproblem. The subproblem can be further decomposed into several subproblems for each roster. These problems are iteratively solved by incorporating cuts into the master problem. We show that the relaxed problem of the master problem can be formulated as a uniform parallel machine scheduling problem to minimize makespan, which is NP-hard. An efficient branch-and-bound algorithm is applied to solve the master problem. Effective valid cuts are developed to reduce feasible search space to tighten the duality gap. Using data provided by the railway company, we demonstrate the effectiveness of the proposed method compared with that of constraint programming techniques for large-scale problems through computational experiments. (C) 2014 Elsevier B.V. All rights reserved.

DOI： 10.1016/j.ejor.2014.02.010

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：TAYLOR & FRANCIS LTD  

A decomposition algorithm for scheduling problems based on timed automata (TA) model is proposed. The problem is represented as an optimal state transition problem for TA. The model comprises of the parallel composition of submodels such as jobs and resources. The procedure of the proposed methodology can be divided into two steps. The first step is to decompose the TA model into several submodels by using decomposable condition. The second step is to combine individual solution of subproblems for the decomposed submodels by the penalty function method. A feasible solution for the entire model is derived through the iterated computation of solving the subproblem for each submodel. The proposed methodology is applied to solve flowshop and jobshop scheduling problems. Computational experiments demonstrate the effectiveness of the proposed algorithm compared with a conventional TA scheduling algorithm without decomposition.

DOI： 10.1080/00207721.2012.724099

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：PERGAMON-ELSEVIER SCIENCE LTD  

We propose a column generation based heuristic algorithm to solve a ship routing and scheduling problem for crude oil transportation with split deliveries. The problem is to find an optimal assignment and sequence and loading volume of demand simultaneously in order to minimize the total distance satisfying the capacity of tankers. The problem can be considered as a multi-product heterogeneous fleet split pickup ship routing problem with finite capacity and loading constraints. An efficient heuristic algorithm based on the column generation method is developed to generate a feasible solution taking into account of practical constraints. The performance of the proposed method is compared with the branch and bound algorithm and that of human operators. Computational results demonstrate the effectiveness of the proposed algorithm for a real case. (C) 2013 Elsevier Ltd. All rights reserved.

DOI： 10.1016/j.compchemeng.2013.09.019

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：PERGAMON-ELSEVIER SCIENCE LTD  

We propose a column generation based heuristic algorithm to solve a ship routing and scheduling problem for crude oil transportation with split deliveries. The problem is to find an optimal assignment and sequence and loading volume of demand simultaneously in order to minimize the total distance satisfying the capacity of tankers. The problem can be considered as a multi-product heterogeneous fleet split pickup ship routing problem with finite capacity and loading constraints. An efficient heuristic algorithm based on the column generation method is developed to generate a feasible solution taking into account of practical constraints. The performance of the proposed method is compared with the branch and bound algorithm and that of human operators. Computational results demonstrate the effectiveness of the proposed algorithm for a real case. (C) 2013 Elsevier Ltd. All rights reserved.

DOI： 10.1016/j.compchemeng.2013.09.019

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：TAYLOR & FRANCIS LTD  

Quantity discount policy is decision-making for trade-off prices between suppliers and manufacturers while production is changeable due to demand fluctuations in a real market. In this paper, quantity discount models which consider selection of contract suppliers, production quantity and inventory simultaneously are addressed. The supply chain planning problem with quantity discounts under demand uncertainty is formulated as a mixed-integer nonlinear programming problem (MINLP) with integral terms. We apply an outer-approximation method to solve MINLP problems. In order to improve the efficiency of the proposed method, the problem is reformulated as a stochastic model replacing the integral terms by using a normalisation technique. We present numerical examples to demonstrate the efficiency of the proposed method.

DOI： 10.1080/00207721.2013.769645

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

Petri net is a mathematical modeling tool that represents wide variety of discrete event systems. Given an initial marking and final marking for a Petri net, an optimal firing sequence problem is defined as the problem to find an optimal transition sequence to minimize the objective function. For the purpose of analysis of 0-1 integer programming problems, we propose a general algorithm to convert general 0-1 integer programming problem into an optimal firing sequence problem of Petri net. By utilizing the proposed algorithm, general 0-1 integer programming problems can be visualized and analyzed by Petri net theory. The property of solutions derived by solving the original 0-1 integer programming and the optimal firing sequence problem is discussed. The solution of 0-1 integer programming problem and that of the optimal firing sequence problem of Petri net are compared. The results show that the solutions for both problems are identical for traveling salesman problem and vehicle routing problems.

DOI： 10.1109/IEEM.2014.7058734

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE Computer Society  

Petri net is a mathematical modeling tool that represents wide variety of discrete event systems. Given an initial marking and final marking for a Petri net, an optimal firing sequence problem is defined as the problem to And an optimal transition sequence to minimize the objective function. For the purpose of analysis of 0-1 integer programming problems, we propose a general algorithm to convert general 0-1 integer programming problem into an optimal firing sequence problem of Petri net. By utilizing the proposed algorithm, general 0-1 integer programming problems can be visualized and analyzed by Petri net theory. The property of solutions derived by solving the original 0-1 integer programming and the optimal firing sequence problem is discussed. The solution of 0-1 integer programming problem and that of the optimal firing sequence problem of Petri net are compared. The results show that the solutions for both problems are identical for traveling salesman problem and vehicle routing problems.

DOI： 10.1109/IEEM.2014.7058734

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We propose a new two-level decomposition algorithm for shift scheduling problems. The problem determines the assignment of duties and rest days to the set of staff members to minimize the given objective function. The constraint on the set of staff members are considered. The objective of this paper is to achieve the minimization of total costs with fairness of working conditions. The proposed method decomposes the original problem into the master and the subproblems. These subproblems are iteratively solved with embedding cuts into the master problem. Computational results show that the performance of the proposed method outperforms a general-purpose constraint logic programming solver.

DOI： 10.1109/SMC.2014.6974518

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

Multi-cluster tools are widely used for semiconductor silicon wafer production. The multi-cluster tools system consists of two or more cluster tools connected with buffer or process modules. Deadlocks are frequently caused in the multi-cluster tools by the restriction of transfer modules and limited number of available spaces. In this paper, we propose a novel deadlock prevention policy for multi-cluster tools. A timed Petri net model for multi-cluster tools is developed. A new deadlock prevention policy is proposed. The proposed deadlock prevention policy is applied to dual-armed multi-cluster tool with dual path. The deadlock-freeness is confirmed by deriving empty siphons for the Petri net model. The computational results show that the performance of scheduling with the proposed deadlock prevention policy is better than the conventional deadlock prevention policy.

DOI： 10.1109/SMC.2014.6974099

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

Global supply chain management has an important role for globalization of marketplaces and global production. The production and distribution planning for suppliers and retailers with different objectives should be coordinated to minimize the total costs. However, it is well-known that the equilibrium solution for decentralized decision making is inferior to the solution of centralized problem. This study focuses on analysis of quantity discounts for multi-period production planning for single supplier and single retailer under uncertain demands. A Stackelberg equilibrium is analytically derived between single supplier and single retailer where supplier is a leader and retailer is a follower. An optimal discount contract is derived through the optimal solution of the centralized problem.

DOI： 10.1109/IEEM.2014.7058765

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Elsevier BV  

DOI： 10.1016/j.procir.2014.01.109

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：JAPAN SOC MECHANICAL ENGINEERS  

We consider an optimization of conflict-free routing problems for automated guided vehicles (AGV) with acceleration and deceleration. A continuous time model is developed to represent the dynamics of vehicles. In the proposed model, the transportation model is discretized into several regions. A network model is created by taking into account the acceleration and deceleration motions. The acceleration and deceleration are represented at curve locations. Column generation heuristic is used to find a near-optimal solution. In this algorithm, we construct a heuristic rule to generate a feasible solution with acceleration and deceleration of vehicles after the column generation. The pricing problem is represented by a resource constrained shortest path problem, which is effectively solved by a labeling algorithm The dominance relation for acceleration and deceleration is addressed. In the proposed model, the dynamics of real speed AGV model are reflected into the routing problems. By comparing the performance of the conventional method, the effectiveness of the proposed method is demonstrated.

DOI： 10.1299/jamdsm.2014jamdsm0067

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Language：Japanese   Publisher：The Japan Joint Automatic Control Conference  

DOI： 10.11511/jacc.57.0_1367

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In this paper, we propose a column generation approach to solve the route planning problem for automated guided vehicles with acceleration and deceleration. The transportation model is discretized into regular intervals. A network model is created by taking into account the acceleration and deceleration motions of AGVs. Column generation heuristics is developed to find a near-optimal solution. The pricing problem is represented by a resource-constrained shortest path problem, which is effectively solved by a labeling algorithm. By comparing the performance of the conventional method, the effectiveness of the proposed method is demonstrated.

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In this paper, a three-echelon supply chain including single manufacturer, multiple retailers and multiple suppliers under demand uncertainty with asymmetric quality information is introduced. The negotiation between the manufacturer and retailers is based on buyback contracts. Due to asymmetric information, the quality information of components purchased from suppliers is unknown to the manufacturer. Thus, we investigate two scenarios for the manufacturer to estimate uncertainty of risk. The problem is modeled by game theory. An optimization approach is proposed to solve the problem under demand uncertainty. Computational results show the feature of the model.

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Authorship：Lead author   Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：The Society of Instrument and Control Engineers  

Cluster tools are used to process a variety of silicon wafers for the fabrication of microelectronic components. The dual-armed cluster tool system consists of several loadlock modules, processing devices, and dual-armed robots for transferring wafers between them. In order to improve total throughput of cluster tools, deadlock avoidance scheduling is highly required for various types of wafer flow patterns or various equipment configurations. In this paper, we propose a Petri net decomposition approach to deadlock avoidance scheduling for dual-armed cluster tools for semiconductor manufacturing. A timed Petri net model is introduced to represent scheduling problems for dual-armed cluster tools. In order to obtain a deadlock-free schedule, we develop a deadlock avoidance control policy that restricts the marking to prevent unmarked siphons. The developed control policy is combined with the Petri net decomposition approach to generate a better feasible solution. Computational results show the effectiveness of the proposed method combined with the deadlock avoidance policy.

DOI： 10.9746/sicetr.49.479

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Other Link： https://jlc.jst.go.jp/DN/JALC/10017886733?from=CiNii

Language：Japanese  

DOI： 10.11509/isciesci.57.3_108

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Elsevier BV  

DOI： 10.1016/j.procir.2013.05.020

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

Cluster tools are widely used for semiconductor manufacturing. Scheduling of multi-cluster tools has been received much attention. A cluster tool consists of several processing modules, transfer module with single or dual-armed handling robot and loadlock modules. The multi-cluster tool consists of two or three cluster tools which are connected with buffer module. In this paper, we present a deadlock-free scheduling for dual-armed multi-cluster tools with multi-flow. We develop a deadlock avoidance policy for dual-armed multi-cluster tools with dual path. A Petri net model is analyzed to derive a deadlock avoidance policy. The deadlock-freeness is confirmed by analyzing siphon of Petri net model. The deadlock-free scheduling method is proposed. The proposed method is applied to the scheduling problem of three-cluster tools with dual path. The computational results show that no deadlock occurs with the proposed deadlock avoidance policy.

DOI： 10.1109/IEEM.2013.6962484

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We study on the modeling and solution of practical airline crew scheduling problems. The problem determines a feasible assignment of flight duties to the set of crews. Two types of integer programming formulations with cell model and graph models are developed. An efficient solution algorithm based on labeling algorithm is proposed. The validity of the proposed models is compared by using a general-purpose solver. The computational results show that cell model is better than those of the graph model with less decision variables.

DOI： 10.1109/IEEM.2013.6962386

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：AMER SOC MECHANICAL ENGINEERS  

The pickup and delivery crude oil transportation scheduling problem is to find an optimal assignment of requests to a fleet of tankers, sequence of visiting places, and loading and unloading volume of demand simultaneously in order to minimize the total cost with the capacity of the tankers. The problem can be formulated as a split pickup and delivery vehicle routing problem. We apply a column generation algorithm to solve the problem efficiently. In order to obtain a feasible solution by column generation, we propose an effective algorithm to generate a feasible solution satisfying the set partitioning constraints. Computational results demonstrate the effectiveness of the proposed method.

DOI： 10.1115/ISFA2012-7169

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

In this paper, we propose a column generation heuristics to a continuous time model of conflict-free pickup and delivery vehicle routing problems. The transportation system is divided into several regions with regular intervals. A network model of conflict-free multi-vehicle routing problem with acceleration and deceleration motions is developed. A column generation heuristics is used to find a near-optimal solution. The pricing problem for each vehicle routing problem is formulated as a resource constrained shortest path problem, which is effectively solved by a labeling algorithm. Computational results demonstrate the effectiveness of the proposed method.

DOI： 10.1109/SMC.2013.74

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Language：Japanese   Publisher：THE INSTITUTE OF SYSTEMS, CONTROL AND INFORMATION ENGINEERS (ISCIE)  

The railway crew rostering problem aims to find an optimal assignment and cyclic sequence of crew duties to a set of rosters satisfying several labor conditions. We propose a novel decomposition approach to solve the railway crew rostering problem with the objective of fair labor condition in order to reduce the computational effort. The proposed method decomposes the original problem into two levels. The upper level master problem determines an assignment of crew duties to the set of rosters without sequencing, and the lower level subproblem generates a feasible cyclic sequence of crew duties including several resting times. Three types of effective cuts are proposed to reduce the feasible search space to tighten the gap between the solutions of the two level problems. Computational results demonstrate the effectiveness of the proposed method compared with that of the constrainted programming technique.

DOI： 10.5687/iscie.25.272

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC  

In this paper, we address a Petri net decomposition approach for simultaneous dispatching and conflict-free routing for bidirectional automated guided vehicle (AGV) systems in dynamic environments. To solve the dynamic problem, static problems for finding near-optimal dispatching and conflict-free routing are solved each time when transportation requests are given. The static problem is converted to an optimal firing sequence problem for a timed Petri net. A Petri net decomposition approach is applied to solve the problem efficiently. In the algorithm, the entire Petri net is decomposed into task and AGV subnets. The penalty function method is used to derive a solution for all subnets. A deadlock avoidance method is embedded in the proposed methodology to ensure the feasibility and the quality of the solution. Computational results show that the proposed method with a deadlock avoidance algorithm efficiently maximizes the throughput for dynamic situations.

DOI： 10.1109/TSMCA.2012.2183353

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Language：Japanese  

DOI： 10.11509/isciesci.56.8_454

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Authorship：Lead author   Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：The Institute of Electrical Engineers of Japan  

In this paper, we propose a column generation for the train-set scheduling problem with regular maintenance constraints. The problem is to allocate the minimum train-set to the train operations required to operate a given train timetable. In the proposed method, a tight lower bound can be obtained from the continuous relaxation for Dantzig-Wolfe reformulation by column generation. The subproblem for the column generation is an elementary shortest path problem with resource constraints. A labeling algorithm is applied to solve the subproblem. In order to reduce the computational effort for solving subproblems, a new state space relaxation of the subproblem is developed in the labeling algorithm. An upper bound is computed by a heuristic algorithm. Computational results demonstrate the effectiveness of the proposed method.

DOI： 10.1541/ieejeiss.132.151

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Other Link： http://id.ndl.go.jp/bib/023417004

Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Institute of Electrical Engineers of Japan  

In this paper, we propose a column generation for the train-set scheduling problem with regular maintenance constraints. The problem is to allocate the minimum train-set to the train operations required to operate a given train timetable. In the proposed method, a tight lower bound can be obtained from the continuous relaxation for Dantzig-Wolfe reformulation by column generation. The subproblem for the column generation is an elementary shortest path problem with resource constraints. A labeling algorithm is applied to solve the subproblem. In order to reduce the computational effort for solving subproblems, a new state space relaxation of the subproblem is developed in the labeling algorithm. An upper bound is computed by a heuristic algorithm. Computational results demonstrate the effectiveness of the proposed method. © 2012 The Institute of Electrical Engineers of Japan.

DOI： 10.1541/ieejeiss.132.151

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)  

The railway crew rostering problem is a scheduling problem to find a set of rosters that is feasible assignment of crew duties satisfying several labor conditions. We propose a novel decomposition approach to solve railway crew rostering problems with the objective of fair labor condition in order to reduce the computational effort. The upper level master problem determines an assignment of crew duties to the set of rosters and the lower level subproblem generates a feasible sequence of rosters including several labor conditions. The effective cuts are proposed to reduce feasible search space to tighten the duality gap between solutions of two level problems. Computational results demonstrate the effectiveness of the proposed method compared with that of the constraint programming technique. © 2012 IEEE.

DOI： 10.1109/CoASE.2012.6386407

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

The energy portfolio and production planning problem for multiple companies under energy constraints is formulated as a mixed integer nonlinear programming problem. A new Lagrangian decomposition and coordination approach is proposed to solve the problem effectively. In this paper, we propose efficient computation algorithms for lower bound and upper bound. The lower bound is computed by relaxing the nonlinear term in the objective function. The upper bound is derived by Lagrangian relax and fix heuristic that successively fixes the solution of subproblems to create a feasible solution. Computational results show that the proposed method can effectively solve the problem compared with conventional Lagrangian decomposition and coordination method.

DOI： 10.1109/IEEM.2012.6837966

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

The energy portfolio and production planning problem for multiple companies under energy constraints is formulated as a mixed integer nonlinear programming problem. A new Lagrangian decomposition and coordination approach is proposed to solve the problem effectively. In this paper, we propose efficient computation algorithms for lower bound and upper bound. The lower bound is computed by relaxing the nonlinear term in the objective function. The upper bound is derived by Lagrangian relax and fix heuristic that successively fixes the solution of subproblems to create a feasible solution. Computational results show that the proposed method can effectively solve the problem compared with conventional Lagrangian decomposition and coordination method.

DOI： 10.1109/IEEM.2012.6837966

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：ELSEVIER SCIENCE BV  

In this paper, we present a game theoretic model in which one manufacturer and its suppliers are coordinated. Quality levels of products from suppliers are considered. Due to fluctuation of demand resulting from varying quality levels, manufacturers are subject to demand-side risks. The objective of the research is to help decision making between manufacturers and suppliers in order to improve level of quality. Stackelberg game is applied to coordinate the manufacturer and suppliers Then, Pareto efficient solutions are utilized to generate supplier's optimal response functions. Computational experiments are conducted to demonstrate the effectiveness of the proposed method. (c) 2012 The Authors. Published by Elsevier B.V. Selection and/or peer-review under responsibility of Professor D. Mourtzis and Professor G. Chryssolouris.

DOI： 10.1016/j.procir.2012.07.044

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：ELSEVIER SCIENCE BV  

In this paper, we propose a distributed optimization approach to energy portfolio and production planning for multiple companies. The production planning problem for multiple companies with the selection of electricity procurement contracts under energy resource constraints is formulated as a mixed integer nonlinear programming problem. A Lagrangian decomposition and coordination technique is applied to solve the problem. The original problem is decomposed into several subproblems. Heuristics are applied to generate a feasible solution. Computational results show the proposed method can solve the problem effectively within a reasonable computation time. (c) 2012 The Authors. Published by Elsevier B.V. Selection and/or peer-review under responsibility of Professor D. Mourtzis and Professor G. Chryssolouris.

DOI： 10.1016/j.procir.2012.07.048

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)  

Semiconductor cluster tools are used to process a variety of silicon wafers for the fabrication of microelectronic components. The cluster tool system consists of several loadlock modules, processing devices, and material handling armed robots for transferring wafers between them. In order to improve total throughput of cluster tools, a deadlock-free scheduling is highly required for various types of wafer flow patterns or various equipment configurations. In this paper, we propose a Petri net decomposition approach to the optimization of scheduling of dual-armed cluster tools for semiconductor manufacturing. A timed Petri net model is introduced to represent scheduling problems for dual-armed cluster tools. In order to obtain a deadlock-free schedule, we develop a deadlock avoidance control policy that restricts the marking to prevent unmarked siphons. The developed control policy is combined with the Petri net decomposition approach to generate a feasible solution. Computational results show the effectiveness of the proposed method combined with deadlock avoidance policy. © 2012 IEEE.

DOI： 10.1109/CoASE.2012.6386362

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Language：English   Publishing type：Research paper (international conference proceedings)  

The railway crew rostering problem is a scheduling problem to find a set of rosters that is feasible assignment of crew duties satisfying several labor conditions. We propose a novel decomposition approach to solve railway crew rostering problems with the objective of fair labor condition in order to reduce the computational effort. The upper level master problem determines an assignment of crew duties to the set of rosters and the lower level subproblem generates a feasible sequence of rosters including several labor conditions. The effective cuts are proposed to reduce feasible search space to tighten the duality gap between solutions of two level problems. Computational results demonstrate the effectiveness of the proposed method compared with that of the constraint programming technique. © 2012 IEEE.

DOI： 10.1109/CoASE.2012.6386407

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：JAPAN SOC MECHANICAL ENGINEERS  

In this paper, we propose Petri net decomposition approach for bi-objective optimization of conflict-free routing for AGV systems. The objective is minimizing total traveling time and equalizing delivery time simultaneously. The dispatching and conflict-free routing problem for AGVs is represented as a bi-objective optimal firing sequence problem for Petri Net. A Petri net decomposition approach is proposed to solve the bi-objective optimization problem efficiently. The convergence of the proposed algorithm is improved reducing search region by the proposed coordination method. The effectiveness of the proposed method is compared with that of a nearest neighborhood dispatching method. Computational results are provided to show the effectiveness of the proposed method.

DOI： 10.1299/jamdsm.6.672

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：JAPAN SOC MECHANICAL ENGINEERS  

In this paper, we propose a heuristic algorithm to solve a practical ship scheduling problem for international crude oil transportation. The problem is considered as a vehicle routing problem with split deliveries. The objective of this paper is to find an optimal assignment of tankers, a sequence of visiting and loading volume simultaneously in order to minimize the total distance satisfying the capacity of tankers. A savings-based meta-heuristic algorithm with lot sizing parameters and volume assignment heuristic is developed. The proposed method is applied to solve a case study with real data. Computational results demonstrate the effectiveness of the heuristic algorithm compared with that of human operators.

DOI： 10.1299/jamdsm.6.687

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：ICIC INTERNATIONAL  

In this paper, we address a new integration of column generation and Lagrangian relaxation for solving flowshop scheduling problems to minimize the total weighted tardiness. In the proposed method, the initial columns are generated by using near-optimal dual variables for linear programming relaxation of Dantzig- Wolfe decomposition derived by the Lagrangian relaxation method. The column generation is executed just after the generation of base columns generated by near-optimal Lagrange multipliers. Computational results demonstrate that the integration of column generation and Lagrangian relaxation can drastically speed up the conventional column generation.

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In this paper, we propose an optimization algorithm for the routing of AGV systems to achieve the minimization of the deviation of delivery time and the minimization of the total transportation time. The dispatching and conflict-free routing problem for AGVs is represented as an optimal firing sequence problem for Petri Net. A Petri Net decomposition approach is applied to solve the multi-objective optimization problem efficiently. The effectiveness of the proposed method is compared with that of the conventional method. Computational results show the effectiveness of the proposed method.

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In this paper, we consider a railway crew scheduling problem. In order to solve it, a mathematical programming method has been used. This method enumerates railway crew pairings, and solves a set covering problem. However, since the general mathematical programming approach requires much computational time, it is difficult to find a good schedule in an acceptable computational time for a large-scale problem. In order to reduce the computational time, we propose some heuristics for enumerating railway crew pairings and solving a set covering problem efficiently. Numerical results using a large-scale actual data show efficiencies of the proposed method.

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：PERGAMON-ELSEVIER SCIENCE LTD  

We address a bilevel decomposition algorithm for solving the simultaneous scheduling and conflict-free routing problems for automated guided vehicles. The overall objective is to minimize the total weighted tardiness of the set of jobs related to these tasks. A mixed integer formulation is decomposed into two levels: the upper level master problem of task assignment and scheduling; and the lower level routing subproblem. The master problem is solved by using Lagrangian relaxation and a lower bound is obtained. Either the solution turns out to be feasible for the lower level or a feasible solution for the problem is constructed, and an upper bound is obtained. If the convergence is not satisfied, cuts are generated to exclude previous feasible solutions before solving the master problem again. Two types of cuts are proposed to reduce the duality gap. The effectiveness of the proposed method is investigated from computational experiments. (C) 2010 Elsevier Ltd. All rights reserved.

DOI： 10.1016/j.cor.2010.08.012

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The railway crew scheduling problem is to determine an optimal crew assignment for the railway timetable data by minimizing the required number of crew members in order to satisfy the set covering constraints. Column generation is one of the optimization methods that can solve the problem efficiently. In order to improve the convergence of column generation, we propose dual inequalities for a Dantzig-Wolfe decomposition of railway crew scheduling problems to reduce the number of replications in the column generation procedure. The dual inequalities are generated from the characteristic of the given railway timetable data. Computational results demonstrate the effectiveness of the proposed method compared with the conventional column generation for an actual railway timetable data. © 2011 Springer-Verlag.

DOI： 10.1007/s12469-011-0037-x

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

We propose a column generation algorithm for solving sequence dependent flowshop scheduling problems (SDST flowshop) to minimize the total weighted tardiness. The continuous relaxation of the set partitioning formulation of the Dantzig-Wolfe decomposition for SDST flowshop is solved by the column generation. The pricing subproblem for the column generation is decomposed into each job-level subproblem. In order to strengthen the lower bound, the additional constraints are imposed to the pricing problems in the proposed algorithm. The cut generation algorithm is developed for the column generation. Computational experiments show that the proposed method can derive the solutions with a smaller duality gap compared with those of the conventional algorithm of ordinary column generation and Lagrangian relaxation with cuts.

DOI： 10.1109/INDIN.2010.5549634

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

We propose a column generation algorithm for solving sequence dependent flowshop scheduling problems (SDST flowshop) to minimize the total weighted tardiness. The continuous relaxation of the set partitioning formulation of the Dantzig-Wolfe decomposition for SDST flowshop is solved by the column generation. The pricing subproblem for the column generation is decomposed into each job-level subproblem. In order to strengthen the lower bound, the additional constraints are imposed to the pricing problems in the proposed algorithm. The cut generation algorithm is developed for the column generation. Computational experiments show that the proposed method can derive the solutions with a smaller duality gap compared with those of the conventional algorithm of ordinary column generation and Lagrangian relaxation with cuts.

DOI： 10.1109/ICSMC.2011.6083859

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

In this paper, we propose a Petri Net decomposition approach for solving the bi-objective conflict-free routing for AGV systems. The objective is to minimize the deviation of delivery time and to minimize the total transportation time. The dispatching and conflict-free routing problem for AGVs is represented as an optimal firing sequence problem for Petri Net. A Petri Net decomposition approach is applied to solve the multi-objective optimization problem efficiently. The convergence of the algorithm is improved by reducing the search region. The effectiveness of the proposed method is compared with that of the conventional method. Computational results show the effectiveness of the proposed method.

DOI： 10.1109/ICSMC.2011.6083754

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Authorship：Lead author   Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：Institute of Electrical Engineers of Japan  

In this paper, we propose a column generation method for the railway crew scheduling problem with a variety of practical constraints such as lower and upper constraints of one continuous traveling, total working time, admissible number of crews, and meal time constraints. The proposed method consists of the two steps. The first step is to derive a lower bound by the column generation and the second step is to generate a feasible solution by a heuristic method. In the proposed method, a label setting algorithm is developed to solve the pricing problem efficiently to reduce computational time. A new dominance condition is developed to eliminate unnecessary states in the label setting algorithm. A heuristic algorithm is also proposed to reduce the generation of the infeasible solutions violating the constraint on the number of the allocated crews. The effectiveness of the proposed method is evaluated by using a real railway data. © 2011 The Institute of Electrical Engineers of Japan.

DOI： 10.1541/ieejeiss.131.1199

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Authorship：Lead author   Language：Japanese   Publishing type：Research paper (scientific journal)  

Quantity discount is the decision making of trade price between suppliers and manufacturers while the production is changeable due to the unknown demand in the real market. In this paper, the supply chain planning problem which considers the selection of the contracted suppliers, production quantity and inventory simultaneously is addressed. By considering those factors, the problem is formulated as a mixed integer nonlinear programming problem (MTNLP). We propose an outer approximation algorithm to solve the supply chain planning problems with quantity discount policy under demand uncertainty. Computational results show that the proposed method is effective for large scale problems compared with the conventional algorithm. The goal of research is to incorporate quantity discount policy into the mathematical model in order to help the decision making in the supply chain management. © 2011 The Japan Society of Mechanical Engineers.

DOI： 10.1299/kikaic.77.4325

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Nowadays, the new trend of global economy has driven the business strategy from the local to the global supply chain optimization. The comprehensive logistics solutions are critical to integrate supply chain management in the global landscape. We propose a column generation algorithm to solve the ship scheduling problem for international crude oil distribution. The problem is to find an optimal assignment and sequence and loading volume of demand simultaneously in order to minimize the total distance satisfying the capacity of the tankers. The problem can be generalized as split delivery routing problem. In this paper, an efficient algorithm by using the column generation is developed to generate a feasible solution taking into account for several practical constraints. The performance of the proposed method is compared with a heuristic algorithm and that of human operators. Computational results demonstrate the effectiveness of the proposed algorithm for a real case study. © 2011 IEEE.

DOI： 10.1109/CASE.2011.6042414

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC  

In this paper, we propose a Petri Net (PN) decomposition approach to the optimization of route planning problems for automated guided vehicles (AGVs) in semiconductor fabrication bays. An augmented PN is developed to model the concurrent dynamics for multiple AGVs. The route planning problem to minimize the total transportation time is formulated as an optimal transition firing sequence problem for the PN. The PN is decomposed into several subnets such that the subnets are made independent by removing the original shared places and creating its own set of resource places for each subnet with the appropriate connections. The partial solution derived at each subnet is not usually making a feasible solution for the entire PN. The penalty function algorithm is used to integrate the solutions derived at the decomposed subnets. The optimal solution for each subnet is repeatedly generated by using the shortest-path algorithm in polynomial time with a penalty function embedded in the objective function. The effectiveness of the proposed method is demonstrated for a practical-sized route planning problem in semiconductor fabrication bay from computational experiments.
Note to Practitioners-For rapid growth of transportation systems for semiconductor fabrication bay, it is required to generate a collision-free route planning for multiple AGVs efficiently to minimize the total traveling time. Conventional decomposition techniques for mathematical programming need reformulation of the original problem when new constraints are added, that makes it extremely difficult to modify the optimization model. PNs can be used as a tool to integrate modeling, simulation, and optimization for designing transportation systems. This paper presents a modeling and optimization of route planning for multiple AGVs by PN. The main difficulty for optimization is the state explosion for PN. To reduce computational complexity, we propose a PN decomposition approach for solving large-scale optimization problems efficiently. The PN model is partitioned into several decomposed subnets. The local solutions of the subnets are integrated by the novel optimization algorithm. The shorter computation time by the proposed method enables us to use the proposed methodology for the rapid design and dynamic optimization for AGVs transportation systems in semiconductor manufacturing.

DOI： 10.1109/TASE.2010.2043096

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：PERGAMON-ELSEVIER SCIENCE LTD  

In this paper, we address a new Lagrangian relaxation (LR) method for solving the hybrid flowshop scheduling problem to minimize the total weighted tardiness. For the conventional LR, the problem relaxing machine capacity constraints can be decomposed into individual job-level subproblems which can be solved by dynamic programming. The Lagrangian dual problem is solved by the subgradient method. In this paper, a Lagrangian relaxation with cut generation is proposed to improve the Lagrangian bounds for the conventional LR. The lower bound is strengthened by imposing additional constraints for the relaxed problem. The state space reductions for dynamic programming for subproblems are also incorporated. Computational results demonstrate that the proposed method outperforms the conventional LR method without significantly increasing the total computing time. (C) 2009 Elsevier Ltd. All rights reserved.

DOI： 10.1016/j.cor.2009.04.008

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Language：Japanese   Publisher：The Institute of Systems, Control and Information Engineers  

DOI： 10.11509/sci.sci10.0.140.0

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Language：Japanese   Publisher：The Institute of Systems, Control and Information Engineers  

DOI： 10.11509/sci.sci10.0.139.0

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Language：Japanese   Publisher：The Institute of Systems, Control and Information Engineers  

DOI： 10.11509/sci.sci10.0.44.0

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

In this paper, we propose a Petri Net decomposition approach for dynamic dispatching and routing for bi-directional AGV systems. For dynamic dispatching and routing, the static problem to determine an optimal firing sequence problem for Petri Net is solved repeatedly. A Petri Net decomposition approach is applied to solve the optimal firing sequence problem efficiently. In the decomposition algorithm, the entire Petri Net is decomposed into task subnets and AGV subnets. The penalty function algorithm is used to coordinate the solution of each subnet. A deadlock avoidance policy for bi-directional lane is used to ensure the generation of conflict-free routing. Computational results show the effectiveness of the proposed Petri Net decomposition approach with deadlock avoidance policy for dynamic situations.

DOI： 10.1109/ICSMC.2010.5641942

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：JAPAN SOC MECHANICAL ENGINEERS  

In this paper, we propose an application of Petri Net decomposition approach for the simultaneous optimization of dispatching and conflict-free routing for automated guided vehicles in the dynamic situation where transport requests are given in real time. The objective is to maximize the total throughput of the AGV transport system during the time horizon. In order to solve the dynamic problem, static problems are periodically solved when the transport requests are given to the AGV system. The dispatching and conflict-free routing are simultaneously optimized by the Petri Net decomposition approach. In the proposed method, the Petri Net is decomposed into several subnets for task subproblems and AGV subproblems that can be solved by the shortest path algorithm on the reachability graph. The local solutions for the subnets are coordinated by a penalty function algorithm. To ensure the generation of conflict-free routing, a new deadlock avoidance strategy is incorporated in the optimization algorithm. The effects of simultaneous optimization of dispatching and conflict-free routing are investigated for routing problems in dynamic environments.

DOI： 10.1299/jamdsm.4.701

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Institute of Electrical Engineers of Japan  

The railway crew scheduling problem is to determine the assignment of crews to each traveling task to minimize the number of crews satisfying set covering constraints. Column generation method is often applied to solve railway crew scheduling problems. In this paper, we propose dual inequalities for railway crew scheduling problems to reduce the number of iterations in the column generation method. Three types of dual inequalities are generated from railway timetable data and they are added to the restricted master problem. Computational results show the effectiveness of the proposed method compared with the conventional column generation method. © 2010 The Institute of Electrical Engineers of Japan.

DOI： 10.1541/ieejeiss.130.275

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：TAYLOR & FRANCIS LTD  

This paper presents an optimisation model and its effective solution technique using beam search heuristic for floor-storage warehousing systems. For a floor-storage system, storage can be accessed from the top of stacks only. The objective is to minimise the number of re-handling operations by optimally determining the storage location and by grouping products for each customer that fit a given sequence for receiving and retrieving operations. An integer programming model is formulated and an approximate solution technique based on the beam search method is proposed to solve the problem by incorporating effective heuristics to reduce the search space using future receiving and retrieving requests. The effectiveness of the proposed method is demonstrated for industrial warehousing problems in a steel plant with 58 storage areas involving more than 3000 retrieving operations. The proposed solution method is shown to be more efficient than the traditional branch-and-bound method for solving integer programming problems.

DOI： 10.1080/00207540802603767

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：JAPAN SOC MECHANICAL ENGINEERS  

In this paper, we propose an application of Petri Net decomposition approach for the simultaneous optimization of dispatching and conflict-free routing for automated guided vehicles in the dynamic situation where transport requests are given in real time. The objective is to maximize the total throughput of the AGV transport system during the time horizon. In order to solve the dynamic problem, static problems are periodically solved when the transport requests are given to the AGV system. The dispatching and conflict-free routing are simultaneously optimized by the Petri Net decomposition approach. In the proposed method, the Petri Net is decomposed into several subnets for task subproblems and AGV subproblems that can be solved by the shortest path algorithm on the reachability graph. The local solutions for the subnets are coordinated by a penalty function algorithm. To ensure the generation of conflict-free routing, a new deadlock avoidance strategy is incorporated in the optimization algorithm. The effects of simultaneous optimization of dispatching and conflict-free routing are investigated for routing problems in dynamic environments.

DOI： 10.1299/jamdsm.4.701

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We propose a new formulation for the column generation for solving flowshop scheduling problems to minimize the total weighted tardiness to derive tighter bounds. In the proposed method, each column represents a two-jobs schedule where the operations of two jobs do not overlap with each other. We show that the lower bound for the original problem can be strengthened by the proposed formulation without significantly increasing the total computing time. Computational experiments demonstrate that the proposed method can derive the solutions with a smaller duality gap compared with those of the conventional formulation. © 2010 IEEE.

DOI： 10.1109/INDIN.2010.5549634

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DOI： 10.9746/sicetr.45.369

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)  

Railway crew scheduling problem is the problem to determine an optimal assignment of crews to each train to minimize the number of crews satisfying set covering constraints. Column generation is an effective technique for solving the railway crew scheduing problems. The problem is formulated as a huge set covering problem. Column generation requires a lot of replications to derive a lower bound. Moreover, it takes considerable computation time to obtain an upper bound using the conventional method (branch and bound for restricted columns). In this paper, we propose an improvement of column generation for railway crew scheduling problems to reduce computational effort. Dual inequalities for deriving a lower bound and a heuristic to construct a feasible solution are proposed. Computational results show the effectiveness of the proposed method compared with those of the conventional column generation.

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Authorship：Lead author   Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：THE INSTITUTE OF SYSTEMS, CONTROL AND INFORMATION ENGINEERS (ISCIE)  

We propose a simultaneous optimization method for task assignment and routing problems with multiple AGVs by the decomposition of Petri Nets. In the proposed method, Petri Net is decomposed into several subnets representing task subproblems and AGV subproblems. Each subproblem is solved by Dijkstra's algorithm. The subproblem on each subnet is repeatedly solved until a feasible solution for the original problem is derived. A solution method for subproblems with no final marking is newly developed. The effectiveness of the proposed method is demonstrated by comparing the performance with CPLEX as well as a nearest neighborhood heuristic method.

DOI： 10.5687/iscie.22.191

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Language：Japanese   Publisher：The Japan Joint Automatic Control Conference  

We propose a column generation with cut generation for flowshop scheduling problems. In the proposed method, constraints for eliminating some of violation for machine capacity constraints are imposed to the pricing problems for arbitrarily selected two jobs. We show that lower bound can be strengthened by the cut generation without significantly increasing total computing time. Computational experiments show that the proposed method can derive solutions with smaller duality gap compared with those of the conventional column generation.

DOI： 10.11511/jacc.52.0.2.0

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Language：Japanese   Publisher：The Japan Joint Automatic Control Conference  

In this paper we consider a railway crew scheduling problem with a variety of practical constraints. The problem consists in finding a minimum crew schedule that covers all of traveling time table satisfying several crew working constraints. We propose a column generation technique to solve the problem. Based on the approach, the pricing problem can be formulated as a huge shortest path problem with resource constraints. We model several constraints such as meal breaks, the upper limitation of riding time and so on. Computational experiments are conducted to show the feasibility of the proposed approach for a practical large scale crew scheduling problem.

DOI： 10.11511/jacc.52.0.128.0

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

In this paper, we address a new integration of column generation and Lagrangian relaxation for solving flowshop scheduling problems to minimize the total weighted tardiness. In the proposed method, initial columns are generated by using near-optimal dual solution using the Lagrange multipliers derived by Lagrangian relaxation method. After the generation of base columns, the column generation is executed. Computational results demonstrate that the integrated column generation and Lagrangian relaxation can drastically speed up the conventional column generation.

DOI： 10.1109/ICSMC.2009.5346159

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：TAYLOR & FRANCIS LTD  

In this paper, we propose a Lagrangian relaxation method for solving routing problems for multiple AGVs by decomposition of timed Petri nets. The AGV routing problem is represented by an optimal transition firing sequence problem for timed Petri nets. The timed Petri net is decomposed into several subnets in which the subproblem for each subnet can be easily solved by Dijkstra&apos;s algorithm. We show that each subproblem generated by each subnet is polynomially solvable. The optimality of the solution can be evaluated by the duality gap derived by the Lagrangian relaxation method. The performance of the proposed method is compared with a conventional optimisation algorithm with the penalty function method. The effectiveness of the proposed method is demonstrated.

DOI： 10.1080/00207540701846244

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

In this paper, we propose a general decomposition method for transition firing sequence problems for first order hybrid Petri Nets. The optimal transition firing sequence problem for first-order hybrid Petri Nets is formulated as a mixed integer programming problem. We propose a Lagrangian relaxation method for solving optimal transition firing sequence problems. The hybrid Petri Net is decomposed into several subnets in which the optimal firing sequence for each subnet is easily solved. The optimality of solution can be evaluated by duality gap derived by Lagrangian relaxation method. The proposed method is applied to a small-scale example. Computational experiments demonstrate the validity of the proposed formulation.

DOI： 10.1109/ICSMC.2009.5345896

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Language：Japanese   Publisher：The Japan Joint Automatic Control Conference  

We propose a decomposition method for jobshop scheduling problems by timed automata. Jobshop problem is represented as an optimal state transition problem for timed automata with the parallel composition of submodels composed of jobs and resources in which the latter can be idle or busy. A new decomposition algorithm is proposed to solve the problem. The validity of the proposed decomposition algorithm is confirmed by satisfying the decomposable condition.

DOI： 10.11511/jacc.52.0.1.0

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Language：Japanese   Publisher：The Japan Joint Automatic Control Conference  

For rapid growth of transportation systems for semiconductor fabrication bays, it is required to generate a conflict-free route planning for multiple AGVs efficiently to minimize total traveling time in real time. In this paper, we propose a simultaneous optimization method for task assignment and routing problems represented by Petri Nets. In the proposed method, the Petri Net is decomposed into several subnets for task assignment subproblem and AGV routing subproblem. Each subproblem can be solved by shortest path algorithm. The effectiveness of the proposed method is confirmed by comparing the performance with the conventional method in dynamic environments where requests are given in real time.

DOI： 10.11511/jacc.52.0.127.0

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Publisher：The Institute of Systems, Control and Information Engineers  

DOI： 10.11509/sci.sci09.0.351.0

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Publisher：The Institute of Systems, Control and Information Engineers  

DOI： 10.11509/sci.sci09.0.279.0

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Publisher：The Institute of Systems, Control and Information Engineers  

DOI： 10.11509/sci.sci09.0.277.0

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DOI： 10.3182/20080706-5-KR-1001.2316

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC  

Planning coordination for multiple companies has received much attention from viewpoints of global supply chain management. In practical situations, a plausible plan for multiple companies should be created by mutual negotiation and coordination without sharing such confidential information as inventory costs, setup costs, and due date penalties for each company. In this paper, we propose a framework for distributed optimization of supply chain planning using an augmented Lagrangian decomposition and coordination approach. A feature of the proposed method is that it can derive a near-optimal solution without requiring all of the information. The proposed method is applied to supply chain planning problems for a petroleum complex, and a midterm planning problem for multiple companies. Computational experiments demonstrate that the average gap between a solution derived by the proposed method and the optimal solution is within 3% of the performance index, even though only local information is used to derive a solution for each company.
Note to Practitioners-Supply Chain Management (SCM) has received much attention with growing mass customization and global market demands. The global performance of SCM is achieved through cooperation and interaction among several organizations. For the petroleum industry, multiple companies within a chemical complex are depending on their suppliers to provide raw materials or intermediates through a pipeline connected with multiple companies to provide efficient delivery with lower costs. In most plants, production instructions are given to each production section in each company. Each company is thoroughly capable in regard to its own production planning and scheduling. Therefore, each company may have its own desirable supply and demand plan for each section. The supply and demand plan must be coordinated by mutual negotiations across the supply chain. Such coordination has been performed by the communications among human operators. However, in recent years, the decision making for each company is becoming increasingly complex with the increase of the number of alternative for the plan with a number of partner companies. Conventional planning systems has been configured to obtain a near-optimal plan using the detailed information about multiple companies. However, organizations generally have their own private coordination methods, and that accessing others' private information or intruding on their decision-making authority should be avoided. In practical situations, a plan must be created without sharing such confidential information as production costs, inventory holding costs, or price of products for competitive companies. In this paper, we propose a framework for distributed supply chain planning system without requiring all of information for multiple companies. Planning coordination can be efficiently automated by the proposed system. Computational experiments demonstrate that the performance of the solution is 3% of gap from an optimal solution with a reasonable computation time, even though only local information is used to derive a solution for each company.

DOI： 10.1109/TASE.2007.894727

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：ICIC INT  

Anew type of interactive master slave manipulation system for tele-operation is developed. The system consists of a master and a slave subsystem with different structure. These two subsystems are governed by bilateral control scheme. For master slave manipulation systems, nonlinear interferences may deteriorate performance in the bilateral control systems. To realize a sense of force for slave manipulation, the bilateral control scheme together with a decoupling control for slave is introduced to the master manipulator with different configurations. Experimental results demonstrate the effectiveness of the proposed control method.

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：ICIC INT  

In this study, we propose a decentralized optimization method for production scheduling, transportation routing for AGVs and motion planning for material handling robots simultaneously. The system consists of a process agent that creates production schedule, AGV agents to generate collision-free routing for multiple AGVs, and a handling agent that determines motion planning for material handling system. Each agent repeats the information exchange and replanning until a feasible solution is derived. The effectiveness of the proposed method is confirmed by comparing the results of the proposed method and those of the conventional method. Experimental study demonstrates the feasibility of the proposed method.

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：ICIC INT  

In this paper, influence of safety parameters for inventory control method on bullwhip effects is studied for supply chain planning for multiple companies under demand uncertainty. The production planning model for multiple companies under uncertain demand situation is developed to represent actual planning environments where demand variances in the future periods are gradually available with respect to the progress of time steps. We examine the bullwhip effect caused by the inventory control method in consideration of safety stock. The relationship between the total profit and bullwhip effects is investigated from numerical simulations.

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

In this paper, we propose a decomposition and coordination method for timed automata for modeling and solution of scheduling problems. Parallel composition of timed automata makes it possible to describe concurrent dynamics for several submodels represented by timed automata. To reduce state space explosion for parallel composition of timed automata, a decomposition and coordination method is developed. In the proposed method, a feasible solution is derived by coordinating the solution of each decomposed subproblem. Computational experiments demonstrate the feasibility of the proposed method.

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

In this paper, we propose a Lagrangian decomposition and coordination method for solving scheduling problems by the decomposition of timed Petri Nets. The timed Petri Net is decomposed into several subnets so that the subproblem for each subnet can be easily solved. The state space analysis is utilized to determine the decomposition strategy for timed Petri Nets. The Lagrangian decomposition and coordination technique is developed to evaluate the optimality of solution. The proposed method is applied to AGV routing problems and flowshop scheduling problems. The effectiveness of the proposed method is demonstrated by comparing the performance with the conventional method.

DOI： 10.1109/SICE.2008.4654914

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

In this paper, we propose a simultaneous optimization method for task assignment and routing problems for multiple AGVs by decomposition of Petri Nets. In the proposed method, the Petri Net is decomposed into several subnets for task subproblems and AGV subproblems. Each subproblem is solved by Dijkstra's algorithm. The solution of subproblem for each subnet is repeated until a feasible solution for the original problem is obtained. In this study, a new solution method for subproblems with no final marking is developed. The effectiveness of the proposed method is confirmed by comparing the performance with conventional methods.

DOI： 10.1109/COASE.2008.4626458

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

In this paper, we propose a decomposition and coordination method for timed automata for modeling and solution of scheduling problems. Parallel composition of timed automata makes it possible to describe concurrent dynamics for several submodels represented by timed automata. To reduce state space explosion for parallel composition of timed automata, a decomposition and coordination method is developed. In the proposed method, a feasible solution is derived by coordinating the solution of each decomposed subproblem. Computational experiments demonstrate the feasibility of the proposed method.

DOI： 10.1109/ICSMC.2008.4811664

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)  

Petri net model is a widely used versatile tool which can represent a discrete event system such as mechanical system, communication system, and production system. The merit of this model is easiness in handling. Other merit of the model is descriptive easiness in parallel, synchronous operations. In addition, it is applicable to a wide variety of discrete event system due to its excellence in the analytical ability and the handling ability. In this paper, application of Petri net solver by decomposition to the Semiconductor Manufacturing. Numerical results showed the practicability of the proposed method. © 2007 IEEE.

DOI： 10.1109/ICICIC.2007.451

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Language：Japanese   Publisher：The Institute of Systems, Control and Information Engineers  

DOI： 10.11509/sci.sci08.0.26.0

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Language：Japanese   Publisher：The Institute of Systems, Control and Information Engineers  

DOI： 10.11509/sci.sci08.0.25.0

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Language：Japanese   Publisher：The Institute of Systems, Control and Information Engineers  

DOI： 10.11509/sci.sci08.0.218.0

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Language：Japanese   Publisher：The Institute of Systems, Control and Information Engineers  

DOI： 10.11509/sci.sci08.0.114.0

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Language：English   Publishing type：Research paper (scientific journal)  

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Authorship：Lead author   Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：THE INSTITUTE OF SYSTEMS, CONTROL AND INFORMATION ENGINEERS (ISCIE)  

In this paper, we propose a decompositon method for simultanenous optimization problem of production scheduling and transportation routing for semiconductor fabrication bays. The original problem is decomposed into an upper level subproblem to determine production scheduling and assignment of requests to AGVs and a lower level subproblem to derive a collision-free routing for AGVs. The novel idea of the proposed method is that the upper level subproblem is solved by using Lagrangian relaxation technique incorporating cuts generated from a solution of the lower level subproblem. The algorithm solves successively the upper level problem and the lower level problem until a feasible solution for original problem is derived. The entire search space is reduced by incorporating cuts. The effectiveness of the proposed method is investigated from numerical experiments.

DOI： 10.5687/iscie.20.465

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)  

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：PERGAMON-ELSEVIER SCIENCE LTD  

In real transportation environments for semi-conductor manufacturing, unexpected disturbances such as motion delays and/or sudden requests for transportation may cause collisions among AGVs increasing total transportation time. It is required to generate a collision-free routing for multiple automated guided vehicles (AGVs) within a few seconds. In this paper, we propose a distributed routing method under motion delay disturbance for multiple AGVs. The proposed method features a characteristic that each AGV subsystem derives its optimal route to minimize the sum of the transportation time and the penalties with respect to collision probability with other AGVs. The penalties reflect the collision probability distribution function for motion delay disturbances. The proposed method is applied to a routing problem for transportation in the semiconductor fabrication bay with 143 nodes and 20 AGVs. The computational results show that the total transportation time obtained by the proposed method is shorter than that of the conventional method. For dynamic transportation environments, an optimal timing for re-routing multiple AGVs under motion delay disturbance is systematically determined by the tradeoff relationship between the total computation time to derive a solution and the uncertainties for re-routings. Markov chain is used to represent uncertainty distribution for re-routings. The proposed method is implemented in an experimental transportation system with 51 nodes and 5 AGVs. The experimental results demonstrate that the proposed method is applicable to real transportation environments. (C) 2006 Elsevier Ltd. All rights reserved.

DOI： 10.1016/j.rcim.2006.10.007

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：PERGAMON-ELSEVIER SCIENCE LTD  

A distributed decision making system for integrated optimization of production scheduling and distribution planning is proposed. The system consists of several subsystems that individually derive the solution of its own subproblem to minimize the objective function obtained by decomposing the problem using an augmented Lagrangian decomposition and coordination technique. The proposed approach is applied to planning and scheduling for an aluminum rolling processing line. Decision variables such as arrival date of raw materials, lot-sizing, production scheduling, and allocation of products to warehouses are optimized simultaneously by repeating local optimization in material resource planning, and scheduling subsystem for each production process and warehouse planning subsystem. The effectiveness of the proposed approach is demonstrated. (c) 2006 Elsevier Ltd. All rights reserved.

DOI： 10.1016/j.compchemeng.2006.10.006

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Authorship：Lead author   Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：The Society of Instrument and Control Engineers  

In this paper, we propose a decomposition and coordination method for timed Petri Nets to determine an optimal firing sequence to minimize the given objective function. The timed Petri Net is decomposed into several subnets in which the optimal firing sequence for each subnet is easily solved by Dijkstra's algorithm in polynomial computing time. The proposed method is applied to a flowshop scheduling problem. The effectiveness of the proposed method is confirmed by comparing the performance between the simulated annealing method.

DOI： 10.9746/ve.sicetr1965.43.338

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Authorship：Lead author   Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：THE INSTITUTE OF SYSTEMS, CONTROL AND INFORMATION ENGINEERS (ISCIE)  

In this paper, we propose an inventory control method for supply chain planning for single stage production systems under uncertain demand. The planning and scheduling model for uncertain demand situation is developed to represent actual environments for planning and scheduling where variances of future demand are gradually available as the time proceeds. In the proposed method, the amount of shipping for raw materials and safety stock for final products are determined by a stochastic approach by using demand average and variation in feedforward. The quantity of safety stock of inventory for final products is determined by incorporating feedback information from the detailed scheduling results. The effectiveness of the proposed methods is investigated from numerical simulations.

DOI： 10.5687/iscie.20.1

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：JAPAN SOC MECHANICAL ENGINEERS  

Considering the need to develop general scheduling problem solver, the recent integration of Petri Nets as modeling tools into effective optimization methods for scheduling problems is very promising. The paper addresses a Petri Net modeling and decomposition method for solving a wide variety of scheduling problems. The scheduling problems are represented as the optimal transition firing sequence problems for timed Petri Nets. The Petri Net is decomposed into several subnets in which each subproblem can be easily solved by Dijkstra' algorithm. The approach is applied to a flowshop scheduling problem. The performance of the proposed algorithm is compared with that of a simulated annealing method.

DOI： 10.1299/jamdsm.1.262

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：JAPAN SOC MECHANICAL ENGINEERS  

Raw materials for industrial steel-making plants need to be stored in material yards in order to satisfy various production requirements. The stored materials are supplied to production plants by belt-conveyors with multiple alternative transportation routes. The simultaneous optimization of the storage allocation and transportation routing for the raw materials yard is required for achieving production that is flexible to changes in demand. In this paper, we propose a Lagrangian decomposition and coordination approach for storage allocation and routing for a belt-conveyor transportation system to handle demand changes. The overall problem is decomposed into an allocation planning subproblem and a transportation routing subproblem. A near-optimal solution for allocation planning and transportation routing is derived by repeating each optimization of subproblems and multiplier updates. The effectiveness of the proposed approach is confirmed in numerical experiments.

DOI： 10.1299/jamdsm.1.250

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

In this paper, we define the basic class of the structure of Petri Nets with polynomial number of states. A decomposition procedure is proposed to decompose the Petri Net into the basic class of Petri Nets. The method is effectively applied to decomposition and coordination algorithm for solving transition firing sequence problems for Petri Nets. The effectiveness of the proposed decomposition procedure is investigated from numerical experiments.

DOI： 10.1109/SICE.2007.4421043

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

Conventional methods for simultaneous production scheduling and routing problem have hierarchical decomposition scheme where production scheduling problem and routing problem are separately solved due to computational complexity. We propose a novel decomposition method with cut generation. The entire problem is decomposed into a upper level subproblem for production scheduling and task assignment problem and a lower level subproblem for collision-free routing of multiple AGVs. The novel idea of the proposed method is the upper level subproblem which is solved by using Lagrangian relaxation technique incorporating cuts generated from a solution of the lower level subproblem. The algorithm solves successively the upper level problem and the lower level problem until a near-optimal solution for original problem is derived. The effectiveness of the proposed method is investigated from numerical experiments.

DOI： 10.1109/COASE.2007.4341680

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

Lagrangian relaxation technique has been used for solving a wide variety of scheduling problems to obtain near optimal solutions. The approach has been successfully applied to jobshop scheduling problems by relaxing the capacity constraints on machines by using Lagrange multipliers. The relaxed problem can be decomposed into independent job-level subproblems which can be solved by dynamic programming. By extending the technique, in this paper, we propose a successive Lagrangian relaxation method for solving flowshop scheduling problems with total weighted tardiness. In the proposed method, the quality of lower bound is improved by successively solving the Lagrangian dual problem embedding cuts into the Lagrangian relaxation problem. The state space reduction for dynamic programming is also incorporated. The effectiveness of the proposed method is demonstrated from numerical experiments.

DOI： 10.1109/COASE.2007.4341703

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：JAPAN SOC MECHANICAL ENGINEERS  

Raw materials for industrial steel-making plants need to be stored in material yards in order to satisfy various production requirements. The stored materials are supplied to production plants by belt-conveyors with multiple alternative transportation routes. The simultaneous optimization of the storage allocation and transportation routing for the raw materials yard is required for achieving production that is flexible to changes in demand. In this paper, we propose a Lagrangian decomposition and coordination approach for storage allocation and routing for a belt-conveyor transportation system to handle demand changes. The overall problem is decomposed into an allocation planning subproblem and a transportation routing subproblem. A near-optimal solution for allocation planning and transportation routing is derived by repeating each optimization of subproblems and multiplier updates. The effectiveness of the proposed approach is confirmed in numerical experiments.

DOI： 10.1299/jamdsm.1.250

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE COMPUTER SOC  

In this paper, we propose a general decomposition and coordination method for timed Petri Nets to determine an optimal transition firing sequence to minimize an objective function. A timed Petri Net model for multiple entities is decomposed into several subnets in which the optimal firing sequence for each subnet is easily solved in polynomial computational complexity. The solution of each subproblem is coordinated by repetitive optimization of the number of tokens for duplicated places. The proposed method is applied to a flowshop scheduling problem. The effectiveness of the proposed method is confirmed by comparing the performance between the simulated annealing method.

DOI： 10.1109/ISADS.2007.28

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Language：Japanese   Publisher：The Institute of Systems, Control and Information Engineers  

DOI： 10.11509/sci.sci07.0.52.0

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Language：Japanese   Publisher：The Institute of Systems, Control and Information Engineers  

DOI： 10.11509/sci.sci07.0.225.0

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Language：Japanese   Publisher：The Institute of Systems, Control and Information Engineers  

DOI： 10.11509/sci.sci07.0.49.0

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Language：Japanese   Publisher：The Institute of Systems, Control and Information Engineers  

DOI： 10.11509/sci.sci07.0.48.0

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Language：Japanese  

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Authorship：Lead author   Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：THE INSTITUTE OF SYSTEMS, CONTROL AND INFORMATION ENGINEERS (ISCIE)  

In this paper, we propose a decomposition and coordination method for routing problems for multiple automated guided vehicles (AGVs) using Petri Nets. An extended Petri Net model is created to represent concurrent motion of multiple AGVs. The routing problem to minimize total transportatin time is formulated by the proposed Petri Net. The optimization model for the Petri Net is decomposed into several subproblems which can be solved by Dijkstra's algorithm in polynomial order. The effectiveness of the proposed method is evaluated by several numerical examples.

DOI： 10.5687/iscie.19.426

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Other Link： https://jlc.jst.go.jp/DN/JALC/00287396681?from=CiNii

Authorship：Lead author   Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：The Society of Instrument and Control Engineers  

In this paper, we propose a distributed routing method for multiple AGVs robots under uncertain motion delays. In real transportation environments, unexpected motion delays and demands may cause re-routings. The feature of the proposed method is that each AGV subsystem creates the routing to minimize the sum of the transportation time and the penalties for collisionn frequency distribution taking into account motion delay disturbances. The proposed method is applied to routing problem for a transportation system with 143 nodes and 20 AGVs in a semiconductor fabrication bay. The computational results show that the total transportation time obtained by the proposed method is shorter than that of the conventional method under various disturbances.

DOI： 10.9746/sicetr1965.42.1042

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)  

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)  

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)  

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Authorship：Lead author   Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：The Institute of Electrical Engineers of Japan  

In this paper, we propose an autonomous decentralized scheduling method with an optimization function of lotsizing and scheduling for multi-stage aluminum production line. The proposed method repeats the generation of schedule at each production process and data exchange among scheduling subsystems until a feasible schedule for the entire plant is derived. Two types of lotsizing and scheduling methods are compared for various size of example problems. The results demonstrate that the simultaneous optimization of lotsizing and scheduling must be considered when intermediate storage cost is embedded in the objective function.

DOI： 10.1541/ieejeiss.126.771

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)  

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：PERGAMON-ELSEVIER SCIENCE LTD  

This paper addresses a local rescheduling procedure for a distributed routing system of multiple Automated Guilided Vehicles in dynamic environments where the requests for transportation are given in real time. The effectiveness of the proposed method is investigated for a transportation system of 143 nodes and 10 30 AGV systems in a semi-conductor fabricating bay, A distributed and parallel routing system (DPRS) is implemented and tested on an experimental five mobile robot system, The results demonstrate that the proposed method can reduce the total computation time by 39% compared with the conventional method without lowering the performance level. It is also experimentally verified that the rescheduling procedure can deal with disturbances in a significantly short computation time. (c) 2005 Elsevier Ltd. All rights reserved.

DOI： 10.1016/j.rcim.2005.02.010

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

In this paper, we propose a decomposition and optimization method for Petri Nets to solve routing problems for automated guided vehicles in semiconductor fabrication bays. An augmented Petri Net model is developed to represent concurrent motion for multiple AGVs. The routing problem to minimize total transportation time is formulated as an optimal firing sequence problem for the proposed Petri Net. The optimization model is decomposed into several subproblems which can be solved by Dijkstra's algorithm in polynomial order. The effectiveness of the proposed method is evaluated by several numerical examples.

DOI： 10.1109/COASE.2006.326886

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Publisher：The Institute of Systems, Control and Information Engineers  

DOI： 10.11509/sci.SCI06.0.114.0

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Publisher：The Institute of Systems, Control and Information Engineers  

DOI： 10.11509/sci.SCI06.0.137.0

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Publisher：The Institute of Systems, Control and Information Engineers  

DOI： 10.11509/sci.SCI06.0.136.0

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Publisher：The Institute of Systems, Control and Information Engineers  

DOI： 10.11509/sci.SCI06.0.116.0

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Publisher：The Institute of Systems, Control and Information Engineers  

DOI： 10.11509/sci.SCI06.0.303.0

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Publisher：The Institute of Systems, Control and Information Engineers  

DOI： 10.11509/sci.SCI06.0.130.0

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Language：Japanese   Publisher：自動制御連合講演会  

DOI： 10.11511/jacc.49.0.167.0

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Publisher：The Japan Joint Automatic Control Conference  

DOI： 10.11511/jacc.49.0.165.0

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC  

To enable efficient transportation in semiconductor fabrication bays, it is necessary to generate route planning of multiple automated guided vehicles (AGVs) efficiently to minimize the total transportation time without collision among AGVs. In this paper, we propose a distributed route-planning method for multiple mobile robots using an augmented Lagrangian decomposition and coordination technique. The proposed method features a characteristic that each AGV individually creates a near-optimal routing plan through repetitive data exchange among the AGVs and local optimization for each AGV. Dijkstra's algorithm is used for local optimization. The optimality of the solution generated by the proposed method is evaluated by comparing the solution with an optimal solution derived by solving integer linear programming problems. A near-optimal solution, within 3% of the average gap from the optimal solution for an example transportation system consisting of 143 nodes and 14 AGVs, can be derived in less than 5 s of computation lime for 100 types of requests. The proposed method is implemented in an experimental system with three AGVs, and the routing plan is derived in the configuration space, taking the motion of the robot into account. It is experimentally demonstrated that the proposed method is effective for various problems, despite the fact that each route for an AGV is created without minimizing the entire objective function.

DOI： 10.1109/TRO.2005.853489

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)  

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)  

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)  

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)  

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)  

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)  

We propose a neural network model generating a robot arm trajectory. The developed neural network model is based on a recurrent-type neural network (RNN) model calculating the proper arm trajectory based on data acquired by evaluation functions of human operations as the training data. A self-learning function has been added to the RNN model. The proposed method is applied to a 2-DOF robot arm, and laboratory experiments were executed to show the effectiveness of the proposed method. Through experiments, it is verified that the proposed model can reproduce the arm trajectory generated by a human. Further, the trajectory of a robot arm is successfully modified to avoid collisions with obstacles by a self-learning function. © ISAROB 2005.

DOI： 10.1007/s10015-004-0334-4

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)  

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Language：English   Publishing type：Research paper (scientific journal)  

We propose a neural network model generating a robot arm trajectory. The developed neural network model is based on a recurrent-type neural network (RNN) model calculating the proper arm trajectory based on data acquired by evaluation functions of human operations as the training data. A self-learning function has been added to the RNN model. The proposed method is applied to a 2-DOF robot arm, and laboratory experiments were executed to show the effectiveness of the proposed method. Through experiments, it is verified that the proposed model can reproduce the arm trajectory generated by a human. Further, the trajectory of a robot arm is successfully modified to avoid collisions with obstacles by a self-learning function. © ISAROB 2005.

DOI： 10.1007/s10015-004-0334-4

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：JAPAN SOC MECHANICAL ENGINEERS  

The paper describes an augmented Lagrangian decomposition and coordination approach for solving single machine scheduling problems to minimize the total weighted tardiness. The problem belongs to the class of NP-hard combinatorial optimization problem. We propose an augmented Lagrangian decomposition and coordination approach, which is commonly used for continuous optimization problems, for solving scheduling problems despite the fact that the problem is nonconvex and non-differentiable. The proposed method shows a good convergence to a feasible solution without heuristically constructing a feasible solution. The performance of the proposed method is compared with that of an ordinary Lagrangian relaxation.

DOI： 10.1299/jsmec.48.299

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：SPRINGER  

In this paper, we propose an autonomous decentralized optimization system for multi-stage production processes. The proposed system consists of a Material Requirement Planning subsystem, a Distribution Planning subsystem and Decentralized Scheduling subsystems belonging to each production stage that constitute the entire plant. In the proposed system, each subsystem repeats both optimization of the schedule at each subsystem and data exchange among the subsystems. Computational results demonstrate that the results of the proposed planning system are superior to those of the hierarchical planning system, despite the fact that the proposed system has wide flexibility for adding the constraints and modifying the criterion of performance evaluation.

DOI： 10.1007/s10845-005-7022-7

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)  

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：IRON STEEL INST JAPAN KEIDANREN KAIKAN  

Recently, various studies have been undertaken to examine the inner furnace phenomena of a blast furnace. These studies are limited, however, to the evaluation of furnace performance while a designed method of furnace control remains unfulfilled. This paper proposes a method of estimating temperature distribution in a reactor furnace using boundary data in order to control inner target furnace temperature distribution. Initially, a simplified furnace simulation program for the calculation of inner furnace gas flow, pressure and temperature distribution is developed. Following that, the simulator is used to estimate and control inner furnace temperature distribution. In the estimation, boundary data such as temperature and pressure, measured near a furnace wall, are used in the furnace simulation. Then, for the control of inner furnace temperature distribution, necessary values for gas blowing at the bottom of the furnace and the burden supply at the top of the furnace are used. Both for the estimation and the control of the inner furnace temperature distribution, a sequential quadratic programming method, a method of the iterative optimization, is applied. In our method, an estimation of inner furnace temperature distribution is done as preparation for the determination of furnace control variables. Through numerical experiments, the validity of our method is demonstrated, showing that the temperature distribution in a furnace can be regulated to the desired one after iterative control operations.

DOI： 10.2355/isijinternational.45.347

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

In this paper, we propose a distributed routing method for multiple mobile robots under uncertain motion delays. The feature of the proposed method is that each AGV subsystem creates the routing to minimize the sum of the transportation time and the penalty for collision avoidance constraints taking into account motion delay disturbances for other AGVs. The computational results show that the total transportation time obtained by the proposed method is shorter than that of the conventional method under various type of motion delay disturbances.

DOI： 10.1109/IROS.2005.1545271

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

We propose a distributed routing system for multi-robot cooperative transportation by parallel multiple processors with an asynchronous data exchanging. In the proposed method, each robot generates a routing to minimize each objective function comprising of traveling time and the penalty for violating constraints of cooperation with other robots. A near optimal routing is generated by repeating the generation of each routing and data exchanging among the robots. The asynchronous data exchanging is adopted to reduce the computation time. In order the solution not to be trapped in a local optimum, the weighting factors violating the constraints are updated with a time-dependent method during parallel computation. The proposed method is applied to a motion planning for cooperative transportation of an experimental 4 real robots system. The effectiveness of the proposed method is investigated on various types of experimental conditions.

DOI： 10.1109/IROS.2005.1545268

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

Coordination and optimization of supply chain planning among multiple companies have widely been received much attention from viewpoints of global supply chain management. Conventional system for supply chain planning is configured on the assumption that correct information for entire company is available by sharing the detailed information among multiple companies. It is required to generate a near optimal plan for multiple companies without sharing the confidential information such as inventory costs, set up costs and due date penalties among competing companies. In this paper, we propose a framework of a distributed supply chain planning for multiple companies by using an augmented Lagrangian relaxation approach. The proposed method features that a feasible solution can be derived without using the entire information by exchanging the data which is not directly related to cost data. From the computational experiments, it has been shown that the average gap between the solution derived by the proposed method and an optimal solution is within 1% of the performance index even though only the local information is used to derive a solution for each company.

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Authorship：Lead author   Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：IRON STEEL INST JAPAN KEIDANREN KAIKAN  

In recent years, many control systems have been developed and applied to hot rolling in order to improve the quality of the finished products. Due to the plant characteristics and the control system performance variations over time, human experts intervene and modulate control gains to maintain and to improve the control system performance. Through a breakthrough, we can expect to attain the fully automated modulation of control gains without human intervention. A neural network model representing gain modulating actions by human was developed for a looper height controller in hot strip mills. The developed neural network model is a recurrent type neural network (RNN) which calculates the appropriate PID gains of the looper height controller based on the modification data Of human operations as training data. Further, a learning algorithm for the RNN model was developed to accelerate convergence of the gain modification process and to stabilize the looper movement. The neural gain tuning model was applied to the inter-stands looper height controller in hot strip mills. The usefulness of the developed model was checked through numerical experiments. From the experimental results, it was verified that the tuning actions by humans can be realized by the model. Through its learning mechanism, the model could also cope with disturbances such as changes in roll gap. This may lead to the stabilization of threading operations of hot strip mills.

DOI： 10.2355/tetsutohagane1955.90.11_933

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：SCRIPTA TECHNICA-JOHN WILEY & SONS  

In this paper, we propose a decentralized scheduling method for flowshop scheduling problems with resource constraints using the Lagrangian decomposition and coordination approach. When a flowshop scheduling problem with resource constraints is decomposed into machine-level subproblems, the decomposed problem becomes very difficult to solve so as to obtain the optimal solution, even when the production sequence of operations is given. In this study, the decomposed subproblems are solved by a simulated annealing algorithm combined with dynamic programming. By decomposing the problem into single machine subproblems, the changeover cost can easily be incorporated in the objective function. In order to reduce the computation time, a heuristic algorithm for calculating the starting times of operations is also proposed. The performance of the proposed method is compared with that of the simulated annealing method by which the schedule of the entire machine is successively improved. Numerical results have shown that the proposed method can generate better solutions than the conventional method. (C) 2004 Wiley Periodicals, Inc.

DOI： 10.1002/eej.10364

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