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

Simulation is widely used for analyzing supply chains with complex structures and stochastic nature. However, optimizing supply chain simulation models is usually computationally expensive. This study proposes a surrogate-assisted evolutionary optimization approach to optimize the inventory policies in multi-echelon distribution systems for perishable items under a limited number of evaluations. The random forest algorithm is used to build the surrogate model for a faster estimation of the performance of the inventory policies. A co-evolutionary differential evolution algorithm is proposed to simultaneously evolve the population through multiple searching strategies. The generated solutions are estimated by the low-cost surrogate model to select the promising solutions, which will be evaluated by the inventory model. Moreover, this study also integrates the surrogate model into two classic metaheuristic algorithms, particle swarm optimization and differential evolution. Also, a new performance indicator is proposed to examine the efficiency of the surrogate model in evolutionary computation. Two case studies are used to investigate the performance of the proposed algorithms. The experimental results show that both particle swarm optimization and differential evolution exhibit performance improvements exceeding 55% by using surrogate models under the limited number of function evaluations. Furthermore, the surrogate model reduces computational time for both algorithms by over 34% to achieve equivalent objective values. Finally, the proposed co-evolutionary differential evolution algorithm is compared with 12 algorithms, and the results show that the proposed algorithm consistently outperforms them. These findings confirm the usefulness of the surrogate model in evolutionary algorithms and the effectiveness of the proposed co-evolutionary strategy for solving perishable inventory problems.

DOI： 10.1016/j.eswa.2023.122179

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

DOI： 10.1109/docs60977.2023.10294547

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

DOI： 10.1109/case56687.2023.10260329

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

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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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：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.ifacol.2023.10.1145

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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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Other Link： https://dblp.uni-trier.de/db/conf/case/case2023.html#FukushimaNL23

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

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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Other Link： https://dblp.uni-trier.de/db/conf/ieem/ieem2022.html#KawabeLNAF22

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

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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Other Link： https://dblp.uni-trier.de/db/conf/ieem/ieem2022.html#NishihataLN22

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

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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Other Link： https://dblp.uni-trier.de/db/conf/ieem/ieem2022.html#OyamaNLAF22

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

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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Other Link： https://dblp.uni-trier.de/db/conf/iros/iros2022.html#BandoNA0F22

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：English   Publishing type：Research paper (scientific journal)   Publisher：{MDPI} {AG}  

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

With recent liberalization and enlarging of trade among companies, it is necessary to generate an optimal supply chain planning by cooperation and coordination of supply chain planning for multiple companies without sharing sensitive information such as costs and profit among competitive companies. A distributed optimization can solve the optimization problems with limited information. A distributed optimization method using subgradient and consensus control methods has been proposed to solve continuous optimization problems. However, conventional distributed optimization methods using subgradient and consensus control methods cannot be applied to the supply chain planning for multiple companies including 0–1 decision variables. In this paper, we propose a new distributed optimization method for solving the supply chain planning problem for multiple companies by subgradient method and consensus control. By branching the cases 0–1 variables, an optimal solution can be obtained by the enumeration. A method to reduce the computational effort has been developed in the proposed method. From numerical experiments, it is confirmed that we can obtain an optimal solution by the reduction of the computation.

DOI： 10.1007/978-3-031-16411-8_27

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

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：Lead 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：Lead author   Publishing type：Research paper (scientific journal)   Publisher：Elsevier BV  

This paper proposes a particle swarm optimization with strategy dynamics (SDPSO) to solve single-objective optimization problems. SDPSO consists of four PSO search strategies. Evolutionary game theory is introduced to control the population state. In evolutionary game theory, through the interaction between players, better strategies will eventually dominate among the players. By extending this idea to PSO, a selection mechanism and a mutation mechanism are proposed. By using the selection mechanism, the adoption probability of the high payoff strategies will increase. The mutation mechanism can examine the stability of the incumbent strategy to evolutionary pressures. The performance of SDPSO is compared with 14 algorithms on the CEC 2014 test suite. The results show that SDPSO has the highest rank. SDPSO is applied to solve a real-world problem. SDPSO can find the best mean results comparing with 4 algorithms. The findings show that the proposed evolutionary game theory-based framework can adaptively control the population state. This study proposes a new application of evolutionary game theory to the design of swarm intelligence and contributes to a better understanding of the usefulness of the evolutionary game theory in the optimization method. The source codes of SDPSO are available at https://github.com/zi-ang-liu/SDPSO.

DOI： 10.1016/j.ins.2021.10.028

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

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 .

DOI： 10.1155/2020/1450985

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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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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   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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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.

DOI： 10.1109/IEEM44572.2019.8978741

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Other Link： https://dblp.uni-trier.de/db/conf/ieem/ieem2019.html#LiuN19

Language：Japanese   Publisher：システム制御情報学会  

CiNii Books

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Language：Japanese   Publisher：システム制御情報学会  

CiNii Books

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Language：Japanese   Publisher：システム制御情報学会  

CiNii Books

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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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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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J-GLOBAL

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J-GLOBAL

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J-GLOBAL

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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：Corresponding author   Publishing type：Article, review, commentary, editorial, etc. (international conference proceedings)  

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Language：Japanese  

DOI： 10.3390/app13031879

CiNii Books

J-GLOBAL

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J-GLOBAL

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J-GLOBAL

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Publisher：Springer  

DOI： 10.1007/978-3-031-16411-8_27

J-GLOBAL

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Other Link： https://dblp.uni-trier.de/db/conf/ifip5-7/apms2022-2.html#DebuchiN022

J-GLOBAL

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J-GLOBAL

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Authorship：Lead author  

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Language：English  

CiNii Article

CiNii Books

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Authorship：Lead author   Publishing type：Article, review, commentary, editorial, etc. (international conference proceedings)  

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Type：Lecture

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