start-ver=1.4 cd-journal=joma no-vol=10 cd-vols= no-issue=1 article-no= start-page=825 end-page=846 dt-received= dt-revised= dt-accepted= dt-pub-year=2023 dt-pub=20230809 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Data-driven evolutionary computation for service constrained inventory optimization in multi-echelon supply chains en-subtitle= kn-subtitle= en-abstract= kn-abstract=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. en-copyright= kn-copyright= en-aut-name=LiuZiang en-aut-sei=Liu en-aut-mei=Ziang kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=NishiTatsushi en-aut-sei=Nishi en-aut-mei=Tatsushi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= affil-num=1 en-affil=Faculty of Environmental, Life, Natural Science and Technology, Okayama University kn-affil= affil-num=2 en-affil=Faculty of Environmental, Life, Natural Science and Technology, Okayama University kn-affil= en-keyword=Evolutionary algorithm kn-keyword=Evolutionary algorithm en-keyword=Inventory management kn-keyword=Inventory management en-keyword=Data-driven kn-keyword=Data-driven en-keyword=Supply chain kn-keyword=Supply chain en-keyword=Digital twin kn-keyword=Digital twin END start-ver=1.4 cd-journal=joma no-vol=13 cd-vols= no-issue=3 article-no= start-page=1879 end-page= dt-received= dt-revised= dt-accepted= dt-pub-year=2023 dt-pub=20230131 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Flexible Route Planning for Multiple Mobile Robots by Combining Q-Learning and Graph Search Algorithm en-subtitle= kn-subtitle= en-abstract= kn-abstract=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. en-copyright= kn-copyright= en-aut-name=KawabeTomoya en-aut-sei=Kawabe en-aut-mei=Tomoya kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=NishiTatsushi en-aut-sei=Nishi en-aut-mei=Tatsushi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=LiuZiang en-aut-sei=Liu en-aut-mei=Ziang kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= affil-num=1 en-affil=Graduate School of Natural Science and Technology, Okayama University kn-affil= affil-num=2 en-affil=Graduate School of Natural Science and Technology, Okayama University kn-affil= affil-num=3 en-affil=Graduate School of Natural Science and Technology, Okayama University kn-affil= en-keyword=Automated Guided Vehicles kn-keyword=Automated Guided Vehicles en-keyword=route planning kn-keyword=route planning en-keyword=Q-learning kn-keyword=Q-learning en-keyword=reinforcement learning kn-keyword=reinforcement learning en-keyword=transportation kn-keyword=transportation END start-ver=1.4 cd-journal=joma no-vol=12 cd-vols= no-issue=19 article-no= start-page=9472 end-page= dt-received= dt-revised= dt-accepted= dt-pub-year=2022 dt-pub=20220921 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Machine Learning and Inverse Optimization for Estimation of Weighting Factors in Multi-Objective Production Scheduling Problems en-subtitle= kn-subtitle= en-abstract= kn-abstract=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. en-copyright= kn-copyright= en-aut-name=TogoHidetoshi en-aut-sei=Togo en-aut-mei=Hidetoshi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=AsanumaKohei en-aut-sei=Asanuma en-aut-mei=Kohei kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=NishiTatsushi en-aut-sei=Nishi en-aut-mei=Tatsushi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=LiuZiang en-aut-sei=Liu en-aut-mei=Ziang kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= affil-num=1 en-affil=Graduate School of Natural Science and Technology, Okayama University kn-affil= affil-num=2 en-affil=Graduate School of Engineering Science, Osaka University kn-affil= affil-num=3 en-affil=Graduate School of Natural Science and Technology, Okayama University kn-affil= affil-num=4 en-affil=Graduate School of Natural Science and Technology, Okayama University kn-affil= en-keyword=multi-objective scheduling kn-keyword=multi-objective scheduling en-keyword=estimation kn-keyword=estimation en-keyword=weighting factors kn-keyword=weighting factors en-keyword=machine learning kn-keyword=machine learning en-keyword=simulated annealing kn-keyword=simulated annealing en-keyword=inverse optimization kn-keyword=inverse optimization END start-ver=1.4 cd-journal=joma no-vol=15 cd-vols= no-issue=6 article-no= start-page=2074 end-page= dt-received= dt-revised= dt-accepted= dt-pub-year=2022 dt-pub=20220311 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Energy-Efficient Robot Configuration and Motion Planning Using Genetic Algorithm and Particle Swarm Optimization en-subtitle= kn-subtitle= en-abstract= kn-abstract=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. en-copyright= kn-copyright= en-aut-name=NonoyamaKazuki en-aut-sei=Nonoyama en-aut-mei=Kazuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=LiuZiang en-aut-sei=Liu en-aut-mei=Ziang kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=FujiwaraTomofumi en-aut-sei=Fujiwara en-aut-mei=Tomofumi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=AlamMd Moktadir en-aut-sei=Alam en-aut-mei=Md Moktadir kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=NishiTatsushi en-aut-sei=Nishi en-aut-mei=Tatsushi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= affil-num=1 en-affil=Graduate School of Natural Science and Technology, Okayama University kn-affil= affil-num=2 en-affil=Graduate School of Natural Science and Technology, Okayama University kn-affil= affil-num=3 en-affil=Graduate School of Natural Science and Technology, Okayama University kn-affil= affil-num=4 en-affil=Graduate School of Natural Science and Technology, Okayama University kn-affil= affil-num=5 en-affil=Graduate School of Natural Science and Technology, Okayama University kn-affil= en-keyword=robot motion planning kn-keyword=robot motion planning en-keyword=robot placement kn-keyword=robot placement en-keyword=optimization kn-keyword=optimization en-keyword=PID kn-keyword=PID en-keyword=genetic algorithm kn-keyword=genetic algorithm en-keyword=particle swarm optimization kn-keyword=particle swarm optimization END start-ver=1.4 cd-journal=joma no-vol=11 cd-vols= no-issue=1 article-no= start-page=397 end-page= dt-received= dt-revised= dt-accepted= dt-pub-year=2021 dt-pub=20210104 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Multi-Period Maximal Covering Location Problem with Capacitated Facilities and Modules for Natural Disaster Relief Services en-subtitle= kn-subtitle= en-abstract= kn-abstract=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. en-copyright= kn-copyright= en-aut-name=AlizadehRoghayyeh en-aut-sei=Alizadeh en-aut-mei=Roghayyeh kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=NishiTatsushi en-aut-sei=Nishi en-aut-mei=Tatsushi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=BagherinejadJafar en-aut-sei=Bagherinejad en-aut-mei=Jafar kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=BashiriMahdi en-aut-sei=Bashiri en-aut-mei=Mahdi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= affil-num=1 en-affil=Division of Mathematical Science for Social Systems, Department of Systems Innovation, Graduate School of Engineering Science, Osaka University kn-affil= affil-num=2 en-affil=Graduate School of Natural Sciences, Department of Industrial Innovation Engineering, Okayama University kn-affil= affil-num=3 en-affil=Department of Industrial Engineering, Faculty of Engineering, Alzahra University kn-affil= affil-num=4 en-affil=School of Strategy and Leadership, Faculty of Business and Law, Coventry University kn-affil= en-keyword=maximal covering location problem kn-keyword=maximal covering location problem en-keyword=capacitated facility kn-keyword=capacitated facility en-keyword=modularity kn-keyword=modularity en-keyword=multi-period kn-keyword=multi-period en-keyword=Lagrangian decomposition heuristic kn-keyword=Lagrangian decomposition heuristic END start-ver=1.4 cd-journal=joma no-vol=10 cd-vols= no-issue=20 article-no= start-page=7110 end-page= dt-received= dt-revised= dt-accepted= dt-pub-year=2020 dt-pub=20201013 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Hybrid Set Covering and Dynamic Modular Covering Location Problem: Application to an Emergency Humanitarian Logistics Problem en-subtitle= kn-subtitle= en-abstract= kn-abstract=This paper presents an extension of the covering location problem as a hybrid covering model that utilizes the set covering and maximal covering location problems. The developed model is a multi-period model that considers strategic and tactical planning decisions. Hybrid covering location problem (HCLP) determines the location of the capacitated facilities by using dynamic set covering location problem as strategic decisions and assigns the constructive units of facilities and allocates the demand points by using dynamic modular capacitated maximal covering location problem as tactical decisions. One of the applications of the proposed model is locating first aid centers in humanitarian logistic services that have been addressed by studying a threat case study in Japan. In addition to validating the developed model, it has been compared to other possible combined problems, and several randomly generated examples have been solved. The results of the case study and model validation tests approve that the main hybrid developed model (HCLP) is capable of providing better coverage percentage compared to conventional covering models and other hybrid variants. en-copyright= kn-copyright= en-aut-name=AlizadehRoghayyeh en-aut-sei=Alizadeh en-aut-mei=Roghayyeh kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=NishiTatsushi en-aut-sei=Nishi en-aut-mei=Tatsushi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= affil-num=1 en-affil=Division of Mathematical Science for Social Systems, Department of Systems Innovation, Graduate School of Engineering Science, Osaka University kn-affil= affil-num=2 en-affil=Graduate School of Natural Sciences, Department of Industrial Innovation Engineering, Okayama University kn-affil= en-keyword=covering location kn-keyword=covering location en-keyword=multi-period kn-keyword=multi-period en-keyword=strategic and tactical planning kn-keyword=strategic and tactical planning en-keyword=modular kn-keyword=modular en-keyword=maximal covering kn-keyword=maximal covering en-keyword=set covering kn-keyword=set covering END