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Advanced Metaheuristic Method for Decision-Making in a Dynamic Job Shop Scheduling Environment

Author

Listed:
  • Hankun Zhang

    (School of E-Business and Logistics, Beijing Technology and Business University, Beijing 100048, China)

  • Borut Buchmeister

    (Faculty of Mechanical Engineering, University of Maribor, 2000 Maribor, Slovenia)

  • Xueyan Li

    (School of Management, Beijing Union University, Beijing 100101, China)

  • Robert Ojstersek

    (Faculty of Mechanical Engineering, University of Maribor, 2000 Maribor, Slovenia)

Abstract

As a well-known NP-hard problem, the dynamic job shop scheduling problem has significant practical value, so this paper proposes an Improved Heuristic Kalman Algorithm to solve this problem. In Improved Heuristic Kalman Algorithm, the cellular neighbor network is introduced, together with the boundary handling function, and the best position of each individual is recorded for constructing the cellular neighbor network. The encoding method is introduced based on the relative position index so that the Improved Heuristic Kalman Algorithm can be applied to solve the dynamic job shop scheduling problem. Solving the benchmark example of dynamic job shop scheduling problem and comparing it with the original Heuristic Kalman Algorithm and Genetic Algorithm-Mixed, the results show that Improved Heuristic Kalman Algorithm is effective for solving the dynamic job shop scheduling problem. The convergence rate of the Improved Heuristic Kalman Algorithm is reduced significantly, which is beneficial to avoid the algorithm from falling into the local optimum. For all 15 benchmark instances, Improved Heuristic Kalman Algorithm and Heuristic Kalman Algorithm have obtained the best solution obtained by Genetic Algorithm-Mixed. Moreover, for 9 out of 15 benchmark instances, they achieved significantly better solutions than Genetic Algorithm-Mixed. They have better robustness and reasonable running time (less than 30 s even for large size problems), which means that they are very suitable for solving the dynamic job shop scheduling problem. According to the dynamic job shop scheduling problem applicability, the integration-communication protocol was presented, which enables the transfer and use of the Improved Heuristic Kalman Algorithm optimization results in the conventional Simio simulation environment. The results of the integration-communication protocol proved the numerical and graphical matching of the optimization results and, thus, the correctness of the data transfer, ensuring high-level usability of the decision-making method in a real-world environment.

Suggested Citation

  • Hankun Zhang & Borut Buchmeister & Xueyan Li & Robert Ojstersek, 2021. "Advanced Metaheuristic Method for Decision-Making in a Dynamic Job Shop Scheduling Environment," Mathematics, MDPI, vol. 9(8), pages 1-22, April.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:8:p:909-:d:539118
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    References listed on IDEAS

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    1. Ali Hosseinabadi & Hajar Siar & Shahaboddin Shamshirband & Mohammad Shojafar & Mohd Nasir, 2015. "Using the gravitational emulation local search algorithm to solve the multi-objective flexible dynamic job shop scheduling problem in Small and Medium Enterprises," Annals of Operations Research, Springer, vol. 229(1), pages 451-474, June.
    2. Xiong, Hegen & Fan, Huali & Jiang, Guozhang & Li, Gongfa, 2017. "A simulation-based study of dispatching rules in a dynamic job shop scheduling problem with batch release and extended technical precedence constraints," European Journal of Operational Research, Elsevier, vol. 257(1), pages 13-24.
    3. Vinod, V. & Sridharan, R., 2011. "Simulation modeling and analysis of due-date assignment methods and scheduling decision rules in a dynamic job shop production system," International Journal of Production Economics, Elsevier, vol. 129(1), pages 127-146, January.
    4. Li, Xue-yan & Li, Xue-mei & Yang, Lingrun & Li, Jing, 2018. "Dynamic route and departure time choice model based on self-adaptive reference point and reinforcement learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 77-92.
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    Cited by:

    1. Fabian Riquelme & Elizabeth Montero & Leslie Pérez-Cáceres & Nicolás Rojas-Morales, 2022. "A Track-Based Conference Scheduling Problem," Mathematics, MDPI, vol. 10(21), pages 1-25, October.
    2. Hankun Zhang & Borut Buchmeister & Xueyan Li & Robert Ojstersek, 2023. "An Efficient Metaheuristic Algorithm for Job Shop Scheduling in a Dynamic Environment," Mathematics, MDPI, vol. 11(10), pages 1-24, May.

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