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An Efficient Metaheuristic Algorithm for Job Shop Scheduling in a Dynamic 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

This paper proposes an Improved Multi-phase Particle Swarm Optimization (IMPPSO) to solve a Dynamic Job Shop Scheduling Problem (DJSSP) known as an non-deterministic polynomial-time hard (NP-hard) problem. A cellular neighbor network, a velocity reinitialization strategy, a randomly select sub-dimension strategy, and a constraint handling function are introduced in the IMPPSO. The IMPPSO is used to solve the Kundakcı and Kulak problem set and is compared with the original Multi-phase Particle Swarm Optimization (MPPSO) and Heuristic Kalman Algorithm (HKA). The results show that the IMPPSO has better global exploration capability and convergence. The IMPPSO has improved fitness for most of the benchmark instances of the Kundakcı and Kulak problem set, with an average improvement rate of 5.16% compared to the Genetic Algorithm-Mixed (GAM) and of 0.74% compared to HKA. The performance of the IMPPSO for solving real-world problems is verified by a case study. The high level of operational efficiency is also evaluated and demonstrated by proposing a simulation model capable of using the decision-making algorithm in a real-world environment.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:10:p:2336-:d:1148965
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    References listed on IDEAS

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    1. Yong Zhou & Jian-jun Yang & Zhuang Huang, 2020. "Automatic design of scheduling policies for dynamic flexible job shop scheduling via surrogate-assisted cooperative co-evolution genetic programming," International Journal of Production Research, Taylor & Francis Journals, vol. 58(9), pages 2561-2580, May.
    2. 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.
    3. 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.
    4. 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.
    5. Ramasesh, R, 1990. "Dynamic job shop scheduling: A survey of simulation research," Omega, Elsevier, vol. 18(1), pages 43-57.
    6. 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.
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    Cited by:

    1. Binzi Xu & Kai Xu & Baolin Fei & Dengchao Huang & Liang Tao & Yan Wang, 2024. "Automatic Design of Energy-Efficient Dispatching Rules for Multi-Objective Dynamic Flexible Job Shop Scheduling Based on Dual Feature Weight Sets," Mathematics, MDPI, vol. 12(10), pages 1-24, May.

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