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Bi-Objective Integrated Scheduling of Job Shop Problems and Material Handling Robots with Setup Time

Author

Listed:
  • Runze Liu

    (Macau Institute of Systems Engineering, Macau University of Science and Technology, Macao 999078, China
    Zhuhai MUST Science and Technology Research Institute, Macau University of Science and Technology, Zhuhai 519031, China)

  • Qi Jia

    (Macau Institute of Systems Engineering, Macau University of Science and Technology, Macao 999078, China
    Zhuhai MUST Science and Technology Research Institute, Macau University of Science and Technology, Zhuhai 519031, China)

  • Hui Yu

    (Macau Institute of Systems Engineering, Macau University of Science and Technology, Macao 999078, China
    Zhuhai MUST Science and Technology Research Institute, Macau University of Science and Technology, Zhuhai 519031, China)

  • Kaizhou Gao

    (Macau Institute of Systems Engineering, Macau University of Science and Technology, Macao 999078, China
    Zhuhai MUST Science and Technology Research Institute, Macau University of Science and Technology, Zhuhai 519031, China)

  • Yaping Fu

    (School of Business, Qingdao University, Qingdao 266071, China)

  • Li Yin

    (Macau Institute of Systems Engineering, Macau University of Science and Technology, Macao 999078, China
    Zhuhai MUST Science and Technology Research Institute, Macau University of Science and Technology, Zhuhai 519031, China)

Abstract

This work investigates the bi-objective integrated scheduling of job shop problems and material handling robots with setup time. The objective is to minimize the maximum completion time and the mean of earliness and tardiness simultaneously. First, a mathematical model is established to describe the problems. Then, different meta-heuristics and their variants are developed to solve the problems, including genetic algorithms, particle swarm optimization, and artificial bee colonies. To improve the performance of algorithms, seven local search operators are proposed. Moreover, two reinforcement learning algorithms, Q-learning and SARSA, are designed to help the algorithm select appropriate local search operators during iterations, further improving the convergence of algorithms. Finally, based on 82 benchmark cases with different scales, the effectiveness of the suggested algorithms is evaluated by comprehensive numerical experiments. The experimental results and discussions show that the genetic algorithm with SARSA is more competitive than its peers.

Suggested Citation

  • Runze Liu & Qi Jia & Hui Yu & Kaizhou Gao & Yaping Fu & Li Yin, 2025. "Bi-Objective Integrated Scheduling of Job Shop Problems and Material Handling Robots with Setup Time," Mathematics, MDPI, vol. 13(3), pages 1-33, January.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:3:p:447-:d:1579350
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    References listed on IDEAS

    as
    1. Andy Ham, 2021. "Transfer-robot task scheduling in job shop," International Journal of Production Research, Taylor & Francis Journals, vol. 59(3), pages 813-823, February.
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    3. Dalila B. M. M. Fontes & Seyed Mahdi Homayouni, 2019. "Joint production and transportation scheduling in flexible manufacturing systems," Journal of Global Optimization, Springer, vol. 74(4), pages 879-908, August.
    4. B. J. Lageweg & J. K. Lenstra & A. H. G. Rinnooy Kan, 1977. "Job-Shop Scheduling by Implicit Enumeration," Management Science, INFORMS, vol. 24(4), pages 441-450, December.
    5. Lacomme, Philippe & Larabi, Mohand & Tchernev, Nikolay, 2013. "Job-shop based framework for simultaneous scheduling of machines and automated guided vehicles," International Journal of Production Economics, Elsevier, vol. 143(1), pages 24-34.
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