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Mathematical modeling and two metaheuristic algorithms for integrated process planning and group scheduling with sequence-dependent setup time

Author

Listed:
  • Mohammad Reza Hosseinzadeh

    (Iran University of Science and Technology)

  • Mehdi Heydari

    (Iran University of Science and Technology)

  • Mohammad Mahdavi Mazdeh

    (Iran University of Science and Technology)

Abstract

The advancement of technology enables manufacturing companies to employ multifunction machines to increase the flexibility of a system in producing miscellaneous products in a short time. In this situation, goods can be usually produced through different process plans, and considering process planning and scheduling in an integrated framework would be essential. Furthermore, group processing is regarded to overcome the difficulty of long setup times and consequently increase the productivity of a manufacturing system. This paper deals with the integrated process planning and group scheduling problem with sequence-dependent setup time between each group of jobs. Two mixed-integer linear programming models with different approaches are presented. Moreover, two metaheuristic algorithms are proposed to solve the problems heuristically. The experiments show the high performance of the combination-based mathematical model for small-size problems as well as the proposed metaheuristic algorithms for medium-size and large-size instances.

Suggested Citation

  • Mohammad Reza Hosseinzadeh & Mehdi Heydari & Mohammad Mahdavi Mazdeh, 2022. "Mathematical modeling and two metaheuristic algorithms for integrated process planning and group scheduling with sequence-dependent setup time," Operational Research, Springer, vol. 22(5), pages 5055-5105, November.
    • Handle: RePEc:spr:operea:v:22:y:2022:i:5:d:10.1007_s12351-022-00700-6
    DOI: 10.1007/s12351-022-00700-6
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    References listed on IDEAS

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    Cited by:

    1. Du, Yu & Li, Jun-qing, 2024. "A deep reinforcement learning based algorithm for a distributed precast concrete production scheduling," International Journal of Production Economics, Elsevier, vol. 268(C).

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