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An effective approach for the dual-resource flexible job shop scheduling problem considering loading and unloading

Author

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  • Xiuli Wu

    (University of Science and Technology Beijing)

  • Junjian Peng

    (University of Science and Technology Beijing)

  • Xiao Xiao

    (University of Science and Technology Beijing)

  • Shaomin Wu

    (University of Kent)

Abstract

Many manufacturing systems need more than one type of resource to co-work with. Commonly studied flexible job shop scheduling problems merely consider the main resource such as machines and ignore the impact of other types of resource. As a result, scheduling solutions may not put into practice. This paper therefore studies the dual resource constrained flexible job shop scheduling problem when loading and unloading time (DRFJSP-LU) of the fixtures is considered. It formulates a multi-objective mathematical model to jointly minimize the makespan and the total setup time. Considering the influence of resource requirement similarity among different operations, we propose a similarity-based scheduling algorithm for setup-time reduction (SSA4STR) and then an improved non-dominated sorting genetic algorithm II (NSGA-II) to optimize the DRFJSP-LU. Experimental results show that the SSA4STR can effectively reduce the loading and unloading time of fixtures while ensuring a level of makespan. The experiments also verify that the scheduling solution with multiple resources has a greater guiding effect on production than the scheduling result with a single resource.

Suggested Citation

  • Xiuli Wu & Junjian Peng & Xiao Xiao & Shaomin Wu, 2021. "An effective approach for the dual-resource flexible job shop scheduling problem considering loading and unloading," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 707-728, March.
    • Handle: RePEc:spr:joinma:v:32:y:2021:i:3:d:10.1007_s10845-020-01697-5
    DOI: 10.1007/s10845-020-01697-5
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    References listed on IDEAS

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    Cited by:

    1. Aidin Delgoshaei & Mohd Khairol Anuar Bin Mohd Ariffin & Zulkiflle B. Leman, 2022. "An Effective 4–Phased Framework for Scheduling Job-Shop Manufacturing Systems Using Weighted NSGA-II," Mathematics, MDPI, vol. 10(23), pages 1-28, December.
    2. Yanwei Sang & Jianping Tan, 2022. "Many-Objective Flexible Job Shop Scheduling Problem with Green Consideration," Energies, MDPI, vol. 15(5), pages 1-17, March.
    3. Wenkang Zhang & Yufan Zheng & Rafiq Ahmad, 2023. "The integrated process planning and scheduling of flexible job-shop-type remanufacturing systems using improved artificial bee colony algorithm," Journal of Intelligent Manufacturing, Springer, vol. 34(7), pages 2963-2988, October.
    4. Abdelmonem M. Ibrahim & Mohamed A. Tawhid, 2023. "An improved artificial algae algorithm integrated with differential evolution for job-shop scheduling problem," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1763-1778, April.
    5. Alejandro Vital-Soto & Mohammed Fazle Baki & Ahmed Azab, 2023. "A multi-objective mathematical model and evolutionary algorithm for the dual-resource flexible job-shop scheduling problem with sequencing flexibility," Flexible Services and Manufacturing Journal, Springer, vol. 35(3), pages 626-668, September.

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