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A new method for a class of parallel batch machine scheduling problem

Author

Listed:
  • Wei Jiang

    (Peking University)

  • Yilan Shen

    (Peking University)

  • Lingxuan Liu

    (Peking University)

  • Xiancong Zhao

    (Peking University
    Beijing Research Institute of Automation for Machinery Industry co., Ltd)

  • Leyuan Shi

    (Peking University
    University of Wisconsin-Madison)

Abstract

This paper studies the scheduling problem of jobs with release times, non-identical sizes, and incompatible job families on unrelated parallel batch machines. The capacities of batch machines and the processing times of each job on the batch machines are different. The processing time of one batch is equal to the longest processing time of jobs in this batch. Different types of jobs are not allowed to be assigned into the same batch, which is known as incompatible job families. Mixed integer linear programming and constraint programming (CP) models are proposed. A new batch-based local search method is designed and an iterated greedy (IG) algorithm is developed to avoid unreasonable exchanging of jobs during the local search. Numerical results show that the CP method can obtain high quality solutions in the small-scale instances. For the large-scale instances, the IG algorithm with the new local search method has a competitive performance.

Suggested Citation

  • Wei Jiang & Yilan Shen & Lingxuan Liu & Xiancong Zhao & Leyuan Shi, 2022. "A new method for a class of parallel batch machine scheduling problem," Flexible Services and Manufacturing Journal, Springer, vol. 34(2), pages 518-550, June.
    • Handle: RePEc:spr:flsman:v:34:y:2022:i:2:d:10.1007_s10696-021-09415-w
    DOI: 10.1007/s10696-021-09415-w
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    References listed on IDEAS

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    6. Zhou, Shengchao & Xie, Jianhui & Du, Ni & Pang, Yan, 2018. "A random-keys genetic algorithm for scheduling unrelated parallel batch processing machines with different capacities and arbitrary job sizes," Applied Mathematics and Computation, Elsevier, vol. 334(C), pages 254-268.
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