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A memetic algorithm for multi-objective flexible job-shop problem with worker flexibility

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  • Xuran Gong
  • Qianwang Deng
  • Guiliang Gong
  • Wei Liu
  • Qinghua Ren

Abstract

In existing scheduling models, the flexible job-shop scheduling problem mainly considers machine flexibility. However, human factor is also an important element existing in real production that is often neglected theoretically. In this paper, we originally probe into a multi-objective flexible job-shop scheduling problem with worker flexibility (MO-FJSPW). A non-linear integer programming model is presented for the problem. Correspondingly, a memetic algorithm (MA) is designed to solve the proposed MO-FJSPW whose objective is to minimise the maximum completion time, the maximum workload of machines and the total workload of all machines. A well-designed chromosome encoding/decoding method is proposed and the adaptive genetic operators are selected by experimental studies. An elimination process is executed to eliminate the repeated individuals in population. Moreover, a local search is incorporated into the non-dominated sorting genetic algorithm II. In experimental phase, the crossover operator and elimination operator in MA are examined firstly. Afterwards, some extensive comparisons are carried out between MA and some other multi-objective algorithms. The simulation results show that the MA performs better for the proposed MO-FJSPW than other algorithms.

Suggested Citation

  • Xuran Gong & Qianwang Deng & Guiliang Gong & Wei Liu & Qinghua Ren, 2018. "A memetic algorithm for multi-objective flexible job-shop problem with worker flexibility," International Journal of Production Research, Taylor & Francis Journals, vol. 56(7), pages 2506-2522, April.
    • Handle: RePEc:taf:tprsxx:v:56:y:2018:i:7:p:2506-2522
    DOI: 10.1080/00207543.2017.1388933
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    Cited by:

    1. Federica Costa & Matthias Thürer & Alberto Portioli-Staudacher, 2023. "Heterogeneous worker multi-functionality and efficiency in dual resource constrained manufacturing lines: an assessment by simulation," Operations Management Research, Springer, vol. 16(3), pages 1476-1489, September.
    2. Alper Türkyılmaz & Özlem Şenvar & İrem Ünal & Serol Bulkan, 2020. "A research survey: heuristic approaches for solving multi objective flexible job shop problems," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1949-1983, December.
    3. Zhilan Lou & Wanchen Jie & Shuzhu Zhang, 2020. "Multi-Objective Optimization for Order Assignment in Food Delivery Industry with Human Factor Considerations," Sustainability, MDPI, vol. 12(19), pages 1-17, September.

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