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Discrete harmony search algorithm for flexible job shop scheduling problem with multiple objectives

Author

Listed:
  • K. Z. Gao

    (Nanyang Technological University
    Liaocheng University)

  • P. N. Suganthan

    (Nanyang Technological University)

  • Q. K. Pan

    (Northeastern University)

  • T. J. Chua

    (Singapore Institute of Manufacturing Technology)

  • T. X. Cai

    (Singapore Institute of Manufacturing Technology)

  • C. S. Chong

    (Singapore Institute of Manufacturing Technology)

Abstract

Flexible job-shop scheduling problem (FJSP) is a practically useful extension of the classical job shop scheduling problem. This paper proposes an effective discrete harmony search (DHS) algorithm to solve FJSP. The objectives are the weighted combination of two minimization criteria namely, the maximum of the completion time (Makespan) and the mean of earliness and tardiness. Firstly, we develop a new method for the initial machine assignment task. Some existing heuristics are also employed for initializing the harmony memory with discrete machine permutation for machine assignment and job permutation for operation sequencing. Secondly, we develop a new rule for the improvisation to produce a new harmony for FJSP incorporating machine assignment and operation sequencing. Thirdly, several local search methods are embedded to enhance the algorithm’s local exploitation ability. Finally, extensive computational experiments are carried out using well-known benchmark instances. Computational results and comparisons show the efficiency and effectiveness of the proposed DHS algorithm for solving the FJSP with weighted combination of two objectives.

Suggested Citation

  • K. Z. Gao & P. N. Suganthan & Q. K. Pan & T. J. Chua & T. X. Cai & C. S. Chong, 2016. "Discrete harmony search algorithm for flexible job shop scheduling problem with multiple objectives," Journal of Intelligent Manufacturing, Springer, vol. 27(2), pages 363-374, April.
    • Handle: RePEc:spr:joinma:v:27:y:2016:i:2:d:10.1007_s10845-014-0869-8
    DOI: 10.1007/s10845-014-0869-8
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    References listed on IDEAS

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    1. Kai-Zhou Gao & Quan-Ke Pan & Jun-Qing Li & Yu-Ting Wang & Jing Liang, 2012. "A Hybrid Harmony Search Algorithm For The No-Wait Flow-Shop Scheduling Problems," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 29(02), pages 1-23.
    2. Demirkol, Ebru & Mehta, Sanjay & Uzsoy, Reha, 1998. "Benchmarks for shop scheduling problems," European Journal of Operational Research, Elsevier, vol. 109(1), pages 137-141, August.
    3. Stéphane Dauzère-Pérès & Jan Paulli, 1997. "An integrated approach for modeling and solving the general multiprocessor job-shop scheduling problem using tabu search," Annals of Operations Research, Springer, vol. 70(0), pages 281-306, April.
    4. M. R. Garey & D. S. Johnson & Ravi Sethi, 1976. "The Complexity of Flowshop and Jobshop Scheduling," Mathematics of Operations Research, INFORMS, vol. 1(2), pages 117-129, May.
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    Cited by:

    1. Hongfeng Wang & Min Huang & Junwei Wang, 2019. "An effective metaheuristic algorithm for flowshop scheduling with deteriorating jobs," Journal of Intelligent Manufacturing, Springer, vol. 30(7), pages 2733-2742, October.
    2. Oscar Castillo & Cinthia Peraza & Patricia Ochoa & Leticia Amador-Angulo & Patricia Melin & Yongjin Park & Zong Woo Geem, 2021. "Shadowed Type-2 Fuzzy Systems for Dynamic Parameter Adaptation in Harmony Search and Differential Evolution for Optimal Design of Fuzzy Controllers," Mathematics, MDPI, vol. 9(19), pages 1-20, October.
    3. Yiyi Xu & M’hammed Sahnoun & Fouad Ben Abdelaziz & David Baudry, 2022. "A simulated multi-objective model for flexible job shop transportation scheduling," Annals of Operations Research, Springer, vol. 311(2), pages 899-920, April.
    4. 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.
    5. Zheng, Zhi-xin & Li, Jun-qing & Duan, Pei-yong, 2019. "Optimal chiller loading by improved artificial fish swarm algorithm for energy saving," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 155(C), pages 227-243.
    6. Fei Luan & Zongyan Cai & Shuqiang Wu & Tianhua Jiang & Fukang Li & Jia Yang, 2019. "Improved Whale Algorithm for Solving the Flexible Job Shop Scheduling Problem," Mathematics, MDPI, vol. 7(5), pages 1-14, April.

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