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An elitist quantum-inspired evolutionary algorithm for the flexible job-shop scheduling problem

Author

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  • Xiuli Wu

    (University of Science and Technology Beijing)

  • Shaomin Wu

    (University of Kent)

Abstract

The flexible job shop scheduling problem (FJSP) is vital to manufacturers especially in today’s constantly changing environment. It is a strongly NP-hard problem and therefore metaheuristics or heuristics are usually pursued to solve it. Most of the existing metaheuristics and heuristics, however, have low efficiency in convergence speed. To overcome this drawback, this paper develops an elitist quantum-inspired evolutionary algorithm. The algorithm aims to minimise the maximum completion time (makespan). It performs a global search with the quantum-inspired evolutionary algorithm and a local search with a method that is inspired by the motion mechanism of the electrons around atomic nucleuses. Three novel algorithms are proposed and their effect on the whole search is discussed. The elitist strategy is adopted to prevent the optimal solution from being destroyed during the evolutionary process. The results show that the proposed algorithm outperforms the best-known algorithms for FJSPs on most of the FJSP benchmarks.

Suggested Citation

  • Xiuli Wu & Shaomin Wu, 2017. "An elitist quantum-inspired evolutionary algorithm for the flexible job-shop scheduling problem," Journal of Intelligent Manufacturing, Springer, vol. 28(6), pages 1441-1457, August.
    • Handle: RePEc:spr:joinma:v:28:y:2017:i:6:d:10.1007_s10845-015-1060-6
    DOI: 10.1007/s10845-015-1060-6
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    References listed on IDEAS

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    1. 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.
    2. Chiang, Tsung-Che & Lin, Hsiao-Jou, 2013. "A simple and effective evolutionary algorithm for multiobjective flexible job shop scheduling," International Journal of Production Economics, Elsevier, vol. 141(1), pages 87-98.
    3. Loukil, T. & Teghem, J. & Tuyttens, D., 2005. "Solving multi-objective production scheduling problems using metaheuristics," European Journal of Operational Research, Elsevier, vol. 161(1), pages 42-61, February.
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    Cited by:

    1. 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.
    2. Yingli Li & Jiahai Wang & Zhengwei Liu, 2022. "A simple two-agent system for multi-objective flexible job-shop scheduling," Journal of Combinatorial Optimization, Springer, vol. 43(1), pages 42-64, January.
    3. Fei Luan & Zongyan Cai & Shuqiang Wu & Shi Qiang Liu & Yixin He, 2019. "Optimizing the Low-Carbon Flexible Job Shop Scheduling Problem with Discrete Whale Optimization Algorithm," Mathematics, MDPI, vol. 7(8), pages 1-17, August.
    4. Gregory A. Kasapidis & Dimitris C. Paraskevopoulos & Panagiotis P. Repoussis & Christos D. Tarantilis, 2021. "Flexible Job Shop Scheduling Problems with Arbitrary Precedence Graphs," Production and Operations Management, Production and Operations Management Society, vol. 30(11), pages 4044-4068, November.
    5. Xiuli Wu & Xianli Shen & Qi Cui, 2018. "Multi-Objective Flexible Flow Shop Scheduling Problem Considering Variable Processing Time due to Renewable Energy," Sustainability, MDPI, vol. 10(3), pages 1-30, March.
    6. Du, Mengyu & Li, Yan-Fu, 2020. "An investigation of new local search strategies in memetic algorithm for redundancy allocation in multi-state series-parallel systems," Reliability Engineering and System Safety, Elsevier, vol. 195(C).

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