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Improved Whale Algorithm for Solving the Flexible Job Shop Scheduling Problem

Author

Listed:
  • Fei Luan

    (School of Construction Machinery, Chang’an University, Xi’an 710064, China
    College of Mechanical and Electrical Engineering, Shaanxi University of Science & Technology, Xi’an 710021, China)

  • Zongyan Cai

    (School of Construction Machinery, Chang’an University, Xi’an 710064, China)

  • Shuqiang Wu

    (School of Construction Machinery, Chang’an University, Xi’an 710064, China)

  • Tianhua Jiang

    (School of Transportation, Ludong University, Yantai 264025, China)

  • Fukang Li

    (School of Construction Machinery, Chang’an University, Xi’an 710064, China)

  • Jia Yang

    (School of Construction Machinery, Chang’an University, Xi’an 710064, China)

Abstract

In this paper, a novel improved whale optimization algorithm (IWOA), based on the integrated approach, is presented for solving the flexible job shop scheduling problem (FJSP) with the objective of minimizing makespan. First of all, to make the whale optimization algorithm (WOA) adaptive to the FJSP, the conversion method between the whale individual position vector and the scheduling solution is firstly proposed. Secondly, a resultful initialization scheme with certain quality is obtained using chaotic reverse learning (CRL) strategies. Thirdly, a nonlinear convergence factor (NFC) and an adaptive weight (AW) are introduced to balance the abilities of exploitation and exploration of the algorithm. Furthermore, a variable neighborhood search (VNS) operation is performed on the current optimal individual to enhance the accuracy and effectiveness of the local exploration. Experimental results on various benchmark instances show that the proposed IWOA can obtain competitive results compared to the existing algorithms in a short time.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:7:y:2019:i:5:p:384-:d:226557
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    References listed on IDEAS

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    6. Oliva, Diego & Abd El Aziz, Mohamed & Ella Hassanien, Aboul, 2017. "Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm," Applied Energy, Elsevier, vol. 200(C), pages 141-154.
    7. Goncalves, Jose Fernando & de Magalhaes Mendes, Jorge Jose & Resende, Mauricio G. C., 2005. "A hybrid genetic algorithm for the job shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 167(1), pages 77-95, November.
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    Cited by:

    1. Li, Maodong & Xu, Guanghui & Lai, Qiang & Chen, Jie, 2022. "A chaotic strategy-based quadratic Opposition-Based Learning adaptive variable-speed whale optimization algorithm," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 193(C), pages 71-99.
    2. Anran Zhao & Peng Liu & Xiyu Gao & Guotai Huang & Xiuguang Yang & Yuan Ma & Zheyu Xie & Yunfeng Li, 2022. "Data-Mining-Based Real-Time Optimization of the Job Shop Scheduling Problem," Mathematics, MDPI, vol. 10(23), pages 1-30, December.
    3. Voravee Punyakum & Kanchana Sethanan & Krisanarach Nitisiri & Rapeepan Pitakaso, 2022. "Hybrid Particle Swarm and Whale Optimization Algorithm for Multi-Visit and Multi-Period Dynamic Workforce Scheduling and Routing Problems," Mathematics, MDPI, vol. 10(19), pages 1-20, October.
    4. Amit Chhabra & Sudip Kumar Sahana & Nor Samsiah Sani & Ali Mohammadzadeh & Hasmila Amirah Omar, 2022. "Energy-Aware Bag-of-Tasks Scheduling in the Cloud Computing System Using Hybrid Oppositional Differential Evolution-Enabled Whale Optimization Algorithm," Energies, MDPI, vol. 15(13), pages 1-36, June.
    5. Zigao Wu & Shaohua Yu & Tiancheng Li, 2019. "A Meta-Model-Based Multi-Objective Evolutionary Approach to Robust Job Shop Scheduling," Mathematics, MDPI, vol. 7(6), pages 1-19, June.

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