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Sustainable scheduling for a flexible job shop considering batch processing and batch transportation strategies

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  • Enguang Guan

    (Shanghai Maritime University)

  • Jiahui Wang

    (Tongji University)

  • Jianguo Duan

    (Shanghai Maritime University)

Abstract

With increasing energy prices and the deteriorating environment, reducing the energy consumption of workshops has become a crucial concern for the manufacturing industry, without compromising production efficiency. Therefore, this paper proposes a multi-objective optimization method for energy-efficient production scheduling in flexible job shops. This method comprehensively considers batch processing strategy (BPS), batch transportation strategy (BTS), machine idle time arrangement (MITA), and machine speed level selection (MSLS). On this basis, a multi-objective scheduling model is constructed, which not only focuses on optimizing the makespan but also thoroughly considers various components of energy consumption, including energy used during machine turning on/off, machine idle periods, machine processing, machine standby, and transportation of jobs. To effectively solve this model, an improved non-dominated sorting genetic algorithm II (NSGA-II) based on real-number encoding is designed. Through this algorithm, we can efficiently seek optimal balance among multiple objectives. Furthermore, an empirical case study fully validates the effectiveness of the proposed method in reducing both the total energy consumption and the makespan in the workshop.

Suggested Citation

  • Enguang Guan & Jiahui Wang & Jianguo Duan, 2025. "Sustainable scheduling for a flexible job shop considering batch processing and batch transportation strategies," Journal of Scheduling, Springer, vol. 28(6), pages 615-637, December.
    • Handle: RePEc:spr:jsched:v:28:y:2025:i:6:d:10.1007_s10951-025-00849-w
    DOI: 10.1007/s10951-025-00849-w
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