An Adaptive Multi-Swarm Gray Wolf Optimizer for Flexible Job Shop Scheduling Problem with Assembly/Disassembly, and Lot Streaming

Authors

  • Liezheng Shen School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China
  • Haiping Zhu School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China | National Centre of Technology Innovation for Intelligent Design and Numerical Control, Wuhan, China
  • Siqi Liu School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

DOI:

https://doi.org/10.23055/ijietap.2025.32.6.11097

Keywords:

Flexible job shop scheduling, assembly/disassembly scheduling, lot streaming, Metaheuristics

Abstract

In the manufacturing/remanufacturing systems of complex products, assembly/disassembly constraints introduce challenges of synchronization and precedence constraints. And large-scale orders have rendered mass production models obsolete. Although existing research on the flexible job shop scheduling problem (FJSP) and its integration with lot streaming (LS) has been extensive, it is still unable to solve these problems simultaneously. To bridge this gap, this paper proposes a novel FJSP with assembly/disassembly and LS (FJSP-AD-LS) to minimize makespan. A MILP model is developed to describe the problem, and an adaptive multi-swarm grey wolf optimizer (AMGWO) is proposed to solve the NP-hard problem. The AMGWO employs a multi-swarm framework with dynamic regrouping to balance exploration and exploitation, and incorporates multiple encoding-decoding methods, evolutionary operators, and knowledge-based neighborhood search methods. A total of 49 new instances are conducted by extending the classical benchmarks. Computational results demonstrate that AMGWO achieves over 10% improvement in makespan compared to the basic algorithm through ablation studies. While commercial MILP solvers could only obtain feasible solutions for 4 small-scale instances within time limits, the proposed algorithm consistently generates high-quality solutions for all test instances. Comparative studies against state-of-the-art methods confirm the superior performance of AMGWO across all 49 instances.

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Published

2025-12-14

How to Cite

Shen, L., Zhu, H., & Liu, S. (2025). An Adaptive Multi-Swarm Gray Wolf Optimizer for Flexible Job Shop Scheduling Problem with Assembly/Disassembly, and Lot Streaming. International Journal of Industrial Engineering: Theory, Applications and Practice, 32(6). https://doi.org/10.23055/ijietap.2025.32.6.11097

Issue

Vol. 32 No. 6 (2025)

Section

Production Planning and Control

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