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The integrated process planning and scheduling of flexible job-shop-type remanufacturing systems using improved artificial bee colony algorithm

Author

Listed:
  • Wenkang Zhang

    (University of Alberta)

  • Yufan Zheng

    (Xi’an Jiaotong-Liverpool University)

  • Rafiq Ahmad

    (University of Alberta)

Abstract

This study considers an integrated process planning and scheduling (IPPS) problem for remanufacturing systems incorporating parallel disassembly workstations, a flexible job-shop-type reprocessing shop, and parallel reassembly workstations. This IPPS problem aims to determine the allocation/sequence of end-of-life products on the disassembly/reassembly shops and make decisions on the process path selection, operation sequencing, workstation allocation, and selection for reprocessing jobs. To solve the problem, a mixed-integer programming model is first built to characterize it mathematically, and a novel extended network graph is designed to represent and solve this problem visually. Then, an improved artificial bee colony algorithm is proposed that can solve the IPPS problem of remanufacturing systems with disassembly, reworking and reassembly shops simultaneously. In this introduced algorithm, a 3-level real-number solution representation scheme is adopted for encoding and decoding processes, and efficient neighborhood search structures are designed to improve the quality and diversity of the population. Computational experiments were systematically conducted on serval test instances. The results show that the proposed algorithm is highly advantageous for solving the IPPS problems in the remanufacturing systems by comparing it with four baseline algorithms.

Suggested Citation

  • Wenkang Zhang & Yufan Zheng & Rafiq Ahmad, 2023. "The integrated process planning and scheduling of flexible job-shop-type remanufacturing systems using improved artificial bee colony algorithm," Journal of Intelligent Manufacturing, Springer, vol. 34(7), pages 2963-2988, October.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:7:d:10.1007_s10845-022-01969-2
    DOI: 10.1007/s10845-022-01969-2
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    References listed on IDEAS

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    1. R. Micale & C. M. La Fata & M. Enea & G. La Scalia, 2021. "Regenerative scheduling problem in engineer to order manufacturing: an economic assessment," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1913-1925, October.
    2. S. Zhang & T. N. Wong, 2018. "Integrated process planning and scheduling: an enhanced ant colony optimization heuristic with parameter tuning," Journal of Intelligent Manufacturing, Springer, vol. 29(3), pages 585-601, March.
    3. 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.
    4. Guiliang Gong & Qianwang Deng & Raymond Chiong & Xuran Gong & Hezhiyuan Huang & Wenwu Han, 2020. "Remanufacturing-oriented process planning and scheduling: mathematical modelling and evolutionary optimisation," International Journal of Production Research, Taylor & Francis Journals, vol. 58(12), pages 3781-3799, June.
    5. Barzanji, Ramin & Naderi, Bahman & Begen, Mehmet A., 2020. "Decomposition algorithms for the integrated process planning and scheduling problem," Omega, Elsevier, vol. 93(C).
    6. Zhigang Jiang & Ya Jiang & Yan Wang & Hua Zhang & Huajun Cao & Guangdong Tian, 2019. "A hybrid approach of rough set and case-based reasoning to remanufacturing process planning," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 19-32, January.
    7. Wenyuan Wang & Daniel Y. Mo & Yue Wang & Mitchell M. Tseng, 2019. "Assessing the cost structure of component reuse in a product family for remanufacturing," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 575-587, February.
    8. Daniel, V. & Guide, R. Jr., 1997. "Scheduling with priority dispatching rules and drum-buffer-rope in a recoverable manufacturing system," International Journal of Production Economics, Elsevier, vol. 53(1), pages 101-116, November.
    9. Min-Geun Kim & Jae-Min Yu & Dong-Ho Lee, 2015. "Scheduling algorithms for remanufacturing systems with parallel flow-shop-type reprocessing lines," International Journal of Production Research, Taylor & Francis Journals, vol. 53(6), pages 1819-1831, March.
    10. Pisut Pongchairerks & Voratas Kachitvichyanukul, 2009. "A two-level Particle Swarm Optimisation algorithm on Job-Shop Scheduling Problems," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 4(4), pages 390-411.
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