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Less is more: variable neighborhood search for integrated production and assembly in smart manufacturing

Author

Listed:
  • Shaojun Lu

    (Hefei University of Technology
    Key Laboratory of Process Optimization and Intelligent Decision-Making of Ministry of Education)

  • Jun Pei

    (Hefei University of Technology
    University of Florida
    Key Laboratory of Process Optimization and Intelligent Decision-Making of Ministry of Education)

  • Xinbao Liu

    (Hefei University of Technology
    Key Laboratory of Process Optimization and Intelligent Decision-Making of Ministry of Education)

  • Xiaofei Qian

    (Hefei University of Technology
    Key Laboratory of Process Optimization and Intelligent Decision-Making of Ministry of Education)

  • Nenad Mladenovic

    (Khalifa University
    Ural Federal University)

  • Panos M. Pardalos

    (University of Florida)

Abstract

This paper investigates an integrated production and assembly scheduling problem with the practical manufacturing features of serial batching and the effects of deteriorating and learning. The problem is divided into two stages. During the production stage, there are several semi-product manufacturers who first produce ordered product components in batches, and then these processed components are sent to an assembly manufacturer. During the assembly stage, the assembly manufacturer will further process them on multiple assembly machines, where the product components are assembled into final products. Through mathematical induction, we characterize the structures of the optimal decision rules for the scheduling problem during the production stage, and a scheme is developed to solve this scheduling problem optimally based on the structural properties. Some useful lemmas are proposed for the scheduling problem during the assembly stage, and a heuristic algorithm is developed to eliminate the inappropriate schedules and enhance the solution quality. We then prove that the investigated problem is NP-hard. Motivated by this complexity result, we present a less-is-more-approach-based variable neighborhood search heuristic to obtain the approximately optimal solution for the problem. The computational experiments indicate that our designed LIMA-VNS (less is more approach–variable neighborhood search) has an advantage over other metaheuristics in terms of converge speed, solution quality, and robustness, especially for large-scale problems.

Suggested Citation

  • Shaojun Lu & Jun Pei & Xinbao Liu & Xiaofei Qian & Nenad Mladenovic & Panos M. Pardalos, 2020. "Less is more: variable neighborhood search for integrated production and assembly in smart manufacturing," Journal of Scheduling, Springer, vol. 23(6), pages 649-664, December.
    • Handle: RePEc:spr:jsched:v:23:y:2020:i:6:d:10.1007_s10951-019-00619-5
    DOI: 10.1007/s10951-019-00619-5
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    References listed on IDEAS

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