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Integrated optimization of non-permutation flow shop scheduling and maintenance planning with variable processing speed

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  • Jiang, Junwei
  • An, Youjun
  • Dong, Yuanfa
  • Hu, Jiawen
  • Li, Yinghe
  • Zhao, Ziye

Abstract

Integrated optimization of production scheduling and maintenance planning has received tremendous attention from academia and industry, but the impacts of variable processing speeds on the integrated optimization are either unconsidered or supposed to be constant in the most existing publications. To remedy this shortcoming, this paper addresses an integrated optimization problem of non-permutation flow-shop scheduling and maintenance planning (including preventive maintenance and replacement) with adjustable processing speeds for minimizing the total production cost. To better analyze the problem, there are five problem properties are derived to describe the relationship among processing speed, processing sequence and maintenance planning. Based on this, an improved genetic algorithm with variable processing speed selection (IGAVPSS) is proposed to deal with the concerned problem. In the numerical simulation, the effectiveness and superiority of extended advanced-postpone balancing strategy, proposed internal operators, IGAVPSS algorithm and variable processing speeds are firstly demonstrated through experimental comparisons. After that, a real application is presented to verify the benefits of the proposed IGAVPSS, and the results show that the newly generated scheduling plan based on the proposed IGAVPSS can reduce up to 42.29% of the total production cost compared with the existing company’s scheduling plan.

Suggested Citation

  • Jiang, Junwei & An, Youjun & Dong, Yuanfa & Hu, Jiawen & Li, Yinghe & Zhao, Ziye, 2023. "Integrated optimization of non-permutation flow shop scheduling and maintenance planning with variable processing speed," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    • Handle: RePEc:eee:reensy:v:234:y:2023:i:c:s0951832023000583
    DOI: 10.1016/j.ress.2023.109143
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