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Modeling and solving for bi-objective cutting parallel machine scheduling problem

Author

Listed:
  • Ronghua Meng

    (Huazhong University of Science and Technology
    China Three Gorges University)

  • Yunqing Rao

    (Huazhong University of Science and Technology)

  • Qiang Luo

    (Huazhong University of Science and Technology)

Abstract

This paper addresses a bi-objective cutting parallel machine scheduling problem aiming to minimize the total makespan and total tardiness. This problem is inspired from a structural metal-cutting plant that combines identical and unrelated parallel machine scheduling problems. To formulate this complicated problem, a new mixed-integer programming (MIP) model is presented in consideration of total makespan and total tardiness. The machine-job-dependent processing times are considered along with the setup times, pickup times, different delivery times, and machine eligibility constraints. Owing to the complex characteristics of the problem, an appropriate non-dominated sorting Genetic Algorithm III (NSGAIII) with an embedded variable neighborhood structure strategy (VNSGAIII) is developed. A number of randomly generated datasets are used to test the performance of VNSGAIII in comparison with NSGAII, and NSGAIII on solving the engineering problem addressed herein. The experimental results demonstrate that the suggested VNSGAIII statistically outperforms the compared algorithms, especially in the distribution of Pareto solutions. The ε-constrained method is implemented in the direct MIP model by CPLEX for comparison with the proposed evolutionary algorithms. The proposed algorithm performs efficiently when obtaining the Pareto solutions.

Suggested Citation

  • Ronghua Meng & Yunqing Rao & Qiang Luo, 2020. "Modeling and solving for bi-objective cutting parallel machine scheduling problem," Annals of Operations Research, Springer, vol. 285(1), pages 223-245, February.
    • Handle: RePEc:spr:annopr:v:285:y:2020:i:1:d:10.1007_s10479-019-03208-z
    DOI: 10.1007/s10479-019-03208-z
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    References listed on IDEAS

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    2. Anghinolfi, Davide & Paolucci, Massimo & Ronco, Roberto, 2021. "A bi-objective heuristic approach for green identical parallel machine scheduling," European Journal of Operational Research, Elsevier, vol. 289(2), pages 416-434.

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