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Optimization of machine tool processing scheduling based on differential evolution algorithm

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  • Yuehong Zhang
  • Mianhao Zhang

Abstract

Machine tool processing scheduling plays a pivotal role in modern manufacturing systems, significantly influencing production efficiency, resource utilization, and timely delivery. Due to its combinatorial and NP-hard characteristics, traditional optimization techniques often face challenges when dealing with large-scale and complex scheduling problems. In this paper, we present an optimization approach for machine tool scheduling that leverages the Differential Evolution (DE) algorithm. By tailoring DE for discrete scheduling environments through specialized encoding and decoding techniques, the algorithm is able to effectively explore the solution space while ensuring the generation of feasible schedules. The results from our experiments reveal that the proposed approach outperforms conventional heuristic methods, particularly in minimizing makespan and achieving a balanced workload distribution across machines. This study underscores the potential of DE as a robust, adaptive, and efficient optimization tool for tackling complex scheduling problems in the context of intelligent manufacturing systems.

Suggested Citation

  • Yuehong Zhang & Mianhao Zhang, 2025. "Optimization of machine tool processing scheduling based on differential evolution algorithm," PLOS ONE, Public Library of Science, vol. 20(10), pages 1-19, October.
    • Handle: RePEc:plo:pone00:0333691
    DOI: 10.1371/journal.pone.0333691
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    References listed on IDEAS

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    1. Antonio Lova & Pilar Tormos, 2001. "Analysis of Scheduling Schemes and Heuristic Rules Performance in Resource-Constrained Multiproject Scheduling," Annals of Operations Research, Springer, vol. 102(1), pages 263-286, February.
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