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<p>In order to address the challenge of frequent disturbance events in ship plate processing, which often disrupt production progress, this study proposes a dynamic scheduling method. This method optimizes the production scheduling process under such disturbances, aiming to minimize completion time, processing costs, and machine load. Considering constraints such as process priority and machine selection, a mathematical model for the dynamic scheduling of ship plate processing is established. The model is solved using an improved NSGA-II algorithm. Firstly, adaptive crossover and mutation probabilities are introduced to adjust these probabilities dynamically based on the evolutionary state of the population. Secondly, an improved elite selection strategy is proposed. This strategy achieves individual adaptive hierarchical selection by considering the number of algorithm iterations and individual non-dominated ranks, thereby enhancing population diversity in the later stages while retaining elite individuals throughout the iterative process. Finally, the feasibility of the improved algorithm and the dynamic scheduling method is verified through experimental analysis of the plate-cutting process in a specific shipyard.</p>

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Research on scheduling optimization of ship plate processing workshop based on improved NSGA-II algorithm

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月16日

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  • Lei Dong,
  • Jinfeng Liu,
  • Shimin Gu,
  • Su Li,
  • Ruhai Jiang,
  • Hao Yang,
  • Zhilong Cheng

摘要

In order to address the challenge of frequent disturbance events in ship plate processing, which often disrupt production progress, this study proposes a dynamic scheduling method. This method optimizes the production scheduling process under such disturbances, aiming to minimize completion time, processing costs, and machine load. Considering constraints such as process priority and machine selection, a mathematical model for the dynamic scheduling of ship plate processing is established. The model is solved using an improved NSGA-II algorithm. Firstly, adaptive crossover and mutation probabilities are introduced to adjust these probabilities dynamically based on the evolutionary state of the population. Secondly, an improved elite selection strategy is proposed. This strategy achieves individual adaptive hierarchical selection by considering the number of algorithm iterations and individual non-dominated ranks, thereby enhancing population diversity in the later stages while retaining elite individuals throughout the iterative process. Finally, the feasibility of the improved algorithm and the dynamic scheduling method is verified through experimental analysis of the plate-cutting process in a specific shipyard.