基于分解多目标进化算法的柔性作业车间调度问题研究

Research on Flexible Job Shop Scheduling Problem Based on Decomposition Multi Objective Evolutionary Algorithm

摘要: 本文聚焦于多目标柔性作业车间调度问题(MOFJSP)，为更贴近实际生产环境的复杂性，提出了一种考虑机器设置时间和工件时滞约束的调度模型，旨在最小化最大完工时间和总能耗。本文基于分解多目标进化算法(MOEA/D)框架，结合启发式方法，对种群初始化过程进行了优化改进。通过在15个标准柔性作业车间调度实例上的对比实验，改进后的MOEA/D算法与5种现有的多目标优化算法进行了性能评估。实验结果表明，改进后的MOEA/D算法能够生成更高质量的初始解集，显著提升了算法的收敛速度，并在帕累托最优解的分散性方面表现出色。这一改进不仅增强了算法在多目标优化问题中的整体性能，还为解决复杂调度问题提供了一种高效且实用的解决方案。

Abstract: This paper focuses on the Multi-objective Flexible Job Shop Scheduling Problem (MOFJSP). In order to be closer to the complexity of the actual production environment, a scheduling model considering machine setup time and job delay constraints is proposed to minimize the maximum completion time and total energy consumption. Based on the framework of Decomposed Multi-objective Evolutionary Algorithm (MOEA/D), combined with heuristic methods, the population initialization process is optimized and improved. Through comparative experiments on 15 standard flexible job shop scheduling instances, the performance of the improved MOEA/D algorithm is evaluated with five existing multi-objective optimization algorithms. Experimental results show that the improved MOEA/D algorithm can generate higher quality initial solution sets, significantly improve the convergence speed of the algorithm, and perform well in the dispersion of Pareto optimal solutions. This improvement not only enhances the overall performance of the algorithm in multi-objective optimization problems, but also provides an efficient and practical solution for solving complex scheduling problems.