基于自适应MOEA/D的柔性车间多目标联合优化调度

Multi-objective joint optimization scheduling of flexible workshop based on adaptive MOEA/D

  • 摘要: 为了实现柔性作业车间完工时间、机器负荷、交货延期时间、车间能耗等多个目标的联合优化,提出了基于自适应惩罚MOEA/D(multi-objective evolutionary algorithm based on decomposition)的柔性车间多目标调度方法。对具有多个生产机床、多个加工任务、多道工序的柔性车间调度问题进行了描述并建立了优化模型。给出了基于MOEA/D算法的柔性车间调度方法,针对常值惩罚因子无法满足不同邻域对收敛性和染色体多样性不同调整需求的问题,提出了能够随邻域染色体密度自适应调整的惩罚因子,并制定了基于自适应惩罚MOEA/D算法的柔性车间调度流程。在具有8个机床、8个工件共28道工序的生产调度实验中,自适应MOEA/D算法搜索的Pareto前沿解能够支配标准MOEA/D和改进NSGA-II算法的Pareto前沿解;在等权重最优解的生产实验中,自适应MOEA/D算法调度方案的完工时间、机器负荷、交货延期时间、车间能耗均小于标准MOEA/D算法和改进NSGA-II算法。实验结果证明了自适应惩罚MOEA/D算法在柔性车间调度中的有效性和优越性。

     

    Abstract: In order to realize the multiple-objective joint optimization of flexible workshop, such as completion time, machine load, delivery delay time and workshop energy consumption, a multi-objective scheduling method for flexible workshop based on adaptive penalty MOEA/D is proposed. The flexible workshop scheduling problem with multiple production machines, multiple processing tasks and multiple processes is described, and an optimization model is established. A flexible job shop scheduling method based on MOEA/D algorithm is proposed. Aiming at the problem that constant penalty factors cannot meet the different adjustment requirements of different neighborhoods for convergence and chromosome diversity, a penalty factor that can adjust adaptively with the density of adjacent chromosomes is proposed, and a flexible workshop scheduling process based on adaptive penalty MOEA/D algorithm is formulated. In the production scheduling experiment with 8 machine tools and 8 workpieces with 28 processes, the Pareto frontier solution searched by the adaptive MOEA/D algorithm can dominate that of the standard MOEA/D and the improved NSGA-II algorithm; In the production experiment of the equal weight optimal solution, the completion time, machine load, delivery delay time and workshop energy consumption of the adaptive MOEA/D algorithm scheduling scheme are less than those of the standard MOEA/D algorithm and the improved NSGA-II algorithm. The experimental results show that the adaptive penalty MOEA/D algorithm is effective and superior in flexible workshop scheduling.

     

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