基于Co-CEM的柔性车间调度优化算法

Flexible job-shop scheduling optimization algorithmbased on Co-CEM

为解决船舶制造中的柔性作业车间调度问题,本文提出一种基于协同进化策略的交叉熵算法来提高船舶制造过程的效率。协同进化策略弥补了交叉熵算法局部搜索能力较弱的问题,提高解的质量;提出基于主动调度的遗传解码算法,保证得到的解属于活动调度;遗传操作将相关调度信息保存在基因中,有效提高算法的搜索效率。本文通过实验对比遗传解码与常用的插入式解码算法,验证了解码算法的有效性及其提升能力,与现有具有竞争力的算法进行对比,证明了基于协同进化策略的交叉熵算法的高效性与优越性,给出了优质的甘特图。

This paper proposes a cooperative coevolution-based CEM(Co-CEM)algorithm to solve the flexible job-shop scheduling problem(FJSP)in shipbuilding and improve the efficiency of the shipbuilding process.The co-evolution strategy makes up for the weak local search capability of the cross-entropy method(CEM),thus achieving improved solution quality.In this paper,a genetic decoding algorithm based on active scheduling is proposed,in which the active scheduling operation ensures that the solutions obtained belong to active scheduling,and the genetic operation saves relevant scheduling information in the gene,effectively improving the search efficiency of the algorithm.The effectiveness of the proposed decoding algorithm and its improvement ability were verified by comparing the genetic decoding algorithm with the commonly used plug-in decoding algorithm via experiments.The comparison with existing competitive algorithms proves the efficiency and superiority of Co-CEM,thereby yielding a corresponding high-quality Gantt chart.