冲压车间调度的动态拥挤度NSGA-Ⅱ多目标优化方法

为了降低冲压车间总能耗、缩短冲压件完成时间,提出了基于动态拥挤度NSGA-Ⅱ算法的冲压车间调度优化方法。分析了冲压机各工作状态,针对多制件、多工序调度问题,建立了以能耗最低、完成时间最短为优化目标的多目标优化模型。针对NSGA-Ⅱ算法中拥挤度比较算子会降低基因的多样性的问题,给出了动态拥挤度策略,该策略在删除多余染色体的同时,动态更新各染色体拥挤度,从而提出了基于动态拥挤度NSGA-Ⅱ算法的模型求解方法。经实例验证,NSGA-Ⅱ算法搜索的Pareto前沿解集不是真正的前沿解,而是陷入了局部最优,动态拥挤度NSGA-Ⅱ算法搜索出了Pareto前沿解集,其优化结果在能耗和完成时间上均优于NSGA-Ⅱ算法。

Multi-object optimization method on stamping workshop scheduling based on dynamic congestion degree NSGA-Ⅱalgorithm

In order to reduce energy consumption of stamping workshop and shorten producing time of stamping parts,the optimization method on stamping workshop scheduling based on dynamic congestion degree NSGA-Ⅱ algorithm was proposed.Then,the different working states of stamping press were analyzed,and for the multi-part and multi-procedure scheduling problem,taking the lowest energy consumption and the shortest completion time as the optimization goal,the multi-object optimization model was built.Aiming at the fact that the congestion degree comparison operator in NSGA-Ⅱ algorithm decreased the diversity of genes,a dynamic congestion degree strategy was given,which dynamically updated the congestion degree of each chromosome when the redundant chromosome was deleted so that the solving method based on dynamic congestion degree NSGA-Ⅱ algorithm for the model was put forward.Instance verification shows that Pareto frontier solution set searched by NSGA-Ⅱ algorithm is not real frontier solution,but falls into a local optimum.Thus,Pareto frontier solution set is searched by dynamic congestion degree NSGA-Ⅱ algorithm,and its optimization results are better than the NSGA-Ⅱ algorithm in terms of energy consumption and completion time.