面向智能车间的动态过道布置问题建模与优化

Modeling and optimization of dynamic corridor layout problems for intelligent workshops

针对传统过道布置鲜少考虑快速响应生产需求变化及加工产品组合的灵活性的不足,构建了一种双目标动态过道布置问题数学模型.通过调整不同阶段的设施位置,以达到总物料搬运成本及设施重组成本之和与布局面积最小化的目的.由于双目标动态过道布置问题具有多项式复杂程度的非确定性问题(NP-hard)的特征,因此又提出了一种基于帕累托(Pareto)占优的多目标改进猫群算法.首先将基本猫群算法离散化;然后引入帕累托占优思想和拥挤距离机制处理双目标结果,并嵌入变邻域搜索结构加快算法寻优;最后采用所提算法测试大量不同规模算例,并与其他算法结果进行对比,验证了该算法的有效性和优越性.

Aiming at the shortcomings of traditional corridor allocation that rarely consider the need to respond quickly to changes in production demand and the flexibility of product mix during processing,a dual objective dynamic corridor allocation problem was constructed.By adjusting the location of facilities at different stages,the sum of material handling cost and facility restructuring cost and layout area were minimized.Since the dual objective corridor allocation problem is characterized as non-deterministic polynomial(NP)-hard problem,a multi-objective improved cat swarm algorithm based on Pareto dominance was also proposed.First,the basic cat swarm optimization was discretized,the Pareto dominance idea and crowding distance mechanism were introduced to process the two-target results,and the variable neighborhood search structure was embedded to speed up the optimization of the algorithm.Finally,the proposed algorithm was used to test a large number of examples of different scales,and the results were compared with other algorithms to verify the effectiveness and superiority of the algorithm.