基于多种群遗传算法的自动化立体库货位优化

Slotting optimization of automated warehouse based on multi-population GA

针对自动化立体仓库作业效率和安全性的要求,以货物出入库作业时间、货架整体等效重心和关联产品间相对聚集程度为目标函数,构建了多目标货位分配优化数学模型。将多目标模型加权归一化处理后,用简单加权遗传算法求解,为避免出现未成熟收敛问题,提出了多种群遗传算法求解货位分配优化数学模型,并结合玩具车组装和自动化立体库实验,求得自动化立体库货位分配结果并对比分析,验证了多种群遗传算法的有效性,为自动化立体库货位分配和优化提供了一种有效的解法。

To meet the requirement of the working performance and security of automated warehouse,the mathematical model of multi-objective slotting allocation optimization is constructed. The outbound-inbound operation time,the equivalent center of gravity of overall shelf and the degree of relative accumulation of related products are taken as the multi-objective functions.A Simple Weighted Genetic Algorithm（SWGA）is used to solve the model obtained by weighted and normalized multi-objective models.To avoid the problem of premature convergence,a Multi-population Genetic Algorithm（MPGA）is proposed to solve the optimization model of slotting allocation.Combined with experiment of toy car assembly and experiment of automated warehouse,the results of slotting allocation are obtained.The effectiveness of the MPGA is verified by comparison and analysis of the two results.This study provides an effective solution for automated warehouse slotting allocation and optimization.