基于遗传算法的汽车零件自动化仓储货位优化

Slotting Optimization of AS/RS for Automotive Parts Based on Genetic Algorithm

汽车零部件仓储对主机厂的正常生产起着至关重要的作用。在分析汽车零部件仓储特点的基础上,探讨了在零部件仓储中应用自动化立体仓库(AS/RS)的可行性与必要性;然后针对自动化立体仓库实施中货位规划的难题,以提高零部件出库作业效率和货架稳定性和安全性为优化目标,建立了货位优化的多目标数学模型。根据某汽车零部件仓储中心的实际情况,通过设置随机目标权重改进传统的遗传算法,在MATLAB软件环境中对模型进行多目标混合遗传算法优化求解,对自动化立体仓库零部件货架的货位进行仿真和优化。结果表明零部件的出库效率、零部件的合成重心、货架的稳定性等都得到很大的改善。

Warehousing of automotive parts plays a key role on the host plant＇s normal production. The necessity and feasibility of AS/RS supplied in auto parts warehousing were researched. To solve the slotting optimization ofAS/RS, a multi-objective mathematical model was built so as to improve the parts operating efficiency and shelf stability and reliability. By setting up random goal weight to improve traditional genetic algorithm, and based on MATLAB software, the optical solution was found with mixed multi-target genetic algorithm. Taking the background of a logistics center of automotive parts to simulate and analyze, the results show that the efficiency of warehousing operation, the stability of racks and the synthesis center of mass of goods have been greatly improved.