基于双种群协同进化遗传算法的电力仓库货位分配方法
Slotting Optimization in Automated Power Warehouse Based on Double-Population Co-evolutionary Genetic Algorithm
DOI: 10.12677/CSA.2018.812210, PDF,  被引量   
作者: 薛劭节*, 宋纪恩：国网江苏省电力有限公司，江苏 南京；杨永成：江苏电力信息技术有限公司，江苏 南京
关键词: 自动化立体库；货位分配；精英策略；遗传算法；Power Warehouse； Slotting Optimization； Elitist Strategy； Genetic Algorithm
摘要: 针对电力自动化立体仓库出入库效率和高层货架稳定性问题，建立了多目标货位分配优化模型。提出了一种基于精英保留策略的双种群协同进化遗传算法并用于求解该模型。仿真实验结果验证了该算法比标准遗传算法具有更好的收敛性，能够有效提高物料出入库效率和货架的稳定性。
Abstract: Slotting optimization greatly affects the efficiency of automated power warehouse. This paper con-structs a multi-objective model of slotting optimization which takes warehousing efficiency and high-rise shelf stability into account. A Double-Population Coevolutionary Genetic Algorithm (DPCGA) based on elite retention strategy is proposed to solve the problem. The simulation result shows that DPCGA is practical and effective. It has better convergence and can effectively improve the efficiency of material storage and shelf stability.
文章引用：薛劭节, 宋纪恩, 杨永成. 基于双种群协同进化遗传算法的电力仓库货位分配方法[J]. 计算机科学与应用, 2018, 8(12): 1887-1894. https://doi.org/10.12677/CSA.2018.812210

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