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Multi-objective storage location allocation optimization and simulation analysis of automated warehouse based on multi-population genetic algorithm

机译：基于多种群遗传算法的自动化仓库多目标仓库位置分配优化与仿真分析

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摘要

Aiming at the requirement of working efficiency and security of automated warehouse and taking the operation time of outbound-inbound, the equivalent center of gravity of overall shelf and the degree of relative accumulation of related products as the multi-objective functions, the mathematical model is constructed for multi-objective storage location allocation optimization. According to the simple weighted genetic algorithm, it is easily prone to the problem of immature convergence when solving multi-objective programming problems. So, the multi-population genetic algorithm is proposed to solve the mathematical model of storage location allocation optimization. Combining with the experiment data of toy car assembly and automated warehouse, the results of the automated warehouse storage location allocation are obtained. FlexSim dynamic simulation model is established based on the storage location allocation solution, the physical parameters of automated warehouse and the experimental requirements plan of vehicle model assembly. The operation effect of the model and the utilization rate of the equipment are analyzed. The result of multi-population genetic algorithm is more reasonable and effective. It is proved that the result of multi-population genetic algorithm is superior to the result of simple weighted genetic algorithm, which provides an effective method for storage location allocation optimization and outbound-inbound dynamic simulation.
机译：针对自动化仓库的工作效率和安全性的要求，以出库入库时间，整体货架的重心当量以及相关产品的相对积累程度作为多目标函数，建立了数学模型。构造用于多目标存储位置分配的优化。根据简单加权遗传算法，在解决多目标规划问题时很容易出现不成熟收敛的问题。因此，提出了一种多种群遗传算法来求解存储位置分配优化的数学模型。结合玩具车组装和自动仓库的实验数据，获得了自动仓库存储位置分配的结果。基于存储位置分配解决方案，自动仓库的物理参数以及车辆模型装配的实验需求计划，建立了FlexSim动态仿真模型。分析了该模型的运行效果和设备利用率。多元遗传算法的结果更加合理有效。证明了多种群遗传算法的结果优于简单加权遗传算法的结果，为存储位置分配优化和出入库动态仿真提供了有效的方法。

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