基于NSGA-Ⅱ算法的分区拣选优化

A Research on Zone Picking Optimization Problem Based on NSGA-Ⅱ Algorithm

电商背景下的客户订单呈现出多品种、小批量、高频次等特点,给仓库拣选工作带来很大的挑战。为提高拣选效率,在订单完全拆分的分批策略和组合优化的行走策略下,设计了以总服务时间最小、分区工作量平衡度最优和二次分拣效率最高的多目标分区拣选模型。由于3个目标函数之间存在矛盾,设计了NSGA-Ⅱ算法对多目标优化模型进行求解。通过数值实验,与传统的不拆分订单的分区拣选系统对比,发现在订单批量环境为[1,4]时,分别使总服务时间减少了43.88%,平衡度改善了84.61%,并分析了区域个数、订单总数和订单批量环境对系统效率的影响。

Under the background of e-commerce, customer orders show the characteristics of multiple varieties, small batch,high frequency and so on, which brings great challenges to the warehouse picking work. In order to improve the efficiency of picking, a multi-objective zone picking model that minimizes the total service time, optimal zone workload balance and achieve the highest secondary sorting efficiency is designed under the batching strategy of the complete splitting of the order and walking strategy of the combinatorial optimization. Due to the contradiction among the three objective functions, the nondominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ) is designed to solve the multi-objective optimization model. Through numerical experiments, it is found that when the order batch environment is [1,4], the total service time is reduced by 43.88%, the balance is improved by 84.61%,respectively, compared with the traditional partition picking system without splitting orders. The influence of the number of zones, the total number of orders and the order batch environment on the system efficiency is analyzed.