改进自适应遗传算法求解“货到人”拣选系统订单分批问题

“货到人”拣选系统采用自动导引运输车(Automated guided vehicle,AGV)实现自动拣选作业，由AGV搬运货架到拣选站台，再由拣选人员从货架上拣取商品。订单分批作为拣选作业的准备工作，是影响AGV搬运次数和人工拣货次数的关键因素，优化订单分批策略对提高“货到人”拣选系统效率至关重要。在电商智能仓库背景下，综合考虑订单需求多种商品、商品多货架分布存储、订单与货架供需匹配关系未知等实际因素，以人工拣选成本和AGV搬运成本之和最小为目标构建数学模型，并设计改进自适应遗传算法求解。该算法采用启发式策略生成初始种群，引入具有自适应变换概率的交叉和变异算子，并加入局部搜索过程以增强寻优能力。最后通过试验测试验证模型和算法的有效性，证明种群初始化方法的优势，并采用灵敏度分析给出合理的周转箱数量配置建议。研究可为电商企业通过订单分批优化提高拣选效率、降低拣选成本提供实践指导，为“货到人”拣选系统的实际应用提供科学依据。

Improved Adaptive Genetic Algorithm for Order Batching of “Part-to-picker” Picking System

“Part-to-picker” picking system adopts automated guided vehicle(AGV) to realize automatic picking operation. Shelves are transported by the AGV to the picking station, where they are used by the pickers to pick the goods. As the preparation for picking operations, order batching is a key factor affecting the number of AGV transporting and manual picking times. Optimizing the order division strategy is critical to improve the efficiency of the “part-to-picker” picking system. In the context of e-commerce intelligent warehouse, practical factors such as order demand for multiple goods, multi-shelf distributed storage of goods, unknown matching relationship between supply and demand of orders and shelves, etc., need to be comprehensively considered. Based on this, the objective of mathematical model construction is to minimize the sum of manual picking cost and AGV transporting cost. To solve the problem, an improved adaptive genetic algorithm is designed. The algorithm uses a heuristic strategy to generate the initial population, introduces crossover and mutation operators with adaptive transformation probability, and adds a local search process to enhance the ability of optimization. Finally, the validity of the model and algorithm is verified by experimental tests, and the superiority of the population initialization method is proved. According to the result, the sensitiveness analysis is used to give the reasonable allocation suggestion of turnover box quantity. This study can not only provide practical guidance for e-commerce enterprises to improve picking efficiency and reduce picking cost through order batch optimization, but also provide scientific basis for the practical application of “part-to-picker” picking system.