双区型仓库订单分批与拣选协同优化研究

Research on collaborative optimization of order batching andpicking in two-block warehouse

针对订单分拣效率低下导致商品出库缓慢的问题,提出一种基于双区型仓库订单分批与拣选的协同优化模型,设计求解模型的CWDP-BSA(clarke-wright and dynamic programming&backtracking search algorithm)协同优化算法。在节约算法中引入快速排序法对订单组合的距离节约值排序,考虑AGV承载量,运用多阶段决策过程最优策略得出状态转移方程求解订单分批模型,确定初始分批方案;并采取多因子选择的回溯搜索算法求解拣选路径模型,以此确定初始拣选方案。再以以上两方案为基础,建立新的基于订单时间窗的订单分批和拣选协同优化模型并求解,进一步优化订单分批和拣选方案。最后通过对比实验得出,平均每批次订单的拣选距离减少了约24.56%,优化后的拣选时间比优化前缩短了约11.4%,在求解不同规模算例时,CWDP-BSA算法的求解结果优于CPLEX软件和其他算法,验证了模型与算法的稳定性和有效性。实验表明,协同优化后的订单分批与物品拣选策略能够有效提升订单出库效率。

In response to the problem of slow delivery of goods due to low order sorting efficiency,this paper proposed a collaborative optimization model based on two-block warehouse order batching and picking,and designed a multi-stage CWDP-BSA algorithm to solve the model.In the saving algorithm,it introduced quick sorting method to sort the distance savings of order combinations.Considering the carrying capacity of AGV,under the guidance of the optimal strategy in the multi-stage decision-making process,it obtained the state transition equation to solve the order batch model and determine the initial batch plan.And it adopted a multi factor selection backtracking search algorithm to solve the picking path model,in order to determine the initial picking plan.Based on the two schemes,establishing and solving a collaborative optimization model for order batching and picking based on order time windows were to further optimize order batching and picking schemes.Finally,through comparative experiments,it can reduce the average picking distance of each batch of orders by about 24.56%,and shorten the optimized picking time by about 11.4%compared to before.When solving examples of different scales,the CWDP-BSA algorithm performs better than CPLEX software and other algorithms,which verifies the stability and effectiveness of the model and algorithm.Experiments show that the collaborative optimization of order batching and item picking strategies can effectively improve the efficiency of order delivery.