穿越策略下考虑相关性的货位优化方法

为研究品项相关性对拣货效率的影响规律,通过分析穿越策略下分区分批拣货时品项间相关性关系的特点,以批次拣货时间最短为目标建立了货位指派优化的数学模型,提出基于品项相关性的货位指派优化算法(SASC_C),该算法以COI法解为初始解,通过货位逐次变换策略将相关性强的品项尽可能的指派到尽可能少的巷道中来提高拣货效率.算法测试结果表明:SASC_C算法的收敛速度明显快于不考虑相关性的随机型算法(SASR);求解质量平均改进约1.06%～10.6%,比COI法的解平均改进0.73%～14.6%;相关性强度越高,改进效果越明显;在穿越策略下,改进效果随访问巷道数的增多而减弱.充分利用品项间的相关性关系进行货位优化,有利于提高穿越策略下的拣货效率.

Slotting optimization based on SKUs correlations with Serpentine routing policy

In order to explore the effect rules of the stock keeping units(SKUs) correlation on picking efficiency in a zone-based wave picking system with serpentine routing policy,a mix integer program model to minimize the pick wave makespan was described.The simulated annealing for slotting considering correlation(SASC_C) heuristic and simulated annealing for slotting randomly(SASR) heuristic were developed.The SASR ignored the SKUs correlation.The SASC_C set the COI solution as the initial solution;the slots sequential movement policy based on the dynamic correlations was proposed to reassign the stronger SKUs to the same aisle and the closed slots as much as possible.The promising computational results show the SASC_C has far better convergence speed than SASR;the solution of SASC_C is better than those of COI and SASC;the average improvement ranges from 0.73% to 14.6% and from1.06% to 10.6% respectively;the more correlation strength,the more improvements on the picking efficiency will be;the more visited aisles in one tour,the more decrease of improvement will be.The effect rules of SKUs correlations are distinct with the Serpentine and Return routing policy.By making the best of SKUs correlation to slotting,the picking efficiency can get some improvement.