基于遗传算法的双区型仓库人工拣货路径优化

为获取捡货作业的最优路径,本文以某配送中心双区型仓库中人工拣货作业为研究对象,探讨了订单批量处理和拣货路径优化问题,建立了拣货车容量受限的TSP模型,并基于遗传算法,设计一种启发式算法对拣货路径进行优化处理,同时,应用Visual 6.0C++程序进行仿真实验,以快速获得任意订单中所有待拣货物的拣取顺序,计算出最短路程。仿真结果表明,将考虑拣货车容量限制的情况下求得的最优路径与未考虑拣货车容量限制的情况下求得的最优路径进行对比,结果拣货的先后次序完全不同,说明考虑运载量是有效的,且更符合实际情况。该研究不但提高拣货效率,而且节约各项成本,对现实仓库的拣货作业具有实际应用价值。

A Genetic Algorithm for Order-Picking Optimization Problem in a Two-Block Warehouse

In order to choose the optimal path for the picking-up work,this paper takes artificial picking operations in a certain distribution center which has double-area warehouse as the studying object.It also discusses the order batching and route optimizing problems,and establishes the TSP model considering the restrained capacity of picking carts.It creates a heuristic algorithm which based on the Genetic Algorithm （GA） to solve the optimized problem.Meanwhile it makes a simulated experiment with the Visual 6.0 C＋ ＋ platform.This way,we can obtain any order-picking routes quickly,and get the shortest one simultaneously.Comparing with the results which neglecting cart＇s limitation,the two results are entirely different.So it is more effective and practical to take carrying capacity into account.The study not only improves the picking efficiency,but also saves costs,and it is more valuable when referring to reality.