B2C电子商务仓库拣货路径优化策略应用研究

当前国内B2C电子商务仓库多为人至物的拣货模式,拣货作业成为其核心作业之一,占据仓库大量时间成本和资金成本,拣货路径优化成为企业亟需解决的问题。本文基于TSP对拣货路径进行建模,利用蚁群算法、模拟退火算法和禁忌搜索对该NP-hard问题进行求解,并同当前企业普遍采用的S型启发式策略进行对比,拣货时间节约13.35%。进一步得出当拣货品数量较少时应采用模拟退火算法求解,而当拣货品数量较大时采用蚁群算法仅进行一次迭代,则可以实现短时间得到相对较优的解。所得结果已应用于某大型电子商务企业,效果明显。

Applied Optimal Strategies for Warehouse Picking Routing in B2C

Men-to-thing picking method is widely used in existing B2C warehouse in China, in which picking is one of the core workloads and it takes a lot of time and capital. Warehouse picking routing is an important problem in e-commerce. Based on TSP, we build a picking routing model which is NP-hard and solved by ant colony algorithm, simulated annealing algorithm, tabu search algorithm and S-shape heuristic algorithms, respectively. The results show that the picking time by ant colony algorithm saves time by 13.35% compared with that by S-shape heuristic algorithms. Furthermore, we obtain a relative optimal solution in shorter time with simulated annealing algorithm when the number of picking goods is small, while the ant colony algorithm can achieve the relative optimal results with only one iteration. These results have been applied to one e-commerce business and have been effective.