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。%Men-to-thing picking method is widely used in existing B 2 C 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 prob-lem 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 .
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