進化計算による在庫配置最適化

抄録

This paper addressed a real-world item stock allocation optimization in ASKUL Corporation, which conducted the electric commerce business covering all over Japan. This work treated a thousand items and eight warehouses. The item stock allocation optimization problem was a combinatorial optimization problem, which determined whether each item should be stocked in each warehouse or not. The problem had multiple constraints, such as the capacities of warehouses and two objectives: the shipping cost and the average number of warehouse stocks. The constraint and objective functions executed an existing complex shipping system internally and should be deemed as a black box. For the black box problem with multiple constraints and objectives, this paper employed CNSGA-II, a representative evolutionary algorithm, and a neighborhood cultivation mechanism. Since the conventional uniform crossover was too destructive for this problem, this paper proposed semantic crossovers grouping and crossing variables in units of item or warehouse. Experiments used real-world data, and results showed that the item crossover crossing each itemunit variable is appropriate for the item stock allocation optimization problem. Also, results showed that the obtained item stock allocation plans are better than an actually used human-made plan in both viewpoints of the shipping cost and the average number of warehouse stocks.