Effectiveness Verification of Crossover by Items and Warehouses in Item Stock Allocation Optimization Used Multi-objective Evolutionary Computation

Abstract

Item stock allocation optimization is a problem for e-commerce companies that operate multiple warehouses to improve the efficiency of item allocation among the warehouses. This problem is very important for reducing shipping and inventory costs. Since the objective function in this problem has some parts that are difficult to represent by formula and to grasp the features, evolutionary computation, one of black box optimization methods, is suitable as the solution. We apply constrained NSGA-II, a multi-objective evolutionary computation method, for minimizing both shipping and inventory costs simultaneously, while considering various constraints such as the capacity of warehouses. In usual evolutionary computation, since the crossover is operated randomly, the charactoristic structure in a good solution may be destructed. Thus we propose a method by which crossover is operated by each item or warehouse, for the purpose to sustain the structure of solutions. By comparing the evolutionary transition of the proposed method with uniform crossover, we confirm the effectiveness.