Generating Products Placement in Warehouse Using BLPSO and MIQCPs

Abstract

Expansion of the e-commerce market due to the development of the Internet has increased in the volume of distribution, and the number of operations in distribution warehouses had also increased. Picking operation is one of the most important tasks, and companies are trying to make this task more efficient by introducing autonomous mobile robots (AMRs), which transports products manually picked to a depot. In this study, we propose a method to generate product assignments that make picking operations more efficient through a two-step optimization process. First, product assignments for utilizing AMRs are generated using particle swarm optimization. Next, in-shelf products layout is generated by mathematical optimization for the products group assigned to the shelves. In product placement optimization, one of the approximate solution methods of the metaheuristic, BLPSO, is fused with a class-based warehouse to obtain an optimal solution. In addition, the problem of in-shelf product layout is formulated in MIQCPs. The constraint expression is used to generate a layout that considers preventing picking mistakes and ensuring the safety of the picker. We have conducted placement optimization experiments using real-world logistic data and discuss the effectiveness of the proposed method.