A Discrete Particle Swarm Optimization for Storage Location Assignment Problem of Retail E-Commerce

Abstract

The storage location assignment of goods is critical to improve the efficiency of warehouse picking and distribution in the era of retail e-commerce. The optimization model of the storage location assignment problem (SLAP) for retail e-commerce is firstly established based on the principles of efficiency priority, shelf stability and similar products adjacency. Then, a new discrete particle swarm optimization (DPSO) algorithm is proposed to solve the NP-hard SLAP. In the DPSO, a new updating mechanism based on multi-fragment crossover and mutation operators is devised. Moreover, the elitist scheme and local search are incorporated into the DPSO to improve global search ability. Finally, five instances are used to compare the performance of the DPSO and state-of-the-art artificial fish swarm algorithm (AFSA). The computational results show that the DPSO is superior to the existing AFSA in solution quality.