Location Assignment Optimization Based on Improved Particle Swarm Optimization Algorithm

• 1、Department of Management and Economics, Tianjin University, Tianjin 300000

Abstract：The implementation of smart supply chain puts forward higher requirements for the lean management of power material warehouse. In order to improve the efficiency of warehouse management, this paper analyzed and optimized material storage management in a power supply bureau. In this paper, through mining association rules for the data of electric power materials and taking the confidence between materials as the distance, the paper used density clustering to cluster the electric power materials with the distance, and established the mathematical model of storage location optimization based on the analysis of the frequency of power materials, so as to optimize the storage location and improve the picking efficiency. In order to improve the shortcoming of particle swarm optimization (PSO) which is easy to fall into local optimum, a random disturbance factor based on linear increasing probability was added to the adaptive inertia weight based on inverted S-curve, which will increase the probability of particles jumping out of local optimal value in the later stage. Taking the warehouse of a power supply bureau as an example, the improved algorithm has better optimization effect and better stability on the material delivery efficiency and the distance between similar goods.

Keywords： slotting optimization PSO association rules density clustering