Optimal Scheduling for Retrieval Jobs in Double-Deep AS/RS by Evolutionary Algorithms

Abstract

We investigate the optimal scheduling of retrieval jobs for double-deep type Automated Storage and Retrieval Systems (AS/RS) in the Flexible Manufacturing System (FMS) used in modern industrial production. Three types of evolutionary algorithms, the Genetic Algorithm (GA), the Immune Genetic Algorithm (IGA), and the Particle Swarm Optimization (PSO) algorithm, are implemented to obtain the optimal assignments. The objective is to minimize the working distance, that is, the shortest retrieval time travelled by the Storage and Retrieval (S/R) machine. Simulation results and comparisons show the advantages and feasibility of the proposed methods.