About this Journal Submit a Manuscript Table of Contents
Abstract and Applied Analysis
Volume 2013 (2013), Article ID 634812, 17 pages
http://dx.doi.org/10.1155/2013/634812
Research Article

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

1Department of Information Management, National Taiwan University of Science and Technology, Taipei 106, Taiwan
2Graduate Institute of Automation and Control, National Taiwan University of Science and Technology, Taipei 106, Taiwan
3Taiwan Information Security Center, National Taiwan University of Science and Technology, Taipei 106, Taiwan

Received 21 January 2013; Revised 7 May 2013; Accepted 27 May 2013

Academic Editor: Jein-Shan Chen

Copyright © 2013 Kuo-Yang Wu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.