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Optimal Scheduling for Retrieval Jobs in Double-Deep ASRS by Evolutionary Algorithms
Joint Authors
Wu, Kuo-Yang
Xu, Sendren Sheng-Dong
Wu, Tzong-Chen
Source
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-17, 17 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-07-01
Country of Publication
Egypt
No. of Pages
17
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Wu, Kuo-Yang& Xu, Sendren Sheng-Dong& Wu, Tzong-Chen. 2013. Optimal Scheduling for Retrieval Jobs in Double-Deep ASRS by Evolutionary Algorithms. Abstract and Applied Analysis،Vol. 2013, no. 2013, pp.1-17.
https://search.emarefa.net/detail/BIM-486890
Modern Language Association (MLA)
Wu, Kuo-Yang…[et al.]. Optimal Scheduling for Retrieval Jobs in Double-Deep ASRS by Evolutionary Algorithms. Abstract and Applied Analysis No. 2013 (2013), pp.1-17.
https://search.emarefa.net/detail/BIM-486890
American Medical Association (AMA)
Wu, Kuo-Yang& Xu, Sendren Sheng-Dong& Wu, Tzong-Chen. Optimal Scheduling for Retrieval Jobs in Double-Deep ASRS by Evolutionary Algorithms. Abstract and Applied Analysis. 2013. Vol. 2013, no. 2013, pp.1-17.
https://search.emarefa.net/detail/BIM-486890
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-486890