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Improved Precedence Preservation Crossover for Multi-objective Job Shop Scheduling Problem

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Ripon, K.S.N., Siddique, N.H. and Torresen, J. (2011) Improved Precedence Preservation Crossover for Multi-objective Job Shop Scheduling Problem. Evolving Systems, 2 (2). pp. 119-129. [Journal article]

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URL: http://www.springerlink.com/content/1868-6478/2/2/

DOI: 10.1007/s12530-010-9022-x

Abstract

Over the last three decades, a great deal of research has been focused on solving the job-shop scheduling problem (JSSP). Researchers have emerged with a wide variety of approaches to solve this stubborn problem. Recently much effort has been concentrated on evolutionary techniques to search for the near-optimal solutions optimizing multiple criteria simultaneously. The choice of crossover operator is very important in the aspect of genetic algorithms (GA), and consequently a wide range of crossover operators have been proposed for JSSP. Most of them represent a solution by a chromosome containing the sequence of all the operations and decode the chromosome to a real schedule from the first gene to the last gene. However, these methods introduce high redundancy at the tail of the chromosome. In this paper, we address this problem in case of precedence preservation crossover (PPX) which is regarded as one of the better crossover operators and propose an improved version, termed as improved precedence preservation crossover (IPPX). Experimental results reveal that our proposed approach finds the near-optimal solutions by optimizing multiple criteria simultaneously with better results and also reduces the execution time significantly.

Item Type:Journal article
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Intelligent Systems Research Centre
ID Code:21207
Deposited By:Dr Mia Siddique
Deposited On:05 Mar 2012 13:52
Last Modified:05 Mar 2012 13:52

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