A problem space genetic algorithm in multiobjective optimization
Author
Türkcan, A.
Aktürk, M. S.
Date
2003Source Title
Journal of Intelligent Manufacturing
Print ISSN
0956-5515
Electronic ISSN
1572-8145
Publisher
Springer New York LLC
Volume
14
Issue
3-4
Pages
363 - 378
Language
English
Type
ArticleItem Usage Stats
110
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84
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Abstract
In this study, a problem space genetic algorithm (PSGA) is used to solve bicriteria tool management and scheduling problems simultaneously in flexible manufacturing systems. The PSGA is used to generate approximately efficient solutions minimizing both the manufacturing cost and total weighted tardiness. This is the first implementation of PSGA to solve a multiobjective optimization problem (MOP). In multiobjective search, the key issues are guiding the search towards the global Pareto-optimal set and maintaining diversity. A new fitness assignment method, which is used in PSGA, is proposed to find a well-diversified, uniformly distributed set of solutions that are close to the global Pareto set. The proposed fitness assignment method is a combination of a nondominated sorting based method which is most commonly used in multiobjective optimization literature and aggregation of objectives method which is popular in the operations research literature. The quality of the Pareto-optimal set is evaluated by using the performance measures developed for multiobjective optimization problems.
Keywords
Bicriteria schedulingFlexible manufacturing systems
Genetic algorithm
Local search
Nonidentical parallel CNC machines
Pareto-optimality
Approximation theory
Genetic algorithms
Problem solving
Scheduling
Multiobjective optimization
Flexible manufacturing systems