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      • Department of Industrial Engineering
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      A problem space genetic algorithm in multiobjective optimization

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      Author
      Türkcan, A.
      Aktürk, M. S.
      Date
      2003
      Source 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
      Article
      Item 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 scheduling
      Flexible manufacturing systems
      Genetic algorithm
      Local search
      Nonidentical parallel CNC machines
      Pareto-optimality
      Approximation theory
      Genetic algorithms
      Problem solving
      Scheduling
      Multiobjective optimization
      Flexible manufacturing systems
      Permalink
      http://hdl.handle.net/11693/24477
      Published Version (Please cite this version)
      http://dx.doi.org/10.1023/A:1024605927329
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      • Department of Industrial Engineering 685
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