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Genetic algorithm with local search for the unrelated parallel machine scheduling problem with sequence-dependent set-up times.

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Title: Genetic algorithm with local search for the unrelated parallel machine scheduling problem with sequence-dependent set-up times.
Authors: Yilmaz Eroglu, Duygu1 (AUTHOR) duygueroglu@uludag.edu.tr, Ozmutlu, H. Cenk1 (AUTHOR), Ozmutlu, Seda1 (AUTHOR)
Source: International Journal of Production Research. Oct2014, Vol. 52 Issue 19, p5841-5856. 16p. 6 Diagrams, 10 Charts, 4 Graphs.
Subjects: Parallel scheduling (Computer scheduling), Setup time, Production scheduling, Genetic algorithms, Search algorithms
Abstract: In this paper, a genetic algorithm (GA) with local search is proposed for the unrelated parallel machine scheduling problem with the objective of minimising the maximum completion time (makespan). We propose a simple chromosome structure consisting of random key numbers in a hybrid genetic-local search algorithm. Random key numbers are frequently used inGAs but create additional difficulties when hybrid factors are implemented in a local search. The best chromosome of each generation is improved using a local search during the algorithm, but the better job sequence (which might appear during the local search operation) must be adapted to the chromosome that will be used in each successive generation. Determining the genes (and the data in the genes) that would be exchanged is the challenge of using random numbers. We have developed an algorithm that satisfies the adaptation of local search results into theGAs with a minimum relocation operation of the genes’ random key numbers – this is the main contribution of the paper. A new hybrid approach is tested on a set of problems taken from the literature, and the computational results validate the effectiveness of the proposed algorithm. [ABSTRACT FROM PUBLISHER]
Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Genetic algorithm with local search for the unrelated parallel machine scheduling problem with sequence-dependent set-up times.
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  Data: <searchLink fieldCode="AR" term="%22Yilmaz+Eroglu%2C+Duygu%22">Yilmaz Eroglu, Duygu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> duygueroglu@uludag.edu.tr</i><br /><searchLink fieldCode="AR" term="%22Ozmutlu%2C+H%2E+Cenk%22">Ozmutlu, H. Cenk</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ozmutlu%2C+Seda%22">Ozmutlu, Seda</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Production+Research%22">International Journal of Production Research</searchLink>. Oct2014, Vol. 52 Issue 19, p5841-5856. 16p. 6 Diagrams, 10 Charts, 4 Graphs.
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  Data: <searchLink fieldCode="DE" term="%22Parallel+scheduling+%28Computer+scheduling%29%22">Parallel scheduling (Computer scheduling)</searchLink><br /><searchLink fieldCode="DE" term="%22Setup+time%22">Setup time</searchLink><br /><searchLink fieldCode="DE" term="%22Production+scheduling%22">Production scheduling</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+algorithms%22">Genetic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Search+algorithms%22">Search algorithms</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: In this paper, a genetic algorithm (GA) with local search is proposed for the unrelated parallel machine scheduling problem with the objective of minimising the maximum completion time (makespan). We propose a simple chromosome structure consisting of random key numbers in a hybrid genetic-local search algorithm. Random key numbers are frequently used inGAs but create additional difficulties when hybrid factors are implemented in a local search. The best chromosome of each generation is improved using a local search during the algorithm, but the better job sequence (which might appear during the local search operation) must be adapted to the chromosome that will be used in each successive generation. Determining the genes (and the data in the genes) that would be exchanged is the challenge of using random numbers. We have developed an algorithm that satisfies the adaptation of local search results into theGAs with a minimum relocation operation of the genes’ random key numbers – this is the main contribution of the paper. A new hybrid approach is tested on a set of problems taken from the literature, and the computational results validate the effectiveness of the proposed algorithm. [ABSTRACT FROM PUBLISHER]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1080/00207543.2014.920966
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 16
        StartPage: 5841
    Subjects:
      – SubjectFull: Parallel scheduling (Computer scheduling)
        Type: general
      – SubjectFull: Setup time
        Type: general
      – SubjectFull: Production scheduling
        Type: general
      – SubjectFull: Genetic algorithms
        Type: general
      – SubjectFull: Search algorithms
        Type: general
    Titles:
      – TitleFull: Genetic algorithm with local search for the unrelated parallel machine scheduling problem with sequence-dependent set-up times.
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            NameFull: Yilmaz Eroglu, Duygu
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            NameFull: Ozmutlu, H. Cenk
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            NameFull: Ozmutlu, Seda
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          Dates:
            – D: 01
              M: 10
              Text: Oct2014
              Type: published
              Y: 2014
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              Value: 52
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              Value: 19
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            – TitleFull: International Journal of Production Research
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