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A multi-objective artificial bee colony algorithm for single machine scheduling with family setup under TOU tariffs.

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Title: A multi-objective artificial bee colony algorithm for single machine scheduling with family setup under TOU tariffs.
Authors: Xue, Ling1 (AUTHOR), Wang, Xiuli1 (AUTHOR) wangdu0816@163.com
Source: International Journal of Production Research. May2025, Vol. 63 Issue 10, p3822-3853. 32p.
Subjects: Bees algorithm, Setup time, Electricity pricing, Search algorithms, Integer programming
Abstract: Time-of-use (TOU) electricity tariffs have been widely implemented in the manufacturing industry in many countries. This paper investigates a single machine scheduling problem involving incompatible job families with sequence-dependent setup times to minimise total electricity cost and total tardiness simultaneously. To tackle this problem, we propose a multi-objective artificial bee colony (MABC) algorithm. Utilising the dominance properties of the problem, we develop tailored heuristics aimed at improving the quality of initial food sources, and design multi-directional neighbourhood structures to explore desirable neighbour solutions along each objective direction. We construct a novel fitness function that not only considers Pareto rank but also incorporates the hypervolume contribution indicator to identify the promising solution space. Moreover, local integer programming is embedded into the MABC algorithm to intensify the search towards Pareto solutions. The experimental results indicate that the MABC algorithm performs significantly better than NSGA-II, SPEA2, and MOEA/D algorithms. [ABSTRACT FROM AUTHOR]
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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  Label: Title
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  Data: A multi-objective artificial bee colony algorithm for single machine scheduling with family setup under TOU tariffs.
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  Data: <searchLink fieldCode="AR" term="%22Xue%2C+Ling%22">Xue, Ling</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Xiuli%22">Wang, Xiuli</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> wangdu0816@163.com</i>
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Production+Research%22">International Journal of Production Research</searchLink>. May2025, Vol. 63 Issue 10, p3822-3853. 32p.
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22Bees+algorithm%22">Bees algorithm</searchLink><br /><searchLink fieldCode="DE" term="%22Setup+time%22">Setup time</searchLink><br /><searchLink fieldCode="DE" term="%22Electricity+pricing%22">Electricity pricing</searchLink><br /><searchLink fieldCode="DE" term="%22Search+algorithms%22">Search algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Integer+programming%22">Integer programming</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Time-of-use (TOU) electricity tariffs have been widely implemented in the manufacturing industry in many countries. This paper investigates a single machine scheduling problem involving incompatible job families with sequence-dependent setup times to minimise total electricity cost and total tardiness simultaneously. To tackle this problem, we propose a multi-objective artificial bee colony (MABC) algorithm. Utilising the dominance properties of the problem, we develop tailored heuristics aimed at improving the quality of initial food sources, and design multi-directional neighbourhood structures to explore desirable neighbour solutions along each objective direction. We construct a novel fitness function that not only considers Pareto rank but also incorporates the hypervolume contribution indicator to identify the promising solution space. Moreover, local integer programming is embedded into the MABC algorithm to intensify the search towards Pareto solutions. The experimental results indicate that the MABC algorithm performs significantly better than NSGA-II, SPEA2, and MOEA/D algorithms. [ABSTRACT FROM AUTHOR]
– 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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    Identifiers:
      – Type: doi
        Value: 10.1080/00207543.2024.2431178
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      – Code: eng
        Text: English
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        PageCount: 32
        StartPage: 3822
    Subjects:
      – SubjectFull: Bees algorithm
        Type: general
      – SubjectFull: Setup time
        Type: general
      – SubjectFull: Electricity pricing
        Type: general
      – SubjectFull: Search algorithms
        Type: general
      – SubjectFull: Integer programming
        Type: general
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            NameFull: Xue, Ling
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              Text: May2025
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              Y: 2025
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            – TitleFull: International Journal of Production Research
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