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Grouping and scheduling multiple sports leagues: an integrated approach.

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Title: Grouping and scheduling multiple sports leagues: an integrated approach.
Authors: Li, Miao1 (AUTHOR) miao.li@ugent.be, Goossens, Dries1,2 (AUTHOR)
Source: Journal of the Operational Research Society. Apr2025, Vol. 76 Issue 4, p739-757. 19p.
Subjects: Simulated annealing, Athletic leagues, Integer programming, Team sports, NP-hard problems
Abstract: This paper introduces the multi-league grouping and scheduling problem, which integrates the grouping of teams into leagues and the scheduling of each league. This involves two possibly conflicting objectives: minimizing travel distance and minimizing capacity violations of venues shared by teams. We formulate this problem as a bi-objective mixed-integer programming model. Given the NP-hardness of the grouping problem, the integrated problem is particularly challenging. Hence, we design a two-layer constructive heuristic to efficiently approximate the Pareto set, using simulated annealing on the outer layer and an integer programming model on the inner layer. We further develop a speed-up version where the inner layer is solved heuristically. We develop a series of large-scale problem instances, including one based on data from the Royal Belgian Football Association. In a computational study, we compare our algorithms with an epsilon-constraint method and evaluate their results using various multi-objective solution quality metrics. [ABSTRACT FROM AUTHOR]
Copyright of Journal of the Operational Research Society 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.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 183540913
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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IllustrationInfo
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  Data: Grouping and scheduling multiple sports leagues: an integrated approach.
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  Data: <searchLink fieldCode="AR" term="%22Li%2C+Miao%22">Li, Miao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> miao.li@ugent.be</i><br /><searchLink fieldCode="AR" term="%22Goossens%2C+Dries%22">Goossens, Dries</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+the+Operational+Research+Society%22">Journal of the Operational Research Society</searchLink>. Apr2025, Vol. 76 Issue 4, p739-757. 19p.
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  Data: <searchLink fieldCode="DE" term="%22Simulated+annealing%22">Simulated annealing</searchLink><br /><searchLink fieldCode="DE" term="%22Athletic+leagues%22">Athletic leagues</searchLink><br /><searchLink fieldCode="DE" term="%22Integer+programming%22">Integer programming</searchLink><br /><searchLink fieldCode="DE" term="%22Team+sports%22">Team sports</searchLink><br /><searchLink fieldCode="DE" term="%22NP-hard+problems%22">NP-hard problems</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This paper introduces the multi-league grouping and scheduling problem, which integrates the grouping of teams into leagues and the scheduling of each league. This involves two possibly conflicting objectives: minimizing travel distance and minimizing capacity violations of venues shared by teams. We formulate this problem as a bi-objective mixed-integer programming model. Given the NP-hardness of the grouping problem, the integrated problem is particularly challenging. Hence, we design a two-layer constructive heuristic to efficiently approximate the Pareto set, using simulated annealing on the outer layer and an integer programming model on the inner layer. We further develop a speed-up version where the inner layer is solved heuristically. We develop a series of large-scale problem instances, including one based on data from the Royal Belgian Football Association. In a computational study, we compare our algorithms with an epsilon-constraint method and evaluate their results using various multi-objective solution quality metrics. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of the Operational Research Society 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: 10.1080/01605682.2024.2391516
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 19
        StartPage: 739
    Subjects:
      – SubjectFull: Simulated annealing
        Type: general
      – SubjectFull: Athletic leagues
        Type: general
      – SubjectFull: Integer programming
        Type: general
      – SubjectFull: Team sports
        Type: general
      – SubjectFull: NP-hard problems
        Type: general
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      – TitleFull: Grouping and scheduling multiple sports leagues: an integrated approach.
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            NameFull: Li, Miao
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              Text: Apr2025
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              Y: 2025
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