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Journal of Quantitative Analysis in Sports

An official journal of the American Statistical Association

Editor-in-Chief: Steve Rigdon, PhD


CiteScore 2018: 1.67

SCImago Journal Rank (SJR) 2018: 0.587
Source Normalized Impact per Paper (SNIP) 2018: 1.970

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1559-0410
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Volume 15, Issue 1

Issues

Volume 15 (2019)

Volume 14 (2018)

Volume 13 (2017)

Volume 12 (2016)

Volume 11 (2015)

Volume 10 (2014)

Volume 9 (2013)

Volume 8 (2012)

Volume 7 (2011)

Volume 6 (2010)

Volume 5 (2009)

Volume 4 (2008)

Volume 3 (2007)

Volume 2 (2006)

Volume 1 (2005)

Analysis of a constructive matheuristic for the traveling umpire problem

Reshma Chirayil Chandrasekharan
  • Corresponding author
  • KU Leuven, Department of Computer Science, CODeS & imec – Gebroeders De Smetstraat 1, 9000 Ghent, Belgium
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
 / Túlio A.M. Toffolo
  • KU Leuven, Department of Computer Science, CODeS & imec – Gebroeders De Smetstraat 1, 9000 Ghent, Belgium
  • Federal University of Ouro Preto, Department of Computing, Ouro Preto, Brazil
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
 / Tony Wauters
Published Online: 2018-09-20 | DOI: https://doi.org/10.1515/jqas-2017-0118

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Abstract

The Traveling Umpire Problem (TUP) is a combinatorial optimization problem concerning the assignment of umpires to the games of a fixed double round-robin tournament. The TUP draws inspiration from the real world multi-objective Major League Baseball (MLB) umpire scheduling problem, but is, however, restricted to the single objective of minimizing total travel distance of the umpires. Several hard constraints are employed to enforce fairness when assigning umpires, making it a challenging optimization problem. The present work concerns a constructive matheuristic approach which focuses primarily on large benchmark instances. A decomposition-based approach is employed which sequentially solves Integer Programming (IP) formulations of the subproblems to arrive at a feasible solution for the entire problem. This constructive matheuristic efficiently generates feasible solutions and improves the best known solutions of large benchmark instances of 26, 28, 30 and 32 teams well within the benchmark time limit. In addition, the algorithm is capable of producing feasible solutions for various small and medium benchmark instances competitive with those produced by other heuristic algorithms. The paper also details experiments conducted to evaluate various algorithmic design parameters such as subproblem size, overlap and objective functions.

Keywords: decomposition; integer programming; matheuristic; sports scheduling; TUP

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About the article

Published Online: 2018-09-20

Published in Print: 2019-02-25

Citation Information: Journal of Quantitative Analysis in Sports, Volume 15, Issue 1, Pages 41–57, ISSN (Online) 1559-0410, ISSN (Print) 2194-6388, DOI: https://doi.org/10.1515/jqas-2017-0118.

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