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Decomposition and local search based methods for the traveling umpire problem

Author

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  • Wauters, Tony
  • Van Malderen, Sam
  • Vanden Berghe, Greet

Abstract

The Traveling Umpire Problem (TUP) is a challenging combinatorial optimization problem based on scheduling umpires for Major League Baseball. The TUP aims at assigning umpire crews to the games of a fixed tournament, minimizing the travel distance of the umpires. The present paper introduces two complementary heuristic solution approaches for the TUP. A new method called enhanced iterative deepening search with leaf node improvements (IDLI) generates schedules in several stages by subsequently considering parts of the problem. The second approach is a custom iterated local search algorithm (ILS) with a step counting hill climbing acceptance criterion. IDLI generates new best solutions for many small and medium sized benchmark instances. ILS produces significant improvements for the largest benchmark instances. In addition, the article introduces a new decomposition methodology for generating lower bounds, which improves all known lower bounds for the benchmark instances.

Suggested Citation

  • Wauters, Tony & Van Malderen, Sam & Vanden Berghe, Greet, 2014. "Decomposition and local search based methods for the traveling umpire problem," European Journal of Operational Research, Elsevier, vol. 238(3), pages 886-898.
    • Handle: RePEc:eee:ejores:v:238:y:2014:i:3:p:886-898
    DOI: 10.1016/j.ejor.2014.04.043
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    References listed on IDEAS

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    1. Michael A. Trick & Hakan Yildiz & Tallys Yunes, 2012. "Scheduling Major League Baseball Umpires and the Traveling Umpire Problem," Interfaces, INFORMS, vol. 42(3), pages 232-244, June.
    2. James R. Evans, 1988. "A Microcomputer-Based Decision Support System for Scheduling Umpires in the American Baseball League," Interfaces, INFORMS, vol. 18(6), pages 42-51, December.
    3. Trick, Michael A. & Yildiz, Hakan, 2012. "Locally Optimized Crossover for the Traveling Umpire Problem," European Journal of Operational Research, Elsevier, vol. 216(2), pages 286-292.
    4. Lamghari, Amina & Ferland, Jacques A., 2011. "Assigning judges to competitions of several rounds using Tabu search," European Journal of Operational Research, Elsevier, vol. 210(3), pages 694-705, May.
    5. de Oliveira, Lucas & de Souza, Cid C. & Yunes, Tallys, 2014. "Improved bounds for the traveling umpire problem: A stronger formulation and a relax-and-fix heuristic," European Journal of Operational Research, Elsevier, vol. 236(2), pages 592-600.
    6. Adam Farmer & Jeffrey S. Smith & Luke T. Miller, 2007. "Scheduling Umpire Crews for Professional Tennis Tournaments," Interfaces, INFORMS, vol. 37(2), pages 187-196, April.
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    Cited by:

    1. Xue, Li & Luo, Zhixing & Lim, Andrew, 2015. "Two exact algorithms for the traveling umpire problem," European Journal of Operational Research, Elsevier, vol. 243(3), pages 932-943.
    2. Özlü, Oğuzhan & Sokol, Joel, 2016. "An optimization approach to designing a baseball scout network," European Journal of Operational Research, Elsevier, vol. 255(3), pages 948-960.
    3. Toffolo, Túlio A.M. & Wauters, Tony & Van Malderen, Sam & Vanden Berghe, Greet, 2016. "Branch-and-bound with decomposition-based lower bounds for the Traveling Umpire Problem," European Journal of Operational Research, Elsevier, vol. 250(3), pages 737-744.
    4. Bender Marco & Westphal Stephan, 2016. "A combined approximation for the traveling tournament problem and the traveling umpire problem," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 12(3), pages 139-149, September.

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