Which algorithm to select in sports timetabling?

David Van Bulck

doi:10.1016/j.ejor.2024.06.005

Which algorithm to select in sports timetabling?

2024-10-01

2

PRE

AI

George Henrique Godim da Fonseca

C

Carlos Lamas‐Fernandez

M

Martin Mariusz Lester

DOI：10.1016/j.ejor.2024.06.005

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Abstract

En 中文

Any sports competition needs a timetable, specifying when and where teams meet each other. The recent International Timetabling Competition (ITC2021) on sports timetabling showed that, although it is possible to develop general algorithms, the performance of each algorithm varies considerably over the problem instances. This paper provides a problem type analysis for sports timetabling, resulting in powerful insights into the strengths and weaknesses of eight state-of-the-art algorithms. Based on machine learning techniques, we propose an algorithm selection system that predicts which algorithm is likely to perform best based on the type of competition and constraints being used (i.e., the problem type) in a given sports timetabling problem instance. Furthermore, we visualize how the problem type relates to algorithm performance, providing insights and possibilities to further enhance several algorithms. Finally, we assess the empirical hardness of the instances. Our results are based on large computational experiments involving about 50 years of CPU time on more than 500 newly generated problem instances.

Keywords:

OR in sports

Sports scheduling

ITC2021

Algorithm selection

Instance space analysis

Which algorithm to select in sports timetabling?

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