Which algorithm to select in sports timetabling?
This work addresses the challenge of algorithm selection for sports timetabling, providing practical insights for competition organizers, but it is incremental as it builds on existing algorithms and benchmarks.
The paper tackles the problem of selecting the best algorithm for sports timetabling by analyzing eight state-of-the-art algorithms using instance space analysis and machine learning, resulting in a system that predicts algorithm performance based on instance characteristics and identifies key factors influencing selection.
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 an instance space 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 when given the characteristics of a sports timetabling problem instance. Furthermore, we identify which characteristics are important in making that prediction, providing insights in the performance of the algorithms, and suggestions to further improve them. 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.