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This paper addresses the general and challenging Sports Timetabling Problem proposed during the International Timetabling Competition of 2021 (ITC2021). The problem is expressed in a flexible format which enables modeling a number of real-world constraints that often occur in Sports Timetabling. An integer programming (IP) formulation and a fix-and...
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Citations
... The Goal algorithm proposed by [12] is based on the fix-and-optimize strategy, which may be categorized as a matheuristic. Matheuristics are heuristics that take advantage of the power of mathematical programming solvers to tackle hard combinatorial optimization problems. ...
... The Goal algorithm proposed by [13] is based on the fix-and-optimize strategy, which may be categorized as a matheuristic. Matheuristics are heuristics that take advantage of the power of mathematical programming solvers to tackle hard combinatorial optimization problems. ...
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.
The fifth International Timetabling Competition (ITC2021) aims to instigate further research on automated sports timetabling. The competition’s problem consists of constructing a compact double round-robin tournament with 16 to 20 teams while respecting various hard constraints and minimizing the penalties from violated soft constraints. This paper focuses on the organization of the ITC2021 competition, with a particular focus on the generation of a set of artificial though challenging, realistic, and diverse problem instances. For the latter, we present a set of features describing the structure of the problem instances, and use these features to construct the so-called instance space for sports timetabling. Several gaps in this space hint that existing problem instances from the literature are not very diverse. We therefore propose a novel integer programming approach to determine the feature values that cover these gaps, and show how to generate associated problem instances. Finally, we provide an overview of the participants and their contributions.
