Generating approximate solutions to the traveling tournament problem using a linear distance relaxation

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Abstract

In some domestic professional sports leagues, the home stadiums are located in cities connected by a common train line running in one direction. For these instances, we can incorporate this geographical information to determine optimal or nearly-optimal solutions to the n-team Traveling Tournament Problem (TTP), an NP-hard sports scheduling problem whose solution is a double round-robin tournament schedule that minimizes the sum total of distances traveled by all n teams. We introduce the Linear Distance Traveling Tournament Problem (LD-TTP), and solve it for n = 4 and n = 6, generating the complete set of possible solutions through elementary combinatorial techniques. For larger n, we propose a novel "expander construction" that generates an approximate solution to the LD-TTP. For n = 4 (mod 6), we show that our expander construction produces a feasible double round-robin tournament schedule whose total distance is guaranteed to be no worse than 4 3 times the optimal solution, regardless of where the n teams are located. This 4 3 -approximation for the LD-TTP is stronger than the currently best-known ratio of 5 3 + ε for the general TTP. We conclude the paper by applying this linear distance relaxation to general (non- linear) n-team TTP instances, where we develop fast approximate solutions by simply "assuming" the n teams lie on a straight line and solving the modified problem. We show that this technique surprisingly generates the distance-optimal tournament on all benchmark sets on 6 teams, as well as close-to-optimal schedules for larger n, even when the teams are located around a circle or positioned in three-dimensional space. © 2012 AI Access Foundation.

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Hoshino, R., & Kawarabayashi, K. I. (2012). Generating approximate solutions to the traveling tournament problem using a linear distance relaxation. Journal of Artificial Intelligence Research, 45, 257–286. https://doi.org/10.1613/jair.3713

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