A Sport Tournament Scheduling by Genetic Algorithm with Swapping Method
This work addresses scheduling efficiency for sports tournaments, offering incremental improvements in travel optimization.
The researchers tackled the mirrored Traveling Tournament Problem (mTTP) to minimize the total number of team travels, using a genetic algorithm with a swapping method to generate optimized scheduling solutions. Their algorithm produced solutions close to theoretical lower bounds and outperformed known results in most cases.
A sport tournament problem is considered the Traveling Tournament Problem (TTP). One interesting type is the mirrored Traveling Tournament Problem (mTTP). The objective of the problem is to minimize either the total number of traveling or the total distances of traveling or both. This research aims to find an optimized solution of the mirrored Traveling Tournament Problem with minimum total number of traveling. The solutions consisting of traveling and scheduling tables are solved by using genetic algorithm (GA) with swapping method. The number of traveling of all teams from obtained solutions are close to the lower bound theory of number of traveling. Moreover, this algorithm generates better solutions than known results for most cases.