The Traveling Tournament Problem Description and Benchmarks
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
A simulated annealing approach to the traveling tournament problem
Journal of Scheduling
Maximizing breaks and bounding solutions to the mirrored traveling tournament problem
Discrete Applied Mathematics - Special issue: Traces of the Latin American conference on combinatorics, graphs and applications: a selection of papers from LACGA 2004, Santiago, Chile
The timetable constrained distance minimization problem
CPAIOR'06 Proceedings of the Third international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Traveling tournament scheduling: a systematic evaluation of simulated annealling
CPAIOR'06 Proceedings of the Third international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
A polynomial-time algorithm to find an equitable home-away assignment
Operations Research Letters
Note: Solving mirrored traveling tournament problem benchmark instances with eight teams
Discrete Optimization
New models for the Mirrored Traveling Tournament Problem
Computers and Industrial Engineering
Journal of Artificial Intelligence Research
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The traveling tournament problem considers scheduling round-robin tournaments that minimize traveling distance, which is an important issue in sports scheduling. Various studies on the traveling tournament problem have appeared in recent years, and there are some variants of this problem. In this paper, we deal with the constant distance traveling tournament problem, which is a special class of the traveling tournament problem. This variant is essentially equivalent to the problem of 'maximizing breaks' and that of 'minimizing breaks', which is another significant objective in sports scheduling. We propose a lower bound of the optimal value of the constant distance traveling tournament problem, and two constructive algorithms that produce feasible solutions whose objective values are close to the proposed lower bound. For some size of instances, one of our algorithms yields optimal solutions.