Boosting combinatorial search through randomization
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
PPDP '99 Proceedings of the International Conference PPDP'99 on Principles and Practice of Declarative Programming
Solving the Sports League Scheduling Problem with Tabu Search
ECAI '00 Proceedings of the Workshop on Local Search for Planning and Scheduling-Revised Papers
Solving the Round Robin Problem Using Propositional Logic
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
CSPLIB: A Benchmark Library for Constraints
CP '99 Proceedings of the 5th International Conference on Principles and Practice of Constraint Programming
Breaking Row and Column Symmetries in Matrix Models
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Scheduling a Major College Basketball Conference
Operations Research
Scheduling a Major College Basketball Conference--Revisited
Operations Research
OPL++: A Modeling Layer for Constraint Programming Libraries
OPL++: A Modeling Layer for Constraint Programming Libraries
A linear-time algorithm to solve the Sports League Scheduling Problem (prob026 of CSPLib)
Discrete Applied Mathematics
Construction of sports schedules with multiple venues
Discrete Applied Mathematics
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
Information Sciences: an International Journal
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This paper presents an enumerative approach for a particular sports league scheduling problem known as ''Prob026'' in CSPLib. Despite its exponential-time complexity, this simple method can solve all instances involving a number T of teams up to 50 in a reasonable amount of time while the best known tabu search and constraint programming algorithms are limited to T=0 or T/2 is odd. Furthermore, solutions were also found for some T values up to 70. The proposed approach relies on discovering, by observation, interesting properties from solutions of small problem instances and then using these properties in the final algorithm to constraint the search process.