Some models of graphs for scheduling sports competitions
Discrete Applied Mathematics
Combinatorial aspects of construction of competition Dutch Professional Football Leagues
Discrete Applied Mathematics - Special issue: Timetabling and chromatic scheduling
A Computing Procedure for Quantification Theory
Journal of the ACM (JACM)
A machine program for theorem-proving
Communications of the ACM
Chaff: engineering an efficient SAT solver
Proceedings of the 38th annual Design Automation Conference
Scheduling a Major College Basketball Conference--Revisited
Operations Research
Scheduling the professional soccer leagues of Austria and Germany
Computers and Operations Research
Scheduling the Italian football league: an ILP-based approach
Computers and Operations Research
Complex Scheduling (GOR-Publications)
Complex Scheduling (GOR-Publications)
Scheduling the Belgian Soccer League
Interfaces
Propagation via lazy clause generation
Constraints
A method for combining complementary techniques for document image segmentation
Pattern Recognition
Construction of sports schedules with multiple venues
Discrete Applied Mathematics
Scheduling the brazilian soccer tournament with fairness and broadcast objectives
PATAT'06 Proceedings of the 6th international conference on Practice and theory of automated timetabling VI
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Tournament schedules of sports leagues have to satisfy several types of constraints such as stadium unavailability, fixed matches, forbidden matches, minimum number of breaks. Usually, there is no schedule satisfying all given constraints and, hence, some of the constraints are considered as `soft' ones. There are various models appropriately describing the environment of sport leagues. Only heuristic methods are known from the literature for solving instances of real life dimensions. We consider here a model which satisfies the demands of many sports leagues. We solve our model by reduction to series of instances of the propositional satisfiability problem and adaption of a satisfiability solver for these specific instances. We test our method on two real life examples and solve the problem optimally within our model in each case. Our solver shows good computational results also on generated test instances, which are motivated by real life requirements. It can be easily extended to meet the demands of other sports leagues.