The U.S. national football league scheduling problem

Authors:
Bistra N. Dilkina;William S. Havens
Affiliations:
Intelligent Systems Lab, Simon Fraser University, Burnaby, British Columbia, Canada and Actenum Corp., Vancouver, British Columbia;Intelligent Systems Lab, Simon Fraser University, Burnaby, British Columbia, Canada and Actenum Corp., Vancouver, British Columbia
Venue:
IAAI'04 Proceedings of the 16th conference on Innovative applications of artifical intelligence
Year:
2004

Citing 7
Cited 0

Partial constraint satisfaction

Artificial Intelligence - Special volume on constraint-based reasoning
Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems

Artificial Intelligence - Special volume on constraint-based reasoning
Weak-commitment search for solving constraint satisfaction problems

AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Boosting combinatorial search through randomization

AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Constraint-based round robin tournament planning

Proceedings of the 1999 international conference on Logic programming
Local search with constraint propagation and conflict-based heuristics

Artificial Intelligence
Scheduling a Major College Basketball Conference

Operations Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

We describe the problem of scheduling the television broadcasts of the U.S. National Football League (NFL). Unlike traditional round-robin tournament scheduling, the NFL problem involves assigning games to broadcast slots under various complex constraints while attempting to satisfy a set of user preferences. As well, a mixed-initiative functionality was required to allow the user to control and assist in the scheduling process. A prototype system was developed for the NFL which produced schedules satisfying many of these constraints and preferences. In this paper, we provide an overview of the constraint solving methodology employed and the implementation of the NFL prototype system.