A filtering algorithm for constraints of difference in CSPs
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Fast Approximation Algorithms for the Knapsack and Sum of Subset Problems
Journal of the ACM (JACM)
A Sufficient Condition for Backtrack-Free Search
Journal of the ACM (JACM)
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
CP '99 Proceedings of the 5th International Conference on Principles and Practice of Constraint Programming
A Framework for Constraint Programming Based Column Generation
CP '99 Proceedings of the 5th International Conference on Principles and Practice of Constraint Programming
An Arc-Consistency Algorithm for the Minimum Weight All Different Constraint
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Fast approximation algorithms for knapsack problems
SFCS '77 Proceedings of the 18th Annual Symposium on Foundations of Computer Science
The practice of approximated consistency for Knapsack constraints
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Upper bounds on the number of solutions of binary integer programs
CPAIOR'10 Proceedings of the 7th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
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We introduce the automatic recording constraint (ARC) that can be used to model and solve scheduling problems where tasks may not overlap in time and the tasks linearly exhaust some resource. Since achieving generalized arc-consistency for the ARC is NP-hard, we develop a filtering algorithm that achieves approximated consistency only. Numerical results show the benefits of the new constraint on three out of four different types of benchmark sets for the automatic recording problem. On these instances, run-times can be achieved that are orders of magnitude better than those of the best previous constraint programming approach.