GRASP: A Search Algorithm for Propositional Satisfiability
IEEE Transactions on Computers
Constraint-directed techniques for scheduling alternative activities
Artificial Intelligence
Constraint-Based Scheduling
A New Approach to Computing Optimal Schedules for the Job-Shop Scheduling Problem
Proceedings of the 5th International IPCO Conference on Integer Programming and Combinatorial Optimization
Integrated Methods for Optimization (International Series in Operations Research & Management Science)
Planning and Scheduling by Logic-Based Benders Decomposition
Operations Research
CPAIOR '09 Proceedings of the 6th International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Nonlinear Pseudo-Boolean Optimization: Relaxation or Propagation?
SAT '09 Proceedings of the 12th International Conference on Theory and Applications of Satisfiability Testing
An Integrated Solver for Optimization Problems
Operations Research
The Knowledge Engineering Review
Checking-up on branch-and-check
CP'10 Proceedings of the 16th international conference on Principles and practice of constraint programming
Explaining the cumulative propagator
Constraints
Explanations for the cumulative constraint: an experimental study
SEA'11 Proceedings of the 10th international conference on Experimental algorithms
A constraint integer programming approach for resource-constrained project scheduling
CPAIOR'10 Proceedings of the 7th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Conflict analysis in mixed integer programming
Discrete Optimization
Scheduling a dynamic aircraft repair shop with limited repair resources
Journal of Artificial Intelligence Research
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Despite the success of constraint programming (CP ) for scheduling, the much wider penetration of mixed integer programming (MIP ) technology into business applications means that many practical scheduling problems are being addressed with MIP, at least as an initial approach. Furthermore, there has been impressive and well-documented improvements in the power of generic MIP solvers over the past decade. We empirically demonstrate that on an existing set of resource allocation and scheduling problems standard MIP and CP models are now competitive with the state-of-the-art manual decomposition approach. Motivated by this result, we formulate two tightly coupled hybrid models based on constraint integer programming (CIP ) and demonstrate that these models, which embody advances in CP and MIP, are able to out-perform the CP, MIP, and decomposition models. We conclude that both MIP and CIP are technologies that should be considered along with CP for solving scheduling problems.