An algorithm for solving the job-shop problem
Management Science
Proceedings of the first international conference on Principles of knowledge representation and reasoning
Artificial Intelligence - Special issue on knowledge representation
Look-ahead techniques for micro-opportunistic job shop scheduling
Look-ahead techniques for micro-opportunistic job shop scheduling
From local to global consistency
Artificial Intelligence
Improved CLP scheduling with task intervals
Proceedings of the eleventh international conference on Logic programming
Generating feasible schedules under complex metric constraints
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Processing disjunctions in temporal constraint networks
Artificial Intelligence
Dynamic problem structure analysis as a basis for constraint-directed scheduling heuristics
Artificial Intelligence
ACM Computing Surveys (CSUR)
Backtracking algorithms for disjunctions of temporal constraints
Artificial Intelligence
Backjump-based backtracking for constraint satisfaction problems
Artificial Intelligence
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Studies in the use and generation of heuristics (greedy algorithms)
Studies in the use and generation of heuristics (greedy algorithms)
Reasoning on interval and point-based disjunctive metric constraints in temporal contexts
Journal of Artificial Intelligence Research
The design and experimental analysis of algorithms for temporal reasoning
Journal of Artificial Intelligence Research
Conditional and composite temporal CSPs
Applied Intelligence
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Scheduling problems can be viewed as a set of temporal metric and disjunctive constraints and so they can be formulated in terms of CSP techniques. In the literature, there are CSP-based methods which sequentially interleave search efforts with the application of consistency enforcing mechanisms and variable/ordering heuristics. Therefore, the number of backtrackings needed to obtain a solution is reduced. In this paper, we propose a new method that effectively integrates the CSP process into a limited closure process: not by interleaving them but rather as a part of the same process. Such an integration allows us to define more informed heuristics. These heuristics are used to limit the complete closure process to a maximum number of disjunctions, thereby reducing its complexity while at the same time reducing the search space. Some open disjunctive solutions can be maintained in the CSP process, limiting the number of backtrackings necessary, and avoiding having to know all the problem constraints in advance. Our experiments with flow-shop and job-shop instances show that this approach obtains a feasible solution/optimal solution without having to use backtracking in most cases. We also analyze the behaviour of our algorithm when some constraints are known dynamically and we demonstrate that it can provide better results than a pure CSP process.