Principles of artificial intelligence
Principles of artificial intelligence
The shifting bottleneck procedure for job shop scheduling
Management Science
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
An algorithm for probabilistic planning
Artificial Intelligence - Special volume on planning and scheduling
Flexibility in a knowledge-based system for solving dynamic resource-constrained scheduling problems
Flexibility in a knowledge-based system for solving dynamic resource-constrained scheduling problems
Constraint-directed techniques for scheduling alternative activities
Artificial Intelligence
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Computing the Envelope for Stepwise-Constant Resource Allocations
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Replanning Using Hierarchical Task Network and Operator-Based Planning
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
Texture measurements as a basis for heuristic commitment techniques in constraint-directed scheduling
Bridging the gap between planning and scheduling
The Knowledge Engineering Review
Interleaving temporal planning and execution in robotics domains
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Proactive algorithms for job shop scheduling with probabilistic durations
Journal of Artificial Intelligence Research
A general framework for scheduling in a stochastic environment
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Dynamic control of plans with temporal uncertainty
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Mixed constraint satisfaction: a framework for decision problems under incomplete knowledge
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Planning under continuous time and resource uncertainty: a challenge for AI
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
A distributed scheduler for air traffic flow management
Journal of Scheduling
Robust local search for solving RCPSP/max with durational uncertainty
Journal of Artificial Intelligence Research
Network modeling and evolutionary optimization for scheduling in manufacturing
Journal of Intelligent Manufacturing
Scheduling a dynamic aircraft repair shop with limited repair resources
Journal of Artificial Intelligence Research
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There are many systems and techniques that address stochastic planning and scheduling problems, based on distinct and sometimes opposite approaches, especially in terms of how generation and execution of the plan, or the schedule, are combined, and if and when knowledge about the uncertainties is taken into account. In many real-life problems, it appears that many of these approaches are needed and should be combined, which to our knowledge has never been done. In this paper, we propose a typology that distinguishes between proactive, progressive, and revision approaches. Then, focusing on scheduling and schedule execution, a theoretic model integrating those three approaches is defined. This model serves as a general template to implement a system that will fit specific application needs: we introduce and discuss our experimental prototypes which validate our model in part, and suggest how this framework could be extended to more general planning systems.