AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Solving Crew Scheduling Problems bu Constraint Programming
CP '95 Proceedings of the First International Conference on Principles and Practice of Constraint Programming
A Two-Stage Hybrid Local Search for the Vehicle Routing Problem with Time Windows
Transportation Science
Dual modelling of permutation and injection problems
Journal of Artificial Intelligence Research
Proactive algorithms for job shop scheduling with probabilistic durations
Journal of Artificial Intelligence Research
Proactive algorithms for scheduling with probabilistic durations
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Online stochastic and robust optimization
ASIAN'04 Proceedings of the 9th Asian Computing Science conference on Advances in Computer Science: dedicated to Jean-Louis Lassez on the Occasion of His 5th Cycle Birthday
Two branch-and-bound algorithms for the robust parallel machine scheduling problem
Computers and Operations Research
Robust local search for solving RCPSP/max with durational uncertainty
Journal of Artificial Intelligence Research
Computers and Operations Research
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Many real-world scheduling problems are subject to change, and scheduling solutions should be robust to those changes. We consider a single-machine scheduling problem where the processing time of each activity is characterized by a normally distributed random variable, with flowtime as the main solution criterion. The objective is to find the @b-robust schedule-the schedule that minimizes the risk of the flowtime exceeding a threshold. We show how to represent this problem as a constraint model, explicitly representing the uncertainty and robustness as input parameters and objectives, and enabling the uncertainty to propagate using constraint propagation. Specifically, we develop three models (primal, dual and hybrid), and we show the effect of dominance rules on the search space.