AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Empirical methods for artificial intelligence
Empirical methods for artificial intelligence
Scheduling Tasks with AND/OR Precedence Constraints
SIAM Journal on Computing
A fast taboo search algorithm for the job shop problem
Management Science
Solving very large weakly coupled Markov decision processes
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Dynamic problem structure analysis as a basis for constraint-directed scheduling heuristics
Artificial Intelligence
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
A Probabilistic Approach to Robust Execution of Temporal Plans with Uncertainty
SETN '02 Proceedings of the Second Hellenic Conference on AI: Methods and Applications of Artificial Intelligence
A Hybrid Exact Algorithm for the TSPTW
INFORMS Journal on Computing
Monte Carlo techniques for stochastic network analysis
Proceedings of the fourth annual conference on Applications of simulation
Enhancing real-time schedules to tolerate transient faults
RTSS '95 Proceedings of the 16th IEEE Real-Time Systems Symposium
Constraint-directed search: a case study of job-shop scheduling
Constraint-directed search: a case study of job-shop scheduling
Guaranteeing fault tolerance through scheduling in real-time systems
Guaranteeing fault tolerance through scheduling in real-time systems
Texture measurements as a basis for heuristic commitment techniques in constraint-directed scheduling
Scheduling with AND/OR Precedence Constraints
SIAM Journal on Computing
Scheduling: Theory, Algorithms, and Systems
Scheduling: Theory, Algorithms, and Systems
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
Complete MCS-based search: application to resource constrained project scheduling
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Proactive algorithms for scheduling with probabilistic durations
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Planning with sharable resource constraints
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Optimal factory scheduling using stochastic dominance A
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
EMSOFT '08 Proceedings of the 8th ACM international conference on Embedded software
Scheduling with uncertain durations: Modeling β-robust scheduling with constraints
Computers and Operations Research
A theoretic and practical framework for scheduling in a stochastic environment
Journal of Scheduling
A constraint programming approach for solving a queueing control problem
Journal of Artificial Intelligence Research
Adaptive stochastic resource control: a machine learning approach
Journal of Artificial Intelligence Research
Strengthening schedules through uncertainty analysis
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Dynamic application model for scheduling with uncertainty on reconfigurable architectures
International Journal of Reconfigurable Computing - Special issue on selected papers from the international workshop on reconfigurable communication-centric systems on chips (ReCoSoC' 2010)
Algorithms and mechanisms for procuring services with uncertain durations using redundancy
Artificial Intelligence
Robust local search for solving RCPSP/max with durational uncertainty
Journal of Artificial Intelligence Research
Physical search problems with probabilistic knowledge
Artificial Intelligence
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Most classical scheduling formulations assume a fixed and known duration for each activity. In this paper, we weaken this assumption, requiring instead that each duration can be represented by an independent random variable with a known mean and variance. The best solutions are ones which have a high probability of achieving a good makespan. We first create a theoretical framework, formally showing how Monte Carlo simulation can be combined with deterministic scheduling algorithms to solve this problem. We propose an associated deterministic scheduling problem whose solution is proved, under certain conditions, to be a lower bound for the probabilistic problem. We then propose and investigate a number of techniques for solving such problems based on combinations of Monte Carlo simulation, solutions to the associated deterministic problem, and either constraint programming or tabu search. Our empirical results demonstrate that a combination of the use of the associated deterministic problem and Monte Carlo simulation results in algorithms that scale best both in terms of problem size and uncertainty. Further experiments point to the correlation between the quality of the deterministic solution and the quality of the probabilistic solution as a major factor responsible for this success.