A fast taboo search algorithm for the job shop problem
Management Science
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
Scheduling with uncertain durations: Modeling β-robust scheduling with constraints
Computers and Operations Research
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
Scheduling conditional task graphs
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
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Proactive scheduling seeks to generate high quality solutions despite execution time uncertainty. Building on work in [Beck and Wilson, 2004], we conduct an empirical study of a number of algorithms for the job shop scheduling problem with probabilistic durations. The main contributions of this paper are: the introduction and empirical analysis of a novel constraint-based search technique that can be applied beyond probabilistic scheduling problems, the introduction and empirical analysis of a number of deterministic filtering algorithms for probabilistic job shop scheduling, and the identification of a number of problem characteristics that contribute to algorithm performance.