Artificial Intelligence - Special issue on knowledge representation
Systematic and nonsystematic search strategies
Proceedings of the first international conference on Artificial intelligence planning systems
Generating feasible schedules under complex metric constraints
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Heuristic-biased stochastic sampling
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Boosting combinatorial search through randomization
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
A Constraint-Based Method for Project Scheduling with Time Windows
Journal of Heuristics
Amplification of Search Performance through Randomization of Heuristics
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
A Constraint-Based Architecture for Flexible Support to Activity Scheduling
AI*IA 01 Proceedings of the 7th Congress of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence
The Knowledge Engineering Review
Enhancing Stochastic Search Performance by Value-Biased Randomization of Heuristics
Journal of Heuristics
An iterative sampling procedure for resource constrained project scheduling ith time windows
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Scheduling a single robot in a job-shop environment through precedence constraint posting
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part II
Schedule robustness through broader solve and robustify search for partial order schedules
AI*IA'05 Proceedings of the 9th conference on Advances in Artificial Intelligence
Annals of Mathematics and Artificial Intelligence
Iterative flattening search for the flexible job shop scheduling problem
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
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In this paper, we investigate the use of stochastic variable and value ordering heuristics for solving job shop scheduling problems with non-relaxable deadlines and complex metric constraints. Previous research in constraint satisfaction scheduling has developed highly effective, deterministic heuristics for this class of problems based on simple measures of temporal sequencing flexibility. However, they are not infallible, and the possibility of search failure raises the issue of how to most productively enlarge the search. Backtracking is one alternative, but such systematicity generally implies high computational cost. We instead design an iterative sampling procedure, based on the intuition that it is more productive to deviate from heuristic advice in cases where the heuristic is less informed, and likewise better to follow the heuristic in cases where it is more knowledgeable. We specify stochastic counterparts to previously developed search heuristics, which are parameterized to calibrate degree of randomness to level of discriminatory power. Experimental results on job shop scheduling CSPs of increasing size demonstrate comparative advantage over chronological backtracking. Comparison is also made to another, recently proposed iterative sampling technique called heuristic-biased stochastic sampling (HBSS). Whereas HBSS assumes a statically specified heuristic bias that is utilized at every application of the heuristic, our approach defines bias dynamically according to how well the heuristic discriminates alternatives.