Artificial Intelligence
Viewing scheduling as an opportunistic problem-solving process
Annals of Operations Research
A task and resource scheduling system for automated planning
Annals of Operations Research
Using iterative repair to automate planning and scheduling of shuttle payload operations
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
An Empirical Evaluation of the Effectiveness of Local Search for Replanning
ECAI '00 Proceedings of the Workshop on Local Search for Planning and Scheduling-Revised Papers
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We present a heuristics-based approach to deep space mission scheduling which is modeled on the approach used by expert human schedulers in producing schedules for planetary encounters. New chronological evaluation techniques are used to focus the search by using information gained during the scheduling process to locate, classify, and resolve regions of conflict. Our approach is based on the assumption that during the construction of a schedule there exist several disjunct temporal regions where the demand for one resource type or a single temporal constraint dominates (bottleneck regions). If the scheduler can identify these regions and classify them based on their dominant constraint, then the scheduler can select the scheduling heuristic.