Chance-constrained Scheduling via Conflict-directed Risk Allocation
Author(s)Wang, Andrew J.; Williams, Brian Charles
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Temporal uncertainty in large-scale logistics forces one to trade off between lost efficiency through built-in slack and costly replanning when deadlines are missed. Due to the difficulty of reasoning about such likelihoods and consequences, a computational framework is needed to quantify and bound the risk of violating scheduling requirements. This work addresses the chance-constrained scheduling problem, where actions’ durations are modeled probabilistically. Our solution method uses conflict-directed risk allocation to efficiently compute a scheduling policy. The key insight, compared to previous work in probabilistic scheduling, is to decouple the reasoning about temporal and risk constraints. This decomposes the problem into a separate master and subproblem, which can be iteratively solved much quicker. Through a set of simulated car-sharing scenarios, it is empirically shown that conflict-directed risk allocation computes solutions nearly an order of magnitude faster than prior art does, which considers all constraints in a single lump-sum optimization.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Aeronautics and Astronautics; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI-15)
Association for the Advancement of Artificial Intelligence
Wang, Andrew J., and Brian C. Williams. "Chance-constrained Scheduling via Conflict-directed Risk Allocation." in Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI-15), January, 25-30, 2015, Austin, Texas, USA.
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