| dc.contributor |
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
|
| dc.contributor |
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics |
|
| dc.contributor |
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
|
| dc.contributor |
Wang, Andrew J. |
|
| dc.contributor |
Williams, Brian Charles |
|
| dc.creator |
Wang, Andrew J. |
|
| dc.creator |
Williams, Brian Charles |
|
| dc.date |
2015-01-20T17:03:31Z |
|
| dc.date |
2015-01-20T17:03:31Z |
|
| dc.date |
2015-01 |
|
| dc.date.accessioned |
2023-03-01T18:04:44Z |
|
| dc.date.available |
2023-03-01T18:04:44Z |
|
| dc.identifier |
http://hdl.handle.net/1721.1/92982 |
|
| dc.identifier |
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. |
|
| dc.identifier |
https://orcid.org/0000-0002-1057-3940 |
|
| dc.identifier.uri |
http://localhost:8080/xmlui/handle/CUHPOERS/278662 |
|
| dc.description |
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. |
|
| dc.format |
application/pdf |
|
| dc.language |
en_US |
|
| dc.publisher |
Association for the Advancement of Artificial Intelligence |
|
| dc.relation |
http://www.aaai.org/Conferences/AAAI/2015/aaai15schedule.pdf |
|
| dc.relation |
Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI-15) |
|
| dc.rights |
Creative Commons Attribution-Noncommercial-Share Alike |
|
| dc.rights |
http://creativecommons.org/licenses/by-nc-sa/4.0/ |
|
| dc.source |
Wang |
|
| dc.title |
Chance-constrained Scheduling via Conflict-directed Risk Allocation |
|
| dc.type |
Article |
|
| dc.type |
http://purl.org/eprint/type/ConferencePaper |
|