TY - GEN
T1 - Benders decomposition for large-scale prescriptive evacuations
AU - Romanski, Julia
AU - Van Hentenryck, Pascal
N1 - Publisher Copyright:
© Copyright 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2016
Y1 - 2016
N2 - This paper considers prescriptive evacuation planning for a region threatened by a natural disaster such a flood, a wildfire, or a hurricane. It proposes a Benders decomposition that generalizes the two-stage approach proposed in earlier work for convergent evacuation plans. Experimental results show that Benders decomposition provides significant improvements in solution quality in reasonable time: It finds provably optimal solutions to scenarios considered in prior work, closing these instances, and increases the number of evacuees by 10 to 15% on average on more complex flood scenarios.
AB - This paper considers prescriptive evacuation planning for a region threatened by a natural disaster such a flood, a wildfire, or a hurricane. It proposes a Benders decomposition that generalizes the two-stage approach proposed in earlier work for convergent evacuation plans. Experimental results show that Benders decomposition provides significant improvements in solution quality in reasonable time: It finds provably optimal solutions to scenarios considered in prior work, closing these instances, and increases the number of evacuees by 10 to 15% on average on more complex flood scenarios.
UR - http://www.scopus.com/inward/record.url?scp=84986230339&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84986230339&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84986230339
T3 - 30th AAAI Conference on Artificial Intelligence, AAAI 2016
SP - 3894
EP - 3900
BT - 30th AAAI Conference on Artificial Intelligence, AAAI 2016
PB - AAAI Press
T2 - 30th AAAI Conference on Artificial Intelligence, AAAI 2016
Y2 - 12 February 2016 through 17 February 2016
ER -