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The Machine Learning and Traveling Repairman Problem

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Show simple item record Tulabandhula, Theja Rudin, Cynthia Jaillet, Patrick 2012-10-12T16:09:40Z 2012-10-12T16:09:40Z 2011-10 2011-10
dc.identifier.isbn 978-3-642-24872-6
dc.identifier.issn 0302-9743
dc.identifier.issn 1611-3349
dc.description Second International Conference, ADT 2011, Piscataway, NJ, USA, October 26-28, 2011. Proceedings en_US
dc.description.abstract The goal of the Machine Learning and Traveling Repairman Problem (ML&TRP) is to determine a route for a “repair crew,” which repairs nodes on a graph. The repair crew aims to minimize the cost of failures at the nodes, but the failure probabilities are not known and must be estimated. If there is uncertainty in the failure probability estimates, we take this uncertainty into account in an unusual way; from the set of acceptable models, we choose the model that has the lowest cost of applying it to the subsequent routing task. In a sense, this procedure agrees with a managerial goal, which is to show that the data can support choosing a low-cost solution. en_US
dc.description.sponsorship Fulbright Program (International Fulbright Science and Technology Award) en_US
dc.description.sponsorship Massachusetts Institute of Technology. Energy Initiative en_US
dc.description.sponsorship National Science Foundation (U.S.) (Grant IIS-1053407) en_US
dc.language.iso en_US
dc.publisher Springer Berlin / Heidelberg en_US
dc.relation.isversionof en_US
dc.rights Creative Commons Attribution-Noncommercial-Share Alike 3.0 en_US
dc.rights.uri en_US
dc.source MIT web domain en_US
dc.title The Machine Learning and Traveling Repairman Problem en_US
dc.type Article en_US
dc.identifier.citation Tulabandhula, Theja, Cynthia Rudin, and Patrick Jaillet. “The Machine Learning and Traveling Repairman Problem.” Algorithmic Decision Theory. Ed. Ronen I. Brafman, Fred S. Roberts, & Alexis Tsoukiàs. LNCS Vol. 6992. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. 262–276. en_US
dc.contributor.department Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science en_US
dc.contributor.department Sloan School of Management en_US
dc.contributor.mitauthor Tulabandhula, Theja
dc.contributor.mitauthor Rudin, Cynthia
dc.contributor.mitauthor Jaillet, Patrick
dc.relation.journal Algorithmic Decision Theory en_US
dc.identifier.mitlicense OPEN_ACCESS_POLICY en_US
dc.eprint.version Author's final manuscript en_US
dc.type.uri en_US
dspace.orderedauthors Tulabandhula, Theja; Rudin, Cynthia; Jaillet, Patrick en

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