The Machine Learning and Traveling Repairman Problem
Author(s)Tulabandhula, Theja; Rudin, Cynthia; Jaillet, Patrick
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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.
Second International Conference, ADT 2011, Piscataway, NJ, USA, October 26-28, 2011. Proceedings
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science; Sloan School of Management
Algorithmic Decision Theory
Springer Berlin / Heidelberg
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.
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