The machine learning and traveling repairman problem

Authors:
Theja Tulabandhula;Cynthia Rudin;Patrick Jaillet
Affiliations:
Massachusetts Institute of Technology, Cambridge MA;Massachusetts Institute of Technology, Cambridge MA;Massachusetts Institute of Technology, Cambridge MA
Venue:
ADT'11 Proceedings of the Second international conference on Algorithmic decision theory
Year:
2011

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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.