Chapter

Algorithmic Decision Theory

Volume 6992 of the series Lecture Notes in Computer Science pp 262-276

The Machine Learning and Traveling Repairman Problem

  • Theja TulabandhulaAffiliated withMassachusetts Institute of Technology
  • , Cynthia RudinAffiliated withMassachusetts Institute of Technology
  • , Patrick JailletAffiliated withMassachusetts Institute of Technology

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.

Keywords

machine learning traveling repairman integer programming uncertainty generalization bound constrained linear function classes