The Machine Learning and Traveling Repairman Problem
Name
Jaillet_The machine learning.pdf
Size
2.75 MB
Format
Adobe PDF
Checksum (MD5)
70a684b7482fd519090d97b220aab224
Author(s)
Tulabandhula, Theja
Rudin, Cynthia
Jaillet, Patrick
Date Issued
October 2011
Journal
Algorithmic Decision Theory
Publisher
Springer Berlin / Heidelberg
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.
Version
Author's final manuscript
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.
Description
Second International Conference, ADT 2011, Piscataway, NJ, USA, October 26-28, 2011. Proceedings
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Sloan School of Management
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/978-3-642-24873-3_20
