Robust Adaptive Routing Under Uncertainty
Name
adaptive-routing-or-17.pdf
Description
Accepted version
Size
1002.34 KB
Format
Adobe PDF
Checksum (MD5)
37fe0571a612b65e3b39fa01aa196e70
Author(s)
Flajolet, Arthur
Blandin, Sébastien
Jaillet, Patrick
Date Issued
2018
Journal
Operations Research
Publisher
Institute for Operations Research and the Management Sciences (INFORMS)
Version
Author's final manuscript
Abstract
© 2017 INFORMS. We consider the problem of finding an optimal history-dependent routing strategy on a directed graph weighted by stochastic arc costs when the objective is to minimize the risk of spending more than a prescribed budget. To help mitigate the impact of the lack of information on the arc cost probability distributions, we introduce a robust counterpart where the distributions are only known through confidence intervals on some statistics such as the mean, the mean absolute deviation, and any quantile. Leveraging recent results in distributionally robust optimization, we develop a general-purpose algorithm to compute an approximate optimal strategy. To illustrate the benefits of the robust approach, we run numerical experiments with field data from the Singapore road network.
MIT Department
Massachusetts Institute of Technology. Operations Research Center
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1287/OPRE.2017.1662
