A Hybrid Inverse Optimization-Stochastic Programming Framework for Network Protection
Optimization and Control
2021-10-04 v1
Abstract
Disaster management is a complex problem demanding sophisticated modeling approaches. We propose utilizing a hybrid method involving inverse optimization to parameterize the cost functions for a road network's traffic equilibrium problem and employing a modified version of Fan and Liu (2010)'s two-stage stochastic model to make protection decisions using the information gained from inverse optimization. We demonstrate the framework using two types of cost functions for the traffic equilibrium problem and show that accurate parameterizations of cost functions can change spending on protection decisions in most of our experiments.
Keywords
Cite
@article{arxiv.2110.00488,
title = {A Hybrid Inverse Optimization-Stochastic Programming Framework for Network Protection},
author = {Stephanie Allen and Daria Terekhov and Steven A. Gabriel},
journal= {arXiv preprint arXiv:2110.00488},
year = {2021}
}