A Hybrid Inverse Optimization-Stochastic Programming Framework for Network Protection

TL;DR

This paper introduces a hybrid inverse optimization and stochastic programming framework to improve network protection strategies by accurately modeling traffic costs and making informed protection decisions.

Contribution

It presents a novel combined approach using inverse optimization and stochastic programming for network protection planning.

Findings

Accurate cost function parameterization influences protection spending.

The framework effectively integrates inverse optimization with stochastic decision-making.

Different cost functions impact protection strategies significantly.

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.