Risk Aversion to Parameter Uncertainty in Markov Decision Processes with an Application to Slow-Onset Disaster Relief

TL;DR

This paper develops a risk-averse framework for Markov Decision Processes that accounts for parameter uncertainty, aiming to minimize the risk of large long-term losses, with applications to disaster relief inventory management.

Contribution

It introduces a novel risk measure-based approach for MDPs under uncertainty, including formulations and heuristics, applied to humanitarian relief scenarios.

Findings

Risk-averse policies reduce potential losses in uncertain environments.

The approach effectively manages slow-onset disaster relief inventory.

Models outperform traditional methods in high-uncertainty settings.

Abstract

In classical Markov Decision Processes (MDPs), action costs and transition probabilities are assumed to be known, although an accurate estimation of these parameters is often not possible in practice. This study addresses MDPs under cost and transition probability uncertainty and aims to provide a mathematical framework to obtain policies minimizing the risk of high long-term losses due to not knowing the true system parameters. To this end, we utilize the risk measure value-at-risk associated with the expected performance of an MDP model with respect to parameter uncertainty. We provide mixed-integer linear and nonlinear programming formulations and heuristic algorithms for such risk-averse models of MDPs under a finite distribution of the uncertain parameters. Our proposed models and solution methods are illustrated on an inventory management problem for humanitarian relief operations during a slow-onset disaster. The results demonstrate the potential of our risk-averse modeling approach for reducing the risk of highly undesirable outcomes in uncertain/risky environments.