Risk aversion to parameter uncertainty in Markov decision processes with an application to slow-onset disaster relief. (2nd August 2020)

• Record Type: Journal Article
• Authors: Meraklı, Merve; Küçükyavuz, Simge
• Is Part Of: IISE transactions. Volume 52:Number 8(2020)
• Page Start: 811
• Page End: 831
• Publication Date: 2020-08-02
• Subjects: Markov decision processes -- parameter uncertainty -- value-at-risk -- chance constraints -- humanitarian supply chains -- disaster relief; Industrial engineering -- Periodicals; Systems engineering -- Periodicals; Industrial engineering; Systems engineering; Electronic journals; Periodicals; 670.285
• DOI: 10.1080/24725854.2019.1674464
• Languages: English
• ISSNs: 2472-5854

Abstract

Abstract: In classic 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.