Risk aversion to parameter uncertainty in Markov decision processes with an application to slow-onset disaster relief

Merve Meraklı, Simge Küçükyavuz*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)811-831
Number of pages21
JournalIISE Transactions
Volume52
Issue number8
DOIs
StatePublished - Aug 2 2020

Keywords

  • Markov decision processes
  • chance constraints
  • disaster relief
  • humanitarian supply chains
  • parameter uncertainty
  • value-at-risk

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

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