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Minimizing risk models in stochastic shortest path problems

Author

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  • Yoshio Ohtsubo

Abstract

We consider a minimizing risk model in a stochastic shortest path problem in which for each node of a graph we select a probability distribution over the set of successor nodes so as to reach a given target node with minimum threshold probability. We formulate such a problem as undiscounted finite Markov decision processes. We show that an optimal value function is a unique solution to an optimality equation and find an optimal stationary policy. A value iteration method is also given. Copyright Springer-Verlag Berlin Heidelberg 2003

Suggested Citation

  • Yoshio Ohtsubo, 2003. "Minimizing risk models in stochastic shortest path problems," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 57(1), pages 79-88, April.
    • Handle: RePEc:spr:mathme:v:57:y:2003:i:1:p:79-88
    DOI: 10.1007/s001860200246
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    Cited by:

    1. Rashed Khanjani-Shiraz & Ali Babapour-Azar & Zohreh Hosseini-Noudeh & Panos M. Pardalos, 2022. "Distributionally robust maximum probability shortest path problem," Journal of Combinatorial Optimization, Springer, vol. 43(1), pages 140-167, January.
    2. Yueyue Fan & Yu Nie, 2006. "Optimal Routing for Maximizing the Travel Time Reliability," Networks and Spatial Economics, Springer, vol. 6(3), pages 333-344, September.
    3. Özlem Çavuş & Andrzej Ruszczyński, 2014. "Computational Methods for Risk-Averse Undiscounted Transient Markov Models," Operations Research, INFORMS, vol. 62(2), pages 401-417, April.

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