Incorporating Bayesian networks in Markov Decision Processes

Rafic Faddoul, Wassim Raphael, Abdul-Hamid Soubra and Alaa Chateauneuf ()
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Rafic Faddoul: TRUST - Contrôle de santé fiabilité et calcul des structures - GeM - Institut de Recherche en Génie Civil et Mécanique - UN UFR ST - Université de Nantes - UFR des Sciences et des Techniques - UN - Université de Nantes - ECN - École Centrale de Nantes - CNRS - Centre National de la Recherche Scientifique
Wassim Raphael: USJ - Université Saint-Joseph de Beyrouth
Abdul-Hamid Soubra: TRUST - Contrôle de santé fiabilité et calcul des structures - GeM - Institut de Recherche en Génie Civil et Mécanique - UN UFR ST - Université de Nantes - UFR des Sciences et des Techniques - UN - Université de Nantes - ECN - École Centrale de Nantes - CNRS - Centre National de la Recherche Scientifique
Alaa Chateauneuf: LAMI - Laboratoire de Mécanique et Ingénieries - IFMA - Institut Français de Mécanique Avancée - UBP - Université Blaise Pascal - Clermont-Ferrand 2

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Abstract: This paper presents an extension to a partially observable Markov decision process so that its solution can take into account, at the beginning of the planning, the possible availability of free information in future time periods. It is assumed that such information has a Bayesian network structure. The proposed approach requires a smaller computational effort than the classical approaches used to solve dynamic Bayesian networks. Furthermore, it allows the user to (1)take advantage of prior probability distributions of relevant random variables that do not necessarily have a direct causal relationship with the state of the system; and (2)rationally take into account the effects of accidental or rare events (such as seismic activities) that may occur during future time periods of the planning horizon. The methodology is illustrated through an example problem that concerns the optimization of inspection, maintenance, and rehabilitation strategies of road pavement over a 14-year planning horizon.

Keywords: Maintenance; Optimization; Markov process; Infrastructure; Bayesian analysis; Decision making (search for similar items in EconPapers)
Date: 2013
Note: View the original document on HAL open archive server: https://hal.science/hal-01006963
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Citations: View citations in EconPapers (2)

Published in Journal of Infrastructure Systems, 2013, 19 (4), pp.415-424. ⟨10.1061/(ASCE)IS.1943-555X.0000134⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-01006963

DOI: 10.1061/(ASCE)IS.1943-555X.0000134

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