Simultaneous network optimization approach for pavement management systems

Aditya Medury, Samer Madanat

    Research output: Contribution to journalArticle

    Abstract

    In the context of sequential decision making under uncertainty, the Markov decision process (MDP) is a widely used mathematical framework. The MDP-based approaches in the infrastructure management literature can be broadly categorized as either top-down or bottom-up. The former, while efficient in incorporating system-level budget constraints, provide randomized policies, which must be mapped to individual facilities using additional subroutines. Conversely, although state-of-the-art bottom-up approaches provide facility-specific decisions, the disjointed nature of their problem formulation does not account for budget constraints in the future years. In this paper, a simultaneous network-level optimization framework is proposed, which seeks to bridge the gap between the top-down and bottom-up MDP-based approaches in infrastructure management. The salient feature of the approach is that it provides facility-specific policies for the current year of decision making while utilizing the randomized policies to calculate the expected future costs. Finally, the proposed methodology is compared to a state-of-the-art bottom-up methodology using a parametric study involving varying network sizes.

    Original languageEnglish (US)
    Article number04014010
    JournalJournal of Infrastructure Systems
    Volume20
    Issue number3
    DOIs
    StatePublished - Sep 1 2014

    Fingerprint

    Pavements
    Decision making
    Subroutines
    Costs
    Uncertainty

    Keywords

    • Maintenance
    • Markov decision process
    • Optimization
    • Pavements

    ASJC Scopus subject areas

    • Civil and Structural Engineering

    Cite this

    Simultaneous network optimization approach for pavement management systems. / Medury, Aditya; Madanat, Samer.

    In: Journal of Infrastructure Systems, Vol. 20, No. 3, 04014010, 01.09.2014.

    Research output: Contribution to journalArticle

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