Robust maintenance policies for Markovian systems under model uncertainty

Kenneth D. Kuhn, Samer Madanat

    Research output: Contribution to journalArticle

    Abstract

    Asset management systems help public works agencies decide when and how to maintain and rehabilitate infrastructure facilities in a cost-effective manner. Many sources of error, some difficult to quantify, can limit the ability of asset management systems to accurately predict how built systems will deteriorate. This article introduces the use of robust optimization to deal with epistemic uncertainty. The Hurwicz criterion is employed to ensure management policies are never "too conservative." An efficient solution algorithm is developed to solve robust counterparts of the asset management problem. A case study demonstrates how the consideration of uncertainty alters optimal management policies and shows how the proposed approach may reduce maintenance and rehabilitation (M&R) expenditures.

    Original languageEnglish (US)
    Pages (from-to)171-178
    Number of pages8
    JournalComputer-Aided Civil and Infrastructure Engineering
    Volume21
    Issue number3
    DOIs
    StatePublished - Apr 1 2006

    Fingerprint

    Asset management
    Public works
    Patient rehabilitation
    Uncertainty
    Costs

    ASJC Scopus subject areas

    • Computational Theory and Mathematics
    • Computer Science Applications
    • Civil and Structural Engineering

    Cite this

    Robust maintenance policies for Markovian systems under model uncertainty. / Kuhn, Kenneth D.; Madanat, Samer.

    In: Computer-Aided Civil and Infrastructure Engineering, Vol. 21, No. 3, 01.04.2006, p. 171-178.

    Research output: Contribution to journalArticle

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