Optimal infrastructure management decisions under uncertainty

    Research output: Contribution to journalArticle

    Abstract

    The planning of maintenance and rehabilitation activities for transportation facilities uses information on facility condition from two sources: measurement and forecasting. Both of these sources are characterized by the presence of significant uncertainties, which have important life-cycle cost implications. State-of-the-art decision-making models ignore the uncertainty either in one or both sources of information. This paper presents a methodology (the Latent Markov Decision Process) that explicitly recognizes the presence of random measurement errors in the measurement of facility condition. The methodology can also be used to quantify the "value of more precise information," which allows an agency to evaluate measurement technologies of different precisions and costs. A parametric study, which demonstrates such an evaluation in the case of highway pavements, is performed.

    Original languageEnglish (US)
    Pages (from-to)77-88
    Number of pages12
    JournalTransportation Research Part C
    Volume1
    Issue number1
    DOIs
    StatePublished - Jan 1 1993

    Fingerprint

    management decision
    uncertainty
    infrastructure
    Random errors
    Information use
    Measurement errors
    Pavements
    Patient rehabilitation
    Costs
    Life cycle
    methodology
    costs
    Decision making
    source of information
    life cycle
    rehabilitation
    Planning
    decision making
    planning
    Uncertainty

    ASJC Scopus subject areas

    • Computer Science Applications
    • Management Science and Operations Research
    • Automotive Engineering
    • Transportation

    Cite this

    Optimal infrastructure management decisions under uncertainty. / Madanat, Samer.

    In: Transportation Research Part C, Vol. 1, No. 1, 01.01.1993, p. 77-88.

    Research output: Contribution to journalArticle

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