Adaptive Optimization and Systematic Probing of Infrastructure System Maintenance Policies under Model Uncertainty

Madanat, Samer; Park, Sejung; Kuhn, Kenneth

doi:10.1061/(asce)1076-0342(2006)12:3(192)

Cited by 57 publications

(25 citation statements)

References 9 publications

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“…We do note, however, that other characterizations of performance model uncertainty have been used in models to support M&R decisions for transportation infrastructure. In particular, Madanat et al (2006) and Kuhn and Madanat (2005) present models where every row in the transition probability matrices is assumed to be a random vector. The former model relies on maximum likelihood estimation to update estimates of the probabilities.…”

Section: Maintenance Optimization Under Performance Model Uncertaintymentioning

confidence: 99%

“…Second, they provide information that is used to update an agency's beliefs, which in turn determine predictions about future condition. Earlier adaptive M&R optimization models, such as Durango and Madanat (2002) or Madanat et al (2006), assume that facilities are inspected in every period and that the inspection process is error-free, i.e., that it reveals the true facility condition. This motivates the development of a methodology that captures both the optimal timing of inspections, and the capabilities of the technologies that are available to inspect a facility.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of inspection and maintenance decisions for infrastructure facilities under performance model uncertainty: A quasi-Bayes approach

Durango-Cohen

Madanat

2008

Transportation Research Part A: Policy and Practice

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We present an optimization model to find joint inspection and maintenance policies for infrastructure facilities under performance model uncertainty. The objective in the formulation is to minimize the total expected social cost of managing facilities over a finite planning horizon.As in recent optimization models, performance model uncertainty is accounted for by representing facility deterioration as a mixture of known models taken from a finite set. The mixture proportions are assumed to be continuous random variables, with probability densities that are updated over time. In this paper, we relax the assumptions of fixed and error-free inspections.We present a parametric study to analyze the effect of initial performance model uncertainty and bias on the expected total cost of managing a facility. The main observation is that reducing the initial variance in model uncertainty may be more important than reducing the initial bias. Our study also shows that cost savings can result from relaxing the constraint of a fixed inspection schedule.

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Section: Maintenance Optimization Under Performance Model Uncertaintymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimization of inspection and maintenance decisions for infrastructure facilities under performance model uncertainty: A quasi-Bayes approach

Durango-Cohen

Madanat

2008

Transportation Research Part A: Policy and Practice

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“…This issue received particular attention in the specialised literature related to the management of civil engineering infrastructures (Gharaibeh et al 2006) and is dealt with in this article. Many of the existing models used in the case of budget constraints for the different time periods were formulated as constrained MDPs using linear programming optimisation for IM&R and/or M&R of a group of structures (Golabi et al 1982, Murakami and Turnquist 1985, Madanat et al 2006. The optimisation variables were chosen as being the probabilities of implementing the different maintenance activities prescribed by randomised policies.…”

Section: Introductionmentioning

confidence: 99%

Extension of dynamic programming models for management optimisation from single structure to multi-structures level

Faddoul

Soubra

Raphaël

et al. 2013

Structure and Infrastructure Engineering

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The aim of this article is the management optimisation (inspection, maintenance and rehabilitation (IM&R)) of a group of structures. It is supposed that the optimisation is constrained by limited available budget at the beginning of each time period during the entire time horizon thus creating an economical dependence between the decisions related to each of the structures. A Lagrangian relaxation technique is used for the extension of existing dynamic programming methods from single structure to multi-structures level. The methodology is illustrated by using a Generalised Partially Observable Markov Decision Process having a decision tree composed of a sequence of two decisions at the beginning of each time period, namely an inspection decision followed by a maintenance action decision. A numerical example concerning the optimisation of IM&R of 16 different bridges is presented.

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“…The problem of network restoration is also closely related to that of network maintenance/rehabilitation project scheduling (see Kiyota et al 1999;Bonyuet et al 2002;Wang et al 2003;Madanat et al 2006). In such problems, the task is to decide which network arcs to rehabilitate or upgrade to optimize user benefit/cost.…”

Section: Introductionmentioning

confidence: 99%

Strategic Network Restoration

2009

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Adaptive Optimization and Systematic Probing of Infrastructure System Maintenance Policies under Model Uncertainty

Cited by 57 publications

References 9 publications

Optimization of inspection and maintenance decisions for infrastructure facilities under performance model uncertainty: A quasi-Bayes approach

Optimization of inspection and maintenance decisions for infrastructure facilities under performance model uncertainty: A quasi-Bayes approach

Extension of dynamic programming models for management optimisation from single structure to multi-structures level

Strategic Network Restoration

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