Optimal Preventive Maintenance Under Decision Dependent Uncertainty

Galenko, Alexander; Popova, Elmira; Kee, Ernie; Grantom, Rick

doi:10.1115/icone14-89093

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…The value of T is obtained by minimizing the long run expected cost per unit time. Galenko et al [11] present optimal maintenance policy for multiple items with decision dependent uncertainty over a finite time horizon (divided into a discrete set) and in the absence of a budget constraint. We address the problem of designing an optimal preventive maintenance policy for a portfolio of items over a finite and continuous time horizon under a constrained total preventive maintenance budget.…”

Section: Icone16-48107mentioning

confidence: 99%

Optimal Preventive Maintenance Policy Under a Budget Constraint

Singh

Popova

Kee

2008

Volume 1: Plant Operations, Maintenance, Installations and Life Cycle; Component Reliability and Materials Issues; Advanced App

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We design an optimal preventive maintenance policy for a system of N items that minimizes the total expected maintenance cost. We assume that the budget for preventive maintenance is limited and constrained. The problem has a finite time horizon and we consider constant inter-preventive maintenance times for every item. The resulting nonlinear optimization problem is reformulated as a binary integer program and computation results are presented on a real data set from South Texas Project Nuclear Operating Company in Bay City, Texas, USA.

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Section: Icone16-48107mentioning

confidence: 99%

Optimal Preventive Maintenance Policy Under a Budget Constraint

Singh

Popova

Kee

2008

Volume 1: Plant Operations, Maintenance, Installations and Life Cycle; Component Reliability and Materials Issues; Advanced App

Self Cite

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A Method to Quantify Plant Availability and Initiating Event Frequency Using a Large Event Tree, Small Fault Tree Model

Kee

Sun

Rodgers

et al. 2006

Volume 1: Plant Operations, Maintenance and Life Cycle; Component Reliability and Materials Issues; Codes, Standards, Licensing

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South Texas Project uses a large fault tree to produce scenarios (minimal cut sets) used in quantification of plant availability and event frequency predictions. On the other hand, the South Texas Project probabilistic risk assessment model uses a large event tree, small fault tree for quantifying core damage and radioactive release frequency predictions. The South Texas Project is converting its availability and event frequency model to use a large event tree, small fault in an effort to streamline application support and to provide additional detail in results. The availability and event frequency model as well as the applications it supports (maintenance and operational risk management, system engineering health assessment, preventive maintenance optimization, and RIAM) are briefly described. A methodology to perform availability modeling in a large event tree, small fault tree framework is described in detail. How the methodology can be used to support South Texas Project maintenance and operations risk management is described in detail. Differences with other fault tree methods and other recently proposed methods are discussed in detail. While the methods described are novel to the South Texas Project Risk Management program and to large event tree, small fault tree models, concepts in the area of application support and availability modeling have wider applicability to the industry.

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Optimal Preventive Maintenance Under Decision Dependent Uncertainty

Cited by 2 publications

References 6 publications

Optimal Preventive Maintenance Policy Under a Budget Constraint

Optimal Preventive Maintenance Policy Under a Budget Constraint

A Method to Quantify Plant Availability and Initiating Event Frequency Using a Large Event Tree, Small Fault Tree Model

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