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Stochastic cost, reliability and maintenance optimization considering uncertain future usage scenarios
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Chatwattanasiri, Nida. Stochastic cost, reliability and maintenance optimization considering uncertain future usage scenarios. Retrieved from https://doi.org/doi:10.7282/T3319XQC

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TitleStochastic cost, reliability and maintenance optimization considering uncertain future usage scenarios
NameChatwattanasiri, Nida (author); Coit, David W. (chair); Gursoy, Melike Baykal (internal member); Jeong, Myong K. (internal member); Tortorella, Michael (outside member); Wattanapongsakorn, Naruemon (outside member); Rutgers University; Graduate School - New Brunswick
Date Created2015
Other Date2015-05 (degree)
SubjectIndustrial and Systems Engineering, Reliability, Mathematical optimization, Stochastic processes
Extent1 online resource (xiii, 163 p. : ill.)
DescriptionA new modeling approach has been developed to optimally design a degradable system structure and maintenance plans for applications exposed to distinct stresses under different environments and diverse usage conditions. When component stresses change or shift in the future and diverse operating conditions, equipment or components may not be operating with the same stress profile that was presented when the historical data was collected and analyzed. A more detailed mathematical perspective is described to analyze the uncertainty of actual system usages in future scenarios that take variations and uncertainties explicitly into consideration. The integrated development of cost-reliability optimization models and a system maintenance policy is an interesting and challenging problem. A new integrated system optimization planning model is developed and applied to provide an insightful mathematical representation of the process that more directly represents the system reliability design process. A four-stage optimization model is constructed to accommodate sequences of decisions over time with random future usage scenarios. In the first and second stage, a two-stage stochastic model with recourse is formulated with a system cost, reliability and maintenance combined objective function to determine an initial design structure and corrective or preventive maintenance intervals. In the third-stage, the system is fielded and data is collected and analyzed to update model parameters and coefficient estimates, and adaptive preventive maintenance optimization is performed. Bayesian posterior distributions are constructed to provide the model with timely and improved estimates to reflect the fielded data. In the fourth-stage, a cost saving strategy is implemented to decide whether the current system design or a new or revised system design can provide sufficient cost savings beyond a threshold to justify design changes. The optimization models are expressed as nonlinear stochastic integer programming models where several tools are used for solving problems. In particular, pattern-search (from MATLAB toolbox), iterative coordinate descent and neighborhood heuristic search are the methods used for searching the feasible region to obtain optimal or recommended model solutions. Theoretical and simulated examples are solved to demonstrate the modeling approach and results.
NotePh.D.
NoteIncludes bibliographical references
Noteby Nida Chatwattanasiri
Genretheses, ETD doctoral
Persistent URLhttps://doi.org/doi:10.7282/T3319XQC
Languageeng
CollectionGraduate School - New Brunswick Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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