Risk-averse periodic preventive maintenance optimization
| dc.contributor.advisor | Popova, Elmira | en |
| dc.contributor.advisor | Morton, David P. | en |
| dc.contributor.committeeMember | Damien, Paul | en |
| dc.contributor.committeeMember | Hasenbein, John J. | en |
| dc.contributor.committeeMember | Kutanoglu, Erhan | en |
| dc.creator | Singh, Inderjeet,1978- | en |
| dc.date.accessioned | 2011-12-21T16:45:29Z | en |
| dc.date.available | 2011-12-21T16:45:29Z | en |
| dc.date.issued | 2011-08 | en |
| dc.date.submitted | August 2011 | en |
| dc.date.updated | 2011-12-21T16:45:57Z | en |
| dc.description | text | en |
| dc.description.abstract | We consider a class of periodic preventive maintenance (PM) optimization problems, for a single piece of equipment that deteriorates with time or use, and can be repaired upon failure, through corrective maintenance (CM). We develop analytical and simulation-based optimization models that seek an optimal periodic PM policy, which minimizes the sum of the expected total cost of PMs and the risk-averse cost of CMs, over a finite planning horizon. In the simulation-based models, we assume that both types of maintenance actions are imperfect, whereas our analytical models consider imperfect PMs with minimal CMs. The effectiveness of maintenance actions is modeled using age reduction factors. For a repairable unit of equipment, its virtual age, and not its calendar age, determines the associated failure rate. Therefore, two sets of parameters, one describing the effectiveness of maintenance actions, and the other that defines the underlying failure rate of a piece of equipment, are critical to our models. Under a given maintenance policy, the two sets of parameters and a virtual-age-based age-reduction model, completely define the failure process of a piece of equipment. In practice, the true failure rate, and exact quality of the maintenance actions, cannot be determined, and are often estimated from the equipment failure history. We use a Bayesian approach to parameter estimation, under which a random-walk-based Gibbs sampler provides posterior estimates for the parameters of interest. Our posterior estimates for a few datasets from the literature, are consistent with published results. Furthermore, our computational results successfully demonstrate that our Gibbs sampler is arguably the obvious choice over a general rejection sampling-based parameter estimation method, for this class of problems. We present a general simulation-based periodic PM optimization model, which uses the posterior estimates to simulate the number of operational equipment failures, under a given periodic PM policy. Optimal periodic PM policies, under the classical maximum likelihood (ML) and Bayesian estimates are obtained for a few datasets. Limitations of the ML approach are revealed for a dataset from the literature, in which the use of ML estimates of the parameters, in the maintenance optimization model, fails to capture a trivial optimal PM policy. Finally, we introduce a single-stage and a two-stage formulation of the risk-averse periodic PM optimization model, with imperfect PMs and minimal CMs. Such models apply to a class of complex equipment with many parts, operational failures of which are addressed by replacing or repairing a few parts, thereby not affecting the failure rate of the equipment under consideration. For general values of PM age reduction factors, we provide sufficient conditions to establish the convexity of the first and second moments of the number of failures, and the risk-averse expected total maintenance cost, over a finite planning horizon. For increasing Weibull rates and a general class of increasing and convex failure rates, we show that these convexity results are independent of the PM age reduction factors. In general, the optimal periodic PM policy under the single-stage model is no better than the optimal two-stage policy. But if PMs are assumed perfect, then we establish that the single-stage and the two-stage optimization models are equivalent. | en |
| dc.description.department | Operations Research and Industrial Engineering | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.slug | 2152/ETD-UT-2011-08-4203 | en |
| dc.identifier.uri | http://hdl.handle.net/2152/ETD-UT-2011-08-4203 | en |
| dc.language.iso | eng | en |
| dc.subject | Risk-averse | en |
| dc.subject | Periodic preventive maintenance | en |
| dc.subject | Two-stage optimization | en |
| dc.subject | Risk-averse cost | en |
| dc.subject | Corrective maintenance | en |
| dc.subject | Preventive maintenance | en |
| dc.subject | Risk-neutral | en |
| dc.subject | Bayesian approach | en |
| dc.subject | Age reduction factors | en |
| dc.subject | Maintenance effectiveness | en |
| dc.subject | Virtual age | en |
| dc.subject | Kijima-I | en |
| dc.subject | Kijima-II | en |
| dc.subject | Effective age | en |
| dc.subject | Increasing failure rate | en |
| dc.subject | Time to first system failures | en |
| dc.subject | Likelihood function | en |
| dc.subject | Parameter estimation | en |
| dc.subject | Simulation-based optimization | en |
| dc.subject | Gibbs sampler | en |
| dc.subject | Efficient algorithm | en |
| dc.subject | As Good As New | en |
| dc.subject | As Good As Old | en |
| dc.subject | Minimal repair | en |
| dc.subject | PM | en |
| dc.subject | CM | en |
| dc.subject | Risk averse maintenance cost | en |
| dc.subject | Computational results | en |
| dc.subject | Simulation based preventive maintenance optimization | en |
| dc.subject | Nonhomogenous poisson process | en |
| dc.subject | Imperfect repair | en |
| dc.subject | Imperfect PM | en |
| dc.subject | Perfect PM | en |
| dc.subject | Finite planning horizon | en |
| dc.subject | Nuclear power plant | en |
| dc.subject | South Texas Project Nuclear Operating Company | en |
| dc.subject | Bayesian methods | en |
| dc.subject | Prior | en |
| dc.subject | Posterior | en |
| dc.subject | ROCOF | en |
| dc.subject | Rate of occurrence of failures | en |
| dc.title | Risk-averse periodic preventive maintenance optimization | en |
| dc.type.genre | thesis | en |
| thesis.degree.department | Operations Research and Industrial Engineering | en |
| thesis.degree.discipline | Operations Research and Industrial Engineering | en |
| thesis.degree.grantor | University of Texas at Austin | en |
| thesis.degree.level | Doctoral | en |
| thesis.degree.name | Doctor of Philosophy | en |