In this paper, a periodic maintenance model is formulated assumingcontinuous monitoring, imperfect preventive maintenance (PM) and perfect correctivemaintenance (CM) using three decision variables, (I, N, Z). The model is derived in aninfinite horizon context where the mean cost per unit time is modelled. PM actionsare performed N − 1 times at time instants iT for i = 1, ..., N − 1, where T = ∆T · Iand ∆T is a fixed positive number representing the minimum time allowed betweenPM actions and I is a time interval multiple representing the decision of how oftenPM actions should be performed. The N:th maintenance activity is either a plannedreplacement (if Z = 0) or a corrective replacement from letting the component runto failure (if Z = 1). Imperfect PM is modelled using age reductions, either using aconstant r or a factor γ. Previous research on assumptions of these types has beenlimited as the assumptions yield models of high complexity which are not analyticallytractable. However, assumptions of this type are considered more realistic than othermore thoroughly researched assumptions, using e.g. minimal CM. Therefore, twocomplimentary optimisation methods are proposed and evaluated, namely, completeenumeration and a specially derived genetic algorithm which can be used for differentproblem sizes respectively. Carefully determined solution bounds enabled completeenumeration to be applicable for many input parameter values which is a great strengthof the proposed model. / I denna rapport modelleras en periodisk underhållsmodell baserat på antagandenakontinuerlig tillsyn,. ofullständigt förebyggande underhåll (FU) och perfektavhjälpande underhåll (AU) genom tre beslutsvariabler (I, N, Z). Modellen härledsinom ramen för en oändlig planeringshorisont där genomsnittskostnaden pertidsenhet modelleras. FU åtgärder utförs vid N − 1 stycken tillfällen vid tidpunkternaiT för i = 1, ..., N − 1, där T = ∆T · I och ∆T är ett givet positivt tal som representerarden minsta tillåtna tiden mellan FU åtgärder och I är en tidsintervallmultipelsom representerar beslutet kring hur ofta FU åtgärder ska utföras. Den N:teunderhållsåtgärden är antingen ett planerat utbyte (om Z = 0) eller ett avhjälpandeutbyte från att låta komponenten arbeta tills dess att den går sönder (om Z = 1).Ofullständigt FU modelleras genom åldersreduktion, antingen genom en konstantr eller en faktor γ. Det har visat sig finnas få tidigare studier som baseras påliknande antaganden då antaganden av denna typ resulterar i modeller av högkomplexitet som inte går att optimera analytiskt. Dock anses antaganden av dennatyp vara mer realistiska än andra mer välstuderade antaganden som exempelvisminimalt AU. Därför föreslås och utvärderas två kompletterande optimeringsmetodertill modellen, nämligen, total genomsökning och en specifikt anpassad genetiskalgoritm som kan användas för olika problemstorlekar. Genom att härleda effektivalösningsavgränsningar kunde optimering med hjälp av total genomsökning bli möjligtför många olika värden på modell parametrarna vilket är en stor fördel med denslutgiltiga modellen.

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. / text