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The purpose of this paper is to address the issues whether or not minimal repair is effective for a repairable item with a known life distribution and when the minimal repair process should be stopped.

The life restoration degree (LRD) following minimal repair is defined and related to the shape parameter of the distribution so that a choice between minimal and perfect repairs can be made based on the shape parameter. Three replacement policies with minimal repair are considered and the corresponding decision rules are derived to determine when the minimal repair process should be stopped.

Main findings are: first, the LRD of minimal repair is inversely or approximately inversely proportional to the shape parameter, second, the effectiveness of minimal repair increases as the cost ratio of perfect and minimal repairs increases and the shape parameter decreases, and third, the unconditional mean residual life equal the mean time between the first and second failures.

The results can be easily used for maintenance strategy selection and maintenance decision optimization of repairable items.

This paper deals with economic manufacturing quantity (EMQ) models with stochastic machine breakdown and repairs. Under two minimal repair policies to maintain a production machine, the expected cost functions per unit time in the steady state, incurred in the manufacturing operation, are formulated and the optimal policies which minimize them are calculated. As a special case, we consider the case where the machine breakdown time follows the homogeneous Poisson process and calculate the optimal EMQ policies numerically. Also, an approximation method is used to represent the expected cost.

When the system fails, the decision to repair or replace a failed unit may depend on the estimated repair cost. Such an idea is called repair limit replacement policy. The repair limit is a limit on the amount of money which can be spent on the repair of a system. The repair limits thus provide an economic replacement policy. Examines optimal repair‐cost limits for a Weibull‐distributed time to failure and an exponentially distributed repair cost. Derives bounds for the optimal repair cost limit that minimizes the average cost per unit time for repairs and replacement. With those bounds, develops a simple algorithm to obtain the optimal repair‐cost limit. Gives numerical examples to illustrate the algorithm.

Examines optimal cost limits for a Weibull‐distributed time to failure and an exponentially distributed repair‐cost limit policy which minimizes the average cost per unit time for repairs and replacement. Presents numerical examples to illustrate the algorithm.

Two-dimensional warranty policies exist for certain consumer products, such as automobiles. Here, warranty is specified in terms of the time since the sale of the product as well as mileage incurred during that period. Thus, at the time of purchasing the product, the manufacturer may offer a warranty of three years or 30,000 miles, whichever occurs first. Failures in the product within this specified period of time or mileage will be covered by the manufacturer.

In this chapter, we consider the scenario of enterprise warranty programs, where customers are given the option of extending the original warranty. Thus, the buyer could be given an option to purchase a five year—50,000 mile warranty, whichever occurs first. Of course, the buyer will be expected to pay a premium to purchase this extended warranty. Such enterprise warranty programs are also found in other consumer durables, such as refrigerators, washers, dryers, and cooking ranges.

This chapter explores determination of the decision variables, such as product price, warranty time, and usage limit under the original conditions and further, for the enterprise warranty, that is, the extended warranty time and extended usage limit, as well as the premium to be charged to the buyer who selects the extended warranty. Mathematical models are developed based on maximizing the expected unit profit by selecting an enterprise warranty program. Additionally, some other objectives are also considered based on the proportional increase in the expected unit profit due to the increased market share attained through the offering of an enterprise warranty program. Some results are obtained through consideration of various goal values of the chosen objectives.

The purpose of this paper is to define conditions under which improved availability of fleet of G-4 jet trainers is obtained, and optimization of intermediate-level maintenance through imperfect maintenance model application. This research has been conducted based on available knowledge, and experience gained by performing intermediate-level maintenance of Serbian Air Force aircrafts.

Analysis of the data collected from daily maintenance reports, and the analysis of maintenance technology and organization, was performed. Based on research results, a reliability study was performed. Implementation of imperfect maintenance with its models of maintenance policies (especially a quasi-renewal process and its treating of reliability and optimal maintenance) was proposed to define new maintenance parameters so that the greater level of availability could be achieved.

The proposed methodology can potentially be applied as a simple tool to estimate the present maintenance parameters and to quickly point out some deficiencies in the analyzed maintenance organization. Validation of this process was done by conducting a reliability case study of G-4 jet trainer fleet, and numerical computations of optimal maintenance policy.

The methodology of the availability estimation when reliability parameters were not tracked by the maintenance organization, and optimization of intermediate-level maintenance, has so far been applied on G-4 jet trainers. Moreover, it can be potentially applied to other aircraft types.

Availability estimation and proposed optimization of intermediate maintenance is based on a survey of data for three years of aircraft fleet maintenance. It enables greater operational readiness (due to a military rationale) with possible cost reduction as a consequence but not as a goal.

Opportunistic and delayed maintenances are increasingly becoming important strategies for sustainable maintenance practices since they increase the lifetime of complex systems like aircrafts and heavy equipment. The objective of the current study is to quantify the optimal time window for adopting these strategies.

The current study considers the trade-offs between different costs involved in the opportunistic and delayed maintenances (of equipment) like the fixed cost of scheduled maintenances, the opportunistic rewards that may be earned and the cost of premature parts replacement. The probability of the opportunistic maintenance has been quantified under two different scenarios – Mission Reliability and Renewal Process. In the case of delayed maintenance, the cost of the delayed maintenance is also considered. The study uses optimization techniques to find the optimal maintenance time windows and also derive useful insights.

Apart from finding the optimal time window for the maintenance activities the study also shows that opportunistic maintenance is beneficial provided the opportunistic reward is significantly large; the cost of conducting scheduled maintenance in the pre-determined slot is significantly large. Similarly, the opportunistic maintenance may not be beneficial if the pre-mature equipment parts replacement cost is significantly high. The optimal opportunistic maintenance time is increasing function of Weibull failure rate parameter “beta” and decreasing function of Weibull failure rate parameter “theta.” In the case of optimal delayed maintenance time, these relationships reverse.

To the best of our knowledge, very few studies exist that have used mission reliability to study opportunistic maintenance or considered the different cost trade-offs comprehensively.

Original equipment manufacturers (OEMs) start providing support to their products, that helped them in beating the competition across the worldwide. The unavailability of spares and crews may also prolong the downtime of equipment, thereby affecting the systems’ performance. The spares and crews have as much effect on the systems’ performance as the design characteristics (i.e. reliability and maintainability). Thus, the OEMs required to extent the support to their products through maintenance, spares, crews, etc., so as to gain the customer satisfaction.

The mathematical model for spares, crews and support quality has been presented in this research work. The problem has been identified from the literature perspective for mechanical systems.

The model has been implemented on a real-life problem, in which the OEMs provide support to their make installed at compressed natural gas workstation in National Capital Region, India.

The research proposed in this work will be helpful to manufacturer, customer, academician, researcher, industrialist and any concerned person, to get the exhaustive benefits from the system.

The purpose of this paper is to explore the impact of the Kijima Type II imperfect repair model on the availability of repairable systems (RS). Since many individuals are interested in measuring the extent to which the system will be available after it has been run for a long time, the specific interest in this study is in the steady-state (limiting) availability behavior of such systems. Furthermore, the authors study the impact of age-based preventive maintenance (PM) on the RS performance.

Because of the complexity of the underlying assumptions of the Kijima Type II model, the authors use simulation modeling to estimate the system availability. Based on preliminary simulation results, the availability function achieves a steady-state value greater than zero. The system steady-state availability is then estimated from the simulation output by computing the average of the availability estimates beyond the initial transient period. Next, the authors develop a meta-model to convert the system reliability and maintainability parameters into the coefficients of the limiting availability estimate without the simulation effort. Using a circumscribed central composite experimental design, the authors confirm the accuracy of the meta-model.

The results show that the meta-model is robust, and provides good estimates of the system limiting availability. Also, the authors find that when using a Kijima Type II model for a system repair process, age-based PM can improve the steady-state availability value. Therefore, an optimal age-based PM policy that maximizes the system’s steady-state availability can be identified.

In practice, it is important to study the system steady-state availability because many individuals, i.e. engineers, are more interested in measuring the extent to which the system will be available after it has been run for a long time. Therefore, this study represents a significant addition to the body of knowledge related to virtual age modeling, in that it incorporates a Kijima type II model and considers system steady-state availability.

The purpose of this paper is to illustrate an uncertain programming model for scheduling of preventive maintenance (PM) actions. The PM scheduling, in which PM actions are performed under fixed intervals, is solved by grey systems theory.

The paper applied the grey evaluation method based on triangular whitenization weight functions which includes two classes: endpoint evaluation method and center-point evaluation method.

Two methods give the same results based on endpoint and center-point triangular whitenization weight functions. For validation, the results were compared by Cassady’s method.

The scheduling of PM is crucial in reliability and maintenance engineering. Hundreds of parts compose complex machines that require replacement and/or repairing. It is helpful to reduce the outage loss on frequent repair/replacement parts and avoid lack of maintenance of the equipment by controlling the equipment maintenance frequency.