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Opportunistic maintenance (OM) policy is a prospective maintenance approach that instigates for a more effective and optimized system. The purpose of this paper is to provide the steps and methods used in model development processes for the application of the OM policy.

Dubbed as opportunistic principle toward optimal maintenance system (OPTOMS) for OM policy toward optimal maintenance system, the model is devised as a decision support system model and contains five phases. The motivation and focus of the model resolve around the need for a practical framework or model of maintenance policy for the application in an industry. In this paper, the OPTOMS model was verified and validated to ensure that the model is applicable in the industry and robust as a support system in decision making for the optimal maintenance system.

From the verification steps conducted in a case study company, it was found that the developed model incorporated simple but practical tools like check sheet, failure mode and effect analysis (FMEA), control chart that has been commonly used in the industry.

This paper provides the general explanations of the developed model and tools used for each phase in implementing OM to achieve an optimal maintenance system. Based on a case study conducted in a semiconductor company, the OPTOMS model can align and prepare the company in increasing machine reliability by reducing machine downtime.

The novelty of this paper is based on the in-depth discussion of all phases and steps in the model that emphasize on how the model will become practical theories in conducting an OM policy in a company. The proposed methods and tools for data collection and analysis are practical and commonly used in the industry. The framework is designed for practical application in the industry. The users would be from the Maintenance and Production Department.

An optimal maintenance policy is key to the improvement of the availability and reliability of a system at an acceptable level without a significant increase in investment. However, the selection process is a complicated task because it requires in-depth knowledge on maintenance policies and on the technical requirements of maintenance. The difficulties and complexity of the selection process arise from the combination of conflicting maintenance constraints such as available spares, size of workforce, and maintenance skills. The paper aims to discuss these issues.

The proposed maintenance policy selection (MPS) model is separated into three major phases. The first phase identifies the critical system (CS) based on failure frequency. The failure mechanism in the CS is then analyzed by using a failure mode and effect analysis in the second phase. In the third phase, a multi-criteria decision making method, called the technique for order of preference by similarity to ideal solution, is adopted to identify an optimal maintenance policy that can minimize the failures.

Through a case study, preventive maintenance was selected as the optimal maintenance policy for the reduction of system failures. The results obtained from the case study not only provide evidence of the feasibility and practicability of the developed model, but also test the acceptability and rationale of the developed model from the industry perspective. Valuable knowledge and experience from employees were extracted and utilized through the proposed model to rank the optimal maintenance policy based on the capability to reduce failure.

The practicality of the MPS model is justified through an implementation in the palm oil industry. The application of the MPS model can also be extended to other manufacturing industries.

This paper reviews the literature on opportunistic maintenance (OM) as new advance maintenance approach and policy. The purpose of this paper is to conceptually identify common principle and thereby provide absolute definition, concept and characteristics of this policy.

A conceptual analysis was conducted on various literatures to clarify a number of principle and concepts as a method for understanding information on OM. The analysis involves the process of separating the compound terms used in the literatures into a few parts, analyse them and then recombining them to have more clear understanding of the policy.

The paper discussed the maintenance approach, genealogy, principle, concept and applications of OM both in numerical analysis and real industry. OM policy is developed based on combination of age replacement policy and block replacement policy and in practical; OM is applied as the combination of corrective maintenance which is applied when any failure occurred, with preventive maintenance (PM) – a planned and scheduled maintenance approach to prevent failure to happen. Any machine shutdown or stoppages due to failure is the “opportunity” to conduct PM even though it is not as planned. The characterization of OM was provided in order to present its theoretical novelty for researchers and practical significance for industries.

To date, there is no publication that reviews the OM in-depth and provides clear understanding on the topic. Therefore, this paper aims to show lineage of OM and the current trend in researches. This discussion will pave the way of new research areas on this optimal maintenance policy. Clear definition and principle of OM provided in this paper will trigger interest in its practicality as well as aid industries to understand and conduct OM in operation plant.

This paper discussed the available literature about OM in various perspectives and scopes for further understanding of the topic by maintenance management professionals and researchers. Therefore, OM can be widely studied and applied in real industry as it is an effective and optimal maintenance policy.

Using a case study for electrical power equipment, the purpose of this paper is to investigate the importance of dependence between series-connected system components in maintenance decisions.

A continuous-time Markov decision model is formulated to find a minimum cost maintenance policy for a circuit breaker as an independent component while considering a downstream transformer as a dependent component. Maintenance of the dependent component is included implicitly in terms of the costs associated with certain state-action pairs. For policy and cost comparisons, a separate model is also formulated that considers only the circuit breaker as the independent component. After uniformizing the continuous-time models to discrete time, standard methods are used to solve for the average-cost-optimal policies of each model.

The optimal maintenance policy and its cost differ significantly depending on whether or not the dependent component is considered.

Data used are from manufacturer databases; additional model validation could be conducted if applied to an electric utility asset fleet within their generation, transmission, and/or distribution system. This model and methodology are already being applied in other contexts such as industrial machinery and equipment, jet engines, amusement park rides, etc.

The outcome of this model can be utilized by asset and operations managers to make maintenance decisions based on prediction rather than more traditional time- or condition-based maintenance methodologies. This model is being developed for use as a module in a larger maintenance information system, specifically linking condition monitor data from the field to a predictive maintenance model. Similar methods are being applied to other applications outside the electrical equipment case detailed herein.

This model provides a structured approach for managers to decide how to best allocate their resources across a network of inter-connected equipment. Work in this area has not fully considered the importance of dependency on systems maintenance, particularly in applications with highly variable repair and replacement costs.

The purpose of this paper is to develop a model that determines whether how much effort of preventive maintenance action is worthwhile for the consumer over the post-sale product life cycle of a repairable complex product where the product is under warranty and subject to stochastic multimode failure process, that is, damaging failure and light failure with different probabilities.

The expected life cycle cost is designed for a warranted product from the consumer perspective. The product failure is quantified with failure rate function, which is the number of failures incurred over the product life cycle. The authors consider the failure rate function reduction method in their model where the scale parameter of a failure rate function is maximized by applying the optimal preventive maintenance level. The scale parameter of any failure distribution refers to the meantime to failure (MTTF). The first-order condition is applied with respect to the maintenance level in order to achieve the convexity of the nonlinear function of the expected life cycle cost function.

The authors have found analytically the close form of the preventive maintenance level, which can be used to find the optimal reduced form of the failure rate function of the product and the minimum product expected life cycle cost under the given condition of multimode stochastic failure process. The authors have suggested different maintenance policies to consumers in order to implement the proposed preventive maintenance model under different conditions. A numerical example further illustrated the analytical model by considering the Weibull distribution.

The consumer may use this study in the accurate modeling of the life cycle cost of a product that is under warranty and fails with a multimode failure process. Also, the suggested preventive maintenance approach of this study helps the consumer in making appropriate maintenance decisions such as to minimize the expected life cycle cost of a product.

This study proposes an accurate estimation of a life cycle cost for a product that is under the support of warranty and fails with multimode. Furthermore, for such a kind of product, which is under warranty and fails with multimode, this study suggests a new preventive maintenance approach that assures the minimum expected life cycle cost.

While steady-state analysis is useful, it does not consider the inherent transient characteristics of repairable systems' behavior, especially in systems that have relatively short life spans, or when their transient behavior is of special concern such as the motivating example used in this paper, military systems. Therefore, a maintenance policy that considers both transient and steady-state availability and aims to achieve the best trade-off between high steady-state availability and rapid stabilization is essential.

This paper studies the transient behavior of system availability under the Kijima Type II virtual age model. While such systems achieve steady-state availability, and it has been proved that deploying preventive maintenance (PM) can significantly improve its steady-state availability, this improvement often comes at the price of longer and increased fluctuating transient behavior, which affects overall system performance. The authors present a methodology that identifies the optimal PM policy that achieves the best trade-off between high steady-state availability and rapid stabilization based on cost-availability analysis.

When the proposed simulation-based optimization and cost analysis methodology is applied to the motivating example, it produces an optimal PM policy that achieves an availability–variability balance between transient and steady-state system behaviors. The optimal PM policy produces a notably lower availability coefficient of variation (by 11.5%), while at the same time suffering a negligible limiting availability loss of only 0.3%. The new optimal PM policy also provides cost savings of about 5% in total maintenance cost. The performed sensitivity analysis shows that the system's optimal maintenance cost is sensitive to the repair time, the shape parameter of the Weibull distribution and the downtime cost, but is robust with respect to changes in the remaining parameters.

Most of the current maintenance models emphasize the steady-state behavior of availability and neglect its transient behavior. For some systems, using steady-state availability as the sole metric for performance is not adequate, especially in systems that have relatively short life spans or when their transient behavior affects the overall performance. However, little work has been done on the transient analysis of such systems. In this paper, the authors aim to fill this gap by emphasizing such systems and applications where transient behavior is of critical importance to efficiently optimize system performance. The authors use military systems as a motivating example.

The authors consider systems that generate damage to environment as they get older and degrade. The purpose of this paper is to develop an optimal condition-based maintenance strategy for such systems in situations where they have a finite operational time requirement. The authors determine simultaneously the optimal number of inspections and the threshold level of environmental damage which minimize the total expected cost over the considered finite time horizon.

The environmental degradation level is monitored through periodic inspections. The authors model the environmental degradation process due to the equipment’s degradation by the Wiener process. A mathematical model and a numerical procedure are developed. Numerical calculations are performed and the influence of the variation of key parameters on the optimal solution is investigated.

Numerical tests indicate that as the cost of the penalty related to the generation of an excess damage to environment increases, inspections should become more frequent and the threshold level should be lowered in order to favor preventive actions reducing the probability to pay the penalty.

Given the complexity of the cost function to be minimized, it is difficult to derive analytically the optimal solution. A numerical procedure is designed to obtain the optimal condition-based maintenance policy. Also, the developed model is based on the assumption that the degradation follows a process with stationary independent increments. This may not be appropriate for all types of degradation processes.

The proposed optimal maintenance policy may be relevant and very useful in the perspective of green operations. In fact, this paper offers to decision-makers a comprehensive approach to implement a green maintenance policy and to rapidly understand the net effect of the maintenance policy with respect to environmental regulation requirements.

The main contribution consists in the modeling and optimization of the condition-based maintenance policy over a finite time horizon. Indeed, existing condition-based maintenance models over an infinite time horizon are not applicable for systems with a finite operational time requirement.

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.

The purpose of this paper is to investigate the optimal production policy and maintenance strategy for leased equipment under a lease contract with warranty periods. In order to have steady revenue, the lessor (owner) of the equipment may provide guaranty periods to encourage the lessee to sign a lease contract with a longer lease period.

Under this production/maintenance scheme, the mathematical model of the expected total cost is developed and the optimal production planning and the corresponding optimal maintenance policy are derived by choosing the optimal warranty periods for the lessee in order to minimize the total cost.

The influence of the production rates variation in the equipment degradation is considered by an increased failure rate according to both time and production rates. The impact of warranty periods on optimal maintenance planning will be studied thereafter. Finally, numerical examples are given to illustrate the analytical study and the effects of the warranty periods variation during the lease periods on the maintenance policy and consequently on the total cost.

The paper proposes a new idea of production and maintenance coupling in the leasing aspect. This study shows that it has a novelty and originality relative to this type of problem which considers and proposes a new maintenance strategy for leasing contract. This originality characterized by the influence of two factors on the equipment maintenance strategy. First factor is the influence of the production variation production rates on the machine degradation degree that is new in the literature charactering by analytical equation that shows the evolution of the machine failure rate according to its use (which is in our case the production rate for each period) respecting in the same time the continuity of the equipment reliability for a period to another.

The purpose of this paper is to develop novel preventive maintenance (PM) modeling methods for a cold standby system subject to two types of failures: random failure and deterioration failure.

The system consists of two components and a single repair shop, assuming that the repair shop can only service for one component at a time. Based on semi-Markov theory, transition probabilities between all possible system states are discussed. With the transition probabilities, Markov renewal equations are established at regenerative points. By solving the Markov regenerative equations, the mean time from the initial state to system failure (MTSF) and the steady state availability (SSA) are formulated as two reliability measures for different reliability requirements of systems. The optimal PM policies are obtained when MTSF and SSA are maximized.

The result of simulation experiments verifies that the derived maintenance models are effective. Sensitivity analysis revealed the significant influencing factors for optimal PM policy for cold standby systems when different system reliability indexes (i.e. MTSF and SSA) are considered. Furthermore, the results show that the repair for random failure has a tremendous impact on prolonging the MTSF of cold standby system and PM plays a greater role in promoting the system availability of a cold standby system than it does in prolonging the MTSF of system.

In practical situations, system not only suffers normal deterioration caused by internal factors, but also undergoes random failures influenced by random shocks. Therefore, multiple failure types are needed to be considered in maintenance modeling. The result of the sensitivity analysis has an instructional role in making maintenance decisions by different system reliability indexes (i.e. MTSF and SSA).

This paper presents novel PM modeling methods for a cold standby system subject to two types of failures: random failure and deterioration failure. The sensitivity analysis identifies the significant influencing factors for optimal maintenance policy by different system reliability indexes which are useful for the managers for further decision making.