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The subject of investigation reported in this paper is the determination of an optimal replacement time for equipment that deteriorates with time. The following hypothesis is proposed and investigated. While a piece of equipment is in the final stages of its life span, i.e. the wear‐out phase, the application of preventive replacement strategy at constant time intervals reduces total down‐time. The novelty of the approach used in this research lies in the conversion of the more complicated classical constant‐interval replacement model to a simplified but nonetheless effective model. Results are shown for a case where the equipment time‐to‐failure has a normal distribution. These results also hold for a Weibull distribution with known shape and scale parameters. The simplified methods proposed in this paper can assist maintenance managers to better make economic decisions about equipment maintenance.

The purpose of this paper is the simultaneous determination of optimal replacement threshold and inspection scheme for a system within condition-based maintenance (CBM) framework.

A proportional hazards model (PHM) is used for risk of failure and a Markovian process to model the system covariates. Total expected long-run cost (including replacement, inspection and downtime costs) is formulated in terms of replacement threshold and inspection scheme. Through an iterative procedure, for all different values of replacement thresholds, their associated optimal inspection scheme is determined using an effective search algorithm. By evaluating the corresponding costs, the optimal replacement threshold and its associated optimal inspection scheme are, then, identified.

The mathematical formulation, that takes into account all different costs, required for the simultaneous determination of optimal replacement threshold and optimal inspection scheme for an item subjected to CBM using PHM is provided. The proposed approach is compared against classical age policy and one state-of-the-art policy through a numerical example. The results show that the proposed approach outperforms other comparing policies.

In practical situations where CBM is implemented, inspections and downtime often incur cost. Under such circumstances, findings of this paper can be utilized for the determination of optimal replacement threshold and optimal inspection scheme so that the CBM cost is minimized.

In most of the reported researches, it is often assumed that inspections have no cost and/or that the time for replacements (either preventive or at failure) is negligible. In the contrary, in this paper the author takes all cost factors including inspection costs, replacement time(s) and their associated downtime costs into account in the simultaneous determination of optimal replacement threshold and optimal inspection scheme.

The purpose of this paper is twofold: an approach is proposed to determine the optimum replacement time for shovel teeth; and a risk-quantification approached is developed to derive a confidence interval for replacement time.

The risk-quantification approach is based on a combination of Monte Carlo simulation and Markov chain. Monte Carlo simulation whereby the wear of shovel teeth is probabilistically monitored over time is used.

Results show that a proper replacement strategy has potential to increase operation efficiency and the uncertainties associated with this strategy can be managed.

The failure time distribution of a tooth is assumed to remain “identically distributed and independent.” Planned tooth replacements are always done when the shovel is not in operation (e.g. between a shift change).

The proposed approach can be effectively used to determine a replacement strategy, along with the level of confidence level, for preventive maintenance planning.

The originality of the paper rests on developing a novel approach to monitor wear on mining shovels probabilistically. Uncertainty associated with production targets is quantified.

An optimization problem to obtain the optimal replacement interval is studied. The system is minimally repaired at failure and is replaced by a new one at age T. The model assumes that the time horizon associated with the number of replacements is random. The total expected cost is calculated, and the optimal replacement interval minimizing the cost is found. A numerical example is used to compare the costs associated with an infinite time line and a random time line, which this model used.

The study aims to design the limited number of random working cycle as a warranty term and propose two types of warranties, which can help manufacturers to ensure the product reliability during the warranty period. By extending the proposed warranty to the consumer's post-warranty maintenance model, besides the authors investigate two kinds of random maintenance policies to sustain the post-warranty reliability, i.e. random replacement first and random replacement last. By integrating depreciation expense depending on working time, the cost rate is constructed for each random maintenance policy and some special cases are provided by discussing parameters in cost rates. Finally, sensitivities on both the proposed warranty and random maintenance policies are analyzed in numerical experiments.

The working cycle of products can be monitored by advanced sensors and measuring technologies. By monitoring the working cycle, manufacturers can design warranty policies to ensure product reliability performance and consumers can model the post-warranty maintenance to sustain the post-warranty reliability. In this article, the authors design a limited number of random working cycles as a warranty term and propose two types of warranties, which can help manufacturers to ensure the product reliability performance during the warranty period. By extending a proposed warranty to the consumer's post-warranty maintenance model, the authors investigate two kinds of random replacement policies to sustain the post-warranty reliability, i.e. random replacement first and random replacement last. By integrating a depreciation expense depending on working time, the cost rate is constructed for each random replacement and some special cases are provided by discussing parameters in the cost rate. Finally, sensitivities to both the proposed warranties and random replacements are analyzed in numerical experiments.

It is shown that the manufacturer can control the warranty cost by limiting number of random working cycle. For the consumer, when the number of random working cycle is designed as a greater warranty limit, the cost rate can be reduced while the post-warranty period can't be lengthened.

The contribution of this article can be highlighted in two key aspects: (1) the authors investigate early warranties to ensure reliability performance of the product which executes successively projects at random working cycles; (2) by integrating random working cycles into the post-warranty period, the authors is the first to investigate random maintenance policy to sustain the post-warranty reliability from the consumer's perspective, which seldom appears in the existing literature.

Despite the existence of multiple asset replacement theories, the economic life replacement method remains a major practical technique for making rational machine replacement decisions. The purpose of this paper is to bridge this method with comprehensive data analytic tools and make it applicable it to modern business reality with abundant data on operating and replacement costs.

This study employs operations research, discrete and continuous optimization, applied mathematical modeling, data analytics, industrial economics and real options theory.

Constructed stochastic algorithms extend the deterministic economic life method and are compared to the contemporary theory of stochastic asset replacement based on real options and dynamic programming. It is proven that both techniques deliver similar results when the cost volatility is small. A major theoretic finding is that the cost uncertainty speeds up the replacement decision.

This research suggests that the proposed stochastic algorithms may become an important tool for managerial decisions about replacement of many similar machines with detailed data on operating and replacement costs.

Compared to the real options replacement theory, major advantages of the proposed algorithms are that they work equally well for any distribution of age-dependent stochastic operating cost. The algorithms are tested on a real industrial case about replacement of medical imaging devices. Numeric simulation supports obtained analytic outcomes.

Unlike point of purchase behavior, not much is known about how payment method impacts post-purchase behavior, especially for durable goods where user experience can last over long periods. The purpose of this paper is to link two strands of literature for the first time by uncovering systematic linkages between the payment method (upfront cash vs loan) used for purchase of durable goods and the replacement timings for the same.

The authors predict that cash purchases are more likely to have shorter replacement horizons compared to loan purchases and propose a psychological mechanism that accounts for the same. Their arguments are based on how the strength of coupling, which is the degree of psychological association between payment and consumption, depends on the payment method and differentially influences the consumption experience and consequently leads to different replacement horizons. They conduct a field study to test their predictions and find support for their model.

The authors find that individuals who financed their durable goods purchases using loan, expressed their intentions to replace the goods after longer period than those who financed their durable goods with cash down payment. As loan installments remind people of painful thoughts of payment, they tend to reduce the dissonance by positively evaluating both retrospective and anticipated usage experiences. This dissonance reduction mechanism eventually leads to reduced willingness to let go of the durable.

Marketers are faced with a tradeoff between increasing purchase likelihood versus ensuring long-term post-purchase satisfaction. In this paper, the authors uncover the psychological mechanisms that can explain how payment method chosen to pay for a durable can have direct effect on post-purchase consumption experiences and subsequently in the replacement intentions. This finding is crucial for marketers who are interested in planning the product line launches and other post-purchase engagement strategies such as buy-back scheme and upgrades.

Understanding the psychological mechanisms that explain individual’s likelihood to replace their durable goods allows policymakers to design appropriate interventions to induce more sustainable and efficient use of durable goods in the market. While on one hand, marketers might be interested in increasing sales of their product line by inducing faster replacement of older product versions, environmentalists nudge towards the opposite. This paper provides a possible way to achieve the dual objectives.

While past research on downstream effects of payment methods on behavioral outcomes focused only on consumables, the authors focus on durable goods. Further, they identify the effect of payment method on both psychological and behavioral outcomes.

The main purpose of present study is to model the replacement policy under uncertainty for managerial application based on grey-reliability approach by considering the subjective views of quality control circle (QCC). The study objectively links the optimality between individual replacement and group replacement policies for determining the minimum operational costs. The integrated framework between QCC, replacement theory, grey set theory and supply chain management is presented to plan replacement actions under uncertainty.

The study proposes the concept of grey-reliability index and built a decision support model, which can deal with the imprecise information for determining the minimum operational costs to plan subsequent maintenance efforts.

The findings of the study establish the synergy between individual replacement and group replacement policies. The computations related to the numbers of failures, operational costs, reliability index and failure probabilities are presented under developed framework. An integrated framework to facilitate the managers in deciding the replacement policy based on operational time towards concerning replacement of assets that do not deteriorate, but fails suddenly over time is presented. The conceptual model is explained with a numerical procedure to illustrate the significance of the proposed approach.

A conceptual model under the framework of such items, whose failures cannot be corrected by repair actions, but can only be set by replacement is presented. The study provides an important knowledge based decision support framework for crafting a replacement model using grey set theory. The study captured subjective information to build decision model in the ambit of replacement.

A common model in the age‐based replacement policy is based on the cost attribute and assumes that the model parameters are known. In practice, the model parameters are estimated from limited historical data, which brings uncertainty into the model. Moreover, minimizing the cost is not the only goal of the maintenance activity. From the decision maker's point of view, the multi‐attributes and the uncertainty of the age‐based replacement policy are two important aspects to take into consideration in the decision‐making process. The purpose of this paper is to propose an approach for a robust‐optimum multi‐attribute age‐based replacement policy.

The proposed approach is based on a combination of the multi‐attribute age‐based replacement policy and robust design problem philosophy. A case study is provided for illustrating the application of the proposed method.

It is found that the proposed approach can determine the interval time for preventive replacement that provides a robust and optimum solution for a multi‐attribute age‐based replacement policy.

The proposed approach can be used by the decision maker in determining a robust‐optimum interval time for preventive replacement of multi‐attribute age‐based replacement, a time interval which is not only optimum, but also robust.

This paper presents an approach that simultaneously considers the multi‐attributes and the uncertainty in the age‐based replacement policy which is, to date, not available.

This paper aims to study the maintenance and replacement problem for a deteriorating repairable system with multiple vacations of one repairman. It proposes a new replacement policy and establishes corresponding replacement models.

It is assumed that the repair after the system failures is not “as good as new” and the repairman is in multiple vacations. The reaching of the effective age of the system is assumed to be mutually stochastic at working state, waiting state for repair and being repaired state. Under these assumptions, a replacement policy based on the effective age of the system is applied. The long-run expected downtime per unit time and the long-run expected profit per unit time as objective functions are chosen, respectively. By using geometric process theory and renewal process theory, the mathematic models have been established and the explicit expressions of the long-run expected downtime per unit time and the long-run expected profit per unit time are derived, respectively.

The optimal replacement policy can be calculated and determined by the computer to minimize the expected downtime or maximize the expected profit. The minimum expected downtime per unit time and maximum expected profit per unit time can also be determined.

This replacement policy and mathematic models can be used as reference to the failure system maintenance and replacement.