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This paper aims to study the mixed after-sales service which simultaneously offers return and replacement services. The authors develop a model to propose what kind of after-sales service the firm should choose and how to make the after-sales service policy to improve the profit. The study aims to extend the literature on the mixed after-sales service and give some support to the managers to make decisions.

In this paper, the authors use the optimization modeling method to describe the situations of a firm offering two exclusive after-sales service policies and a mixed after-sales service policy, respectively. They compare the results in different cases and analyze the impact of different parameters on the boundary values and other results. Finally, the authors include three numerical examples to illustrate the major results.

The authors find that the mixed after-sales service can successfully segment the market, meet various customers’ distinct needs and differentiate the service prices to improve the total profit. Moreover, the authors find the boundary values which indicate the optimal interval for each service. Then, for a certain situation, they can clearly tell which after-sales service dominates and provides the optimal selling price, order quantity and total profit. Besides, the authors show the impact of different parameters on the boundary values and other results.

This paper combines after-sales service into traditional models and provides a new mixed service to segment the market and improve total revenue. It provides some managerial implications for the decision-makers.

A new policy for joint optimization of age replacement and spare provisioning has been proposed by incorporating a continuous review (s, S) type inventory policy, where s is the reorder level and S is the maximum stock level. Gives cost formulations for a single operating unit situation and outlines simulation procedure to determine optimal values of the decision variables by minimizing total cost of replacement and inventory. Studies the behaviour of this policy for a large number of case problems and highlights the effects of different cost elements, item failure characteristics and lead time characteristics. Also determines, for all case problems, optimal (s, S) policies to support Barlow‐Proschan age policy. Simulation results clearly indicate that the jointly optimal policy is more cost‐effective than Barlow‐Proschan policy.

The purpose of this paper is to investigate age replacement policies for two-component parallel system with stochastic dependence. The stochastic dependence considered, is modeled by a one-sided domino effect. The failure of component 1 at instant t may induce the failure of component 2 at instant t+τ with probability p _1→2. The time delay τ is a random variable with known probability density function h _{p 1→2} (.). The system is considered in a failed state when both components are failed. The proposed replacement policies suggest to replace the system upon failure or at age T whichever occurs first.

In the first policy, costs and durations associated with maintenance activities are supposed to be constant. In the second replacement policy, the preventive replacement cost depends on the system’s state and age. The expected cost per unit of time over an infinite span is derived and numerical examples are presented.

In this paper and especially in the second policy, the authors find that the authors can get a more economical policy if the authors consider that the preventive replacement cost is not constant but depends on T.

In this paper, the authors take into account of the stochastic dependence between system components. This dependence affects the global reliability of the system and replacement’s periodicity. It can be used to measure the performance of the system et introduced into design phase of the system.

We formulate a multi‐attribute decision model for preventive replacement of a “magnetic sealing head” in a soft‐drink producing factory in the Kingdom of Saudi Arabia. In case of failure of this part, the opportunity cost (for production losses) is very important, as the entire production line will be idle. We determine in a first case the replacement policy that minimizes the total expected unit cost of replacement (preventive and corrective). Next, we determine the optimal policy that maximizes the expected multi‐attribute utility of the decision‐maker in the factory. Four attributes are considered in the replacement problem, namely cost, quality, labor productivity, and cash flow availability. The optimal policy in each case outperforms by far the one applied in the plant, which turns out to be costly and inefficient with respect to the utility of the decision‐maker.

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.

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.

The purpose of this paper is to study a geometric process (GP) maintenance model and policy for a repairable system.

Lam first introduced the GP and its application to maintenance model. Assume that a replacement policy N is applied by which the system will be replaced by a new, identical one following the Nth failure.

For a deteriorating system, an optimal replacement policy is determined analytically, and the monotonicity properties of the optimal replacement policy are then studied.

For an improving system, the paper shows that the optimal replacement policy is the ∞ policy, i.e., the policy without replacement.

This paper develops a framework for selecting the most efficient and effective preventive maintenance policy using multiple-criteria decision making and multi-objective optimization.

The critical component is identified with a list of maintenance policies, and then its failure data are collected and the optimization objective functions are defined. Fuzzy AHP is used to prioritize each objective based on the experts' questionnaire. Weighted comprehensive criterion method is used to solve the multi-objective models for each policy. Finally, the effectiveness and efficiency are calculated to select the best maintenance policy.

For a fleet of buses in hot climate environment where coolant pump is identified as the most critical component, it was found that block-GAN policy is the most efficient and effective one with a 10.24% of cost saving and 0.34 expected number of failures per cycle compared to age policy and block-BAO policy.

Only three maintenance policies are compared and studied. Other maintenance policies can also be considered in future.

The proposed methodology is implemented in UAE for selecting a maintenance scheme for a critical component in a fleet of buses. It can be validated later in other Gulf countries.

This research lays a solid foundation for selecting the most efficient and effective preventive maintenance policy for different applications and sectors using MCDM and multi-objective optimization to improve reliability and avoid economic loss.

This paper aims to investigate the optimization of the replacement with minimal repair policy for a system which experiences a time horizon of random length. Under such policy system replacement occurs at multiples of some period while minimal repair is performed at system failure between two successive replacements.

The objective function is the expected total cost composed of minimal repairs and replacements costs. A simple and compact expression is derived for the expected total costs and conditions under which an optimal replacement period exits are given. For sake of illustration, a numerical example is provided.

The paper finds that by the recent great technological development, the life cycle of present products is seen to be reduced more and more. This has motivated the development of maintenance optimization models for systems which experience an exact finite time horizon.

To ensure the benefits from the improved technologies, the information concerning the technological change must be taken into account. Such information is based on technological forecasting and difficult to obtain and merely rely on uncertainties.