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To determine the optimal spare part order‐replacement policy for any high cost single unit complex system in a discrete‐time circumstance.

The expected total discounted cost over an infinite planning horizon is taken as a criterion of optimality as it allows us to put emphasis on the present behavior of the system.

The problem under consideration is a two‐dimensional discrete optimization problem (regular ordering time and inventory time limit for the spare are decision variables) which is difficult to handle, in general. However, it is explored that the problem can be reduced to a simple one‐dimensional one and the optimal ordering time is to be determined under the two extreme situations: no replacement of the spare until the original unit fails and replacement of the spare as soon as it is delivered.

For modeling simplicity, deterministic lead time is considered for both regular and expedited orders. A more appropriate assumption would be to consider randomized lead time for both the orders.

The research provides a useful order‐replacement strategy for a single‐unit system where the failure of the unit is better measured by the number of cycles completed before failure rather than the instant of failure.

The work done in this paper carries certain values as any continuous time model for the problem under consideration can be regarded as only an approximate model.

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.

In this paper, we consider a repair‐time limit replacement problem with imperfect repair and develop a graphical method to determine the optimal repair‐time limit which minimizes the expected total discounted cost over an infinite time horizon. The method proposed can be applied to an estimation problem of the optimal repair‐time limit from the empirical repair‐time data. Then, the modified scaled total time on test transform of the underlying repair‐time distribution function is used. Numerical examples are devoted to examine asymptotic properties of the nonparametric estimator for the optimal repair‐time limit.

The purpose of this paper is to focus on an optimizing maintenance policy with repair limit time for a new type of aircraft component, in which the lifetime is assumed to be an uncertain variable due to no historical operation data, and the repair time is a random variable that can be described by the experimental data.

To describe this repair limit time policy over an infinite time horizon, an extended uncertain random renewal reward theorem is firstly proposed based on chance theory, involves uncertain random interarrival times and stochastic rewards. Accordingly, the uncertain random programming model, which minimized the expected maintenance cost rate, is formulated to find the optimal repair limit time.

A numerical example with sensitivity analysis is provided to illustrate the utility of the proposed policy. It provides a useful reference and guidance for aircraft optimization. For maintainers, it plays an important guiding role in engineering practice.

The proposed uncertain random renewal reward process proved useful for the optimization of maintenance strategy with maintenance limited time for a new type of aircraft components, which provides scientific support for aircraft maintenance decision-making for civil aviation enterprises.

The present study proposes two types of periodical inspection policies for a scale which weighs products in the final stage of manufacturing some specific products, such as chemical products. The scale is inspected at time iT(i=1,2, … ). Under ModelI, an inspection operation involves detection of a scale malfunction or inaccuracy as well as an adjustment activity. Under ModelII, an inspection is conducted only to detect a scale malfunction. If a scale malfunction is detected, an adjustment activity follows. For each model, the fraction defective and the long‐run average cost are formulated. We then examine the existence of an inspection interval T_α which makes the fraction defective under a prespecified value α (0< α <1). An economical inspection interval T^* minimizing the long‐run average cost is also discussed.

Develops a generalized ordering policy for one‐unit systems in which a lead time depends on the failure time. When an ordered spare is delivered after the lead time it is put into the inventory if an original unit is still operating, and the original one is replaced/ exchanged by the stocked spare when the original one fails/passes a prespecified time, whichever occurs first. Applying the expected cost per unit time in the steady state as a criterion of optimality, the optimal ordering policy which minimizes it is obtained. Numerical examples show the dependence of the optimal ordering policies on the lead time change.

This paper derives three evaluation measures of file recovery with n archive copies: when a failure is detected uniformly during (0, T),the mean recovery overhead, the overhead rate and the availability are obtained. Optimal archive copies intervals T* which minimize or maximize those measures are analytically discussed. When faults occur at random, they are given by unique solutions of the same type equations. Finally, a numerical example is shown.

There exist some reliability models whose performances increase by partitioning them into parts. Such a typical model is the basic inspection policy in which an operating unit is checked at suitable times for a finite time span and its failure is detected. This paper applies the concept of the basic inspection policy to five models: back‐up for hard disk, checkpoint for double modular redundancy, job partition, garbage collection, and network partition. The performances of each model are analytically evaluated.

This paper addresses special reliability-centered maintenance (RCM) strategies for one-shot devices by providing fuzzy inferences system with the assumption that, to data, there is no data available on their maintenance. As far as one-shot devices are concerned, the relevant data is inadequate.

In this paper, a fuzzy expert system is proposed to effectively select RCM strategies for one-shot devices. In this research: (1) a human expert team is provided, (2) spatial RCM strategies for one-shot devices and parameters bearing upon those strategies are determined, (3) the verbal variables of the expert team are transformed into fuzzy sets, (4) the relationship between parameters and strategies are designed whereupon a model is developed by MATLAB software, (5) Finally, the model is applied to a real-life one-shot system.

The finding of this study indicates that the proposed fuzzy expert system can determine the parameters affecting the choice of the appropriate one-shot RCM strategies, and a fuzzy inference system can help for effective decision making.

The developed model can be used as a fast and reliable method for determining an appropriate one-shot RCM strategy, whose results can be relied upon with a suitable approximation in respect of the behavior test. To the best authors’ knowledge, this problem is not addressed yet.

Elevator manufacturers are responsible for securing the safety and maintaining the performance, cleanliness and cosmetic appearance of the elevators which they sell.The purpose of this paper is to develop a cost‐optimized after‐sales strategy for the maintenance of an elevator part. First, in order to find an optimum maintenance method, the replacement probability and the inspection probability of the part are theoretically derived using each of the three maintenance methods: Time Based Maintenance (TBM), Condition Based Maintenance (CBM), and Breakdown Maintenance (BM). Then, the approach to plan a cost‐optimized maintenance method is presented and the relationship between the failure characteristics of the part and the cost‐optimized maintenance method is discussed.The results of using the proposed approach are compared with those obtained by Online Quality Engineering (Taguchi methods). Finally, from numerical experiments, it is shown that this approach can provide a more cost‐effective and better maintenance method than the method provided by Online Quality Engineering.