An Extended Sequential Imperfect Preventive Maintenance Model with Improvement Factors

This article presents a generalization of the imperfect sequential preventive maintenance (PM) policy with minimal repair. As failures occur, the system experiences one of two types of failures: a Type-I failure (minor), rectified by a minimal repair; or a Type-II failure (catastrophic) that calls for an unplanned maintenance. In each maintenance period, the system is maintained following the occurrence of a Type-II failure or at age, whichever takes place first. At the Nth maintenance, the system is replaced rather than maintained. The imperfect PM model adopted in this study incorporates with improvement factors in the hazard-rate function. Taking age-dependent minimal repair costs into consideration, the objective consists of finding the optimal PM and replacement schedule that minimize the expected cost per unit time over an infinite time-horizon.     

An optimal age-replacement policy for a simple repairable system with delayed repair

In this article, a simple repairable system (i.e., a repairable system consisting of one component and one repairman) with delayed repair is studied. Assume that the system after repair is not “as good as new”, and the degeneration of the system is stochastic. Under these assumptions, using the geometric process repair model, we consider a replacement policy T based on system age under which the system is replaced when the system age reaches T. Our problem is to determine an optimal replacement policy T*, such that the average cost rate (i.e., the long-run average cost per unit time) of the system is minimized. The explicit expression of the average cost rate is derived, the corresponding optimal replacement policy T* can be determined by minimizing the average cost rate of the system. Finally, a numerical example is given to illustrate some theoretical results and the model's applicability.     

Optimal Maintenance Time for Repairable Systems Under Two Types of Failures

Determination of preventive maintenance is an important issue for systems under degradation. A typical maintenance policy calls for complete preventive repair actions at pre-scheduled times and minimal repair actions whenever a failure occurs. Under minimal repair, failures are modeled according to a non homogeneous Poisson process. A perfect preventive maintenance restores the system to the as good as new condition. The motivation for this article was a maintenance data set related to power switch disconnectors. Two different types of failures could be observed for these systems according to their causes. The major difference between these types of failures is their costs. Assuming that the system will be in operation for an infinite time, we find the expected cost per unit of time for each preventive maintenance policy and hence obtain the optimal strategy as a function of the processes intensities. Assuming a parametrical form for the intensity function, large sample estimates for the optimal maintenance check points are obtained and discussed.     

Bayes sequential estimation in a life test and asymptotic properties

For a life test without replacement on M machines, assuming an exponential distribution for failure times, the Bayes sequential procedure for estimating the failure rate is studied. Estimation error is assumed to be measured by one of a family of loss functions, and the cost of sampling consists of a cost per machine failure c, >, 0 and a cost per unit time c > 0. Assuming a conjugate prior on 9, the Bayes sequential procedure and its asymptotic Bayesian and sampling theory properties are obtained as c1, z9 - 0 and M + jointly.     

OPTIMAL CONTROL OF A DETERIORATING PRODUCTION PROCESS

We study the optimal control of a production process subject to a deterministicdrift and to random shocks. The process mean is observable at discrete points of time after producing a batch and, at each such point, a decision is made whether to reset the process mean to some initial value or to continue with the production. The objective is to find the initial setting of the process mean and the resetting time that minimizes the expected average cost per unit time. It is shown that the optimal control policy is of a control limit type. An algorithm for finding the optimal control parameters is presented.     

Asymptotically pointwise optimal and asymptotically optimal stopping times in the Bayesian inference

The paper deals with the problem of determining asymptotically pointwise optimal and asymptotically optimal stopping times in the Bayesian inference. The sufficient conditions are given for a family of stopping times to be asymptotically pointwise optimal and asymptotically optimal with respect to a continuous time process. As an example a sequential estimation of the intensity of the Poisson process is considered. Under a gamma prior distribution, an asymptotically pointwise optimal and asymptotically optimal rule is given using a LINEX loss function and the cost c per unit time.     

Routing in a queueing system with two heterogeneous servers in speed and in quality of resolution

Heterogeneous servers, in manufacturing and service systems, may have different speeds and different quality levels for the provided service or good For a two-server queueing model, we formulate the job routing problem for minimizing the stationary weighted sum of the expected time spent in the system and the number of unsatisfied customers per time unit. Using a Markov decision process approach, we prove that the optimal routing policy of jobs to service is a threshold policy that depends on the queue length. When the number of waiting jobs in the queue is below a certain threshold, only one server should work and the other one remains idle. At or above this threshold, both servers should serve jobs. This is an extension of the known result where only the heterogeneity in speed is considered.     

A Bayesian sequential design with adaptive randomization for 2‐sided hypothesis test

Bayesian sequential and adaptive randomization designs are gaining popularity in clinical trials thanks to their potentials to reduce the number of required participants and save resources. We propose a Bayesian sequential design with adaptive randomization rates so as to more efficiently attribute newly recruited patients to different treatment arms. In this paper, we consider 2‐arm clinical trials. Patients are allocated to the 2 arms with a randomization rate to achieve minimum variance for the test statistic. Algorithms are presented to calculate the optimal randomization rate, critical values, and power for the proposed design. Sensitivity analysis is implemented to check the influence on design by changing the prior distributions. Simulation studies are applied to compare the proposed method and traditional methods in terms of power and actual sample sizes. Simulations show that, when total sample size is fixed, the proposed design can obtain greater power and/or cost smaller actual sample size than the traditional Bayesian sequential design. Finally, we apply the proposed method to a real data set and compare the results with the Bayesian sequential design without adaptive randomization in terms of sample sizes. The proposed method can further reduce required sample size.     

Optimization problems for consecutive-2-out-of-n:G system

Abstract

This paper considers the optimization problems for a consecutive-2-out-of-n:G system where n is considered to be fixed or random. When the number of components is constant, the optimal number of components and the optimal replacement time are discussed by minimizing the expected cost rates. Furthermore, we focus on the above discussions again when n is a random variable. We give an approximate value of MTTF and propose the preventive replacement policy, respectively.     

A model of preventive maintenance for parallel,series, and single-item replacement systems based on statistical analysis

Preventive maintenance (PM) scheduling of units is addressed as a crucial issue that effects on both economy and reliability of power systems. In this paper, we describe an application of statistical analysis for determining the best PM strategy in the case of parallel, series, and single-item replacement systems. A key aspect of industrial maintenance is the trade-off between cost and time of performing PM operations. The goals of this study is to determine the best time for performing PM operations in each system and also finding the number of spare parts and facilities in single-item replacement and parallel systems respectively so that the average cost per unit time is minimized. In this proposed maintenance strategy, PM operations are regularly performed on the production unit in equal time intervals. Finally, three examples are presented to demonstrate the effectiveness of the proposed models.     

An extended geometric process repair model with imperfect delayed repair under different objective functions

This article studies an extended geometric process repair model for a simple repairable system with imperfect delayed repair. Assume that the system after repair is not always successively degenerative, and the repair is not also always delayed. Under these assumptions, based on the failure number N of the system, an optimal replacement policy N* is determined respectively by minimizing the average cost rate (ACR), maximizing the average availability rate (AAR), and optimizing the trade-off model of the ACR and the AAR. Finally, a numerical example is given to illustrate some theoretical results and the model applicability.     

A Finite Horizon Dynamic Programming Model for Production and Repair Decisions

In this research, a novel optimal single machine replacement policy in finite stages based on the rate of producing defective items is proposed. The primary objective of this paper is to determine the optimal decision using a Markov decision process to maximize the total profit associated with a machine maintenance policy. It is assumed that a customer order is due at the end of a finite horizon and the machine deteriorates over time when operating. Repair takes time but brings the machine to a better state. Production and repair costs are considered in the model and revenue is earned for each good item produced by the end of the horizon, there is also a cost for the machine condition at the end of the horizon. In each period, we must decide whether to produce, repair, or do nothing, with the objective of maximizing expected profit during the horizon.     

A geometric process repair model for a cold standby repairable system with imperfect delay repair and priority in use

In this article, a two-dissimilar-component cold standby repairable system with one repairman is studied. Assume that the repair after failure for each component is delayed or undelayed. Component 2 after repair is “as good as new” while Component 1 after repair is not, but Component 1 has priority in use. Under these assumptions, using a geometric process, we consider a replacement policy N based on the failure number of Component 1. An optimal replacement policy N* is determined by minimizing the average cost rate C(N) of the system. Finally, a numerical example is given to illustrate some theoretical results and the model applicability.     

Optimal control of the surplus in an insurance policy

A classical continuous time surplus process is modified by adding two actions. If the level of the surplus goes below $\tau \ge 0$, we increase the level of the surplus up to initial level $u>\tau$ by injecting capital to the surplus. Meanwhile, the excess amount of the surplus over $V>u$ is invested continuously to other business. After assigning several costs related to managing the surplus, we obtain the long-run average cost per unit time and illustrate a numerical example to show how to find an optimal investment policy minimizing the cost.     

A new economic scheme for CCC charts with run rules based on average number of inspected items

New statistical techniques and procedures have been developed to control high-yield processes along with looking for process improvement opportunities and minimizing production cost. Cumulative count of conforming control chart is generally a technique for high-quality processes, when nonconforming items are rarely produced. The objective of this study is to design control chart based on cumulative count of conforming items and run rules that develops an economic model based on the average number of inspected items to design m-of-m CCC chart in order to facilitate minimum average cost per item produced. The optimal design parameters for different values of nonconforming fraction and different cost parameters in each scenario are determined. Finally, to analyze the behavior of optimal economic solutions, sensitivity analysis of the model parameters is performed.     

Adaptive nonparametric empirical Bayes estimation via wavelet series: The minimax study

In the present paper, we derive lower bounds for the risk of the nonparametric empirical Bayes estimators. In order to attain the optimal convergence rate, we propose generalization of the linear empirical Bayes estimation method which takes advantage of the flexibility of the wavelet techniques. We present an empirical Bayes estimator as a wavelet series expansion and estimate coefficients by minimizing the prior risk of the estimator. As a result, estimation of wavelet coefficients requires solution of a well-posed low-dimensional sparse system of linear equations. The dimension of the system depends on the size of wavelet support and smoothness of the Bayes estimator. An adaptive choice of the resolution level is carried out using Lepski et al. (1997) method. The method is computationally efficient and provides asymptotically optimal adaptive EB estimators. The theory is supplemented by numerous examples.     

The optimal allocation combination for the two-sided sequential screening procedure based on the individual misclassification error

In this paper, the expected total costs (ETCs) of three kinds of quality cost functions for the two-sided sequential screening procedure (SQSP) based on the individual misclassification error are obtained, where the ETC is the sum of the expected cost of inspection, the expected cost of rejection and the expected cost of quality. The general formulas for all the desired probabilities and three ETCs when k screening variables are allocated into r-stages are derived. The optimal allocation combination for each ETC is determined based on the criterion of minimum ETC. Finally, we give two examples to illustrate the selection of the optimal allocation combination for the SQSP.     

The Optimal Allocation Combination for the One-Sided Sequential Screening Procedure Based on the Individual Misclassification Error

In this article, the expected total costs of three kinds of quality cost functions for the one-sided sequential screening procedure based on the individual misclassification error are obtained, where the expected total cost is the sum of the expected cost of inspection, the expected cost of rejection, and the expected cost of quality. The computational formulas for three kinds of expected total costs are derived when k screening variables are allocated into r stages. The optimal allocation combination is determined based on the criterion of minimum expected total cost. At last, we give one example to illustrate the selection of the optimal allocation combination for the sequential screening procedure.     

A geometric process warranty model using a combination policy

This paper proposes a geometric process warranty model. Assume that a combination policy (W, T) is applied after selling a product, so that a free warranty is offered in [0, W), followed by a pro-rata warranty in [W, T). Assume further the successive operating times (repair times) of the product form a decreasing (increasing) geometric process. The average cost rate of the product to the manufacturer and a consumer can be derived respectively. For exponential distribution case, the explicit formulas of the average cost rate are obtained, and an finite algorithm for determination of an optimal combination policy is suggested.     

Inventory model for convertible item with deterioration

Abstract

In this article, we developed a model for a convertible item (or product) where initial form of the item converts into another product by consuming conversion cost and time both. After duration, it converts again into a new product of a different nature. It is a sequential-type conversion from initial into two other products over states. The demand pattern and deterioration rate differ at each converted state. An inventory model is developed for such a kind of sequential convertible item. Expressions for total cost and other related costs (as per states) are derived and optimal time to convert the product in different states are calculated under model assumptions. A numerical example is incorporated in support of the theoretical findings and it validates the strength of the model.