Optimal maintenance of a production-inventory system with idle periods

In this paper we consider a production-inventory system in which an input generating installation supplies a buffer with a raw material and a production unit pulls the raw material from the buffer with constant rate. The installation deteriorates in time and the problem of its optimal preventive maintenance is considered. It is assumed that the installation after the completion of its maintenance remains idle until the buffer is evacuated. Under a suitable cost structure it is shown that the average-cost optimal policy for fixed buffer content is of control-limit type, i.e. it prescribes a preventive maintenance of the installation if and only if its degree of deterioration is greater than or equal to a critical level. Using the usual regenerative argument, the average cost of a control-limit policy is computed exactly and then, the optimal control-limit policy is determined. Furthermore, the stationary probabilities of the system under the optimal policy are computed.     

Monotone optimal replacement policies for a Markovian deteriorating system in a controllable environment

We consider the optimal replacement of a periodically inspected system under Markov deterioration that operates in a controlled environment. Provided are sufficient conditions that characterize an optimal control-limit replacement policy with respect to the system’s condition and its environment. The structure of the optimal policy is illustrated by a numerical example.     

An improved algorithm for the computation of the optimal repair/replacement policy under general repairs

We consider a system which deteriorates with age and may experience a failure at any time instant. On failure, the system may be replaced or repaired. The repair can partially reset the failure intensity of the unit. Under a suitable cost structure it has been proved in the literature that the average-cost optimal policy is of control-limit type, i.e. it conducts a replacement if and only if, on the nth failure, the real age of the system is greater than or equal to a critical value. We develop an efficient special-purpose policy iteration algorithm that generates a sequence of improving control-limit policies. The value determination step of the algorithm is based on the embedding technique. There is strong numerical evidence that the algorithm converges to the optimal policy.     

Controlling the GI/M/1 queue by conditional acceptance of customers

In this paper we consider the problem of controlling the arrival of customers into a GI/M/1 service station. It is known that when the decisions controlling the system are made only at arrival epochs, the optimal acceptance strategy is of a control-limit type, i.e., an arrival is accepted if and only if fewer than n customers are present in the system. The question is whether exercising conditional acceptance can further increase the expected long run average profit of a firm which operates the system. To reveal the relevance of conditional acceptance we consider an extension of the control-limit rule in which the nth customer is conditionally admitted to the queue. This customer may later be rejected if neither service completion nor arrival has occurred within a given time period since the last arrival epoch. We model the system as a semi-Markov decision process, and develop conditions under which such a policy is preferable to the simple control-limit rule.     

Optimal preventive maintenance of a production system with an intermediate buffer

In this paper we consider a model consisting of a deteriorating installation that transfers a raw material to a production unit and a buffer which has been built between the installation and the production unit. The deterioration process of the installation is considered to be nonstationary, i.e. the transition probabilities may depend not only on the working conditions of the installation but on its age as well. The problem of the optimal preventive maintenance of the installation is considered. Under a suitable cost structure it is shown that, for fixed age of the installation and fixed buffer level, the optimal policy is of control-limit type. When the deterioration process is stationary, an efficient Markov decision algorithm operating on the class of control-limit policies is developed. There is strong numerical evidence that the algorithm converges to the optimal policy. Two generalizations of this model are also discussed.     

Hamilton-Jacobi-Bellman inequality for the average control of piecewise deterministic Markov processes

ABSTRACT

The main goal of this paper is to study the infinite-horizon long run average continuous-time optimal control problem of piecewise deterministic Markov processes (PDMPs) with the control acting continuously on the jump intensity λ and on the transition measure Q of the process. We provide conditions for the existence of a solution to an integro-differential optimality inequality, the so called Hamilton-Jacobi-Bellman (HJB) equation, and for the existence of a deterministic stationary optimal policy. These results are obtained by using the so-called vanishing discount approach, under some continuity and compactness assumptions on the parameters of the problem, as well as some non-explosive conditions for the process.     

Optimal Breast Biopsy Decision-Making Based on Mammographic Features and Demographic Factors

Breast cancer is the most common non-skin cancer affecting women in the United States, where every year more than 20 million mammograms are performed. Breast biopsy is commonly performed on the suspicious findings on mammograms to confirm the presence of cancer. Currently, 700,000 biopsies are performed annually in the U.S.; 55%-85% of these biopsies ultimately are found to be benign breast lesions, resulting in unnecessary treatments, patient anxiety, and expenditures. This paper addresses the decision problem faced by radiologists: When should a woman be sent for biopsy based on her mammographic features and demographic factors? This problem is formulated as a finite-horizon discrete-time Markov decision process. The optimal policy of our model shows that the decision to biopsy should take the age of patient into account; particularly, an older patient's risk threshold for biopsy should be higher than that of a younger patient. When applied to the clinical data, our model outperforms radiologists in the biopsy decision-making problem. This study also derives structural properties of the model, including sufficiency conditions that ensure the existence of a control-limit type policy and nondecreasing control-limits with age.     

An optimal replacement policy for a multistate degenerative simple system

In this paper, a degenerative simple system (i.e. a degenerative one-component system with one repairman) with k + 1 states, including k failure states and one working state, 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, we consider a new replacement policy T based on the system age. 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 can be determined, the explicit expression of the minimum of the average cost rate can be found and under some mild conditions the existence and uniqueness of the optimal policy T^∗ can be proved, too. Further, we can show that the repair model for the multistate system in this paper forms a general monotone process repair model which includes the geometric process repair model as a special case. We can also show that the repair model in the paper is equivalent to a geometric process repair model for a two-state degenerative simple system in the sense that they have the same average cost rate and the same optimal policy. Finally, a numerical example is given to illustrate the theoretical results of this model.     

A deteriorating cold standby repairable system with priority in use

In this paper, a cold standby repairable system consisting of two dissimilar components and one repairman is studied. In this system, it is assumed that the working time distributions and the repair time distributions of the two components are both exponential and component 1 is given priority in use. After repair, component 2 is “as good as new” while component 1 follows a geometric process repair. Under these assumptions, using the geometric process and a supplementary variable technique, some important reliability indices such as the system availability, reliability, mean time to first failure (MTTFF), rate of occurrence of failure (ROCOF) and the idle probability of the repairman are derived. A numerical example for the system reliability R(t) is given. And it is considered that a repair-replacement policy based on the working age T of component 1 under which the system is replaced when the working age of component 1 reaches T. Our problem is to determine an optimal policy T^∗ such that the long-run average cost per unit time of the system is minimized. The explicit expression for the long-run average cost per unit time of the system is evaluated, and the corresponding optimal replacement policy T^∗ can be found analytically or numerically. Another numerical example for replacement model is also given.     

On an optimal harvesting problem

Kaplan [1972] treated a harvesting problem as a discrete time stochastic control model with independent disturbances and with decreasing mean value increase depending either on the value or on the age of the asset. We consider the more general model where the mean value increase depends on the value and also on the age. As main result we obtain the existence of optimal control-limit policies with respect to three different natural orders in state space. A basic role play additional convexity and boundedness assumptions. Our findings extend and correct the main result of Kaplan. The paper contains further detailed information about the solution.     

Managing an integrated production inventory system with information on the production and demand status and multiple non-unitary demand classes

In this paper, we study a system consisting of a manufacturer or supplier serving several retailers or clients. The manufacturer produces a standard product in a make-to-stock fashion in anticipation of orders emanating from n retailers with different contractual agreements hence ranked/prioritized according to their importance. Orders from the retailers are non-unitary and have sizes that follow a discrete distribution. The total production time is assumed to follow a k₀-Erlang distribution. Order inter-arrival time for class l demand is assumed to follow a k_l-Erlang distribution. Work-in-process as well as the finished product incur a, per unit per unit of time, carrying cost. Unsatisfied units from an order from a particular demand class are assumed lost and incur a class specific lost sale cost. The objective is to determine the optimal production and inventory allocation policies so as to minimize the expected total (discounted or average) cost. We formulate the problem as a Markov decision process and show that the optimal production policy is of the base-stock type with base-stock levels non-decreasing in the demand stages. We also show that the optimal inventory allocation policy is a rationing policy with rationing levels non-decreasing in the demand stages. We also study several important special cases and provide, through numerical experiments, managerial insights including the effect of the different sources of variability on the operating cost and the benefits of such contracts as Vendor Managed Inventory or Collaborative Planning, Forecasting, and Replenishment. Also, we show that a heuristic that ignores the dependence of the base-stock and rationing levels on the demands stages can perform very poorly compared to the optimal policy.     

A condition-based maintenance model

Condition-based maintenance (CBM) is very appealing becauseit enables one to make maintenance decisions based on the currentinformation about the system. Various monitoring techniqueshave been developed to obtain this information, but there arestill very few mathematical models capable of utilizing it foreffective maintenance decision making. In this paper, we propose a CBM model for situations where onlypartial information is available through monitoring a signalprocess that does not necessarily exhibit monotone behaviour.The evolution of the signal process is determined by randomfactors and minor maintenance actions between inspections. Theobjective is to find the replacement policy that maximizes thetotal expected profit during the lifetime of the system. Wewill show that, under weak monotonicity assumptions, the optimalpolicy is of a control-limit type, and we will develop an algorithmfor finding the limit for an -optimal policy.     

Markov Decision Processes with Variance Minimization: A New Condition and Approach

Abstract

This article deals with the limiting average variance criterion for discrete-time Markov decision processes in Borel spaces. The costs may have neither upper nor lower bounds. We propose another set of conditions under which we prove the existence of a variance minimal policy in the class of average expected cost optimal stationary policies. Our conditions are weaker than those in the previous literature. Moreover, some sufficient conditions for the existence of a variance minimal policy are imposed on the primitive data of the model. In particular, the stochastic monotonicity condition in this paper has been first used to study the limiting average variance criterion. Also, the optimality inequality approach provided here is different from the “optimality equation approach” widely used in the previous literature. Finally, we use a controlled queueing system to illustrate our results.     

单部件可修系统的一个几何过程维修模型

本文研究了单部件、一个修理工组成的可修系统的最优更换问题,假定系统不能修复如新,以系统年龄T为策略,利用几何过程求出了最优的策略T^*,使得系统经长期运行单位时间内期望效益达到最大,并求出了系统经长期运行单位时间内期望效益的显式表达式。在一定条件下证明了T^*的唯一存在性。最后还证明了策略T^*比文献[6]中的策略T^*优。     

基于基准可靠度和预防维修次数上限的两参数预防维修策略

本文研究了一类修旧非新的两参数预防维修策略.在预防维修依赖于基准可靠度R的条件下,利用系统的相关可靠性指标建立了平均费用关于R和N(预防维修次数上限)的函数关系.进一步找到了该函数的最小值点,即得到了最优策略(R,N)*.同时通过实例说明了本文的维修策略优于文献[8].     

一个可修系统的最优更换模型

本文考虑了单部件、一个修理工组成的可修系统，在故障系统不能“修复如新”的前提下，我们利用几何过程，以系统年龄Ｔ为策略，选择最优的Ｔ使得系统经长期运行单位时间的期望效益达到最大．本文还在一定的条件下证明了最优更换策略Ｔ的唯一存在，且求出了系统经长期运行单位时间的最大期望效益的明显表达式．     

退化可修系统最优更换数学模型研究

研究了单部件组成的退化可修系统,在假定故障部件“修复非新”的条件下,以系统中部件的故障次数N为更换策略进行了研究,我们推导出系统经长期运行后,单位时间内期望效益的明显表达式,而且在一定条件下证明了最优策略N*是所有更换策略中最优的.最后还通过几何过程对此进行了讨论.