Planning gamma accelerated degradation tests with two accelerating variables

Gamma accelerated degradation tests (ADT) are widely used to assess timely lifetime information of highly reliable products with degradation paths that follow a gamma process. In the existing literature, there is interest in addressing the problem of deciding how to conduct an efficient, ADT that includes determinations of higher stress‐testing levels and their corresponding sample‐size allocations. The existing results mainly focused on the case of a single accelerating variable. However, this may not be practical when the quality characteristics of the product have slow degradation rates. To overcome this difficulty, we propose an analytical approach to address this decision‐making problem using the case of two accelerating variables. Specifically, based on the criterion of minimizing the asymptotic variance of the estimated q quantile of lifetime distribution of the product, we analytically show that the optimal stress levels and sample‐size allocations can be simultaneously obtained via a general equivalence theorem. In addition, we use a practical example to illustrate the proposed procedure.     

Maintaining systems with heterogeneous spare parts

We consider the problem of optimally maintaining a stochastically degrading, single‐unit system using heterogeneous spares of varying quality. The system's failures are unannounced; therefore, it is inspected periodically to determine its status (functioning or failed). The system continues in operation until it is either preventively or correctively maintained. The available maintenance options include perfect repair, which restores the system to an as‐good‐as‐new condition, and replacement with a randomly selected unit from the supply of heterogeneous spares. The objective is to minimize the total expected discounted maintenance costs over an infinite time horizon. We formulate the problem using a mixed observability Markov decision process (MOMDP) model in which the system's age is observable but its quality must be inferred. We show, under suitable conditions, the monotonicity of the optimal value function in the belief about the system quality and establish conditions under which finite preventive maintenance thresholds exist. A detailed computational study reveals that the optimal policy encourages exploration when the system's quality is uncertain; the policy is more exploitive when the quality is highly certain. The study also demonstrates that substantial cost savings are achieved by utilizing our MOMDP‐based method as compared to more naïve methods of accounting for heterogeneous spares.     

用伴随方程研究烟幕施放的优化问题

从描述烟幕扩散的基本方程出发,提出了一类烟幕施放的优化问题．文中指出,这类问题由于约束条件为复杂的偏微分方程,计算量很大,难以直接求解．为了解决这一问题,引入了一种伴随方法以降低计算量．推导了三维非平坦地形下大气平流一扩散方程的伴随方程,利用伴随方程和原方程的关系,对优化问题进行了等价变形,大大降低了计算量．最后通过一个算例,对这一方法的有效性进行了演示。     

Optimal control of a production process subject to AOQL constraint

A production process which exhibits a decreasing pattern in the mean quality during the course of production is considered. A lower limit is specified for the quality characteristic of interest, and an item is classified as defective if its quality characteristic is below this lower limit. The major concern of the manufacturer is the average outgoing quality (AOQ). Hence, the process has to be adjusted after a time to avoid producing a large proportion of defective items due to deteriorating quality. To meet consumer specifications, an average outgoing quality limit (AOQL) is specified. A process-control scheme is developed in which decisions as to the upper and lower limit of the process mean are made based upon the choice of an AOQL. We discuss the decision problem of selecting the starting level of the process mean, and the level at which the process mean should be adjusted back to the starting level, so that the AOQ is not larger than AOQL. We consider a cost model which includes a fixed cost for adjustment, and a production cost which is assumed to be a function of the quality level. Due to the complexity of the model, a search procedure is used to find the optimal solution. In addition, an approximate solution which requires only simple calculations is developed, and is shown to be very effective in finding near-optimal solutions.     

Supplier certification and quality investment in supply chains

Global sourcing has made quality management a more challenging task, and supplier certification has emerged as a solution to overcome suppliers' informational advantage about their product quality. This article analyzes the impact of certification standards on the supplier's investment in quality, when a buyer outsources the production process. Based on our results, deterministic certification may lead to under‐investment in quality improvement technology for efficient suppliers, thereby leading to potential supply chain inefficiency. The introduction of noisy certification may alleviate this under‐investment problem, when the cost of information asymmetry is high. While allowing noisy certification always empowers the buyer to offer a menu to screen among heterogeneous suppliers, the buyer may optimally choose only a limited number of certification standards. Our analysis provides a clear‐cut prediction of the types of certifiers the buyer should use for heterogeneous suppliers, and we identify the conditions under which the supplier benefits from noisy certification. © 2013 Wiley Periodicals, Inc. Naval Research Logistics, 2013     

Preventive maintenance at opportunities of restricted duration

This article deals with the problem of setting priorities for the execution of maintenance packages at randomly occurring opportunities. These opportunities are of restricted duration, implying that only a limited number of packages can be executed. The main idea proposed is to set up a model for determining the optimal execution time for the individual maintenance packages and to develop cost criteria for deviations from the optimal time. In this article we use the block replacement model, but the approach can be easily extended to include other optimization models as well. Using Monte Carlo simulation the performance of the method is compared with various heuristics, both for a two-package and a multipackage case. © 1994 John Wiley & Sons, Inc.     

Note: Dynamic lot sizing for a finite rate input process

The basic single-product dynamic lot-sizing problem involves determining the optimal batch production schedule to meet a deterministic, discrete-in-time, varying demand pattern subject to linear setup and stockholding costs. The most widely known procedure for deriving the optimal solution is the Wagner-Whitin algorithm, although many other approaches have subsequently been developed for tackling the same problem. The objective of this note is to show how these procedures can readily be adapted when the input is a finite rate production process. © 1997 John Wiley & Sons, Inc. Naval Research Logistics 44: 221–228, 1997     

Production to order and off‐line inspection when the production process is partially observable

This study combines inspection and lot‐sizing decisions. The issue is whether to INSPECT another unit or PRODUCE a new lot. A unit produced is either conforming or defective. Demand need to be satisfied in full, by conforming units only. The production process may switch from a “good” state to a “bad” state, at constant rate. The proportion of conforming units in the good state is higher than in the bad state. The true state is unobservable and can only be inferred from the quality of units inspected. We thus update, after each inspection, the probability that the unit, next candidate for inspection, was produced while the production process was in the good state. That “good‐state‐probability” is the basis for our decision to INSPECT or PRODUCE. We prove that the optimal policy has a simple form: INSPECT only if the good‐state‐probability exceeds a control limit. We provide a methodology to calculate the optimal lot size and the expected costs associated with INSPECT and PRODUCE. Surprisingly, we find that the control limit, as a function of the demand (and other problem parameters) is not necessarily monotone. Also, counter to intuition, it is possible that the optimal action is PRODUCE, after revealing a conforming unit. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2007     

Optimal sample size allocation for tests with multiple levels of stress with extreme value regression

In this article, we discuss the optimal allocation problem in a multiple stress levels life‐testing experiment when an extreme value regression model is used for statistical analysis. We derive the maximum likelihood estimators, the Fisher information, and the asymptotic variance–covariance matrix of the maximum likelihood estimators. Three optimality criteria are defined and the optimal allocation of units for two‐ and k‐stress level situations are determined. We demonstrate the efficiency of the optimal allocation of units in a multiple stress levels life‐testing experiment by using real experimental situations discussed earlier by McCool and Nelson and Meeker. Monte Carlo simulations are used to show that the optimality results hold for small sample sizes as well. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2007     

The effect of process adjustment error on X̄ chart design

The existing literature on economic design of X̄ process control charts generally assumes perfect process adjustment, such that the process mean is returned to an exactly centered “in control” state following any real or false alarm control chart signal. This paper presents a model which demonstrates the effects of imperfect process adjustment on the economically designed control chart parameters. The model demonstrates that the optimal control limit width depends fundamentally on the precision with which the process can be adjusted. The greater the process adjustment error, all else constant, the wider will be the optimal control limits, in order to alleviate the potential for process overcontrol and tampering effects. By endogenously modeling these effects, the new model helps to rectify the problem of poor statistical properties for which the economic design approach has been criticized. © 1999 John Wiley & Sons, Inc. Naval Research Logistics 46: 597–612, 1999     

Sequential and systematic sampling plans for the Markov‐dependent production process

Acceptance sampling plans are used to assess the quality of an ongoing production process, in addition to the lot acceptance. In this paper, we consider sampling inspection plans for monitoring the Markov‐dependent production process. We construct sequential plans that satisfy the usual probability requirements at acceptable quality level and rejectable quality level and, in addition, possess the minimum average sample number under semicurtailed inspection. As these plans result in large sample sizes, especially when the serial correlation is high, we suggest new plans called “systematic sampling plans.” The minimum average sample number systematic plans that satisfy the probability requirements are constructed. Our algorithm uses some simple recurrence relations to compute the required acceptance probabilities. The optimal systematic plans require much smaller sample sizes and acceptance numbers, compared to the sequential plans. However, they need larger production runs to make a decision. Tables for choosing appropriate sequential and systematic plans are provided. The problem of selecting the best systematic sampling plan is also addressed. The operating characteristic curves of some of the sequential and the systematic plans are compared, and are observed to be almost identical. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48: 451–467, 2001     

Capacity allocation with machine duplication in semiconductor manufacturing

The manufacturing process for a computer chip is complex in that it involves a large number of distinct operations requiring a substantial lead‐time for completion. Our observations of such a manufacturing process at a large plant in the United States led us to identify several tactical and operational problems that were being addressed by the production planners on a recurring basis. This paper focuses on one such problem. At a tactical level, given a demand forecast of wafers to be manufactured, one specific problem deals with specifying which machine or machine groups will process different batches of wafers. We address this problem by recognizing the capacity limitations of the individual machines as well as the requirement for reducing operating and investment costs related to the machines. A mathematical model, which is a variation of the well‐known capacitated facility location problem, is proposed to solve this problem. Given the intractability of the model, we first develop problem specific lower bounding procedures based on Lagrangean relaxation. We also propose a heuristic method to obtain “good” solutions with reasonable computational effort. Computational tests, using hypothetical and industry‐based data, indicate that our heuristic approach provides optimal/near optimal solutions fairly quickly. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005     

Monitoring declining quality of ammunition stockpile under step stress

Most ammunition is produced long before its ultimate consumption and stored in a series of different depots for a considerably long period of time. During storage, the quality of the ammunition stockpile deteriorates proportionally to the condition of the depots. We view different conditions associated with a series of depots as step stress. A random effects logistic regression model is employed to predict the quality of ammunition stockpile in terms of the routing information such as a series of locations and duration of storage of ammunition lots. The resultant prediction model can be used to determine the appropriate time for reorder or renovation of ammunition before the quality becomes substandard. An example is given to illustrate the implementation procedure of the prediction model suggested in this article. © 1994 John Wiley & Sons, Inc.     

The single-machine absolute-deviation early-tardy problem with random completion times

We consider sequencing n jobs on a single machine subject to job completion times arising from either machine breakdowns or other causes. The objective is to minimize an expected weighted combination of due dates, completion times, earliness, and tardiness penalties. The determination of optimal distinct due dates or optimal common due dates for a given schedule is investigated. The scheduling problem for a fixed common due date is considered when random completion times arise from machine breakdowns. The optimality of a V-shaped about (a point) T sequence is established when the number of machine breakdowns follows either a Poisson or a geometric distribution and the duration of a breakdown has an exponential distribution. © 1996 John Wiley & Sons, Inc.     

Bicriteria scheduling on a single batching machine with job transportation and deterioration considerations

We study a single batching machine scheduling problem with transportation and deterioration considerations arising from steel production. A set of jobs are transported, one at a time, by a vehicle from a holding area to the single batching machine. The machine can process several jobs simultaneously as a batch. The processing time of a job will increase if the duration from the time leaving the holding area to the start of its processing exceeds a given threshold. The time needed to process a batch is the longest of the job processing times in the batch. The problem is to determine the job sequence for transportation and the job batching for processing so as to minimize the makespan and the number of batches. We study four variations (P1, P2, P3, P4) of the problem with different treatments of the two criteria. We prove that all the four variations are strongly NP‐hard and further develop polynomial time algorithms for their special cases. For each of the first three variations, we propose a heuristic algorithm and analyze its worst‐case performance. For P4, which is to find the Pareto frontier, we provide a heuristic algorithm and an exact algorithm based on branch and bound. Computational experiments show that all the heuristic algorithms perform well on randomly generated problem instances, and the exact algorithm for P4 can obtain Pareto optimal schedules for small‐scale instances. © 2014 Wiley Periodicals, Inc. Naval Research Logistics 61: 269–285, 2014     

Optimal lot sizing and inspection policy for an EMQ model with imperfect inspections

An EMQ model with a production process subject to random deterioration is considered. The process can be monitored through inspections, and both the lot size and the inspection schedule are subject to control. The “in-control” periods are assumed to be generally distributed and the inspections are imperfect, i.e., the true state of the process is not necessarily revealed through an inspection. The objective is the joint determination of the lot size and the inspection schedule, minimizing the long-run expected average cost per unit time. Both discrete and continuous cases are examined. A dynamic programming formulation is considered in the case where the inspections can be performed only at discrete times, which is typical for the parts industry. In the continuous case, an optimum inspection schedule is obtained for a given production time and given number of inspections by solving a nonlinear programming problem. A two-dimensional search procedure can be used to find the optimal policy. In the exponential case, the structure of the optimal inspection policy is established using Lagrange's method, and it is shown that the optimal inspection times can be found by solving a nonlinear equation. Numerical studies indicate that the optimal policy performs much better than the optimal policy with periodic inspections considered previously in the literature. The case of perfect inspections is discussed, and an extension of the results obtained previously in the literature is presented. © 1998 John Wiley & Sons, Inc. Naval Research Logistics 45: 165–186, 1998     

The discrete max-min problem

The historic max-min problem is examined as a discrete process rather than in its more usual continuous mode. Since the practical application of the max-min model usually involves discrete objects such as ballistic missiles, the discrete formulation of the problem seems quite appropriate. This paper uses an illegal modification to the dynamic programming process to obtain an upper bound to the max-min value. Then a second but legal application of dynamic programming to the minimization part of the problem for a fixed maximizing vector will give a lower bound to the max-min value. Concepts of optimal stopping rules may be applied to indicate when sufficiently near optimal solutions have been obtained.     

Bounding the optimal burn‐in time for a system with two types of failure

Burn‐in is a widely used method to improve the quality of products or systems after they have been produced. In this paper, we consider the problem of determining bounds to the optimal burn‐in time and optimal replacement policy maximizing the steady state availability of a repairable system. It is assumed that two types of system failures may occur: One is Type I failure (minor failure), which can be removed by a minimal repair, and the other is Type II failure (catastrophic failure), which can be removed only by a complete repair. Assuming that the underlying lifetime distribution of the system has a bathtub‐shaped failure rate function, upper and lower bounds for the optimal burn‐in time are provided. Furthermore, some other applications of optimal burn‐in are also considered. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004     

压电界面电极的应力强度因子

建立了压电界面电极的力学模型,利用有限傅里叶变换方法将相应的力电耦合问题描述为奇异积分方程,并得到了界面应力及其强度因子的解析解。讨论了结构几何参数对应力强度因子的影响。基于强度设计的观点得到两方面的结论：一是界面中心电极优于偏置电极;二是界面长度的最优值为中心电极长度的2倍。     

Continuous quality production and machine maintenance

This paper formulates a problem of continuous quality production and maintenance of a machine. Quality is assumed to be a known function of the machine's (Markov diffusion process) degradation states. Applications to a specific quality function are used to obtain analytical solutions to an open-loop and feedback stochastic control maintenance problem.