Application of renewal theory to continuous sampling plans

Renewal theory is used to study the effectiveness of a class of continuous sampling plans first introduced by Dodge. This approach provides a simple way of viewing and computing the long-run Average Outgoing Quality (AOQ) and its maximum AOQL. More importantly, it is used to study the average outgoing quality in a short production run through an approximation formula AOQ^*(t). Formulas for AOQ and AOQ^*(t) are provided. By simulation, it is found that AOQ^*(t) is sufficiently accurate in situations corresponding to actual practice.     

Systems approach to the multistage manufacturing connected-unit situation

This paper investigates the relationships among incoming quality limit (AIQL), manufacturing quality level in each stage of production, and outgoing quality limit (AOQL), for a multistage manufacturing connected-unit situation from the systems cost-effectiveness point of view. An objective function is developed and the formulated problem is solved using discrete optimization technique. The study is restricted to the development of single sampling plans where inspection is by attribute. Lot size and sample size are fixed for incoming and outgoing inspection for each stage of manufacturing.     

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     

Design of sequential sampling inspection plans with screening based on minimal lattice paths

In this article we propose new sequential sampling inspection plans with screening indexed by LTPD and AOQL, in which the alternative of accepting or rejecting a lot is decided by the results of sequential sampling plans, based on the minimal lattice paths. It is illustrated that the average total inspection can be economized by using both of the proposed sequential sampling inspection plans, with screening indexed by LTPD and AOQL, respectively. © 1994 John Wiley & Sons, Inc.     

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.     

Nonlinear one-parametric linear programming and t-norm transportation problems

Previous methods for solving the nonlinear one-parametric linear programming problem min {c(t)^Tx |Ax = b, x ≥ 0} for t ? [α,β] were based on the simplex method using a considerably extended tableau. The proposed method avoids such an extension. A finite sequence of feasible bases (B^k | k = 1, 2, …, r) optimal in [t^k, t^k+1] for k = 1, 2, …,^r with α = t¹ < t² < … < t^r+1 = β is determined using the zeroes of a set of nonlinear functions. Computational experience is discussed in the special case of t-norm transportation problems.     

Reliability of a 2‐dimensional k‐within‐consecutive‐r × s‐out‐of‐m × n:F system

A 2‐dimensional rectangular (cylindrical) k‐within‐consecutive‐r × s‐out‐of‐m × n:F system is the rectangular (cylindrical) m × n‐system if the system fails whenever k components in a r × s‐submatrix fail. This paper proposes a recursive algorithm for the reliability of the 2‐dimensional k‐within‐consecutive‐r × s‐out‐m × n:F system, in the rectangular case and the cylindrical case. This algorithm requires min ( O (mk^r(n?s)), O (nk^s(m?r))), and O (mk^rn) computing time in the rectangular case and the cylindrical case, respectively. The proposed algorithm will be demonstrated and some numerical examples will be shown. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48: 625–637, 2001.     

Qmp/USP: A modern alternative to MIL-STD-105D

The Quality Measurement Plan (QMP) and the Universal Sampling Plan (USP) are the data analysis and sampling plans for the AT&T Technologies quality audit. This article describes QMP/USP, an acceptance sampling plan based on QMP and USP principles. QMPIUSP is a complete acceptance sampling system. It combines the elements of classical rectification inspection plans with those of MIL-STD-IOSD. There is no switching between plans, no tables of numbers to look through, and no discontinue state. QMP/USP is a computerized, self-contained system that features:

• Acceptance decisions based on the QMP Bayes empirical Bayes analysis of current and past sampling result
• Sample size selection based on USP, i.e., lot size, AQL, a cost ratio, the QMP analysis, and a budget constraint
• Guaranteed AOQ
• A complete statistical analysis of the quality process.

In this article, we describe the operation of QMP/USP and compare its performance with that of MIL-STD-IOSD. The comparison is made under many different quality environments with many metrics. Our results show that QMP/USP and MIL/STD/IOSD perform similarly for the environments where quality could be described as “in control”; and that QMPlUSP is superior in the environments where quality is “out of control”.     

Evaluating methods for the reliability of a large 2‐dimensional rectangular k‐within‐consecutive‐(r,s)‐out‐of‐(m,n):F system

A 2‐dimensional rectangular k‐within‐consecutive‐(r, s)‐out‐of‐(m, n):F system consists of m × n components, and fails if and only if k or more components fail in an r × s submatrix. This system can be treated as a reliability model for TFT liquid crystal displays, wireless communication networks, etc. Although an effective method has been developed for evaluating the exact system reliability of small or medium‐sized systems, that method needs extremely high computing time and memory capacity when applied to larger systems. Therefore, developing upper and lower bounds and accurate approximations for system reliability is useful for large systems. In this paper, first, we propose new upper and lower bounds for the reliability of a 2‐dimensional rectangular k‐within‐consecutive‐(r, s)‐out‐of‐(m, n):F system. Secondly, we propose two limit theorems for that system. With these theorems we can obtain accurate approximations for system reliabilities when the system is large and component reliabilities are close to one. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005     

A note on a prediction interval for a first-order Gauss Markov process

Let X_t, t = 1,2, ?, be a stationary Gaussian Markov process with E(X_t) = μ and Cov(X_t, X_t+k) = σ²ρ^k. We derive a prediction interval for X_2n+1 based on the preceding 2n observations X₁,X₂, ?,X_2n.     

Variable sampling plans for normal distribution indexed by Taguchi's loss function

Taguchi has presented an approach to quality improvement in which reduction of deviation from the target value is the guiding principle. In this approach any measured value x of a product characteristic X brings a loss to consumer in general, where the loss is expressed as a quadratic form with respect to the difference between the measured value x and the target value T of a product characteristic. Then, it is natural to reject the lot which may bring a large loss to consumer. This concept induces us to construct new variable sampling plans based on the Taguchi's loss criterion. In this article, a design procedure of the sampling plans for assuring the loss in the Taguchi's method is proposed. Some numerical results based on the proposed design procedures are illustrated. © 1997 John Wiley & Sons, Inc. Naval Research Logistics 44: 591–603 (1997)     

A markov chain version of the secretary problem

This paper deals with the Secretary Problem where n secretaries are interviewed sequentially and the best k must be hired. The values of the secretaries are observed as they are interviewed, but beforehand only the distributions of these values are known. Furthermore, the distributions of two successive secretaries' values are governed by a Markov chain. Optimal hiring policies for finite n and limiting optimal policies as k and n approach infinity are obtained.     

One- and two-sided sampling plans based on the exponential distribution

In this paper we examine the one- and two-sided sampling plans for the exponential distribution. Solutions are provided for several situations arising out of the assumptions on the knowledge of the parameters of the distribution. The values of the constants are tabled in the special case of p₁ = p₂ for the two-sided plans.     

Measures of component importance for markov chain imbeddable reliability structures

In this article we study the reliability importance of the components for the wide class of Markov chain imbeddable systems (MIS). Methods for the evaluation of Birnbaum importance are developed for a general MIS, and some generating function techniques are demonstrated for the special case of homogeneous MISs. As an application, the reliability ordering for the components of a k‐out‐of‐n and consecutive‐k‐out‐of‐n structure is examined in some detail. © 1999 John Wiley & Sons, Inc. Naval Research Logistics 46: 613–639, 1999     

A recursive algorithm for a summed multinomial density function

An algorithm for calculating the probabilities of a summed multinomial density function which is recursive with n (the number of trials) is presented. Having application in inspector error models for auditing and quality control problems with Cartesian product structures, the algorithm is discussed in the context of computing optimal economic sampling plans. Computational experience with the algorithm is presented.     

Reliability of linear multistate multiple sliding window systems

This paper proposes a new model that generalizes the linear consecutive k‐out‐of‐r‐from‐n:F system to multistate case with multiple failure criteria. In this model (named linear multistate multiple sliding window system) the system consists of n linearly ordered multistate elements (MEs). Each ME can have different states: from complete failure up to perfect functioning. A performance rate is associated with each state. Several functions are defined for a set of integer numbers ρ in such a way that for each r ∈ ρ corresponding function f_r produces negative values if the combination of performance rates of r consecutive MEs corresponds to the unacceptable state of the system. The system fails if at least one of functions f_r for any r consecutive MEs for r ∈ ρ produces a negative value. An algorithm for system reliability evaluation is suggested which is based on an extended universal moment generating function. Examples of system reliability evaluation are presented. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.     

Economic design of sampling plans with multi-decision alternatives

For situations where there are several markets with different profit-cost structures, economic attributes sampling plans are developed for determining the market to ship the lot to. Two sampling plans are considered; the fixed-size sampling plan with several levels of acceptance numbers, and the inverse sampling plan with several levels of sample sizes. Linear profit models are constructed which involve four profit/cost components; profit from a conforming item, inspection cost, replacement cost, and cost from an accepted nonconforming item. For fixed-size and inverse sampling plans, methods of finding optimal sampling plans are presented and examples with beta-binomial prior distributions are given.     

An economic two-stage screening procedure with a prescribed outgoing quality in logistic and normal models

An economic two-stage screening procedure based on a dichotomous performance variable T and a continuous screening variable X is proposed. X is measured first to decide whether an item should be accepted, rejected, or additional observations should be taken. If no terminal decision is reached, T is then observed to classify the undecided items. Two models are considered; (i) the logistic model, where P(T = 1|X = x) is assumed to be a logistic function of x, and (ii) the normal model, where X given T is assumed to be normally distributed. A simple economic model based on inspection and misclassification costs is constructed. Optimal cutoff values on the screening variable are obtained by minimizing the expected cost subject to the constraint that the average outgoing quality attains a pre-specified level. Solutions are provided for both known-parameter and unknown-parameter cases. © 1995 John Wiley & Sons, Inc.     

Distribution of sample correlation coefficients

Let (Y, X_l,…, X_K) be a random vector distributed according to a multivariate normal distribution where X_l,…, X_K are considered as predictor variables and y is the predictand. Let r_i, and R_i denote the population and sample correlation coefficients, respectively, between Y and X_i. The population correlation coefficient r_i is a measure of the predictive power of X_i. The author has derived the joint distribution of R_l,…, R_K and its asymptotic property. The given result is useful in the problem of selecting the most important predictor variable corresponding to the largest absolute value of r_i.     

Estimating critical path and arc probabilities in stochastic activity networks

This article describes a new procedure for estimating parameters of a stochastic activity network of N arcs. The parameters include the probability that path m is the longest path, the probability that path m is the shortest path, the probability that arc i is on the longest path, and the probability that arc i is on the shortest path. The proposed procedure uses quasirandom points together with information on a cutset ? of the network to produce an upper bound of O[(log K)^N?|?|+1/K] on the absolute error of approximation, where K denotes the number of replications. This is a deterministic bound and is more favorable than the convergence rate of 1/K^1/2 that one obtains from the standard error for K independent replications using random sampling. It is also shown how series reduction can improve the convergence rate by reducing the exponent on log K. The technique is illustrated using a Monte Carlo sampling experiment for a network of 16 relevant arcs with a cutset of ? = 7 arcs. The illustration shows the superior performance of using quasirandom points with a cutset (plan A) and the even better performance of using quasirandom points with the cutset together with series reduction (plan B) with regard to mean square error. However, it also shows that computation time considerations favor plan A when K is small and plan B when K is large.