Economic Multiattribute Acceptance Sampling

Schmidt, J. W.; Bennett, G. Kemble

doi:10.1080/05695557208974849

Cited by 32 publications

(9 citation statements)

References 6 publications

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“…Thus, the conditional distribution of the number of defective items Xij in a lot given Oij can be described by a binomial mass function with parameters of N and 0~i; i.e., P(XiH Oij) = Bi(N, O~j) (Hald, 1960;Case et al, 1975;Tang et al, 1986). Furthermore, similar to the treatment of other researchers (Schmidt and Bennett, 1972;Case et al, 1975;Tang et al, 1986), we assume probabilistic independence of occurrences and distributions respectively among stages and attributes.…”

Section: The Decision Problemmentioning

confidence: 96%

“…Since Beta distribution has many rich properties in terms of descriptive generality and mathematical tractability and the sensitivity analysis of Schmidt and Bennett (1972) indicates that knowledge of the prior distribution need not be very precise, it is reasonable to assume that Oij of any lot is distributed randomly according to a Beta distribution with parameters aij and/3ij attainable from estimation using data generated by experiments or previous productions; i.e., f(Oij) = Be (aij,~ij). Schmidt and Bennett (1972), use Poisson distribution to describe conditional probability P(X/O). Case et al (1975), ; Tang et al (1986) use binomial distribution for P(X/O).…”

Section: The Decision Problemmentioning

confidence: 99%

“…More general and realistic situations with respect to the manufacturer's preference and risk attitude also could be taken into consideration. In recent years, research on multi-attribute and multi-stage inspections incorporating different classes of defect attributes (Schmidt and Bennett, 1972;Case et al, 1975;Tang et al, 1986), explicitly non-commensurate criteria (Moskowitz et al, 1982;Malakooti and Balhorn, 1987), manufacturer's preference and risk behaviour and consecutive outcomes (Lindsay and Bishop, 1964;Pruzan, 1967;Hurst, 1973;Hsu, 1984) in determining optimal acceptance sampling plans has received more attention.…”

Section: Introductionmentioning

confidence: 99%

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Neurally-inspired stochastic algorithm for determining multi-stage multi-attribute sampling inspection plans

Wang

Chankong

1991

J Intell Manuf

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In manufacturing industries, sampling inspection is a common practice for quality assurance and cost reduction. The basic decisions in sampling inspection are how many manufactured items to be sampled from each lot and how many identified defective items in the sample to accept or reject each lot. Because of the combinatorial nature of alternative solutions on the sample sizes and acceptance criteria, the problem of determining an optimal sampling plan is NP-complete. In this paper, a neurally-inspired approach to generating acceptance sampling inspection plans is proposed. A Bayesian cost model of multi-stage-multi-attribute sampling inspections for quality assurance in serial production systems is formulated. This model can accommodate various dispositions of rejected lots such as scraping and screening. The model also can reflect the relationships between stages and among attributes. To determine the sampling plans based on the formulated model, a neurally-inspired stochastic algorithm is developed. This algorithm simulates the state transition of a primal-dual stochastic neural network to generate the sampling plans. The simulated primal network is responsible for generation of new states whereas the dual network is for recording the generated solutions. Starting with an arbitrary feasible solution, this algorithm is able to converge to a near optimal or an optimal sampling plan with a sequence of monotonically improved solutions. The operating characteristics and performance of the algorithm are demonstrated via numerical examples.

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Section: The Decision Problemmentioning

confidence: 96%

Section: The Decision Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Neurally-inspired stochastic algorithm for determining multi-stage multi-attribute sampling inspection plans

Wang

Chankong

1991

J Intell Manuf

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“…Choosing between these two policies depends on the tradeoff between scrapping costs and outgoing quality; see also Ding and Gong (2008) and Quinino et al (2010) for variations of this problem. Sequencing issues and imperfect testing have both been considered in the inspection of multi-characteristic components; we refer to Raouf et al (1983), Schmidt and Bennett (1972) and Tang and Tang (1994) for examples. The results in the foregoing references, however, do not apply for the case where retesting is not allowed or economically infeasible.…”

Section: Literature Reviewmentioning

confidence: 99%

Sequential Testing of <i>k</i>-out-of-<i>n</i> Systems with Imperfect Tests

et al. 2015

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Abstract. A k-out-of-n system configuration requires that, for the overall system to be functional, at least k out of the total of n components be working. We consider the problem of sequentially testing the components of a k-out-of-n system in order to learn the state of the system, when the tests are costly and when the individual component tests are imperfect, which means that a test can identify a component as working when in reality it is down, and vice versa. Each component is tested at most once. Since tests are imperfect, even when all components are tested the state of the system is not necessarily known with certainty, and so reaching a lower bound on the probability of correctness of the system state is used as a stopping criterion for the inspection.We define different classes of inspection policies and we examine global optimality of each of the classes. We find that a globally optimal policy for diagnosing k-out-of-n systems with imperfect tests can be found in polynomial time when the predictive error probabilities are the same for all the components. Of the three policy classes studied, the dominant policies always contain a global optimum, while elementary policies are compact in representation. The newly introduced class of so-called 'interrupted block-walking' policies combines these merits of global optimality and of compactness.

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“…Yet another force complicating this issue is the situation where the items requiring inspection have many different attributes or characteristics that require inspection [8][9][10][11][12]. An electronic assembly is a good example of this -many different features of an electronic assembly may require inspection.…”

Section: Introductionmentioning

confidence: 99%

A Genetic Algorithm for Multiple Inspections with Multiple Objectives

McMullen¹

2013

AJOR

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This research presents a genetic algorithm to address the problem where multiple inspections are done to test conformity of multiple product characteristics. The genetic algorithm is employed to find an inspection plan where the multiple inspections are carried out, motivated to optimize two objectives: minimization of the total cost associated with the inspection; and maximization of probability of accepting conforming units. The genetic algorithm includes a constraint to induce variety into the characteristics being tested, so that the inspections are not dominated by "specialized" product characteristics. The resulting solutions are compared to optimal solutions, and it is determined that formidable solutions are found via the Genetic Algorithm approach.

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Economic Multiattribute Acceptance Sampling

Cited by 32 publications

References 6 publications

Neurally-inspired stochastic algorithm for determining multi-stage multi-attribute sampling inspection plans

Neurally-inspired stochastic algorithm for determining multi-stage multi-attribute sampling inspection plans

Sequential Testing of <i>k</i>-out-of-<i>n</i> Systems with Imperfect Tests

A Genetic Algorithm for Multiple Inspections with Multiple Objectives

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