Design Of Acceptance Sampling Plans Research Articles

Design of acceptance sampling plans based on interval valued neutrosophic sets

Design of acceptance sampling plans based on interval valued neutrosophic sets

Optimal design of multivariate acceptance sampling plans by variables

Quality-control via acceptance sampling is a technique well established for monitoring the quality of production by lots. The design of acceptance sampling plans for univariate characteristics that follow the Gaussian distribution is reported in various references. Formulations for finding statistically and economically optimal acceptance sampling plans have been consistently proposed. Contrarily, the extension of acceptance sampling to multivariate characteristics has limited applications and the methods available to design plans are still elusive as they involve complex numerical computation procedures. We propose optimization-based formulations to design acceptance sampling plans by variables for multivariate characteristics. First, we consider the independence of characteristics and address plans that satisfy all the controlled quality levels of each one. A Mixed Integer Nonlinear Programming formulation is introduced for such a purpose. Then, we extend the analysis to dependent characteristics and use Surrogate-based optimization to handle the problem. The formulations are demonstrated with simulated scenarios and an industrial case of practical interest.

A new approach in the economic design of acceptance sampling plans based on process yield index and Taguchi loss function

Acceptance sampling plan is used for decision making in the field of products quality control. These plans provide the producer and the consumer with decision rules for accepting or rejecting the lot. In this paper, a new approach for the economic design of resubmitted and multiple dependent state (MDS) sampling plans is proposed based on the process yield index considering Taguchi loss function and the average number of sample size n, which minimizes the costs related to the yield index Spk. In the proposed model the loss of a batch is considered rather than a single product. Also, the loss of both producer and consumer based on the yield index is minimized simultaneously through solving an optimization model and the parameters of the proposed sampling plans are obtained. The performance of both proposed sampling plans is analyzed and compared in terms of average sample number and total expected loss. The results indicate that both proposed sampling plans show satisfactory performance. Moreover, the advantages of the proposed model over the existing resubmitted and MDS sampling plans are presented. Finally, the application of the proposed model and sampling plans is illustrated through a real case in batch manufacturing processes.

Designing an economical acceptance sampling plan in the presence of inspection errors based on maxima nomination sampling method

One of the most useful and effective methods with an extensive application in companies with purpose of examining the quality of the raw material in addition to final products, is acceptance sampling plans. The inspection process is assumed free of errors in most design of acceptance sampling plans. However, this assumption may not be true. In this research, an optimization model for acceptance sampling plan based on the Maxima Nomination Sampling (MNS) method which is developed for single acceptance sampling plan at the presence of inspection errors is presented. What we have accomplished in this article is to propose an economical model which involves two types of inspection errors and investigates the impact of these errors from an economical point of view then it's been compared with the a classical method for proving it 's efficiency. Furthermore, the sensitivity analysis is carried out to analyze the behavior of the MNS scheme optimal solution. The numerical studies indicate that MNS method is always more economical than classical one.

Economic design of acceptance sampling plans for truncated life test using Frechet distribution

ABSTRACTIn this article designing of acceptance sampling plans for the truncated life test is proposed using economic approach by assuming that the lifetime of a product follows Frechet distribution based on median. The optimal sampling plans to meet the consumer’s confidence level with minimum total cost are designed. Efficiency of the proposed plan is also analyzed.

A new economical scheme of acceptance sampling plan in a two-stage approach based on the maxima nomination sampling technique

An acceptance sampling plan plays a very important role in any quality assurance system. In this new economical design of acceptance sampling plan, three types of costs are included in the objective function by considering average outgoing quality limit (AOQL), average quality level (AQL) and lot tolerance percent defective (LTPD) constraints based on the maxima nomination sampling (MNS) method in a two-stage approach. The design of this sampling inspection plan involves the minimum average total inspection (ATI). The model is designed to minimize the summation of costs and the proposed MNS economical sampling plan is compared with the classical one. Practitioners can use the proposed model to decrease the total cost of inspection.

Economic Design of Acceptance Sampling Plans Based on Conforming Run Lengths Using Loss Functions

Abstract In this paper, a new sampling plan is introduced based on the run length of conforming items and Taguchi loss function. Run length of conforming items is used for decision making about the quality of a lot. The proposed sampling plan is based on the Markov modeling. A continuous Taguchi loss function is used to obtain loss of deviations between the value of quality characteristics and its target. An optimization model is developed for obtaining the optimal control tolerances and the corresponding critical acceptance and rejection thresholds based on the geometric distribution, which minimizes the loss function for both producers and consumers. For practical purpose, a numerical example is presented to illustrate how the proposed procedure can be applied to design acceptance sampling plans. In addition, the results of a comparison study denoted the merits of proposed model. At the end, a general model regarding the risk of consumer and producer is developed. A sensitivity analysis is carried out on the different values of the required parameters in order to validate the results of optimization model.

Acceptance Sampling Plans from Life Tests Based on Percentiles of Half Normal Distribution

The design of acceptance sampling plans is developed under truncated life testing based on the percentiles of half normal distribution. The minimum sample size necessary to ensure the specified life percentile is obtained under a given consumer’s risk. The operating characteristic values of the sampling plans as well as the producer’s risk are presented. The results are illustrated by examples.

Equal-Risk Acceptance Sampling Plan

The common acceptance sampling plan needs to specify two points in the operating characteristic curve, which represent the producers and customers risks, respectively. The points are determined in a subjective way and hence may result in unfairness. This paper presents a new approach, which determines the acceptance sampling plan based on the condition that the risks of producer and customer at acceptable quality level are the same. Another condition is introduced to control the mean risk of producer. The approach can considerably simplify the design of acceptance sampling plan, and is illustrated by an example.

Optimal Design of Acceptance Sampling Plans by Variables for Nonconforming Proportions When the Standard Deviation Is Unknown

This article presents an optimization-based approach for the design of acceptance sampling plans by variables for controlling nonconforming proportions when the standard deviation is unknown. The variables are described by rigorous noncentral Student’s t-distributions. Single and double acceptance sampling (AS) plans are addressed. The optimal design results from minimizing the average sampling number (ASN), subject to conditions holding at producer’s and consumer’s required quality levels. The problem is then solved employing a nonlinear programming solver. The results obtained are in close agreement with previous sampling plans found in the literature, outperforming them regarding the feasibility.

Economic Design of Acceptance Sampling Plans in a Two-Stage Supply Chain

Supply Chain Management, which is concerned with material and information flows between facilities and the final customers, has been considered the most popular operations strategy for improving organizational competitiveness nowadays. With the advanced development of computer technology, it is getting easier to derive an acceptance sampling plan satisfying both the producer's and consumer's quality and risk requirements. However, all the available QC tables and computer software determine the sampling plan on a noneconomic basis. In this paper, we design an economic model to determine the optimal sampling plan in a two-stage supply chain that minimizes the producer's and the consumer's total quality cost while satisfying both the producer's and consumer's quality and risk requirements. Numerical examples show that the optimal sampling plan is quite sensitive to the producer's product quality. The product's inspection, internal failure, and postsale failure costs also have an effect on the optimal sampling plan.

An optimization‐based framework for designing acceptance sampling plans by variables for non‐conforming proportions

Purpose – This paper seeks to present an optimization‐based approach to design acceptance sampling plans by variables for controlling non‐conforming proportions in lots of items. Simple and double sampling plans with s known and unknown are addressed. Normal approximation distributions proposed by Wallis are employed to handle plans with s unknown. The approach stands on the minimization of the average sampling number (ASN) taking into account the constraints arising from the two point conditions on the operating characteristic (OC) curve. The resulting optimization problems fall under the class of mixed integer non‐linear programming (MINLP), and are solved employing GAMS. The results obtained strongly agree with classical acceptance sampling plans found in the literature, although outperforming them in some cases, and providing a general approach to address other cases.Design/methodology/approach – The approach takes the form of formulation of the design of acceptance sampling plans by variables for non...

An optimization‐based approach for designing attribute acceptance sampling plans

PurposeThis purpose of this paper is to present an optimization‐based approach to support the design of attribute sampling plans for lot acceptance purposes, with the fraction of non‐conforming items being modeled by a Poisson probability distribution function.Design/methodology/approachThe paper approach stands upon the minimization of the error of the probability of acceptance equalities in the controlled points of the operating curve (OC) with respect to sample size and acceptance number. It was applied to simple and double sampling plans, including several combinations of quality levels required by the producer and the consumer. Formulation of the design of acceptance sampling plans as an optimization problem, having as a goal the minimization of the squared error at the controlled points of the OC curve, and its subsequent solution employing GAMS.FindingsThe results are in strong agreement with acceptance sampling plans available in the open literature. The papers approach in some scenarios outperforms classical sampling plans and allows one to identify the lack of feasible solutions.Originality/valueAn optimization‐based approach to support the design of acceptance sampling plans for attributes was conceived and tested. It allows for a general treatment of these problems, including the identification of a lack of feasible solutions, as well as making possible the determination of feasible alternatives by relaxing some model constraints.

Design of Acceptance Sampling Plans Under Varying Inspection Error

Abstract A mathematical programming model is developed for the design of sampling plans under varying inspection errors. The model minimizes average lot inspection cost subject to a given outgoing lot quality. An efficient search method is developed and is compared to complete enumeration based on six varying error models. The method always finds a global optimum and its efficiency increases on the maximum sample size. For a set of test problems, the method has examined at most 3.63% of all sampling plans to find a global optimum. This study verifies that the assumption of constant error or no error must be reconsidered. Also, the fact that the sampling plan must be adjusted depending on the inspection error models is justified. Handled by the Department of Quality and Reliability Engineering.

Automatic inspection by lots in the presence of classification errors

The paper is concerned with the application of pattern recognition to the problem of automatic inspection of products by lots. It is shown that in the presence of classification errors, the classical approach to the design of acceptance sampling plans cannot be used. An alternative quality control procedure is proposed for the model assuming an arbitrary distribution of patterns in the lot.