Single Acceptance Sampling Plans Research Articles

Designing of Single Acceptance Sampling Plan based on Time Truncated Life Test under New Weibull-Pareto Distribution

Designing of Single Acceptance Sampling Plan based on Time Truncated Life Test under New Weibull-Pareto Distribution

New Lifetime Distribution with Applications to Single Acceptance Sampling Plan and Scenarios of Increasing Hazard Rates

This article is an extension of the Chris-Jerry distribution (C-JD) in that a two-parameter Chris-Jerry distribution (TPCJD) is suggested and its characteristics are studied. Based on the determined domain of attraction and other major statistical properties, the proposed TPCJD seems to fit into the Gumbel domain. Additionally, it has been confirmed that the stress strength is reliable. The tail study suggests that the TPCJD’s substantial tail makes it suited for a range of applications. The study took into account the single acceptance sampling approach using both simulation and real-life situations. The parameters of the TPCJD were estimated by some classical and Bayesian approaches. The mean squared errors (MSE), linear-exponential, and generalized entropy loss functions were deployed to obtain the Bayesian estimators aided by the Markov chain Monte Carlo (MCMC) simulation. An analysis of lifetime data on two events justified the use of the proposed distribution after comparing the results with some standard lifetime models.

Simulation Study of the Bayesian and Non-Bayesian Estimation of a new Lifetime Distribution Parameters with Increasing Hazard Rate

In this paper, a new distribution known as the Shifted Chris-Jerry (SHCJ) distribution is proposed. The proposition is motivated by the need to compare the efficiency of various classical estimation methods as well as the bayesian estimation using gamma prior at linear-exponential loss, squared error loss and generalized entropy loss functions. Some useful mathematical properties are derived. Single acceptance sampling plans (SASPs) are created for the distribution when the life test is truncated at a predetermined period. The median lifetime of the SHCJ distribution with pre-defined constants is taken as the truncation time. To guarantee that the specific life test is obtained at the defined risk to the user, the minimum sample size is required. For a particular consumer’s risk, the SHCJ distribution’s parameters, and the truncation time including numerical results are obtained. A simulation study is carried out for the bayesian and non-bayesian estimation of the parameters. Data on blood cancer patients is used to demonstrate the usefuleness of the proposed distribution.

Single Acceptance Sampling Plan Based on Truncatedlife Tests for the Exponentiated Moment Exponential Distribution With Application in Bladder Cancer Data

Background and Purpose: In statistical quality control, inspecting complete products is very hard due to its cost and time. This research uses a time-truncated single acceptance sampling plan to investigate bladder cancer data for the exponentiated moment exponential distribution. Materials and Methods: A single acceptance sampling plan is used to investigate bladder cancer data when a patient’s remission times following an exponentiated moment exponential distribution. Minimum sample sizes, operating characteristic function, and the associated producer’s risks are obtained and calculated. Results: Minimum sample sizes necessary to ensure a certain mean lifetime for selected acceptance numbers and consumer confidence levels are obtained. The operating characteristic function and the associated producer’s risks are provided. We also analyzed the minimum ratios of the mean life to the specified life. Conclusion: The results show optimal sample sizes decrease when the time to pre-determined mean lifetime ratio increases. Besides, the operating characteristic values of the proposed single sampling plan increase when the ratios of the mean life to the specified life increase.

Application of statistical quality control methods in a textile manufacturing company

Statistical quality control is an effective method employed to increase the finished product quality of enterprises, and this method is used to increase the quality standards of the textile company presented in this study. The company's knitted fabric quality control data of the previous years are analyzed, and appropriate statistical quality control methods are determined to improve the quality control system currently used. U control chart is considered to assess the quality limits of the fabrics received from the suppliers. A single acceptance sampling plan is structured to standardize the acceptance sampling method applied by the company to the knitted fabric lots. MIL-STD 105 D military standard is considered while creating a single acceptance sampling plan. The operating characteristic (OC Curve) for the sampling plan is designed and the acceptance probabilities of the lots with different defective rates are examined. As stated in the computational results, suppliers need quality improvement efforts and followed by that it would be possible to reach high quality levels in the enterprise.

Single Acceptance Sampling Plan Based on Truncated Life Tests for Zubair-Exponential Distribution

In this paper, a single acceptance sampling plan based on a truncated life test is proposed for a lifetime that follows the Zubair-Exponential (ZE) distribution. For some acceptance numbers, confidence levels, values of the ratio of the fixed experiment time to the particular mean lifetime, and the minimum sample sizes required to assert the specified mean life are obtained. The operating characteristic function values of the proposed sampling plans, consumer's and the producer's risk are presented. Other useful tables are presented and the results are discussed.

A Novel G Family for Single Acceptance Sampling Plan with Application in Quality and Risk Decisions

A Novel G Family for Single Acceptance Sampling Plan with Application in Quality and Risk Decisions

Single acceptance sampling plans based on truncated lifetime tests for two-parameter Xgamma distribution with real data application.

Recently, the two-parameter Xgamma distribution (TPXGD) is suggested as a new lifetime distribution for modeling some real data. The TPXGD is investigated in different areas and generalized to other forms by many of the researchers. The acceptance sampling plans are one of the main important statistical tools in production and engineering fields. In this paper, modified acceptance sampling plans for the TPXGD are proposed with the assumption that the lifetime is truncated at a predetermined level. The mean of the TPXGD model is utilized as a quality parameter. The variables of the acceptance sampling plans including the acceptance numbers, the minimum sample sizes, operating characteristic function and the producer's risk are investigated for various values of the model parameters. Numerical examples are offered to illustrate the process of the proposed plans. Also, a real data is fitted to the TPXGD and an application based on the suggested acceptance sampling plans is considered for explanation.

Cost model of variable multiple dependent state sampling plan with rectifying inspection

It is always foreseen to upturn the efficiency of an acceptance sampling plan for lot sentencing. In this research, variable multiple dependent state sampling plan considering the quality cost with rectifying inspection is proposed for normally distributed quality characteristics. The staple objective is to curb the total cost of the lot under inspection and improve the quality of outgoing lots. Tables are constructed for optimal plan parameters by increasing forgoing lots. The behavior of cost parameters on total cost has been investigated. Protection and cost curves are portrayed to show the efficacy of the proposed plan with rectifying single acceptance sampling plan using the cost model. Using the cost model, the proposed plan turns out to be more effectual than the single acceptance sampling plan in terms of sample size, average outgoing quality, and total cost. has emerged having a substantial effect on total cost trailed by and with the tiniest effect. Finally, an example is given to demonstrate the proposed plan.

Contribution to time truncating reliability single sampling plan for weighted Rayleigh distribution

The Rayleigh distribution, a well-known one, is naturally applicable in life testing studies. This article is indeed an attempt made to study the Weighted Rayleigh Distribution as a model for a life time random variable. It discusses the concept of truncated single acceptance sampling plan at a pre-assigned time. With the given probability levels, different acceptance numbers and the values of the ratio of the specified test time to the specified mean life are obtained, further it is found that the minimum sample sizes with assured the specified mean life time are discussed. This paper also elaborates the operating characteristic values of the sampling plans and producer’s risk. Tables are given and illustrated with example.

Economic Design of Acceptance Sampling Plans for Truncated Life Tests Using Three-Parameter Lindley Distribution

A single acceptance sampling plan for the three-parameter Lindley distribution under a truncated life test is developed. For various consumer’s confidence levels, acceptance numbers, and values of the ratio of the experimental time to the specified average lifetime, the minimum sample size important to assert a certain average lifetime are calculated. The operating characteristic (OC) function values as well as the associated producer’s risks are also provided. A numerical example is presented to illustrate the suggested acceptance sampling plans.

Acceptance sampling plans from truncated life test based on Tsallis q-exponential distribution

ABSTRACTIn this study, we propose a new single acceptance sampling plan from truncated life test assuming that the quality characteristics follow the Tsallis q-exponential distribution. The proposed plan is given, and then we derived the operation characteristics function and calculate the optimal sample size and producer’'s risk for some given parameter values to measure the performance of this plan. Also, a comparative study with other sampling plan is discussed to show the benefit of the proposed plan, and real data analysis is given to illustrate the applicability of the proposed plan in the industry.

An economic design of rectifying single sampling plans via maxima nomination sampling in the presence of inspection errors

Economic design for acceptance sampling plans (ASP) has attracted the attention of many researchers. In this article, rectifying single ASP is checked by using the maxima nomination sampling method and the plan’s parameters are obtained while sum of consumer and producer loss is minimized. In the presence of inspection errors, the lots are rectified in two ways: the detected defective items are discarded or replaced with non-defective items. To illustrate the advantages of the economic design of MNS, it is compared with SRS plan by using a real world example.

Design of Fuzzy Sampling Plan Using the Birnbaum-Saunders Distribution

Acceptance sampling is one of the essential areas of quality control. In a conventional environment, probability theory is used to study acceptance sampling plans. In some situations, it is not possible to apply conventional techniques due to vagueness in the values emerging from the complexities of processor measurement methods. There are two types of acceptance sampling plans: attribute and variable. One of the important elements in attribute acceptance sampling is the proportion of defective items. In some situations, this proportion is not a precise value, but vague. In this case, it is suitable to apply flexible techniques to study the fuzzy proportion. Fuzzy set theory is used to investigate such concepts. It is observed there is no research available to apply Birnbaum-Saunders distribution in fuzzy acceptance sampling. In this article, it is assumed that the proportion of defective items is fuzzy and follows the Birnbaum-Saunders distribution. A single acceptance sampling plan, based on binomial distribution, is used to design the fuzzy operating characteristic (FOC) curve. Results are illustrated with examples. One real-life example is also presented in the article. The results show the behavior of curves with different combinations of parameters of Birnbaum-Saunders distribution. The novelty of this study is to use the probability distribution function of Birnbaum-Saunders distribution as a proportion of defective items and find the acceptance probability in a fuzzy environment. This is an application of Birnbaum-Saunders distribution in fuzzy acceptance sampling.

DEVELOPING SINGLE-ACCEPTANCE SAMPLING PLANS BASED ON A TRUNCATED LIFETIME TEST FOR AN ISHITA DISTRIBUTION

Abstract Acceptance sampling plans are statistical procedures that are used for quality control and improvement in cases where it is not possible to test every item in a lot of materials. The outcome of this test determines whether the entire lot is accepted or rejected based on a random sample. In this procedure, an important characteristic of the materials is their lifetime sampling distribution, and this can vary from sample to sample. In this article, a new lifetime distribution, known as an Ishita distribution, is considered for developing a new single-acceptance sampling plan. The new acceptance sampling plan is developed under a condition wherein the mean lifetime test is truncated within a pre-specified time period. Based on this condition, the parameters of the acceptance sampling plan including the operating characteristics function and the minimum sample size are obtained. The producer’s risk in relation to the entire lot of materials is derived and illustrated by numerical examples.

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.

A variables multiple dependent state sampling plan based on a one-sided capability index

ABSTRACTAcceptance sampling plan has been considered as one of most practical tools for quality assurance applications. While various types of acceptance sampling plans have been developed for different purposes, single acceptance sampling plan is the most popular because it is simple to administrate. However, a new concept called multiple dependent state sampling has gained the attention of scholars in recent years. The underlying principle is that the acceptance of a submitted lot should not only depend on the quality of the current lot but also consider the quality of the preceding lots. This research develops a variables multiple dependent state sampling plan (VMDSSP) for unilateral specification limit based on a one-sided capability index. The operating characteristic (OC) curve is prepared based on the exact sampling distribution. The plan parameters are determined by minimizing the average sample number while satisfying the quality levels demanded by both the producer and the consumer. The performance of the proposed plan is compared with the traditional variables single sampling plan (VSSP) and is examined in a case study.

Sampling Schemes by Variables Inspection for the First-Order Autoregressive Model between Linear Profiles

We present four new sampling schemes by variables inspection to deal with the first-order autoregressive model between linear profiles. The first plan is based on exponentially weighted moving average (EWMA) and the rest of three plans are using the resubmitted sampling, repetitive group sampling (RGS), and multiple dependent state (MDS) sampling schemes. The nonlinear optimization problem is developed to find the number of profiles and the corresponding acceptance criteria, such that the producer’s and consumer’s risk are satisfied simultaneously. The efficiency of the proposed plans is compared with the conventional single sampling plan in terms of average sample number and the probability of acceptance. The result implies that all of the proposed sampling plans are superior to the single acceptance sampling plan by variables. In addition, the EWMA method appeared to be better than the others. The applications of proposed plans are shown with the help of industrial examples taken from calibration of an optical imaging system, and tire cornering stiffness test.

Integrated production, sampling quality control and maintenance of deteriorating production systems with AOQL constraint

This paper considers the problem of integrated production, preventive maintenance and quality control for a stochastic production system subject to both reliability and quality deteriorations. A make-to-stock production strategy is used to provide protection to the serviceable stock against uncertainties. The quality control is performed using a single acceptance sampling plan by attributes. The preventive maintenance strategy consists in carrying out an imperfect maintenance as a part of the setup activity at the beginning of each lot production, while a major maintenance (overhaul) is undertaken once the proportion of defectives in a rejected lot reaches or exceeds a given threshold. The main objective of this study is to jointly optimize the production lot size, the inventory threshold, the sampling plan parameters and the overhaul threshold by minimizing the total incurred cost. To meet customer requirements, the optimization problem is subject to a specified constraint on the average outgoing quality limit (AOQL). A stochastic mathematical model is developed and solved using a simulation-based optimization approach. Numerical examples and thorough sensitivity analyses are provided to illustrate the efficiency of the proposed integrated model. Compared with the 100% inspection policy which is widely used in the literature on integrated production, maintenance and quality control, the results obtained show that an economic design of acceptance sampling in such an integrated context can lead to important cost savings of more than 20%.

Two-Stage Variables Acceptance Sampling Plans Using Process Loss Functions

In this article, a variable two-stage acceptance sampling plan is developed when the quality characteristic is evaluated through a process loss function. The plan parameters of the proposed plan are determined by using the two-point approach and tabulated according to various quality levels. Two cases are discussed when the process mean lies at the target value and when it does not, respectively. Extensive tables are provided for both cases and the results are explained with examples. The advantage of the proposed plan is compared with the existing variable single acceptance sampling plan using the process loss function.