Acceptance Sampling Research Articles

Impact of Inspection Error of an Economic Production Quantity Model with Acceptance Sampling, Defective Items, and Rework

Abstract: In this model, the manufacturer needs to inspect and rework defective items due to the flawed manufacturing process. When checking for defective products, two types of errors, namely Type 1 error and Type 2 error, may occur at the inspection stage. If a lot of defective materials are rejected during the inspection, a non-destructive screening process separates the material into reworkable and non-defective categories. This study includes inspection rate insufficiency (IRI) and inspection rate sufficiency (IRS) based on the relationship between inspection and production rates. Rework Priority Policy (RPP) is followed in every scenario. This paper aims to minimize the inspection error and optimize the total profit. Numerical examples help demonstrate the value of this well-established model. A sensitivity analysis of this study was carried out to check its accuracy.

Investigating green initiatives at South African public universities

Purpose: All institutions of higher learning including public universities in South Africa must participate in green programmes in order to be considered socially responsible. This requires a better understanding of the green environment and green initiatives. Hence, the primary objective is to identify and investigate the existing green initiatives implemented at South African universities.
 Design/Methodology/Approach: The study investigated five key green initiatives: 1) Renewable energy generation and consumption. 2) Water-saving technology and consumption. 3) Waste management. 4) Sustainable buildings. 5) Personnel training and awareness creation. A five-point Likert scale collected data across 18 criteria measuring the five green initiatives. University managers completed and returned 149 questionnaires but only 144 were usable.
 Findings: The data has marginal reliability (α=0.412), acceptable sample adequacy (KMO=0.531) and significance at the 95% confidence level (p≤0.05). The results show that universities implement 20-25% of their green initiatives. The exploratory factor analysis identified seven factors explaining a cumulative variance of 56.14%. They are: 1) Recovering natural resources and recycling. 2) Supportive infrastructure. 3) Reallocation of resources. 4) Conservation of natural resources. 5) Leadership involvement and improved communication. 6) Improved technology.7) Efficient use of financial resources.
 Conclusion: This research contributes to the "going green at universities" of knowledge in South Africa. It is valuable to students, researchers, communities and the department of education in their pursuit to implement green initiatives at universities.

Group Acceptance Sampling Plans Based on Truncated Life Tests for Gamma Lindley Distribution with Real Data Application

Group Acceptance Sampling Plans Based on Truncated Life Tests for Gamma Lindley Distribution with Real Data Application

Two sample Bayesian acceptance sampling plan

Two sample Bayesian acceptance sampling plan

A modified reliability acceptance sampling plan with warranty return rate consideration under Weibull‐distributed accelerated lifetime data

AbstractWarranty is a standard and essential policy for today's products. The period and cost of warranty are majorly affected by the reliability of products. Therefore, validation and verification of product reliability are required to control warranty costs and sustain the brand reputation. In this paper, we propose a modified reliability acceptance sampling plan (MRASP) for product reliability validation. The operating characteristic function of the MRASP is derived by considering the accelerated time‐to‐failure data with Type‐II censoring, which follows a Weibull distribution. To minimize the number of failures required for the accelerated life test and consider the sampling risk constraints, we constructed an optimization model to determine the optimal plan design of the proposed MRASP. Following the optimal plan design, practitioners can set the number of failures required in the accelerated life test to estimate the lifetime capability of the submitted lot and decide the lot disposition using the acceptance benchmark. In addition, the lifetime capability estimation can be used to infer the warranty return rate and warranty cost of the lot, which is valuable for practitioners to formulate the warranty budget. Finally, we illustrate a real‐world case to demonstrate the practicality and applicability of the proposed method.

An acceptance sampling plan for the odd exponential-logarithmic Fréchet distribution: applications to quality control data

An acceptance sampling plan for the odd exponential-logarithmic Fréchet distribution: applications to quality control data

Estimation of the Smallest Acceptable Sample Size in Bilateral Approaches to Coefficient Estimation and Accuracy Prediction

This study is about smallest acceptable sample size determination in experimental design studies involving a driving simulator. The smallest acceptable sample size should be specified so researchers can make accurate inferences about their studied populations. However, the number of samples typically collected is largely subject to the expense of data collection. Working out the methodology of estimating the required number of subjects based on an initially small number is a better way for researchers to determine the smallest acceptable sample size in the experiment. Predictor estimate precision and prediction accuracy are major factors for conducting experiments. Accordingly, this study estimates the smallest acceptable sample size, with emphasis on coefficient estimation and prediction accuracy for selected significant variables. The smallest acceptable sample size is chosen to be the maximum value returned by both coefficient estimation calculation and accuracy prediction calculation approaches. This methodology is flexible and scalable, and can be tailored to other experimental situations. To validate the appropriateness of this procedure, a more than sufficient sample of 50 drivers was recruited. The smallest acceptable sample size was determined backwardly, based on the variable coefficient convergence trends of the mean squared error (MSE) curves of the significant variables. Both the clear converging trends of the MSE curves and the proposed method indicated that 30 was an acceptable sample size.

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

Group acceptance sampling plan based on truncated life tests using extended odd Weibull exponential distribution with application to the mortality rate of COVID-19 patients

This study examines group acceptance sampling plans for many items evaluated together, with the product lifetime following the extended odd Weibull exponential (EOWEx) distribution. Significant design parameters are derived when the consumer risk and the test termination period have been established. Different quality levels are used to define the values of the operational characteristic function. The minimum ratios of the specified average life to the real average life are also computed at the risk of the designated producer. The results of this study provide a foundation for future research on a wide variety of situations at the micro-quality level, where the items follow varied probability distributions under the extended odd Weibull exponential scheme. The mortality rate of COVID-19 patients in the UK is used to display the group acceptance sampling plan for the EOWEx distribution. The performance of the presented sampling plan is compared to the ordinary sampling plan when the quality of the product follows the EOWEx distribution.

Effect of Ethical Leadership on Knowledge Sharing: A Mediating Model of Moral Efficacy and Positive Reciprocity

In the banking industry of Peshawar, Khyber Pakhtunkhwa, the current study examined the direct and indirect effects of EL on employees' KS through positive reciprocity and moral efficacy. The study used G*power analysis to determine an acceptable sample size in order to meet its objectives. Based on power, effect size, and significant level, G*Power was used to determine that the sample size of 291 is suitable for this investigation. A survey questionnaire was used to gather information from the intended respondents. It was verified that the scale was reliable. It was discovered that EL and employee KS have a strong correlation. It implies that if a business has EL, then its personnel will exhibit high levels of KS behaviors. Furthermore, it was discovered that the association between EL and KS is partially mediated by moral efficacy and positive reciprocity. Because of this, the link between EL KS and moral efficacy is not entirely direct; rather, some effects flow directly through moral efficacy and positive reciprocity. Future research directions and implications were also emphasized.

Are tube fill volumes below 90% a rejection criterion for all coagulation tests?

Rejected samples lead to prolonged turnaround time and delayed diagnosis and treatment of patients. This study was conducted to determine minimum acceptable sample volume in Sarstedt brand coagulation tubes to reduce high sample rejection rate. Blood samples were drawn from 20 participants (10 healthy volunteers and 10 patients receiving oral anticoagulant) into coagulation tubes. Six samples were taken from each participant, with tube fill volumes of 100%, 90%, 80%, 70%, 60%, and 50%. Prothrombin time (PT), active partial thromboplastin time (aPTT), and fibrinogen tests were analyzed. According to quality performance specifications, the tube fill volume must be at least 70% for PT and aPTT and 50% for fibrinogen. There was no statistical difference in samples from healthy volunteers for PT, aPTT, and fibrinogen tests when the minimum tube fill volume was at least 80%, 90%, and 50%, respectively. These percentages were 50%, 70%, and 60%, respectively, in patients receiving oral anticoagulant. Sarstedt tubes meet quality standard specifications at a 70% fill rate for PT and aPTT and a 50% fill rate for fibrinogen. Comprehensive studies with larger populations are needed to accept these values as sample acceptance criteria for the laboratory.

Acceptance sampling plan based on difference in difference estimator with application

An acceptance sampling plan has been designed in this study based on the Difference-in-Difference estimator. This plan is designed for the inspection of those product units whose life follows the normal distribution. The operating characteristic function is discussed for the two respective cases of the standard deviation known and unknown. The parameters of the proposed plan are determined by minimizing the sample size and followed by the satisfying optimization rule. The results are computed and tabulated for various parametric combinations of acceptable quality levels and limiting quality levels. The computations are performed by using R statistical programming software for all respective cases. The real-life application of the proposed sampling plan has been discussed and elaborated in detail.

A New Cosine-Originated Probability Distribution with Symmetrical and Asymmetrical Behaviors: Repetitive Acceptance Sampling with Reliability Application

Several new acceptance sampling plans using various probability distribution methods have been developed in the literature. However, there is no published work on the design of new sampling plans using trigonometric-based probability distributions. In order to cover this amazing and fascinating research gap, we first introduce a novel probabilistic method called a new modified cosine-G method. A special member of the new modified cosine-G method, namely, a new modified cosine-Weibull distribution, is examined and implemented. The density function of the new model possesses symmetrical as well as asymmetrical behaviors. The usefulness and superior fitting power of the new modified cosine-Weibull distribution are demonstrated by analyzing an asymmetrical data set. Furthermore, based on the new modified cosine-Weibull distribution, we develop a new repetitive acceptance sampling strategy for attributes with specified shape parameters. Finally, a real-world application is presented to illustrate the proposed repetitive acceptance sampling strategy.

SPSRec: An Efficient Signal Phase Recommendation for Signalized Intersections With GAN and Decision-Tree Model

Signal phase optimization at signalized intersection is of great importance to urban traffic control and management yet is very challenging. The traditional approaches for signal phase optimization heavily rely on the traffic engineering practitioners’ experience. To tackle these challenges, a novel data-driven method is proposed to realize signal phase optimization and recommendation solely using limited amount of real signalized intersection samples. Firstly, all of discrete features related to signal phase design, encoded by one-hot representation, are sampled by the Gumbel-SoftMax distribution, which is a continuous approximation to a multinomial distribution. With this approximation distribution, the generative adversarial network (GAN) is applied to produce the most acceptable signal phase samples among all acceptable choices, dealing with the problem of insufficient samples and uneven sample distribution in real word. Thirdly, a decision-tree based classifier is established to realize signal phase recommendation automatically. We conducted extensive experiments to evaluate our proposed method on three cities in China, including Beijing, Tongxiang and Chaozhou. The experimental results showed that the proposed method could effectively improve the signalized intersection operation efficiency. Moreover, the proposed method has already been deployed in several cities, and it successfully keeps serving hundreds of signalized intersections. This confirms that SPSRec is a practical and robust solution for large-scale real-world signal control services.

Optimal Acceptance Sampling Testing Plan With Pivotal Quantity for Log-Location-Scale Distributions

Global market competition has led to manufacturers encountering a decision problem as the reliability of their products exceeds a given standard. Acceptance sampling tests (ASTs) can be expensive and time-consuming, especially for products with high reliability or quality. Thus, careful planning of the acceptance rule and determination of a suitable sample size for the test are important. In this article, an exact method for the optimal planning of ASTs is proposed for cases in which the product lifetimes follow a distribution in the general log-location-scale family of distributions. A novel procedure using the pivotal quantity is established based on the method-of-moments estimators of the model parameters, and the distribution of the reliability estimator is derived. An algorithm for obtaining the optimal sample size and the corresponding acceptability constant is presented in which the producer's and consumer's risks are constrained according to certain acceptance criteria. We apply the proposed method to a censored AST by generating censored observations with a quantile-filling method under Type-II censoring. The performance of the proposed AST with a complete or censored sample is studied and compared with an AST based on the maximum likelihood method. The robustness of the proposed AST is also examined under model misspecification. The results show that the proposed AST performs well under different scenarios.

Construction of acceptance sampling schemes for exponential lifetime products with progressive type II right censoring

Ensuring product quality is crucial for enhancing a company's competitiveness, and product lifetime serves as a vital metric, particularly in the context of electronic goods, for evaluating product quality. To meet quality requirements and mitigate risks for both producers and consumers, thorough examinations of components and final products are necessary. Acceptance sampling plans are commonly employed to achieve these objectives. When assessing product lifetime under non-normal distributions, the lifetime performance index emerges as a valuable and frequently utilized metric. In practical scenarios, lifetime tests may encounter challenges such as the absence of experimenters to observe all product lifetimes and the possibility of accidental product breakage. To address these issues, this study introduces two sampling schemes: a single sampling plan and a repetitive group sampling plan. These plans are specifically designed for exponential lifetime products, utilizing the progressively type II right censored sample as the data type for lifetime testing. The study provides tabulated plan parameters for practical implementation and presents a comparative analysis between the two proposed plans. Furthermore, a practical example is presented to demonstrate the efficacy of the developed sampling plans.

Surrogate-assisted hyper-parameter search for portfolio optimisation: multi-period considerations

AbstractPortfolio management is a multi-period multi-objective optimisation problem subject to various constraints. However, portfolio management is treated as a single-period problem partly due to the computationally burdensome hyper-parameter search procedure needed to construct a multi-period Pareto frontier. This study presents the Pareto driven surrogate (ParDen-Sur) modelling framework to efficiently perform the required hyper-parameter search. ParDen-Sur extends previous surrogate frameworks by including a reservoir sampling-based look-ahead mechanism for offspring generation in evolutionary algorithms (EAs) alongside the traditional acceptance sampling scheme. We evaluate this framework against, and in conjunction with, several seminal multi-objective (MO) EAs on two datasets for both the single- and multi-period use cases. When considering hypervolume ParDen-Sur improves marginally (0.8%) over the state-of-the-art (SOTA)-NSGA-II. However, for generational distance plus and inverted generational distance plus, these improvements over the SOTA are 19.4% and 66.5%, respectively. When considering the average number of evaluations and generations to reach a 99% success rate, ParDen-Sur is shown to be 1.84× and 2.02× more effective than the SOTA. This improvement is statistically significant for the Pareto frontiers, across multiple EAs, for both datasets and use cases.

Impact of the total measurement error on sampling plan for bulk materials – An optimal sampling plan under ToS framework

Acceptance sampling plans are used in quality control between a producer and a buyer. However, in the literature of acceptance sampling for bulk materials, the term “sampling” refers to obtaining simple random samples in the traditional statistical sense, which may lead to heavy financial losses. In contrast, the Theory of Sampling (ToS) defines sampling as the collection of physical samples based on a thorough understanding of the material concerned. The goal of this research is to investigate the influence of incorrect sampling procedures on acceptance sampling in a mining value chain. To achieve this, the representative sampling approach of ToS was utilized, total measurement error was measured, and possibilities for improving sampling procedures were identified. A variable Quick Switch Sampling plan (QSS) based on the Beta distribution is applied. A nonlinear optimization model is used to estimate the plan parameters, which minimizes the necessary average sample number (ASN) while meeting the producer and consumer quality levels and maximum allowable risks using real-life data. Finally, A collaborative quality program is suggested for a solid producer-buyer relationship by using a correct sampling facility of the producer.

Multiple dependent repetitive group sampling plan for Marshall-Olkin logistic-exponential distribution assuring percentile lifetime with applications

Acceptance sampling plans are commonly utilized for planning, process control, and implementation of industrial data sets. In addition to this, they also provide a solution for process quality control. They could be used for executing a decision for acceptance or rejection of a lot. A good acceptance sampling plan can be formulated depending on the requirements of the project. Usually, the plan should follow a defined lifetime distribution. Hence, the appropriate plan would not only determine the plan parameters but would also be cost and time efficient. In this article, an attribute multiple dependent repetitive group sampling (MDRGS) plan is developed assuming that the lifetime of the product follows the Marshall-Olkin logistic-exponential (MOLE) distribution. The parameters of plan are examined by considering a two-point approach. The suggested plan is contrasted with the single sampling and repeated group sampling plans. Finally, the implementation of the proposed plan is discussed using industrial as well as simulated data sets. It is concluded that the proposed plan reduces the sampling inspection cost and time as compared to existing acceptance sampling plans.

Optimal Bayesian generalized multiple-dependent state sampling plan for attributes

Over the years, acceptance sampling plans have been crucial to quality assurance in manufacturing. Sample plans are designed using operating characteristic curve conditions to safeguard producers and customers. We propose a conditional probability-based Bayesian generalized multiple-dependent state sampling technique in this paper. The technique relies on Gamma-Poisson distribution. Other performance indicators and acceptance probability are calculated. Also, the new plan's operational method is discussed. The proposed technique is also compared to current attribute sampling schemes for efficacy. Optimal plan parameters for the plan's economic structure are also generated, adding managerial insights to the suggested plan. The entire cost study showed that the suggested plan is cheaper than existing sample plans under identical conditions. To account for inspection flaws, the plan is adjusted. We examine how Type I and Type II errors affect sampling plan outcomes. The plan is demonstrated with numerical examples and a data-driven application.