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The purpose of this paper is to develop a group acceptance sampling plan (GASP) for a resubmitted lot when the lifetime of a product follows odds exponential log logistic distribution introduced by Rao and Rao (2014). The parameters of the proposed plan such as minimum group size and acceptance number are determined for a pre-specified consumer’s risk, number of testers and the test termination time. The authors compare the proposed plan with the ordinary GASP, and the results are illustrated with live data example.

The parameters of the proposed plan such as minimum group size and acceptance number are determined for a pre-specified consumer’s risk, number of testers and the test termination time.

The authors determined the group size and acceptance number.

No specific limitations.

This methodology can be applicable in industry to study quality control.

This methodology can be applicable in health study.

The parameters of the proposed plan such as minimum group size and acceptance number are determined for a pre-specified consumer’s risk, number of testers and the test termination time.

This paper introduces a Bayesian two-sided group chain sampling plan (BT-SGChSP) by using binomial distribution to estimate the average proportion of defectives. In this Bayesian approach, beta distribution is used as a suitable prior of binomial distribution. The proposed plan considers both consumer's and producer's risks. Currently, group chain sampling plans only consider the consumer's risk and do not account for the producer's risk. All existing plans are used to estimate only a single point, but this plan gives a quality region for the pre-specified values of different design parameters. In other words, instead of point wise description for the designing of sampling plan based on a range of quality by involving a novel approach called quality region.

The methodology is based on five phases, which are (1) operating procedure, (2) derivation of the probability of lot acceptance, (3) constructing plans for given acceptable quality level (AQL) and limiting quality level (LQL), (4) construction of quality intervals for BT-SGChSP and (5) selection of the sampling plans.

The findings show that the operating characteristic (OC) curve of BT-SGChSP is more ideal than the existing Bayesian group chain sampling plan because the quality regions for BT-SGChSP give less proportion of defectives for same consumer's and producer's risks.

There are four limitations in this study: first is the use of binomial distribution when deriving the probability of lot acceptance. Alternatively, it can be derived by using distributions such as Poisson, weighted Poisson and weighted binomial. The second is that beta distribution is used as prior distribution. Otherwise, different prior distributions can be used like: Rayleigh, exponential and generalized exponential. The third is that we adopt mean as a quality parameter, whereas median and other quintiles can be used. Forth, this paper considers probabilistic quality region (PQR) and indifference quality region (IQR).

The proposed plan is an alternative of traditional group chain sampling plans that are based on only current lot information. This plan considers current lot information with preceding and succeeding lot and also considers prior information of the product.

This paper first time uses a tight (three acceptance criteria) and introduces a BT-SGChSP to find quality regions for both producer's and consumer's risk.

Acceptance sampling plans are a decision-making process on the basis of a randomly selected sampling from a party, where it is not possible to completely scan the products for reasons such as time and cost being limited or the formation of damaged products during the inspection. For some products, the life span (time from beginning to failure) may be an important quality characteristic. In this case, the quality control adequacy of the products can be checked with an acceptance sampling plan based on the truncated life test with a censored scheme for the lifetime of the products. In this study, group acceptance sampling plans (GASPs) based on life tests are studied under the Type-I censored scheme for the compound Weibull-exponential (CWE) distribution.

GASPs based on life tests under the Type-I censored scheme for the CWE distribution are developed by using both the producer's risk and the consumer's risk.

In this study, optimum sample size, optimum number of groups and acceptance number are obtained under the Type-I censored scheme for the CWE distribution. Real data set illustration is given to show GASPs how to be used for the industry applications.

Different from acceptance sampling plans with just considering the producer's risk, GASPs are constructed by using two-point approach included both the producer's risk and the consumer's risk for CWE distribution.

This paper introduces group chain acceptance sampling plans (GChSP) for a truncated life test at preassumed time by using the minimum angle method. The proposed method is an approach, where both risks associated with acceptance sampling, namely consumers’ and producer’s risks, are considered. Currently, the GChSP only considers the consumer's risk (CR), which means the current plan only protects the consumer not the producer since it does not take into account the producer's risk (PR) at all.

There are six phases involved when designing the GChSP, which are (1) identifying the design parameters, (2) implementing the operating procedures, (3) deriving the probability of lot acceptance, (4) deriving the probability of zero or one defective, (5) deriving the proportion defective and (6) measuring the performance.

The findings show that the optimal number of groups obtained satisfies both parties, i.e. consumer and producer, compared to the established GChSP, where the number of group calculated only satisfies the consumer not the producer.

There are three limitations identified for this paper. The first limitation is the distribution, in which this paper only proposes the GChSP for generalized exponential distribution. It can be extended to different distribution available in the literature. The second limitation is that the paper uses binomial distribution when deriving the probability of lot acceptance. Also, it can be derived by using different distributions such as weighted binomial distribution, Poisson distribution and weighted Poisson distribution. The final limitation is that the paper adopts the mean as a quality parameter. For the quality parameter, researchers have other options such as the median and the percentile.

The proposed GChSP should provide an alternative for the industrial practitioners and for the inspection activity, as they have more options of the sampling plans before they finally decide to select one.

This is the first paper to propose the minimum angle method for the GChSP, where both risks, CR and PR, are considered. The GChSP has been developed since 2015, but all the researchers only considered the CR in their papers.

The purpose of this paper is to propose the group acceptance sampling plans for when the lifetime of the submitted product follows the Pareto distribution.

The single‐point approach (only consumer's risk) is used to find the plan parameter of the proposed plan for specified values of consumer's risk, producer's risk, acceptance number, number of testers and experiment time.

Tables are constructed using the Poisson and the weighted Poisson distribution. Extensive tables are provided for practical use.

The tables in this paper can be used only when the lifetime of a product follows the Pareto distribution of 2nd kind.

The result can be used to test the product to save cost and time of the experiment. The use of the weighted Poisson distribution provides the less group size (sample size) as than the plans in the literature.

By implementing the proposed plan, the experiment cost can be minimized.

The novelty of this paper is that Poisson and the weighted Poisson distributions are used to find the plan parameter of the proposed plan instead of the binomial distribution when the lifetime of submitted product follows the Pareto distribution of 2nd kind.

This paper aims to create a new decision-making procedure that uses “Lot-by-Lot Acceptance Sampling Plan by Attributes” methodology in the production processes when any production interruption is observed in tobacco industry, which is a significant example of batch production.

Based on the fish bone diagram, the reasons of the production interruptions are categorized, then Lot-by-Lot Acceptance Sampling Plan by Attributes is studied to overcome the reasons of the production interruptions. Furthermore, managerial aspects of decision making are not ignored and hence, acceptance sampling models are determined by an Analytical Hierarchy Process (AHP) among the alternative acceptance sampling models.

A three-phased acceptance sampling model is generated for determination of the reasons of production interruptions. Hence, the necessary actions are provided according to the results of the proposed acceptance sampling model. Initially, 729 alternative acceptance sampling models are found and 38 of them are chosen by relaxation. Then, five acceptance sampling models are determined by AHP.

The current experience dependent decision mechanism is suggested to be replaced by the proposed acceptance sampling model which is based on both statistical and managerial decision-making procedure.

Acceptance sampling plans are considered as a decision-making procedure for various cases in production processes. However, to the best of our knowledge Lot-by-Lot Acceptance Sampling Plan by Attributes has not been considered as a decision-making procedure for batch production when any production interruption is investigated.

The purpose of this paper is to design a modified version of the double sampling plan to handle the inspection processes requiring a minimum sample size to assure the median life for the products under the new Weibull–Pareto distribution. The economic design of the proposed plan is also considered to assure the product's lifetime with minimum cost.

The authors have developed an optimization model for obtaining the required plan parameters by solving simultaneously two non-linear inequalities and such inequalities have been formed based on the two points on the operating characteristic curve approach.

The results show that the average sample number, average total inspection and total inspection cost under the proposed plan are smaller than the same of a single sampling plan. This means that the proposed plan will be more efficient than a single sampling plan in reducing inspection effort and cost while providing the desired protection.

The proposed modified double sampling plan designed to assure the median life of the products under the new Weibull–Pareto distribution is not available in the literature. The proposed plan will be very useful in assuring the product median lifetime with minimum sample size as well as minimum cost in all the manufacturing industries.

The purpose of this paper is to design a modified chain sampling plan for assuring the product percentile life where the lifetime follows Weibull or generalized exponential distributions (GEDs). In order to reduce the cost of inspection when implementing the proposed modified chain sampling plan, it is also considered the economic aspect of designing of proposed plan in this paper.

The authors have designed the proposed plan on the basis of two points on the operating characteristic (OC) curve approach. The optimization problem is used to determine the plan parameters of the proposed plan so that the specified values of producer’s risk and consumer’s risk are satisfied simultaneously.

The results we have obtained, confirm that the proposed plan will be very effective in reducing the sample size rather than other existing sampling plans. The OC curves of proposed plan, chain sampling plan and zero acceptance number single sampling plan show that the performance of proposed plan in discriminating the good and poor quality lots is better than other two plans. In this paper, it is proved that the value of number of preceding lots required for current lot disposition plays an important role.

The proposed modified chain sampling plan for assuring the percentile lifetime of the products under Weibull or GEDs is not available in the literature. The proposed plan can be used in all the manufacturing industries to assure the product percentile lifetime with minimum sample size as well as minimum cost.

Lifetime data are used in many different applied sciences, like biomedicine, engineering, insurance and finance and others. The purpose of this paper is to develop a new acceptance sampling plans for Rama distribution when the mean lifetime test is truncated at a pre-determined time. The minimum sample sizes required to assert the specified life mean is obtained for a given customer’s risk. The operating characteristic function values of the sampling plans and producer’s risk are calculated.

The results are illustrated using numerical examples and a real data set is considered to illustrate the performance of the suggested acceptance sampling plans and how it can be used for the industry applications.

This paper shows a new acceptance sampling plans based on Rama distribution in the particular case when the mean life time test is truncated.

The results calculated in this paper demonstrate the differences between OC values for different distributions taken into account. In particular, OC values of Rama distribution are found to be less than the proposed distribution counterparts.

The purpose of this paper is to present the single sampling attribute plan for given acceptance quality level (AQL) and limiting quality level (LQL) involving minimum sum of risks using weighted Poisson distribution.

For the given AQL and LQL, sum of producer's and consumer's risks have been attained. Based on weighted Poisson distribution, the sum of these risks has been arrived at, along with the acceptance number and the rejection number. Also, the operating characteristic function for the single sampling attribute sampling plan, using weighted Poisson distribution, has been derived.

In the final inspection, the producer and the consumer represent the same party. So, the sum these two risks should be minimized. In this paper, the sum of risks has been tabulated using weighted Poisson distribution for different operating ratios. These tabulated values are comparatively less than the sum of risks derived using Poisson distribution.

The sampling plan presented in this paper is particularly useful for testing the quality of finished products in shop floor situations.