The present study aims to develop a new variable repetitive group sampling plan using the Exponentially Weighted Moving Average (EWMA) statistic based on the yield index for the submitted lot. The optimal parameters of the proposed plan were determined under three scenarios based on the Average Sample Number (ASN). ASN should be minimized to decrease the inspection time and cost using the optimization problem for the required quality levels and sundry combinations of producer's and consumer's risks. A comparison study was conducted to determine the e ciency of the proposed plan. Furthermore, the proposed plan was presented with an example elaborating its applicability in the industry. The proposed plan was compared with the single sampling plan and repetitive group sampling plan based on the yield index. The upshots were tabulated for di erent quality levels. The obtained results demonstrated that with respect to performance, the proposed sampling plan was more lucrative than the existing sampling plans in terms of ASN.

In this paper, we develop an optimization model for the economic design of repetitive sampling plan in the presence of two markets. The process under consideration produces a product with a normally distributed quality characteristic with unknown mean and known variance. The quality characteristic has a lower specification limit. The quality of the product is controlled via lot-by-lot acceptance sampling plan. The objective function used in the model is maximizing profit and product conformity using the Taguchi loss function as a surrogate for product conformity. Risks of producer and consumer in two different markets are considered as constraints. We demonstrate the application of the model using a numerical example. Sensitivity analysis on model parameters shows that the result of the model is sensitive to changes in model parameters.

Acceptance sampling models have been widely applied in companies for evaluation the raw material as well as the final products. Meanwhile, process capability indices (PCIs) have been used in various industrial environments as capability measures that are obtained based on process departure from a target, process yield, process consistency and process loss. In this research, first a repetitive group sampling (RGS) plan based on process capability index is developed for Variables inspection. Then the optimal parameters of proposed RGS plan are determined and also a new multiple dependent state (MDS) sampling plan, a double sampling plan (DSP) and a sampling plan for resubmitted lots are developed and finally, a comparison study is carried out between the proposed sampling plans and the results are elaborated.

In this paper a new sequential sampling plan is introduced in which the acceptable quality level (AQL) and the lot tolerance percent defective (LTPD) are a fuzzy number. This plan is well defined, since, if the parameters are crisp, it changes to a classical plan. For such a plan, a particular table of rejection and acceptance is calculated and compared with the classical one.

In the present paper, the acceptance single sampling plan is developed by the generalized intuitionistic fuzzy numbers. The $\alpha_1$-cut and $\alpha_2$-cut sets of the generalized intuitionistic fuzzy numbers are applied to construct the acceptance single sampling plan. We also investigate the operating characteristic curve, where the parameter is considered the generalized intuitionistic fuzzy number. The $(\alpha_1,\alpha_2)$-cut set of generalized intuitionistic fuzzy operating characteristics is constructed. The bands are represented with upper and lower bounds instead of curves for operating characteristics and evaluated in detail. Finally, the numerical example is given to illustrate the proposed approach.

This study presents a repetitive group sampling plan and a multiple dependent state sampling plan based on the EWMA (exponentially weighted moving average) yield index for product acceptance. The proposed plans utilize the current and previous information through EWMA statistic to reach a decision of lot sentencing. A non-linear optimization model is developed to determine the plan parameters of the proposed plans for various speci, ed conditions. The performance of the proposed plans over several existing sampling plans is analyzed, showing that the proposed plans are e, cient in reducing the sample size for lot sentencing. For industrial application, a real example is given to demonstrate the implementation of the proposed plans.