This paper presents the designing of a sampling plan using the process loss consideration for the multiple dependent state sampling under the neutrosophic statistics. The operating characteristics under the neutrosophic statistical interval method (NSIM) are developed to nd the neutrosophic plan parameters of the proposed sampling plan. A non-linear optimization under NSIM is used to nd the optimal neutrosophic plan parameters under the given conditions. The advantages of the proposed sampling plan are discussed over the existing sampling plans. A real example having some uncertain observations is given for the illustration purpose.

The theory of classical statistics assumes crisp, certain, and clear observations/parameters in engineering applications. However, in such engineering applications, due to their complex functions, it may not possible to obtain clear or crisp values of certain parameters. So, there is a chance of obtaining indeterminate, imprecise, vague, and incomplete parameters. In this situation, neutrosophic statistics can be applied, which is the generalization of classical statistics. This is reduced to classical statistics when no parameters are found to be indeterminate, imprecise, vague, or incomplete in actual practice. In this paper, we design a new sampling plan using the neutrosophic approach for the process loss function. The neutrosophic non-linear problem is given and applied to determine the neutrosophic plan parameters of the proposed sampling plan. A table is given and discussed with the help of factory data.

The acceptance sampling plan plays an important role in maintaining the high quality of a product. The variable control chart, using classical statistics, helps in making acceptance or rejection decisions about the submitted lot of the product. Furthermore, the sampling plan, using classical statistics, assumes the complete or determinate information available about a lot of product. However, in some situations, data may be ambiguous, vague, imprecise, and incomplete or indeterminate. In this case, the use of neutrosophic statistics can be applied to guide the experimenters. In this paper, we originally proposed a new variable sampling plan using the neutrosophic interval statistical method.

If the sample or population has vague, inaccurate, unidentified, deficient, indecisive, or fuzzy data, then the available sampling plans could not be suitable to use for decision-making. In this article, an improved group-sampling plan based on time truncated life tests for Weibull distribution under neutrosophic statistics (NS) has been developed. We developed improved single and double group-sampling plans based on the NS. The proposed design neutrosophic plan parameters are obtained by satisfying both producer’s and consumer’s risks simultaneously under neutrosophic optimization solution.

This paper extends the idea of multiple dependent state sampling plans to the case of using process capability index when the quality characteristic of the product follows the normal distribution. The plan parameters are determined using the optimization process with minimum values of sample size so that the specified producer's risk and consumer's risk should be satisfied simultaneously for given values of acceptable quality level and limiting quality level in terms of fraction defective beyond two specification limits. The plan parameters are determined under symmetric and asymmetric cases of fraction defective. The advantage of the proposed plan is discussed over the single variable sampling plan. A real example is presented to illustrate the proposed plan in practice.

The sampling plan using the classical statistics under the exponential distribution can be applied only when there are certainty and clearness and in observations and parameters. But, in practice, it is not necessary that under some circumstances all the observations/parameters are determined. So, we cannot analyze them using the classical statistics which provides results in the determined values. The neutrosophic statistics which is the generation of classical statistics can be applied to the analysis when parameters/observations are incomplete, indeterminate, and vague imprecise. In this paper, we will design originally a sampling plan for the exponential distribution under the neutrosophic interval statistical method. The neutrosophic plan parameters of the proposed plan will be determined through the neutrosophic non-linear problem. The tables for various values of risk are presented for the use in the industrial. An example from the automobiles manufacturing industry is given to explain for the exponential distribution under the neutrosophic interval statistical method.

The aim of reducing the inspection cost and time using acceptance sampling can be achieved by utilizing the features of allocating more than one sample item to a single tester. Therefore, group acceptance sampling plans are occupying an important place in the literature because they have the above-mentioned facility. In this paper, the designing of a group acceptance sampling plan is considered to provide assurance on the product’s mean life. We design the proposed plan based on neutrosophic statistics under the assumption that the product’s lifetime follows aWeibull distribution. We determine the optimal parameters using two specified points on the operating characteristic curve. The discussion on how to implement the proposed plan is provided by an illustrative example.