Multiple Dependent State Sampling Inspection Plan for Lindley Distributed Quality Characteristic

This article develops multiple dependent state (MDS) sampling inspection plans based on the mean of lifetime quality characteristic that follows non-normal distributions viz., exponential and Lindley distribution. In this plan, the lot quality is measured by the lot mean (𝜇). We have estimated the optimal plan parameters of the proposed technique by non-linear optimization approaches considering acceptable quality level and rejection quality level. We have compared the sample size between the MDS sampling inspection plan and the single sampling inspection plan for the variable. Finally, we have taken two examples to illustrate the proposed technique.

Design of Multiple Dependent State Sampling Plan Application for COVID-19 Data Using Exponentiated Weibull Distribution

The proposed sampling plan in this article is referred to as multiple dependent state (MDS) sampling plans, for rejecting a lot based on properties of the current and preceding lot sampled. The median life of the product for the proposed sampling plan is assured based on a time-truncated life test, when a lifetime of the product follows exponentiated Weibull distribution (EWD). For the proposed plan, optimal parameters such as the number of preceding lots required for deciding whether to accept or reject the current lot, sample size, and rejection and acceptance numbers are obtained by the approach of two points on the operating characteristic curve (OC curve). Tables are constructed for various combinations of consumer and producer’s risks for various shape parameters. The proposed MDS sampling plan for EWD is demonstrated using the coronavirus (COVID-19) outbreak in China. The performance of the proposed sampling plan is compared with the existing single-sampling plan (SSP) when the quality of the product follows EWD.

A New X-bar Control Chart for Multiple Dependent State Sampling Using Neutrosophic Exponentially Weighted Moving Average Statistics with Application to Monitoring Road Accidents and Road Injuries

In this article, an efficient mean chart for symmetric data have been presented for multiple dependent state (MDS) sampling using neutrosophic exponentially weighted moving average (NEWMA) statistics. The existing neutrosophic exponentially weighted moving average charts are not capable of seizure the unusual changes threatened to the manufacturing processes. The control chart coefficients have been estimated using the symmetry property of the Gaussian distribution for the uncertain environment. The neutrosophic Monte Carlo simulation methodology has been developed to check the efficiency and performance of the proposed chart by calculating the neutrosophic average run lengths and neutrosophic standard deviations. The proposed chart has been compared with the counterpart charts for confirmation of the proposed technique and found to be a robust chart.