Multiple Dependent State Sampling Plan Research Articles

Life truncated multiple dependent state plan for imprecise Weibull distributed data

This paper aims to provide a multiple dependent state (MDS) sampling technique for light-emitting diode luminous intensities under indeterminacy by employing time truncated sampling schemes and the Weibull distribution. This indicates that ASN is significantly impacted by the indeterminacy parameter. Furthermore, a comparison is shown between the existing, indeterminate sampling plans and the recommended sample designs. The projected sampling technique is illustrated by calculating the luminous intensities of LEDs using the Weibull distribution. Based on the findings and practical example, we conclude that the suggested strategy needs a smaller sample size than SSP and the current MDS sampling plan.

Designing of multiple dependent state sampling plan for Type-II generalized half logistic distribution

Designing of multiple dependent state sampling plan for Type-II generalized half logistic distribution

Application of the multiple dependent state sampling strategy to late adolescent suicide rates

A multiple dependent state sampling plan (MDSSP) is designed when the lifetimes of the variables follow New Lomax Rayleigh Distribution (NLRD). The decision to accept or reject a lot in the proposed methodology is based on the quality of the given present or previous lots. A binomial model-based operating characteristic curve (OC curve) for continuous lots of variables under similar settings in healthcare is used in finding the probability of acceptance, acceptance number, rejection number, and the number of preceding (succeeding) lots to consider. Time truncated life test based on the specified median of the NLRD is used in designing the current acceptance sampling plan. For specified values of the parameters of NLRD, quantile ratios, consumer’s risk and producer’s risk, average sampling number (ASN) and probability of acceptance of a lot are reported in tables. Real data on worldwide suicide rates of 15–19 years in the year 2019 from the World Health Organization (WHO) website is considered to illustrate this methodology. The minimum sample size required from the selected data to comment on worldwide suicide rates in late adolescents is explained with MDSSP. The results of the proposed acceptance sampling method are compared with the single-stage sampling plan.

A new multiple dependent state sampling plan based on one-sided process capability indices

A new multiple dependent state sampling plan based on one-sided process capability indices

Improving Multiple Dependent State Sampling Plan Based on Capability Index S^T_pk

Improving Multiple Dependent State Sampling Plan Based on Capability Index S^T_pk

Designing Adaptive Multiple Dependent State Sampling Plan for Accelerated Life Tests

A novel adaptive multiple dependent state sampling plan (AMDSSP) was designed to inspect products from a continuous manufacturing process under the accelerated life test (ALT) using both double sampling plan (DSP) and multiple dependent state sampling plan (MDSSP) concepts. Under accelerated conditions, the lifetime of a product follows the Weibull distribution with a known shape parameter, while the scale parameter can be determined using the acceleration factor (AF). The Arrhenius model is used to estimate AF when the damaging process is temperature-sensitive. An economic design of the proposed sampling plan was also considered for the ALT. A genetic algorithm with nonlinear optimization was used to estimate optimal plan parameters to minimize the average sample number (ASN) and total cost of inspection (TC) under both producer’s and consumer’s risks. Numerical results are presented to support the AMDSSP for the ALT, while performance comparisons between the AMDSSP, the MDSSP and a single sampling plan (SSP) for the ALT are discussed. Results indicated that the AMDSSP was more flexible and efficient for ASN and TC than the MDSSP and SSP plans under accelerated conditions. The AMDSSP also had a higher operating characteristic (OC) curve than both the existing sampling plans. Two real datasets of electronic devices for the ALT at high temperatures demonstrated the practicality and usefulness of the proposed sampling plan.

Generalized multiple dependent state sampling plan for quality inspection in ocean dataset

The present article develops the generalized multiple dependent state sampling plan for testing the quality of the ocean data. The plan parameters for the inspection of the ocean data are selected through the optimization process. The operational procedure for the inspection of ocean data is given under the proposed sampling scheme. The efficiency of the proposed plan is compared with the existing plan used for the inspection of the quality of ocean data in terms of sample size. From the comparative study, it is concluded that the proposed plan is efficient than the existing plan in an inspection of the ocean data.

Sampling Inspection Plan to Test Daily COVID-19 Cases Using Gamma Distribution under Indeterminacy Based on Multiple Dependent Scheme.

The purpose of this paper is to develop a multiple dependent state (MDS) sampling plan based on time-truncated sampling schemes for the daily number of cases of the coronavirus disease COVID-19 using gamma distribution under indeterminacy. The proposed sampling scheme parameters include average sample number (ASN) and accept and reject sample numbers when the indeterminacy parameter is known. In addition to the parameters of the proposed sampling schemes, the resultant tables are provided for different known indeterminacy parametric values. The outcomes resulting from various sampling schemes show that the ASN decreases as indeterminacy values increase. This shows that the indeterminacy parameter plays a vital role for the ASN. A comparative study between the proposed sampling schemes and existing sampling schemes based on indeterminacy is also discussed. The projected sampling scheme is illustrated with the help of the daily number of cases of COVID-19 data. From the results and real example, we conclude that the proposed MDS sampling scheme under indeterminacy requires a smaller sample size compared to the single sampling plan (SSP) and the existing MDS sampling plan.

An optimal construction of yield-based EWMA repetitive multivariate sampling plan

In this paper, a variable repetitive group sampling (VRGS) plan is proposed based on the exponentially weighted moving average (EWMA) statistic with yield index for inspection of mutually independent and multivariate normally distributed characteristics. Plan parameters are found optimally based on asymptotic distribution of using a non linear optimization. A simulation study indicates that the reported parameters can thoroughly warrant determined risks for finite sample sizes. This work also extends application of the proposed plan to the class of correlated characteristics. The obtained findings show that the proposed plan significantly reduces required average sample number compared to the existing modified VRGS plan, single, double, and multiple dependent state sampling plans with yield-based EWMA statistic. Two industrial examples are presented to illustrate the application of the proposed plan in real world.

Product Acceptance Determination Based on EWMA Yield Index Using Repetitive and MDS Sampling Schemes

In this manuscript, we present 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 the 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 specified conditions. The performance of the proposed plans over several existing sampling plans is analyzed, which shows that the proposed plans are efficient in reducing the sample size for lot sentencing. For industrial application, a real example is given to demonstrate the implementation of the proposed plans.

A Novel Multiple Dependent State Sampling Plan Based on Time Truncated Life Tests Using Mean Lifetime

The design of a new adaptive version of the multiple dependent state (AMDS) sampling plan is presented based on the time truncated life test under the Weibull distribution. We achieved the proposed sampling plan by applying the concept of the double sampling plan and existing multiple dependent state sampling plans. A warning sign for acceptance number was proposed to increase the probability of current lot acceptance. The optimal plan parameters were determined simultaneously with nonlinear optimization problems under the producer’s risk and consumer’s risk. A simulation study was presented to support the proposed sampling plan. A comparison between the proposed and existing sampling plans, namely multiple dependent state (MDS) sampling plans and a modified multiple dependent state (MMDS) sampling plan, was considered under the average sampling number and operating characteristic curve values. In addition, the use of two real datasets demonstrated the practicality and usefulness of the proposed sampling plan. The results indicated that the proposed plan is more flexible and efficient in terms of the average sample number compared to the existing MDS and MMDS sampling plans.

Construction of multiple dependent state sampling plan for variables based on logistic distribution

Acceptance sampling plans are specific rules used in statistical quality control to reach a decision on acceptance or rejection of lots of manufactured products submitted for inspection subject to the quality requirements of the sampled units. In order to reduce the inspection time and cost, the variable sampling plans are used as it needs comparatively small sample size. The multiple dependent state sampling plan is one of the conditional sampling plans which acquires the sample information from present lot as well as the prior lots for disposition of a lot. The MDS plan is applicable in the situations where lots are submitted for inspection in sequence of production from a process having a constant proportion non-conforming. MDS plan under the assumption of normality are found in literature. In this article, a multiple dependent state sampling plan is developed for sentencing lots of products whose quality characteristics follows a Logistic distribution. The proposed plan is designed by considering the two points on the OC curve, by formulating it as a non-linear optimization problem, when standard deviation is known and unknown. The tables are constructed for the selection of the proposed plan parameters. The proposed plan is compared with the single variable sampling plan based on Logistic distribution.

Adjustable variables multiple-dependent-state sampling plans based on a process capability index

Acceptance sampling plans are practical and economical methods of statistical quality control. In modern manufacturing processes, supplier’s products are produced from continuous-flow manufacturing processes; consequently, the quality of successive lots is expected to be homogeneous and dependent. The multiple dependent state (MDS) sampling plan considers the cumulative lot-sentencing results to prevent the primary disadvantage of past-lot exclusion from the single sampling plan (SSP). Unfortunately, the sampling efficiency of MDS plans declines as additional preceding-lot results are included. In this paper, an adjustable MDS (AMDS) sampling plan based on the most popular process capability index with bilateral specifications is developed. The proposed AMDS plans not only increase sampling performance as additional preceding lots are considered in the lot-sentencing decision but also garner economic gains from reducing the required sample size. After comparing the performance of different plans, our proposed AMDS plans demonstrate superior cost-effectiveness and discriminatory powers to that of the SSP and MDS plans; moreover, with adjustable mechanisms, they can be progressively implemented for developing a solid supplier-buyer partnership. Finally, the industrial applicability of the AMDS plans is illustrated in a case study.

An integrated failure-censored sampling scheme for lifetime-performance verification and validation under a Weibull distribution

Acceptance sampling plans (ASPs), practically providing an economic size of sampling inspection and a definite standard for lot-sentencing determination, have been widely used for the products' quality verification and validation. Simply sentencing the submitted lot according to the inspection of only one group of samples, the single sampling plan (SSP) has an advantage of lot-by-lot simple administration. When encountering costly, destructive, and time-consuming life testing, the SSP’s simplicity, however, comes at the high expense of requiring large-tested samples. To overcome the drawback, the multiple-dependent-state sampling plan (MDSP) and repetitive group sampling plan (RGSP) have been recently developed. The former activates a backtracking mechanism to include valuable information inspected from preceding lots, whereas the latter actuates a resampling mechanism to alleviate psychological skepticism raised from both entities of supplier and buyer. In this paper, an integrated failure-censored sampling scheme (IFCSS), consolidating both advantages gained from the MDSP and the RGSP, is developed based on the Weibull-lifetime performance index. We construct an optimization model to determine the plan criteria under the economical objective. Moreover, we further create a Weibull-lifetime IFCSS web-based app with QR code access for the plan’s easy setup. By comparing with inspection performances, our proposed IFCSS exhibits superior cost-efficiency and discriminating capability than the recently developed MDSP and RGSP. Finally, the Weibull-lifetime IFCSS’s applicability is illustrated by a ceramic-bearing lifetime-testing inspection.

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.

Cost model of variable multiple dependent state sampling plan with rectifying inspection

It is always foreseen to upturn the efficiency of an acceptance sampling plan for lot sentencing. In this research, variable multiple dependent state sampling plan considering the quality cost with rectifying inspection is proposed for normally distributed quality characteristics. The staple objective is to curb the total cost of the lot under inspection and improve the quality of outgoing lots. Tables are constructed for optimal plan parameters by increasing forgoing lots. The behavior of cost parameters on total cost has been investigated. Protection and cost curves are portrayed to show the efficacy of the proposed plan with rectifying single acceptance sampling plan using the cost model. Using the cost model, the proposed plan turns out to be more effectual than the single acceptance sampling plan in terms of sample size, average outgoing quality, and total cost. has emerged having a substantial effect on total cost trailed by and with the tiniest effect. Finally, an example is given to demonstrate the proposed plan.

A new approach in the economic design of acceptance sampling plans based on process yield index and Taguchi loss function

Acceptance sampling plan is used for decision making in the field of products quality control. These plans provide the producer and the consumer with decision rules for accepting or rejecting the lot. In this paper, a new approach for the economic design of resubmitted and multiple dependent state (MDS) sampling plans is proposed based on the process yield index considering Taguchi loss function and the average number of sample size n, which minimizes the costs related to the yield index Spk. In the proposed model the loss of a batch is considered rather than a single product. Also, the loss of both producer and consumer based on the yield index is minimized simultaneously through solving an optimization model and the parameters of the proposed sampling plans are obtained. The performance of both proposed sampling plans is analyzed and compared in terms of average sample number and total expected loss. The results indicate that both proposed sampling plans show satisfactory performance. Moreover, the advantages of the proposed model over the existing resubmitted and MDS sampling plans are presented. Finally, the application of the proposed model and sampling plans is illustrated through a real case in batch manufacturing processes.

Economic Determination of Modified Multiple Dependent State Sampling Plan under Some Lifetime Distributions

In this paper, the modification of multiple dependent state sampling plan is proposed and designed for assuring a mean lifetime of the products under Birnbaum–Saunders distribution and Weibull distribution. The optimal parameters of the proposed plan are determined based on two points on the operating characteristic curve approach. Different combinations of producer’s risk and consumer’s risk are considered for plan parameters determination. The efficacy of the proposed plan is compared with those of other existing sampling plans using an average sample number and operating characteristic function. The economic designing of the proposed plan is also considered and the comparative study is done based on the total cost of the inspection.

Generalized multiple dependent state sampling plans for coefficient of variation

Sampling plans using the coefficient of variation (CV) attract increasing attention by many authors in the literature due to its importance to measure the product quality. A generalized multiple dependent state (GMDS) sampling plan for accepting a lot is proposed based on the coefficient of variation when a quality characteristic comes from a normal distribution. The optimal plan parameters of the proposed plan are solved by a nonlinear optimization model, which minimizes the sample size required for inspection while satisfying the given producer’s risk and the consumer’s risk at the same time. A comparative study of the proposed GMDS sampling plan over the two existing sampling plans is considered. A real example is given to demonstrate the proposed plan.

A variables-type multiple-dependent-state sampling plan based on the lifetime performance index under a Weibull distribution

Verification and validation of product quality highly affects the success of product design and manufacturing processes as well as the long-term collaboration between buyers and suppliers. Lifetime testing for acceptance is the main bottleneck in verification and validation and often requires considerable time and expense, especially in the current high-product-yield era. To establish a cost-effective and risk-controllable life testing sampling scheme, we propose a variables-type multiple-dependent-state (MDS) sampling plan using the lifetime performance index under a Weibull distribution with Type-II right censoring. The design parameters of the lifetime-capable MDS sampling plan are formulated as optimization models to minimize the required number of testing failures subject to nonlinear constraints for the desired lifetime capability levels and allowable risks regulated by the supplier and buyer. Compared with variable single sampling plans, the plan proposed herein is more cost-effective sampling and has greater discriminatory power. The applicability of the proposed plan is demonstrated through an example.