In this article, we propose a sampling plan called a multiple deferred state sampling plan for sentencing a lot based on the information of current and successive lot samples. Based on this samplin...

Designing of multiple deferred state sampling plan for generalized inverted exponential distribution

In this paper, we propose a multiple deferred state repetitive group sampling plan which is a new sampling plan developed by incorporating the features of both multiple deferred state sampling plan...

Optimal design of repetitive group sampling plans for Weibull and gamma distributions with applications and comparison to the Birnbaum–Saunders distribution

All traditional acceptance sampling plan are constructed with crisp proportion of defective items (p). But in the manufacturing processes, p may not be precisely known. So, in this paper fuzzy multiple deferred state sampling plan by attribute is proposed in the presence of imprecise p by applying fuzzy sets theory and probability theory. Here, the authors concentrate more on a specific case of developed plan for preparing a table to find its parameters for given specified fuzzy acceptable and limiting quality levels. Also, some practical examples are given to be fully informed. The obtained results show that the proposed plan not only is well-defined, but also has higher discriminatory power than fuzzy single sampling plan with zero and one acceptance numbers.

Construction of fuzzy multiple deferred state sampling plan

Traditional multiple deferred state sampling plan by attribute is an inspection with crisp parameter. In this plan, in addition to using the current lot information, the future successive lots information can also be utilized on sentencing the current lot. In this study, a fuzzy multiple deferred state (FMDS) sampling plan by attribute is proposed when the proportion of defective items (p) is uncertain. So, the fuzzy probability theory is applied to construct the operating characteristic curve of mentioned plan. Then, we concentrate more on a special feature of FMDS(0,1,2) plan. We show that the proposed plan is well-defined because it converts to classic case when p is not imprecise. In order to be fully informed, some examples will also be discussed. The comparisons between the FMDS(0,1,2) plan and the existing fuzzy single sampling plan (FSSP) with zero and one acceptance number show that the proposed plan is more powerful than the FSSP in distinguishing good and bad quality of the lot. It also enjoys the least sum of the consumer and producer risks.

Multiple Deferred State Sampling Plan with Fuzzy Parameter

SYNOPTIC ABSTRACTThis article proposes the multiple deferred (dependent) state sampling plan for a time-truncated life test when the lifetime of product follows the Weibull distribution. The quality of the product is considered in terms of mean lifetime. The optimal plan parameters of the proposed plan, such as the sample size, the acceptance and rejection numbers, and the number of successive (preceding) lots needed for making the decision whether to accept or reject the current lot, are determined using the approach of two points on the operating characteristic curve. Tables are constructed for different combinations of producer's and consumer's risks. The implementation of the proposed plan is also explained with a descriptive example. Comparison is also made with the existing sampling plans. In addition, an economic design of the proposed plan is also discussed and the optimal plans are determined, such that both producer's and consumer's risks are satisfied with minimum total cost of inspection.

Optimal Designing of a Multiple Deferred State Sampling Plan for Weibull Distributed Life Time Assuring Mean Life

Since the first acceptance sampling plans have been developed 80 years ago, a number of selection principles have emerged. The majority of these principles are characterised by the fact that they look upon producer and consumer as two opposing parties. However, in many occasions, e.g., in final inspection, producer and consumer represent the same party and, therefore, the used sampling plan should not make an attempt to discriminate between their interests. In this case the interest is to avoid wrong decisions, i.e., reject product of sufficient quality and accept product of insufficient quality. Thus, the natural objective in these cases is to use overall risk for a wrong decision as optimisation criteria. Optimum result can be arrived further by the weighted Poisson distribution. In this paper, a table and procedure are given for finding the multiple deferred state-(c1, c2) sampling plan involving minimum sum of producer’s and consumer’s risks for specified acceptance quality level and limiting quality level using the weighted Poisson distribution. This is the case with so called ‘multiple deferred state-(c1, c2) sampling plan for given acceptance quality level and limiting quality level involving minimum risks using the weighted Poisson distribution’.

Development of multiple deferred state sampling plan based on minimum risks using the weighted Poisson distribution for given acceptance quality level and limiting quality level

K. Subramani

Papers

Development of multiple deferred state sampling plan based on minimum risks using the weighted Poisson distribution for given acceptance quality level and limiting quality level