A Group Acceptance Sampling Plans for Lifetimes Following a Generalized Exponential Distribution

2009 ◽  
Vol 24 (1) ◽  
Author(s):  
G. Srinivasa Rao
2011 ◽  
Vol 39 (4) ◽  
pp. 102921 ◽  
Author(s):  
M. R. Mitchell ◽  
R. E. Link ◽  
Muhammad Aslam ◽  
Debasis Kundu ◽  
Chi- Hyuck Jun ◽  
...  

2021 ◽  
Vol 50 (4) ◽  
pp. 1121-1129
Author(s):  
Mohd Azri Pawan Teh ◽  
Nazrina Aziz ◽  
Zakiyah Zain

The established group chain acceptance sampling plans (GChSP-1) functions with five acceptance criteria, while the modified group of chain acceptance sampling plans (MGChSP-1) operates with three acceptance criteria. Since the acceptance criteria affect the performances of the sampling plans, therefore, this article suggests a balanced approach by introducing a new group of chain acceptance sampling plans (NGChSP-1), where it functions with four acceptance criteria. The NGChSP-1 is developed by using minimum angle method which caters for producer’s and consumer’s risks. The generalized exponential distribution is selected as the lifetime distribution and the simulation for the NGChSP-1 is conducted at various values of design parameters using the Scilab programming. The finding shows that the optimal number of groups and the corresponding smallest theta for NGChSP-1 are smaller compared to those for the GChSP-1. For illustration purposes, the NGChSP-1 is then applied to real data of air conditioning equipment.


2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohd Azri Pawan Teh ◽  
Nazrina Aziz ◽  
Zakiyah Zain

PurposeThis paper introduces group chain acceptance sampling plans (GChSP) for a truncated life test at preassumed time by using the minimum angle method. The proposed method is an approach, where both risks associated with acceptance sampling, namely consumers’ and producer’s risks, are considered. Currently, the GChSP only considers the consumer's risk (CR), which means the current plan only protects the consumer not the producer since it does not take into account the producer's risk (PR) at all.Design/methodology/approachThere are six phases involved when designing the GChSP, which are (1) identifying the design parameters, (2) implementing the operating procedures, (3) deriving the probability of lot acceptance, (4) deriving the probability of zero or one defective, (5) deriving the proportion defective and (6) measuring the performance.FindingsThe findings show that the optimal number of groups obtained satisfies both parties, i.e. consumer and producer, compared to the established GChSP, where the number of group calculated only satisfies the consumer not the producer.Research limitations/implicationsThere are three limitations identified for this paper. The first limitation is the distribution, in which this paper only proposes the GChSP for generalized exponential distribution. It can be extended to different distribution available in the literature. The second limitation is that the paper uses binomial distribution when deriving the probability of lot acceptance. Also, it can be derived by using different distributions such as weighted binomial distribution, Poisson distribution and weighted Poisson distribution. The final limitation is that the paper adopts the mean as a quality parameter. For the quality parameter, researchers have other options such as the median and the percentile.Practical implicationsThe proposed GChSP should provide an alternative for the industrial practitioners and for the inspection activity, as they have more options of the sampling plans before they finally decide to select one.Originality/valueThis is the first paper to propose the minimum angle method for the GChSP, where both risks, CR and PR, are considered. The GChSP has been developed since 2015, but all the researchers only considered the CR in their papers.


2016 ◽  
Vol 40 (3) ◽  
Author(s):  
G. Srinivasa Rao

In this paper, double acceptance sampling plans are developed for a truncated life test, when the lifetime of an item follows the Marshall-Olkin extended exponential distribution. The probability of acceptance is calculated for different consumer’s confidence levels fixing the producer’s risk at 0.05. The probability of acceptance and the producer’s risk are explained by means of examples.


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