BAYESIAN SAMPLING PLANS WITH PROGRESSIVE CENSORING AND WARRANTY POLICY

Author(s):  
TZONG-RU TSAI

The paper investigates the design of Bayesian sampling plan for the exponential lifetime model under progressive type-II censoring, in which items are manufactured in batches and sold to consumers with a general rebate warranty policy. Assume that the mean lifetime of items is random and varies from lot to lot. A cost model consists of the cost per item on test, the cost per item of test time and the costs of rejecting and accepting an item is established, and an algorithm is provided to determine the optimal Bayesian sampling plan which minimizes the expected average cost per lot. The use of the proposed method is illustrated by numerical results and an example. A sensitivity study is conducted to evaluate the influences of the removal scheme and using incorrect estimates for the hyper-parameters on the proposed sampling plans. The proposed method can be extended to the Weibull lifetime model as its shape parameter is known.

2017 ◽  
Vol 40 (7) ◽  
pp. 2240-2248 ◽  
Author(s):  
Saminathan Balamurali ◽  
Jambulingam Subramani

Skip-lot sampling plans have been widely used in industries to reduce the inspection efforts on products that have an excellent quality history. These skip-lot sampling schemes are economically advantageous and useful to minimize the cost of the inspection of the final lots. Also, the skip-lot concept is sound and useful in the design of sampling plans. In this paper, we propose a designing methodology to determine the optimal parameters of a skip-lot sampling plan of type SkSP-2 when the quality characteristic under study follows a normal distribution. The optimal plan parameters are determined to minimize the average sample number subject to satisfying the producer’s and consumer’s risks simultaneously at the acceptable and limiting quality levels, respectively. An optimization problem is formulated in order to construct tables for determining the optimal parameters of the proposed sampling plan for both known and unknown standard deviation cases and the results are compared with the variables single sampling plans.


2010 ◽  
Vol 450 ◽  
pp. 393-396
Author(s):  
Hsin Rau ◽  
Yi Hsiang Wang ◽  
Kuo Hua Cho

This study explores sampling plans for optimal inspection allocation in multi-station systems with consideration of rework. After components have processed from each workstation in a serial production system, we can apply different sampling plans for inspection in order to maintain the outgoing quality level. The defective components that are detected at each sampling plan will return to the specific workstation to rework. This study proposes a minimum total cost model with a proper sampling plan after each workstation, which can help people to manager production better.


2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Saminathan Balamurali ◽  
Muhammad Aslam ◽  
Chi-Hyuck Jun

Skip-lot sampling plans have been widely used in industries to reduce the inspection efforts when products have good quality records. These schemes are known as economically advantageous and useful to minimize the cost of the inspection of the final lots. A new system of skip-lot sampling plan called SkSP-R is proposed in this paper. The performance measures for the proposed SkSP-R plan are derived using the Markov chain formulation. The proposed plan is found to be more efficient than the single sampling plan and the SkSP-2 plan.


2013 ◽  
Vol 44 (2) ◽  
pp. 113-122
Author(s):  
Tachen Liang

We compare the performances of two sampling plans, namely, the Lin-Liang-Huang (2002)'s Bayesian sampling plan $(n^*,\xi^*)$ and the Lin-Huang-Balakrishnan (2008a, 2010a)'s exact Bayesian sampling plan $(n_0,r_0,t_0,\xi_0)$. We also comment the accuracy of the values of the design parameters $(n_0,r_0,t_0,\xi_0)$ provided in Lin-Huang-Balakrishnan (2010a). We conclude that among the class of sampling plans $(n,r,t,\xi)$ of Lin et al.~(2008a, 2010a), the exact Bayesian sampling plan does not exist.


2019 ◽  
Vol 8 (2) ◽  
pp. 124
Author(s):  
Fathy H. Eissa ◽  
Shuo-Jye Wu ◽  
Hamid H. Ahmed

Based on progressive type-II censored sample with random removals, point and interval estimations for the shape parameters of the exponentiated Weibull distribution are discussed. Computational formula for the expected total test time are derived for different situations of sampling plans. This is useful in planning a life test experiment. The efficiency of the estimators are compared in terms of the root mean square error, the variance and the coverage probability of the corresponding confidence intervals. A simulation study is presented for several values of removal probability and different values of failure percentage. Also, numerical applications are conducted to illustrate and compare the usefulness of the different sampling plans in terms of expected test times for different patterns of failure rates.


Author(s):  
DAMING LIN ◽  
W.K. CHIU

A cost model for an accelerated life test sampling plan is constructed. This model incorporates the cost of a life test and the cost of decision on a batch of components. The Arrhenius model is adopted for the life-stress relationship. Then the method of deriving an economic sampling plan is presented. A numerical example is given for illustration purpose.


2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Fulgence Niyibitegeka ◽  
Arthorn Riewpaiboon ◽  
Sitaporn Youngkong ◽  
Montarat Thavorncharoensap

Abstract Background In 2016, diarrhea killed around 7 children aged under 5 years per 1000 live births in Burundi. The objective of this study was to estimate the economic burden associated with diarrhea in Burundi and to examine factors affecting the cost to provide economic evidence useful for the policymaking about clinical management of diarrhea. Methods The study was designed as a prospective cost-of-illness study using an incidence-based approach from the societal perspective. The study included patients aged under 5 years with acute non-bloody diarrhea who visited Buyenzi health center and Prince Regent Charles hospital from November to December 2019. Data were collected through interviews with patients’ caregivers and review of patients’ medical and financial records. Multiple linear regression was performed to identify factors affecting cost, and a cost model was used to generate predictions of various clinical and care management costs. All costs were converted into international dollars for the year 2019. Results One hundred thirty-eight patients with an average age of 14.45 months were included in this study. Twenty-one percent of the total patients included were admitted. The average total cost per episode of diarrhea was Int$109.01. Outpatient visit and hospitalization costs per episode of diarrhea were Int$59.87 and Int$292, respectively. The costs were significantly affected by the health facility type, patient type, health insurance scheme, complications with dehydration, and duration of the episode before consultation. Our model indicates that the prevention of one case of dehydration results in savings of Int$16.81, accounting for approximately 11 times of the primary treatment cost of one case of diarrhea in the community-based management program for diarrhea in Burundi. Conclusion Diarrhea is associated with a substantial economic burden to society. Evidence from this study provides useful information to support health interventions aimed at prevention of diarrhea and dehydration related to diarrhea in Burundi. Appropriate and timely care provided to patients with diarrhea in their communities and primary health centers can significantly reduce the economic burden of diarrhea. Implementing a health policy to provide inexpensive treatment to prevent dehydration can save significant amount of health expenditure.


2020 ◽  
Vol 10 (24) ◽  
pp. 9154
Author(s):  
Paula Morella ◽  
María Pilar Lambán ◽  
Jesús Royo ◽  
Juan Carlos Sánchez ◽  
Jaime Latapia

The purpose of this work is to develop a new Key Performance Indicator (KPI) that can quantify the cost of Six Big Losses developed by Nakajima and implements it in a Cyber Physical System (CPS), achieving a real-time monitorization of the KPI. This paper follows the methodology explained below. A cost model has been used to accurately develop this indicator together with the Six Big Losses description. At the same time, the machine tool has been integrated into a CPS, enhancing the real-time data acquisition, using the Industry 4.0 technologies. Once the KPI has been defined, we have developed the software that can turn these real-time data into relevant information (using Python) through the calculation of our indicator. Finally, we have carried out a case of study showing our new KPI results and comparing them to other indicators related with the Six Big Losses but in different dimensions. As a result, our research quantifies economically the Six Big Losses, enhances the detection of the bigger ones to improve them, and enlightens the importance of paying attention to different dimensions, mainly, the productive, sustainable, and economic at the same time.


Author(s):  
Elvira Albert ◽  
Jesús Correas ◽  
Pablo Gordillo ◽  
Guillermo Román-Díez ◽  
Albert Rubio

Abstract We present the main concepts, components, and usage of Gasol, a Gas AnalysiS and Optimization tooL for Ethereum smart contracts. Gasol offers a wide variety of cost models that allow inferring the gas consumption associated to selected types of EVM instructions and/or inferring the number of times that such types of bytecode instructions are executed. Among others, we have cost models to measure only storage opcodes, to measure a selected family of gas-consumption opcodes following the Ethereum’s classification, to estimate the cost of a selected program line, etc. After choosing the desired cost model and the function of interest, Gasol returns to the user an upper bound of the cost for this function. As the gas consumption is often dominated by the instructions that access the storage, Gasol uses the gas analysis to detect under-optimized storage patterns, and includes an (optional) automatic optimization of the selected function. Our tool can be used within an Eclipse plugin for which displays the gas and instructions bounds and, when applicable, the gas-optimized function.


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