Optimal variable acceptance sampling plan for exponential distribution using Bayesian estimate under Type I hybrid censoring

Abstract

In this study, variable acceptance sampling plans under Type I hybrid censoring is designed for a lot of independent and identical units with exponential lifetimes using Bayesian estimate of the parameter $\vartheta$. This approach is new from the conventional methods in acceptance sampling plan which relay on maximum likelihood estimate and minimising of Bayes risk. Bayesian estimate is obtained using squared error loss and Linex loss functions. Optimisation problem is solved for minimising the testing cost under each methods and optimal values of the plan parameters $n, t_1$ and $t_2$ are calculated. The proposed plans are illustrated using various examples and a real life case study is also conducted. Expected testing cost of the sampling plan obtained using squared error loss function is much lower than the cost of existing plans using maximum likelihood estimate.