Journal of Quality Engineering and Management

Abstract Purpose: This paper aims to estimate the parameters of the Weibull distribution using a genetic algorithm and compare its performance with traditional estimation methods.
Methodology: A simulation study was conducted under different sample sizes and censoring levels. The genetic algorithm was applied to maximize the likelihood function.
Findings: The results show that the genetic algorithm provides more accurate and stable parameter estimates compared to the maximum likelihood method, especially in the presence of censored data.
Originality/Value: This study presents a novel application of genetic algorithms in reliability analysis, demonstrating their effectiveness in parameter estimation for censored datasets.

Abstract In this paper, the Bayesian estimation of the reliability parameter, R = P (X <Y), in the two-parameter Riley distribution, is investigated under increasingly censored bond samples. This issue is studied in three different ways. In the first case, assuming that the variables stress, X, and resistance, Y, both have a common location parameter and non-common scale parameters, and all of these parameters are unknown, the Bayesian estimate R is examined. Since in this case Bayesian estimation does not have a closed form, it is approximated by both Lindley and MCMC methods. In the second case, assuming that the stress and resistance variables have a known common place parameter and the common and unknown scale parameters, the exact Bayesian estimate for R is calculated. In the third case, assuming that all parameters are different and unknown, the Bayesian R estimate is calculated using the MCMC approximate method. Bayesian belief intervals are also obtained in all methods. Finally, using Monte Carlo simulations, the performance of different estimators is compared. 
 

Abstract The purpose of this paper is to investigate the reliability parameter R = P (X <Y) based on samples with type 2 incremental censorship in which X and Y are independent random variables with generalized inverse exponential distribution with different shape parameters and the same scale parameter. The maximum likelihood estimator (MLE) and the nonlinear estimator with uniform uniform variance (UMVUE) of the parameter R are obtained and different confidence intervals are provided. Also, Bayesian R estimator and HPD confidence interval using Gibbs sampling method are proposed. Monte Carlo simulations have been performed to compare the performance of different methods.