en Discrete Optimization Discrete Optimization پژوهشی Original <span style="font-size:10.0pt"><span style="line-height:115%"><span new="" roman="" style="font-family:" times=""><span style="letter-spacing:-.25pt">This paper considers the progressively Type-II censoring and determines the optimal sample size using a Bayesian prediction approach. To this end, two criteria, namely the Bayes risk function of the point predictor for a future progressively censored order statistic and the designing cost of the experiment are considered. In the Bayesian prediction, the general entropy loss function is applied. We find the optimal sample size such that the Bayes risk function and the cost of the experiment do not exceed two pre-fixed values. To show the usefulness of the results, some numerical computations are presented. </span></span></span></span> optimization problem, general entropy loss function, Bayes risk function, prediction. 47 56 http://iors.ir/journal/browse.php?a_code=A-10-6038-1&slc_lang=en&sid=1 Elham Basiri elhambasiri@kub.ac.ir 00031947532846002795 00031947532846002795 No Kosar University of Bojnord S.M.T.K. MirMostafaee m.mirmostafaee@umz.ac.ir 00031947532846002796 00031947532846002796 Yes University of Mazandaran