In this paper the conditional location problem is discussed. conditional location problems have a wide range of applications in location science. A new meta-heuristic algorithm for solving conditional p-median problems is proposed and results are compared to those of the previous studies. This algorithm produces much better results than the previous formulations.

In this paper‎, ‎we consider a k-component coherent system while the system lifetimes are observed‎, ‎the system structure is known and the component lifetime follows the proportional hazard rate model‎. ‎We discuss the prediction problem based on Type-II censored coherent system lifetime data‎. ‎For predicting the future system failures‎, ‎we obtain the maximum likelihood predictor‎, ‎the best unbiased predictor‎, ‎the conditional median predictor and the Bayesian predictors‎. ‎As it seems that the integrals of the Bayes prediction do not possess closed forms‎, ‎the Metropolis-Hastings method is applied to approximating these integrals‎. ‎Different interval predictors based on classical and Bayesian approaches are derived‎. ‎A numerical example is presented to illustrate the prediction methods used in this paper‎. ‎A Monte Carlo simulation study is performed to evaluate and compare the performances of different prediction methods.

In this paper, the problem of predicting times to failure of units censored in multiple stages of progressively hybrid censoring for the proportional hazards family is considered. We discuss different classical predictors. The best unbiased predictor (BUP), the maximum likelihood predictor (MLP) and conditional median predictor (CMP) are all derived. As an example, the obtained results are computed for exponential distribution. A numerical example is presented to illustrate the prediction methods discussed here. Using simulation studies, the predictors are compared in terms of bias and mean squared prediction error (MSPE).

In this paper, we discuss different predictors of times to failure of units censored in multiple stages in a progressively censored sample from proportional hazard rate models. The maximum likelihood predictors, best unbiased predictors and conditional median predictors are considered. We also consider Bayesian point predictors for the times to failure of units. A numerical example and a Monte Carlo simulation study are presented to illustrate all the prediction methods discussed in this paper.