This paper considers the problem of identifying some probability distributions using the concepts of right and left truncated moments of some function of the kth order statistic. Furthermore, it investigates the necessary and sufficient conditions to characterize distributions by the equation as well as Some wellknown results follow from our results as special cases.

The assignment problem (AP) is a special case of the transportation problem, in which the objective is to assign a number of resources to the equal number of activities at a minimum cost (or maximum profit). It has great significance subject discussed in real physical world for e.g. production planning, particular job tasks, economic etc. We endeavor in this paper to introduce a new approach to assignment problem namely “Revised Ones Assignment Method (ROA)” for solving wide range of problem. In ROA method first we define assignment matrix, then reduced matrix till it has at least one in each row and column. The new method is based on creating some ones in the assignment matrix and to complete exact assignment to their ones, we have added new step to ROA algorithm can be utilized for all types of AP with maximize or minimize objective functions and at last we have illustrate some numerical examples.

The aim of this paper is to provide an approximate 100(1-α)% calibrated CAN, Exact t, Standard Bootstrap, Bootstrap-t, Variance-stabilized Bootstrap-t, Bayesian Bootstrap, Percentile Bootstrap and Bias-corrected and accelerated bootstrap confidence intervals for intensity parameters of a two stage open queueing network with feedback with distribution-free interval and service times. Numerical simulation study is conducted to demonstrate performances of the confidence intervals by using calibration technique. We consider a measure, named relative coverage, to evaluate performances of the said intervals.