We give a generalization of the method of pessimistic estimators [14], in which we compose estimators by multiplying them. We give conditions on the pessimistic estimators of two expectations, under which the product of the pessimistic estimators is a pessimistic estimator of the product of the two expectations. This approach can be useful when derandomizing algorithms for which one needs to bo...

data envelopment analysis (dea) is a technique for measuring the efficiency of decision making units. in all models of the dea, for each unit under assessment, the numerical efficiency is obtained which may be less than or equal to one. given the possible large number of functional units, we use various ranking methods for evaluating units. one of the rating methods is balance index and topsis....

One of the fundamental problems in the classic DEA is lack of ability to distinguish unit's performance scores that is considered as a disadvantage. Recently, Parkan et al. [9] tried to address this problem.   They proposed to assess each unit both optimistic and pessimistic views are taken into account. In contrast to traditional evaluation, one index is considered for each unit based on the l...

this paper proposes an alternative approach for efficiency analysis when a set of dmus uses interval scale variables in the productive process. to test the influence of these variables, we present a general approach of deriving dea models to deal with the variables. we investigate a number of performance measures with unrestricted-in-sign interval and/or interval scale variables.

The basic assumption in data envelopment analysis patterns (DEA) (such as the CCR andBCC models) is that the value of data related to the inputs and outputs is a precise andpositive number, but most of the time in real conditions of business, determining precisenumerical value is not possible in for some inputs or outputs. For this purpose, differentmodels have been proposed in DEA for imprecis...

on account of the existence of uncertainty, dea occasionally faces the situation of imprecise data, especially when a set of dmus include missing data, ordinal data, interval data, stochastic data, or fuzzy data. therefore, how to evaluate the efficiency of a set of dmus in interval environments is a problem worth studying. in this paper, we discussed the new method for evaluation and ranking i...