‎In this paper, we present a two-stage model for ranking of decision making units (DMUs) using interval analytic hierarchy process (AHP). Since the efficiency score of unity is assigned to the efficient units, we evaluate the efficiency of each DMU by basic DEA models and calculate the weights of the criteria using proposed model. In the first stage, the proposed model evaluates decision making...

in models of data envelopment analysis (dea), an optimal set of input and output weights is generally as-sumed to represent the assessed decision making unit (dmu) in the best light in comparison to all the other dmus. these sets of weights are, typically, different for each of the participating dmus. thus, it is important to find a common set of weights (csw) across the set of dmus. in this pa...

BACKGROUND When analysing spatial data, it is important to account for spatial autocorrelation. In Bayesian statistics, spatial autocorrelation is commonly modelled by the intrinsic conditional autoregressive prior distribution. At the heart of this model is a spatial weights matrix which controls the behaviour and degree of spatial smoothing. The purpose of this study is to review the main spe...

Within the context of renewed global emphasis on pharmaceutical sector as a result new crown epidemic, this article investigates how to develop portfolio within industry. This focus comes direct outbreak. The primary source data is investing, and time period covered from December 31, 2016, July 2022. study organized around four main components: research different market sectors, stock selection...

Data envelopment analysis operates as a tool for appraising the relative efficiency of a set of homogenous decision making units. This methodology is applied widely in different contexts. Regarding to its logic, DEA allows each DMU to take its optimal weight in comparison with other DMUs while a similar condition is considered for other units. This feature is a bilabial characteri...