Data envelopment analysis (DEA) is a well-Known method in efficiency evaluation of a set of decision making units (DMUs) such as organizations and banks. An advantage of DEA technique is selection of weights at random. Weight selection is of crucial importance in efficiency evaluation. In this regard, it is important to employ models that have more freedom in selecting weights. One such model i...

This paper addresses comprehensive ranking systems determining an ordering of entities by aggregating quantitative data for multiple attributes. We propose a DEA-CP (Data Envelopment Analysis Compromise Programming) model for the comprehensive ranking, including preference voting (ranked voting) to rank candidates in terms of aggregate vote by rank for each candidate. Although the DEA-CP model ...

Two-stage data envelopment analysis (2-DEA) is commonly used in productive efficiency analysis to estimate the effects of operational conditions and practices on performance. In this method the DEA efficiency estimates are regressed on contextual variables representing the operational conditions. We reexamine the statistical properties of the 2-DEA estimator, and find that it is statistically c...

Abstract Technologies have varied strengths and weaknesses which require careful assessment by the purchasers. One of the uses of Data Envelopment Analysis (DEA) is technology selection. The traditional models of DEA do not consider dual-role factors. The objective of this paper is to use a model for selecting the best technologies in the presence of dual-role factors. The model determines whet...

The field of productive efficiency analysis is currently divided between two main paradigms: the deterministic, nonparametric Data Envelopment Analysis (DEA) and the parametric Stochastic Frontier Analysis (SFA). This paper examines an encompassing semiparametric frontier model that combines the DEA-type nonparametric frontier, which satisfies monotonicity and concavity, with the SFA-style stoc...

In this study, Data Envelopment Analysis (DEA) and Principal Component Analysis (PCA) were compared when these two methods are used for ranking Decision Making Units (DMU) with multiple inputs and outputs. DEA, a nonstatistical technique, is a methodology using a linear programming model for evaluating and ranking DMU’s performance. PCA, a multivariate statistical method, uses new measures defi...

Some specific geometric data envelopment analysis (DEA) models are well known to the researchers in DEA through so-called multiplicative or log-linear efficiency models. Valuable properties of these models were noted by several authors but the models still remain somewhat obscure and rarely used in practice. The purpose of this paper is to show from a mathematical perspective where the geometri...

Data Envelopment Analysis (DEA) is a linear programming methodology for measuring the efficiency of Decision Making Units (DMUs) to improve organizational performance in private and public sectors. However, if new DMU needs be known its score, DEA analysis would have re-conducted, especially nowadays, datasets from many fields been growing rapidly real world, which will need huge amount computa...

One of the issues in Data Envelopment Analysis (DEA) is sensitivity and stability region of the speci c decision making unit (DMU), included ecient and inecient DMUs. In sensitivity analysis of ecient DMUs,the largest region should be found namely stability region thatdata variations are only for ecient DMU under evaluation and the data for the remainingDMUs are assumed xed. Also ecient DMU u...