Data envelopment analysis (DEA) calculates the relative efficiency of homogenous decision-making units (DMUs) with multiple inputs and outputs. Classic DEA models usually suffer from several issues such as: discrimination power, variable weights of inputs/outputs, inaccurate efficiency estimation for small number of DMUs, incapability in working with zero and negative data, and not having exter...

Data envelopment analysis (DEA) is a representative method to estimate efficient frontiers and derive efficiency. However, in a situation with weight restrictions on individual input–output pairs, its suitability has been questioned. Therefore, the main purpose of this paper is to develop a mathematical method, which we call the input-oriented ratio-based comparative efficiency model, DEA-R-I, ...

data envelopment analysis (dea) as a non-parametric method for efficiency measurement allows decision making units (dmus) to select the most advantageous weight factors in order to maximize their efficiency scores.  in most practical applications of dea presented in the literature, the presented models assume that all inputs are fully desirable. however, in many real situations undesirable inpu...

abstract. a practical common weight scalarizing function methodology with an improved discriminating power for technology selection is introduced. the proposed scalarizing function methodology enables the evaluation of the relative efficiency of decision-making units (dmus) with respect to multiple outputs and a single exact input with common weights. its robustness and discriminating power are...

technology selection is an important part of management of technology. recently karsak and ahiska (2005) proposed a novel common weight multiple criteria decision making (mcdm) methodology for selection of the best advanced manufacturing technology (amt) candidates based on a number of attributes. however, amin et al. (2006), by means of a numerical example demonstrated the convergence difficul...

In data envelopment analysis (DEA), mul-tiplier and envelopment CCR models eval-uate the decision-making units (DMUs) under optimal conditions. Therefore, the best prices are allocated to the inputs and outputs. Thus, if a given DMU was not efficient under optimal conditions, it would not be considered efficient by any other models. In the current study, using common weights in DEA, a number of...

Deriving weights from a pairwise comparison matrix (PCM) is a subject for which a wide range of methods have ever been presented. This paper proposes a common weight multi criteria decision analysis-data envelopment analysis (MCDA-DEA) approach with assurance region for weight derivation from a PCM. The proposed model has several merits over the competing approaches and removes the drawbacks of...

Data envelopment analysis (DEA) is recognized as a powerful analytical research tool for performance evaluations by obtaining empirical estimates of relations between multiple inputs and multiple outputs. In order to further embody the hierarchical structures of numerous performance evaluation problems in the DEA framework, a generalized multiple layer DEA (MLDEA) model is proposed, and its lin...

Technology selection is an important part of management of technology. Recently Karsak and Ahiska (2005) proposed a novel common weight multiple criteria decision making (MCDM) methodology for selection of the best Advanced Manufacturing Technology (AMT) candidates based on a number of attributes. However, Amin et al. (2006), by means of a numerical example demonstrated the convergence difficul...