Data envelopment analysis is a method for evaluating the relative efficiency of a collection of decision making units. The DEA classic models calculate each unit’s efficiency in the best condition, meaning that finds a weight that the DMU is at its maximum efficiency. In this paper, utilizing the directional distance function model in the presence of undesirable outputs, the efficiency of each ...

In this paper we introduce a scale elasticity measure based on directional distance function for multi-output-multi-input technologies and explore its fundamental properties. Specifically, we derive necessary and sufficient condition for equivalence of the scale elasticity measure based on the directional distance function with the input oriented and output oriented scale elasticity measures. W...

Undesirable outputs are often produced together with desirable. This joint production of good and bad outputs bring about a difficulty for productivity measurement. Here we introduce a directional distance function and use it as a component in a new productivity index. This index, as an empirical example shows, seems to solve the problem caused by the joint production of good and bad outputs. P...

In this paper, two non-linear technologies are proposed based on weak disposability definitions: weak disposability with non-uniform abatement factors and new weak disposability. Both technologies are applied to Spanish airport systems and the existing technologies are modified. To remove the computational complexity of non-linear approaches, the linearization methods are proposed. Then, in ord...

This paper develops a new factor-analysis-based (FAB) approach for choosing the optimal direction in directional distance function (DDF) analysis. It has combined merits of factor analysis and slacks-based measure (SBM) incorporates relative ease with which various input-output could be adjusted. development relieves reliance on price information that is difficult to obtain provision public goo...