Using Directional Distance Functions to Determine Ranking Ranges in Cross-efficiency Evaluations

Ranking ranges in cross-efficiency evaluations

The existence of alternate optima for the DEA weights may reduce the usefulness of the cross-efficiency evaluation, since the ranking provided depends on the choice of weights that the different DMUs make. In this paper, we develop a procedure to carry out the cross-efficiency evaluation without the need to make any specific choice of DEA weights. The proposed procedure takes into consideration...

Decomposing profit efficiency using a slack-based directional distance function

This paper develops a slack-based decomposition of profit efficiency based on a directional distance function.

Dynamic Efficiency Analysis using a Directional Distance Function

Ranking efficient DMUs using minimizing distance in DEA

In many applications, ranking of decision making units (DMUs) is a problematic technical task procedure to decision makers in data envelopment analysis (DEA), especially when there are extremely efficient DMUs. In such cases, many DEA models may usually get the same efficiency score for different DMUs. Hence, there is a growing interest in ranking techniques yet. The main purpose of this paper ...

Preference Voting and Ranking Using Dea Game Cross Efficiency Model

Abstract Data envelopment analysis (DEA), a useful assessment tool, has been used to solve the problem of preference voting and aggregation which require the determination of the weights associated with different ranking places. Instead of applying the same externally imposed weighting scheme to all candidates, DEA models allow each candidate to choose his/her own weights in order to maximize h...

Using Neural Network to Determine Input Excesses, Output Shortfalls and Efficiency of Dmus in Russell Mode

Data Envelopment Analysis (DEA) has two fundamental approaches for assessing theefficiency with different characteristics; radial and non-radial models. This paper isconcerned the non-radial model of Russell which is a non linear model. Conventional DEAfor a large dataset with many inputs/outputs would require huge computer resources in termsof memory and CPU time. Artificial Neural Network (AN...