Crowding distance development for ranking efficient units in DEA

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 ...

Ranking efficient units by regular polygon area in DEA

Ranking Efficient Units in DEA by Using TOPSIS Method

There exist many different ranking methods for ranking efficient Decision Making Units (DMUs) in Data Envelopment Analysis (DEA). However, since each of these methods considers a certain theory for ranking, they may give different ranks. In practice, choosing a ranking method, the results of which the Decision Maker (DM) would be able to trust is an important issue. In this paper, we consider s...

Ranking DEA Efficient Units with the Most Compromising Common Weights

One may employ Data Envelopment Analysis (DEA) to discriminate decision-making units (DMUs) into efficient and inefficient ones base upon the multiple inputs and output performance indices. In this paper we consider that there is a centralized decision maker (DM) who ‘owns’ or ‘supervises’ all the DMUs. In such intraorganizational scenario the DM has an interest in discriminating the efficient ...

Ranking units with fuzzy data in DEA

In this study, both optimistic and pessimistic approaches of data envelopment analysis are applied to propose an equitable ranking method in fuzzy environments. To this end, we suppose that the sum of efficiency scores of all decision making units (DMUs) equals to unity. Using the worst-best and best-worst approaches, the minimum and maximum possible efficiency scores of each DMU are estimated ...