New insights on clustering DMUs based on congestion: A DEA approach

Congestion is one of the important concepts in data envelopment analysis that is a decrease (increase) in one or more inputs of a decision making unit (DMU) causes an increase (decrease) in one or more outputs. In this vein, Tone and Sahoo’s congestion approach is one of the most robust congestion approaches in DEA (data envelopment analysis). However, in the presence of alternative optimal solutions, Tone and Sahoo’s approach is unable to detect congestion (strong and weak). Moreover, in their approach, all inputs and outputs of decision making units (DMUs) have been considered positive, while in real world, data is often non-negative. In this research, a slack-based DEA approach is first proposed to recognize congestion (strong and weak) of DMUs and then, DMUs are clustered in three clusters. One of the advantages of our approach is capable of detecting congestion (strong and weak) in the presence of alternative optimal solutions. Anther advantage is capable of identifying congesting (strong and weak) DMUs with non-negative inputs and outputs. Lastly, we apply the approach to the data sets for making comparisons between the proposed approach and Tone and Sahoo’s approach then some directions for future research are suggested.

کلیدواژه ها:

Data envelopment Congestion ، Slack ، Efficient ، Returns to scale (RTS)