Volume 20, Issue 1 (3-2023)
jor 2023, 20(1): 79-95 |
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Nazari S, Rostamy Malkhalifeh M, Hamzehee A. Measuring Congestion of the Undesirable Outputs Using the Fuzzy Data. jor 2023; 20 (1) :79-95
URL: http://jamlu.liau.ac.ir/article-1-2069-en.html
URL: http://jamlu.liau.ac.ir/article-1-2069-en.html
Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran, Iran , ohsen_rostamy@yahoo.com
Abstract: (989 Views)
Congestion is one of the main issues in the Data Envelopment Analysis (DEA) literature which the decision maker (DM) can use it to decide about changing the size of a particular decision making unit (DMU). Many scholars have been attracted to recognizing and measuring the congestion of DMUs. Congestion occurs when the reduction (increment) in some inputs results in the maximum possible increase (decrease) in some outputs without worsening (improving) other input/output. If a DMU shows the input congestion, then we can reduce the production cost by reducing its inputs. On the other hand, congestion results in decreasing the outputs, hence, if the congestion is omitted, then the outputs should be increased and so the obtained profit is increased. The existing methods in the literature only consider the congestion in the presence of the desirable outputs. The problem of the congestion assessment of DMUs by using DEA may not be straightforward due to the data uncertainty. Several studies have been developed to incorporate uncertainty into input/output values in the DEA literature. On the other hand, while traditional DEA models focus more on crisp data, there exist many applications in which data are reported in the form of fuzzy data. This study focuses on measuring the congestion in the presence of the undesirable outputs by using the fuzzy data. For this purpose, we propose a model to recognize and measuring the congestion of DMUs.
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