Volume 56, Number 4, July-August 2022
Recent developments of operations research and data sciences
|Page(s)||2067 - 2092|
|Published online||11 July 2022|
Congestion and non-congestion areas: identify and measure congestion in DEA
Department of mathematics, Yadegar-e-Imam Khomeini (RAH), Shahre Rey Branch, Islamic Azad University, Tehran, Iran
Accepted: 31 May 2022
Detecting the weak and strong congestion statuses of decision-making units (DMUs) and measuring them via data envelopment analysis (DEA) is an important issue that has been discussed in several studies and with different views. The efficiency frontier is a concept derived from the underlying production possibility set (PPS), and the congestion concept is related to them. Still, researchers have defined congestion for each DMU in many previous studies and ignored that congestion is linked with the underlying production technology. In the congestion measurement matter, this paper presents two new insights into a congestion area and non-congestion area for production technology and two new mathematical definitions of congestion based on the PPS properties and detecting the weak and strong congestion status of DMUs (CSOD). We prove our definitions are equal to the original definition of congestion. First, we describe the congestion and non-congestion areas based on the PPS; then provide full details of how to measure congestion built on these new insights. Our approaches are very accurate and fast to calculate; they are theoretically elementary and efficient in performance. Our proposed methods can deal with both non-negative and negative data. Finally, an empirical example is provided to illustrate our approaches.
Mathematics Subject Classification: 90C08 / 97M40 / 60K30
Key words: Data envelopment analysis (DEA) / congestion starting point (CSP) / efficiency / congestion area (CA) / non-congestion area (NCA) / production possibility set (PPS)
© The authors. Published by EDP Sciences, ROADEF, SMAI 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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