This study examines the concept of congestion in Data Envelopment Analysis (DEA), with particular emphasis on the complications arising from the existence of multiple optimal solutions. Unlike conventional approaches that assume a unique optimal solution, the presence of multiple projections can lead to ambiguity in measuring congestion and weaken its economic interpretation. To address this issue, a modified DEA-based framework is proposed to identify and measure congestion under mSultiple solutions. The study also investigates the relationship between congestion and returns to scale, highlighting their close theoretical connection. In addition, an alternative method is presented and compared with traditional approaches to evaluate its performance in detecting congestion. To validate the proposed analysis, numerical examples from the textile and automobile industries are examined. The comparative results reveal that while different methods may produce similar outcomes for some inputs, notable differences emerge in others, particularly when efficient units with higher input consumption exist. These findings indicate that not all inefficiencies can be attributed to congestion, and improper measurement may lead to misleading conclusions.
Research Article | Open Access | Download Full Text
Volume 4 | Issue 1 | Year 2026 | Article Id: MS-V4I1P102 DOI: https://doi.org/10.59232/MS-V4I1P102
Congestion Measurement in DEA under Multiple Optimal Solutions: A Comparative Study
Susan Peterson
| Received | Revised | Accepted | Published |
|---|---|---|---|
| 24 Nov 2025 | 20 Dec 2025 | 22 Jan 2026 | 28 Feb 2026 |
Citation
Susan Peterson. “Congestion Measurement in DEA under Multiple Optimal Solutions: A Comparative Study.” DS Journal of Modeling and Simulation, vol. 4, no. 1, pp. 7-21, 2026.
Abstract
Keywords
Data envelopment analysis, Congestion, Linear Programming, Efficiency, Decision Making Unit (DMU).
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