Issue |
RAIRO-Oper. Res.
Volume 56, Number 4, July-August 2022
|
|
---|---|---|
Page(s) | 2159 - 2179 | |
DOI | https://doi.org/10.1051/ro/2022100 | |
Published online | 20 July 2022 |
Improving weak efficiency frontier in a variable returns to scale stochastic data envelopment analysis model
1
Department of Industrial Engineering, Faculty of Engineering, Eastern Mediterranean University, Famagusta, TRNC, Via Mersin 10, Famagusta, Turkey
2
Industrial Engineering Technology, Higher Colleges of Technology, Sharjah, UAE
3
CERIS – Civil Engineering Research and Innovation for Sustainability, Instituto Superior, Técnico, University of Lisbon, Lisbon, Portugal
* Corresponding author: mibrahim1@hct.ac.ae; dalahs2011@gmail.com
Received:
31
January
2022
Accepted:
11
June
2022
The conventional stochastic data envelopment analysis (SDEA) model suffers from biased efficiency scores for units located at the weak efficient frontier or compared to the weak frontier. This study modifies the weak efficient hyperplane(s) while maintaining the general production function by restricting the gradients of weak efficient hyperplanes in the original model using facet analysis. Empirical analysis on environmental efficiency of sustainable development goals validates the results of the modification. Results of the modified model compared to the conventional model show change in efficiency scores of weak efficient units and those compared to the weak part of the frontier while the efficiency scores of the strong efficient frontier remain the same. Furthermore, the proposed model shows greater discriminatory power compared to the conventional model, hence, providing a reliable benchmark and improvement strategy post efficiency analysis.
Mathematics Subject Classification: 90B50 / 90C05 / 90C90
Key words: Efficiency stochastic data envelopment analysis (SDEA) / weak efficient frontier / facet analysis / sustainable development goals
© 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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