A SBM DEA Model of Performance Generation via Input–Output Ratios under Pseudo-Returns to Scale

Authors

  • Sohiela Dehghan-Chenari * Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran, Iran.
  • Farhad Hossienzadeh-Lotfi Research Center of Performance and Productivity Analysis, Istinye University, Istanbul, Turkiye.

https://doi.org/10.22105/raise.vi.98

Abstract

Data Envelopment Analysis with ratio measures (DEA-R) provides an effective framework for evaluating the performance of decision-making units based on ratio indicators. However, existing DEA-R studies generally treat outputs as a homogeneous set and overlook systems in which outputs play different functional roles and exhibit distinct scaling behaviors. This study introduces a novel ratio-based technology with two categories of outputs. The first category is combined with inputs to construct ratio-based performance indicators, whereas the second category represents outcome outputs generated through the utilization of these performance structures. A new axiomatic framework is developed, and the concept of pseudo-returns to scale is extended to the ratio–output space. The proposed approach characterizes pseudo-returns to scale within ratio-based technologies. The framework provides a new perspective for analyzing organizations in which performance indicators and outcome indicators respond differently to resource utilization and operational development.

Keywords:

Data envelopment analysis, Ratio-based technology, Pseudo-returns to scale, Ratio performance indicators, Outcome outputs

References

  1. [1] Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444. https://doi.org/10.1016/0377-2217(78)90138-8

  2. [2] Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management science, 30(9), 1078–1092. https://doi.org/10.1287/mnsc.30.9.1078

  3. [3] Thanassoulis, E., Boussofiane, A., & Dyson, R. G. (1996). A comparison of data envelopment analysis and ratio analysis as tools for performance assessment. Omega, 24(3), 229–244. https://doi.org/10.1016/0305-0483(95)00060-7

  4. [4] Despić, O., Despić, M., & Paradi, J. C. (2007). DEA-R: Ratio-based comparative efficiency model, its mathematical relation to DEA and its use in applications. Journal of Productivity Analysis, 28(1), 33–44. https://doi.org/10.1007/s11123-007-0050-x%0A%0A

  5. [5] Emrouznejad, A., & Amin, G. R. (2009). DEA models for ratio data: Convexity consideration. Applied Mathematical Modelling, 33(1), 486–498. https://doi.org/10.1016/j.apm.2007.11.018

  6. [6] Mozaffari, M. R., Gerami, J., & Jablonsky, J. (2014). Relationship between DEA models without explicit inputs and DEA-R models. Central European Journal of Operations Research, 22(1), 1–12. https://doi.org/10.1007/s10100-012-0273-4%0A%0A

  7. [7] Azizi, H. (2017). Output-input ratio analysis and data envelopment analysis inefficient frontier. International Journal of Applied Operational Research-An Open Access Journal, 7(3), 11–21. https://ijorlu.lahijan.iau.ir/article-1-564-en.pdf

  8. [8] Hatami-Marbini, A., & Toloo, M. (2019). Data envelopment analysis models with ratio data: A revisit. Computers & Industrial Engineering, 133, 331–338. https://doi.org/10.1016/j.cie.2019.04.041

  9. [9] Gerami, J., Mozaffari, M. R., & Wanke, P. F. (2020). A multi-criteria ratio-based approach for two-stage data envelopment analysis. Expert Systems With Applications, 158, 113508. https://doi.org/10.1016/j.eswa.2020.113508

  10. [10] Gerami, J., Mozaffari, M. R., Wanke, P. F., & Correa, H. (2022). A novel slacks-based model for efficiency and super-efficiency in DEA-R. Operational Research, 22(4), 3373–3410. https://doi.org/10.1007/s12351-021-00679-6%0A%0A

  11. [11] Ostovan, S., Mozaffari, M. R., Jamshidi, A., & Gerami, J. (2020). Evaluation of two-stage networks based on average efficiency using DEA and DEA-R with fuzzy data. International Journal of Fuzzy Systems, 22(5), 1665–1678. https://doi.org/10.1007/s40815-020-00896-9%0A%0A

  12. [12] Sohrabi, A., Gerami, J., & Mozaffari, M. R. (2022). A novel inverse DEA-R model for inputs/output estimation. Journal of Mathematical Extension, 16. https://ijmex.com/index.php/ijmex/article/download/2047/1546

  13. [13] Gerami, J., Kiani Mavi, R., Farzipoor Saen, R., & Kiani Mavi, N. (2023). A novel network DEA-R model for evaluating hospital services supply chain performance. Annals of Operations Research, 324(1), 1041–1066. https://doi.org/10.1007/s10479-020-03755-w%0A%0A

  14. [14] Hosseinzadeh Lotfi, F., Allahviranloo, T., Pedrycz, W., Mozaffari, M. R., & Gerami, J. (2023). Relationship between ratio analysis, DEA-R and DEA models. Comparative Efficiency in Data Envelopment Analysis Based on Ratio Analysis (pp. 1–21). Springer. https://doi.org/10.1007/978-3-031-43181-4_1%0A%0A

  15. [15] Wanke, P., Ostovan, S., Mozaffari, M. R., Gerami, J., & Tan, Y. (2023). Stochastic network DEA-R models for two-stage systems. Journal of Modelling in Management, 18(3), 842–875. https://doi.org/10.1108/JM2-10-2021-0256

  16. [16] Keshtkar, B., Mozaffari, M. R., Feylizadeh, M. R., & Maddahi, R. (2024). Two-stage network models in DEA and DEA-R with desirable and undesirable outputs. Journal of Mathematical Extension, 18. https://www.ijmex.com/index.php/ijmex/article/download/3011/1612

  17. [17] Cooper. (1962). Programming with linear fractional functionals. Naval Research Logistics Quarterly, 9(3), 181–186. https://doi.org/10.1002/nav.3800090303

Published

2026-08-25

How to Cite

Dehghan-Chenari, S. ., & Hossienzadeh-Lotfi, F. . (2026). A SBM DEA Model of Performance Generation via Input–Output Ratios under Pseudo-Returns to Scale. Research Annals of Industrial and Systems Engineering, 3(3), 160–169. https://doi.org/10.22105/raise.vi.98

Similar Articles

11-20 of 61

You may also start an advanced similarity search for this article.