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Improving the discrimination power with a new multi-criteria data envelopment model

Author

Listed:
  • Aneirson Francisco Silva

    (São Paulo State University)

  • Fernando Augusto S. Marins

    (São Paulo State University)

  • Erica Ximenes Dias

    (São Paulo State University)

Abstract

Data envelopment analysis (DEA) allows evaluation of the relative efficiencies of similar entities, known as decision making units (DMUs), which consume the same types of resources and offer similar types of products. It is known that under certain circumstances, when the number of DMUs does not meet the DEA Golden Rule, that is, this number is not sufficiently large compared to the total number of inputs and outputs, traditional DEA models often yield solutions that identify too many DMUs as efficient. In fact, this weak discrimination power and unrealistic weight distribution presented by DEA models remain a major challenge, leading to the development of models to improve this performance, such as: multiple criteria data envelopment analysis (MCDEA), bi-objective multiple criteria data envelopment analysis, goal programming approaches to solve weighted goal programming (WGP–MCDEA) and extended–MCDEA. This paper proposes a new MCDEA model which is based on goal programming, with and without super efficiency concepts, and presents test results that show its advantages over the above cited models. A set of problems from the literature and real-world applications are used in these tests. The results show that the new MCDEA model provides better discrimination of DMUs in all tested problems, and provides a weight dispersion that is statistically equal to that obtained by other MCDEA models. An additional feature of the proposed model is that it allows the identification of the input and output variables that are most important to the problem, to make it easier for the decision maker to improve the efficiency of the DMUs involved. This is very useful in practice, because in general, the available resources are scarce, so it is a further advantage of the proposed MCDEA model over the others tested.

Suggested Citation

  • Aneirson Francisco Silva & Fernando Augusto S. Marins & Erica Ximenes Dias, 2020. "Improving the discrimination power with a new multi-criteria data envelopment model," Annals of Operations Research, Springer, vol. 287(1), pages 127-159, April.
    • Handle: RePEc:spr:annopr:v:287:y:2020:i:1:d:10.1007_s10479-019-03446-1
    DOI: 10.1007/s10479-019-03446-1
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    References listed on IDEAS

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    1. Per Andersen & Niels Christian Petersen, 1993. "A Procedure for Ranking Efficient Units in Data Envelopment Analysis," Management Science, INFORMS, vol. 39(10), pages 1261-1264, October.
    2. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    3. Adler, Nicole & Friedman, Lea & Sinuany-Stern, Zilla, 2002. "Review of ranking methods in the data envelopment analysis context," European Journal of Operational Research, Elsevier, vol. 140(2), pages 249-265, July.
    4. Cook, Wade D. & Seiford, Larry M., 2009. "Data envelopment analysis (DEA) - Thirty years on," European Journal of Operational Research, Elsevier, vol. 192(1), pages 1-17, January.
    5. Rubem, Ana Paula dos Santos & Soares de Mello, João Carlos C.B. & Angulo Meza, Lidia, 2017. "A goal programming approach to solve the multiple criteria DEA model," European Journal of Operational Research, Elsevier, vol. 260(1), pages 134-139.
    6. Yongbo Li & Amir-Reza Abtahi & Mahya Seyedan, 2019. "Supply chain performance evaluation using fuzzy network data envelopment analysis: a case study in automotive industry," Annals of Operations Research, Springer, vol. 275(2), pages 461-484, April.
    7. Timothy Anderson & Keith Hollingsworth & Lane Inman, 2002. "The Fixed Weighting Nature of A Cross-Evaluation Model," Journal of Productivity Analysis, Springer, vol. 17(3), pages 249-255, May.
    8. Hadi Moheb-Alizadeh & Robert Handfield, 2018. "An integrated chance-constrained stochastic model for efficient and sustainable supplier selection and order allocation," International Journal of Production Research, Taylor & Francis Journals, vol. 56(21), pages 6890-6916, November.
    9. Renata Machado de Andrade & Suhyung Lee & Paul Tae-Woo Lee & Oh Kyoung Kwon & Hye Min Chung, 2019. "Port Efficiency Incorporating Service Measurement Variables by the BiO-MCDEA: Brazilian Case," Sustainability, MDPI, vol. 11(16), pages 1-18, August.
    10. Hashem Omrani & Farzane Adabi & Narges Adabi, 2017. "Designing an efficient supply chain network with uncertain data: a robust optimization—data envelopment analysis approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(7), pages 816-828, July.
    11. A. Charnes & W. W. Cooper, 1962. "Programming with linear fractional functionals," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 9(3‐4), pages 181-186, September.
    12. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    13. Li, Xiao-Bai & Reeves, Gary R., 1999. "A multiple criteria approach to data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 115(3), pages 507-517, June.
    14. Dimitrov, Stanko & Sutton, Warren, 2010. "Promoting symmetric weight selection in data envelopment analysis: A penalty function approach," European Journal of Operational Research, Elsevier, vol. 200(1), pages 281-288, January.
    15. William W. Cooper & Lawrence M. Seiford & Kaoru Tone, 2006. "Introduction to Data Envelopment Analysis and Its Uses," Springer Books, Springer, number 978-0-387-29122-2, December.
    16. Lin, Ruiyue & Liu, Yue, 2019. "Super-efficiency based on the directional distance function in the presence of negative data," Omega, Elsevier, vol. 85(C), pages 26-34.
    17. Ghasemi, M.-R. & Ignatius, Joshua & Emrouznejad, Ali, 2014. "A bi-objective weighted model for improving the discrimination power in MCDEA," European Journal of Operational Research, Elsevier, vol. 233(3), pages 640-650.
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