SpringerLink
Log in

Exploring Congestion in Fuzzy DEA by Solving One Model; Case Study: Hospitals in Tehran

  • Chapter
  • First Online:
Decision Making in Healthcare Systems

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 513))

  • 178 Accesses

Abstract

In recent decades, the number of private hospitals has increased in many countries, leading to an oversupply of drugs in the country's medical industry. This leads to hospital congestion and a decline in hospital profits, efficiency, and performance. One of the appropriate methods to detect congestion is Data Envelopment Analysis (DEA). In this study, we identified congested Tehran hospitals with fuzzy and vague data. For this purpose, we developed for the first time the methods proposed by Shadab et al. (Shadab, M., Saati, S., Farzipoor Saen, R., Mostafaee. (2021). Detecting congestion in DEA by solving one model. Operations Research and Decisions. 77–97.) and presented a novel fuzzy nonlinear DEA model for identifying congested hospitals as Decision Making Units (DMUs). We improved the method suggested by Saati et al. (Saati et al. in Fuzzy Optimization Decision making. 1:255–267, 2002) and transformed our proposed possibilistic model into a crisp nonlinear DEA model. The type of congestion (weak and strong) and its magnitude are also determined. Finally, the proposed model is used in an empirical example to identify 15 congested hospitals in Tehran (the capital of Iran) and measure the role of congestion and its magnitude to improve their performance. The result showed that congestion occurred in 39.9% of 15 hospitals.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
EUR 29.95
Price includes VAT (Germany)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 117.69
Price includes VAT (Germany)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
EUR 160.49
Price includes VAT (Germany)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Charnes, A., Cooper, W.W., Rhodes, E.: Measuring the efficiency of decision-making units. Eur. J. Oper. Res. 2, 429–444 (1978)

    Google Scholar 

  2. Banker, R.D., Charnes, A., Cooper, W.W: Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manag. Sci. 30(9), 1078–1092 (1984)

    Google Scholar 

  3. Banker, R.D., Amirteimoori, A., Allahviranloo, T., Sinha, R.P.: Performance analysis and managerial ability in the general insurance market: a study of India and Iran. Inf. Technol. Manage. (2023). https://doi.org/10.1007/s10799-023-00405-y

    Article  Google Scholar 

  4. Amirteimoori, A., Allahviranloo, T., Kordrostami, S., Bagheri, S.F.: Improving decision-making units in performance analysis methods: a data envelopment analysis approach. Math. Sci. (2023). https://doi.org/10.1007/s40096-023-00512-5

    Article  Google Scholar 

  5. Amirteimoori, A., Allahviranloo, T., Zadmirzaei, M.: Scale elasticity and technical efficiency analysis in the European forest sector: a stochastic value-based approach. Eur. J. Forest Res. (2023). https://doi.org/10.1007/s10342-023-01589-2

    Article  Google Scholar 

  6. Mahmoodirad, A., Allahviranloo, T., Niroomand, S.: A new effective solution method for fully intuitionistic fuzzy transportation problem. Soft Comp. 23(12), 4521–4530 (2019)

    Google Scholar 

  7. Fare, R., Svensson, L.: Congestion of production factors. Econ. J. Econ. Soc. 48, 1745–1753 (1984)

    Google Scholar 

  8. Fare, R., Grosskopf, S.: Measuring congestion in production. Zeitsc hrift fur National Okonomie 43, 257–271 (1983)

    Google Scholar 

  9. Jahanshahloo, G.R., Khodabakhshi, M.: Suitable combination of inputs for improving outputs in DEA with determining input congestion considering textile industry of China. Appl. Math. Comp. 151, 263–273 (2004)

    Google Scholar 

  10. Khodabakhshi, M.: Chance constrained additive input relaxation model in stochastic data envelopment analysis. J. Inform Syst. Sci. 6(1), 99–112 (2010)

    MathSciNet  Google Scholar 

  11. Noura, A.A., Hosseinzadeh Lotfi, F., Jahanshahloo, G.R., Fanati Rashidi, S., Parker, B.R: A new method for measuring congestion in data envelopment analysis. Socio-Econ. Plan. Sci. 44, 240–246 (2010)

    Google Scholar 

  12. Wei, Q.L., Yan, H.: Congestion and returns to scale in data envelopment analysis. Eur. J. Oper. Res. 153, 641–660 (2004)

    Google Scholar 

  13. Cooper, W.W., Thompson, R.G., Thrall, R.: Introduction extensions and new developments in DEA. Ann. Operat. Res. 66, 3–45 (1996)

    Google Scholar 

  14. Cooper, W.W., Seiford, L.M., Zhu, J.: A unified additive model approach for evaluating inefficiency and congestion with associated measures in DEA. Socioecon. Plann. Sci. 4, 1–25 (2000)

    Google Scholar 

  15. Cooper, W.W., Deng, H., Huang, Z.M., Li, S.L: A one model approach to congestion in DEA. Socio-Econ. Plan. Sci. 36, 231–238 (2002)

    Google Scholar 

  16. Tone, K., Sahoo, B.K: Degree of scale economics and economics and congestion: a unified DEA approach. Eur. J. Operat. Res. 153, 641–660 (2004)

    Google Scholar 

  17. Sueyoshi, T., Sekitiani, K.: DEA congestion and returns to scale under an occurrence of multiple optimal projections. Eur. J. Oper. Res. 194, 592–607 (2009)

    Google Scholar 

  18. Mehdiloozad, M., Zhu, J., Sahoo, B.K: Identification of congestion in data envelopment analysis under the occurrence of multiple projections: A reliable method capable of dealing with negative data. Eur. J. Operat. Res. 265, 644–654 (2018)

    Google Scholar 

  19. Ebrahimzade Adimi, M., Rostamy-Malkhalifeh, M., Hosseinzadeh Lotfi, F., Mehrjoo, R.: A new linear to find the congestion hyperplane in DEA. J. Math. Sci. 13, 43–52 (2019)

    Article  MathSciNet  Google Scholar 

  20. Kerstens, K., Van de Woestyne, I.: The remarkable incidence of congestion in production: a review, empirical illustration, and research agenda. Data Envelop. Anal. J. 4(2), 109–147 (2019)

    Google Scholar 

  21. Shadab, M., Saati, S., Farzipoor Saen, R., Mostafaee, A.: Measuring congestion by anchor points in DEA. J. Sadhana 45(1), 37–47 (2020)

    Google Scholar 

  22. Shadab, M., Saati, S., Farzipoor Saen, R., Mostafaee, A.: Detecting congestion in DEA by solving one model. Operat. Res. Decis. 77–97 (2021)

    Google Scholar 

  23. Bloom, N., Propper, C., Seiler, S., Reenen, J.V.: The impact of competition on management quality: evidence from public hospitals. Natl. Bureau Econ. Res. 82, 37–77 (2010)

    Google Scholar 

  24. Cooper, Z., Gibbons, S., Skellern, M.: Does competition from private surgical centres improve public hospitals’ performance? Evidence from the english national health service. J. Public Econ. 166, 63–80 (2018)

    Article  Google Scholar 

  25. Park, S.H., Kim, D.C.: Congestion and efficiency analysis of public hospitals. Prod. Rev. 29, 61–29 (2015)

    Google Scholar 

  26. Simões, P., Marques, R.C.: Performance and congestion analysis of the Portuguese hospital services centre. Eur. J. Operat. Res. 19, 39–63 (2009)

    Google Scholar 

  27. Park, S., Ko, J.H., Bae, E.S., Chang, M., Kim, D.: The impact of hospital specialization on congestion and efficiency. Sustain. J. 14, 32–46 (2019)

    Google Scholar 

  28. Cooper, W.W., Deng, H., Huang, Z.M., Li, S.L.: A one model approach to congestion in DEA. Socio-Econ. Plan. Sci. 36, 231–238 (2002)

    Article  Google Scholar 

  29. Besancenot, D., Sirven, N., Vranceanu, R.: A model of hospital congestion in develo** countries. ESSEC Work. Paper 1804, 72–94 (2018)

    Google Scholar 

  30. Hou, W., Qin, S., Thompson, C.H.: A vertual evaluation of options for managing risk of hospital congestion with minimum intervention. Sci. Rep. 12, 34–46 (2022)

    Article  Google Scholar 

  31. Zadeh, L.A.: Fuzzy sets. Inf. Control. 8, 338–353 (1965)

    Article  Google Scholar 

  32. Zhou, W., Xu, Z.: An overview of the fuzzy data envelopment analysis research and its successful applications. Int. J. Fuzzy Syst. 22, 1037–1055 (2020)

    Article  MathSciNet  Google Scholar 

  33. Ahmadvand, S., Pishvaee, M.S.: An efficient method for kindly allocation problem: a credibility- based fuzzy common weights data envelopment analysis aproach. Health Care Manage. 21(4), 587–603 (2018)

    Article  Google Scholar 

  34. Saati, S., Hatami Marbini, A., Tavana, M., Agrell, P.J.: A fuzzy data envelopment analysis for clustring operating units with imprecies data. Int. J. Uncert. Fuzz. Knowl. Based Syst. 21, 29–54 (2014)

    Google Scholar 

  35. Esfandiari, M., Saati, S.: Data Envelopment Analysis with fuzzy complex numbers with an emprical case on power plans of Iran. Rairo-Oerat. Res. 2013–2025 (2021)

    Google Scholar 

  36. Saati, S., Memariani, A., Jahanshahloo, G.R.: Efficiency analysis and ranking of DMUs with fuzzy data. Fuzzy Optim. Decis. Mak. 1, 255–267 (2002)

    Article  Google Scholar 

  37. Amirteimoori, A., Allahviranloo, T., Zadmirzaei, M., Hasanzadeh, F.: On the environmental performance analysis: a combined fuzzy data envelopment analysis and artificial intelligence algorithms. Expert Syst. Appl. 224, 119953 (2023). https://doi.org/10.1016/j.eswa.2023.119953

    Article  Google Scholar 

  38. Rahmani, A., Lotfi, F.H., Rostamy-Malkhalifeh, M., Allahviranloo, T.: A new method for defuzzification and ranking of fuzzy numbers based on the statistical beta distribution. Adv. Fuzzy Syst. 2016, 1–8 (2016)

    Google Scholar 

  39. Arya, A., Yadav, S.P.: Development of FDEA models to measure the performance efficiencies of DMUs. Int. J. Fuzzy Syst. 20, 163–173 (2018)

    Google Scholar 

  40. Sengupta, J.K.: A fuzzy systems approach in data envelopment analysis. Comput. Math. Appl. 24(8–9), 259–266 (1992)

    Article  MathSciNet  Google Scholar 

  41. Dubois, D., Prade, H.: Possibility Theory: An Approach to Computerized Processing of Uncertainty. Plenum Press, New York (1988)

    Google Scholar 

  42. Kaufmann, A., Gupta, M.M.: Introduction to Fuzzy Arithmetic: Theory and Applications. Van Nostrand Reinhold, New York (1991)

    Google Scholar 

  43. Zimmermann, H.J.: Fuzzy Set Theory-and Its Applications, 4th edn. Kluwer Acadmic Publishers, Norwell, MA (2001)

    Book  Google Scholar 

  44. Meada, Y., Entani, T., Tanaka, H.: Fuzzy DEA with interval efficiency. In: 6th European Congress on Intelligent Techniques and Soft Computing vol. 2, pp. 1067–1071 (1998)

    Google Scholar 

  45. Wang, Y.M., Chin, K.S., Yang, J.B.: Measuring the performance of decision-making units using geometric average efficiency. J. Operat. Res. Soc. 58, 929–937 (2007)

    Article  Google Scholar 

  46. Allahviranloo, T., Ezadi, S.: Z-Advanced numbers processes. Inf. Sci. 480, 130–143 (2019)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Departement of Mathematics, North Tehran Branch, Islamic Azad University, Tehran, Iran

    Saber Saati, Maryam Shadab & Sajedeh Mohamadniaahmadi

Authors
  1. Saber Saati
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maryam Shadab
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sajedeh Mohamadniaahmadi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Saber Saati .

Editor information

Editors and Affiliations

  1. Faculty of Engineering and Natural Sciences, Istinye University, Istanbul, Türkiye

    Tofigh Allahviranloo

  2. Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Farhad Hosseinzadeh Lotfi

  3. School of Information Technology, Deakin University, Waurn Ponds Campus, Geelong, VIC, Australia

    Zohreh Moghaddas

  4. Department of Mathematics, Islamic Azad University, Rasht, Iran

    Mohsen Vaez-Ghasemi

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Saati, S., Shadab, M., Mohamadniaahmadi, S. (2024). Exploring Congestion in Fuzzy DEA by Solving One Model; Case Study: Hospitals in Tehran. In: Allahviranloo, T., Hosseinzadeh Lotfi, F., Moghaddas, Z., Vaez-Ghasemi, M. (eds) Decision Making in Healthcare Systems. Studies in Systems, Decision and Control, vol 513. Springer, Cham. https://doi.org/10.1007/978-3-031-46735-6_15

Download citation

Publish with us

Policies and ethics

Search

Navigation