Synergizing IFTOPSIS and DEA for Enhanced Efficiency Analysis in Inpatient Units

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Cholida Usi Wardani
Sobri Abusini
Isnani Darti


Efficiency, Data envelopment analysis, fuzzy, TOPSIS


The pursuit of efficiency in the business sector is a multifaceted endeavor, extending beyond mere cost reduction to encompass a strategic optimization of operational performance. The enhancement of efficiency is not solely for the benefit of investors or proprietors but is also a concerted effort to maximize resource utilization and minimize waste. This study introduces an integrative approach combining IFTOPSIS and DEA methodologies to deliver a robust efficiency evaluation framework.The fusion of IFTOPSIS's qualitative analysis with DEA's quantitative assessments addresses the complexity of operational performance, providing a balanced evaluation that transcends subjective bias with data-driven insights. IFTOPSIS articulates decision-makers' preferences in uncertain scenarios, assigning weights to criteria, while DEA discriminates between efficient and inefficient operational units. This confluence of methods is applied to the assessment of inpatient healthcare units—a sector that has traditionally relied on patient-centric evaluations, neglecting the comprehensive review of resource deployment. The results of this amalgamated approach reveal dimensions of operational efficiency previously unexplored, offering stakeholders a data-enriched foundation for strategic decision-making. The study's findings have significant implications for the healthcare industry, providing a template for resource evaluation that could inform policy and drive improvements in patient care services.


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[1] World Health Organization, “The World Health Report 2000,” Heal. Syst. Improv. Perform., 2000.
[2] J. Cylus, I. Papanicolas, and P. C. Smith, “How to make measurement matter for policy and management,” Heal.
Syst. efciency, 2016.
[3] B. Hollingsworth, “Cost, production, efficiency, or effectiveness: where should we focus?,” Lancet Glob Heal., vol.
1, no. 5, pp. e249–50, 2013.
[4] K. S. Cavalluzzo and C. D. Ittner, “Implementing Performance Measurement Innovations: Evidence from
Government,” Accounting, Organ. Soc., vol. 29, pp. 243–267, 2004.
[5] M. Zeydan and C. Çolpan, “A new decision support system for performance measurement using combined fuzzy TOPSIS/DEA approach," Int. J. Prod. Res., vol. 47, no. 15, pp. 4327–4349, 2009, doi:
[6] K. F. Čiković and J. Lozić, “Application of Data Envelopment Analysis (DEA) in Information and
Communication Technologies,” Teh. Glas., vol. 1, pp. 129–134, 2022.
[7] B. S. Sahay, “Multi-factor productivity measurement model for service organizations,” Int. J. Product. Perform.
Manag., vol. 54, no. 1, pp. 7–22, 2005, doi: 10.1108/17410400510571419.
[8] C. W. Churchman, R. L. Ackoff, and E. L. Arnoff, Introduction to operations research. Wiley, New York, 1957. [9] C. L. Hwang and K. P. Yoon, “Multiple attribute decision making: methods and applications,” Lect. notes Econ.
Math. Syst., vol. 186, 1981.
[10] H. Deng, C. H. Yeh, and R. J. Willis, “Inter-company comparison using modified TOPSIS with objective weights,” Comput. Oper. Res., vol. 27, no. 10, pp. 963–973, 2000.
[11] T. L. Saaty, e analytic hierarchy process. New York: McGraw Hill, 1980.
[12] T. L. Saaty, Fundamentals of decision making and priority theory with the analytic hierarchy process, 6th ed.
Rws Publications, 2000.
[13] V. Belton and T. Gear, “On a short-coming of Saaty’s method of analytic hierarchies,” Omega, vol. 11, no. 3, pp.
228–230, 1983.
[14] F. A. Lootsma, Multi-criteria decision analysis via ratio and difference judgement, 29th ed. Springer Science & Business Media, 2007.
[15] B. Roy, The outranking approach and the foundations of ELECTRE methods. University of Paris-Dauphine: Document Du Lamsade, 1989.
[16] B. Roy, “The outranking approach and the foundations of ELECTRE methods,” Theory Decis., vol. 31, no. 1, pp.
49–73, 1991.
[17] B. Roy and P. Vincke, “Multicriteria analysis: survey and new directions,” Eur. J. Oper. Res., vol. 8, no. 3, pp.
207–218, 1981.
[18] P. L. Yu, “A class of solutions for group decision problems,” Manage. Sci., vol. 19, no. 8, pp. 936– 946, 1973. [19] M. Zeleny, Multiple criteria decision making. New York: McGraw-Hill, 1982.
[20] S. Opricovic, “Multicriteria optimization of civil engineering systems,” Fac. Civ. Eng. Belgrade, vol. 2, no. 1, pp.
5–21, 1998.
[21] J. P. Brans, B. Mareschal, and P. Vincke, “PROMITHEE: A new family of outranking methods in MCDM,” Oper.
Res., vol. 84, pp. 477–490, 1984.
[22] M. El Alaoui, Fuzzy TOPSIS. 2021. doi: 10.1201/9781003168416.
[23] S. Pascoe, T. Cannard, N. A. Dowling, C. M. Dichmont, F. Asche, and L. R. Little, “Use of Data Envelopment Analysis (DEA) to assess management alternatives in the presence of multiple objectives,” Mar. Policy, vol. 148, no. November 2022, p. 105444, 2023, doi: 10.1016/j.marpol.2022.105444.
[24] M. S. Gharibdousti and A. Azadeh, “Performance Evaluation of Organizations Based on Human Factor
Engineering Using Fuzzy Data Envelopment Analysis (FDEA),” J. Soc. Comput. Civ. Eng., vol. 3, no. 1, pp. 63–
90, 2019, doi: 10.22115/SCCE.2019.177180.1101.
[25] Y. Ersoy, “Performance Evaluation in Distance Education by Using Data Envelopment Analysis (DEA) and
TOPSIS Methods,” Arab. J. Sci. Eng., vol. 46, no. 2, pp. 1803–1817, 2021, doi: 10.1007/s13369-020-05087-0. [26] S. Akkoç and K. Vatansever, “Fuzzy Performance Evaluation with AHP and Topsis Methods: Evidence from
Turkish Banking Sector after the Global Financial Crisis,” Eurasian J. Bus. Econ., vol. 6, no. 11, pp. 53–74, 2013. [27] A. Bhattacharyya and S. Chakraborty, “A DEA-TOPSIS-based approach for performance evaluation of Indian
technical institutes,” Decis. Sci. Lett., vol. 3, no. 3, pp. 397–410, 2014, doi: 10.5267/j.dsl.2014.2.003.
[28] W. Yinghui and L. Wenlu, “e Application of Intuitionistic Fuzzy Set TOPSIS Method in Employee
Performance Appraisal,” Int. J. u- e- Serv. Sci. Technol., vol. 8, no. 3, pp. 329–344, 2015.
[29] B. D. Rouyendegh, A. Yildizbasi, and I. Yilmaz, “Evaluation of retail ındustry performance ability through ıntegrated ıntuitionistic fuzzy TOPSIS and data envelopment analysis approach,” Soc. Comput., vol. 24, no. 16, pp. 12255–12266, 2020, doi: 10.1007/s00500-020-04669-2.
[30] Kemenkes RI, “Permenkes No 3 Tahun 2020 Tentang Klasifikasi dan Perizinan Rumah Sakit,” Tentang Klasifikasi dan Perizinan Rumah Sakit, no. 3, pp. 1–80, 2020, [Online]. Available:
[31] B. Flokou, V. Aletras, and D. Niakas, “Awindow-DEA based efficiency evaluation of the public hospital sector
in Greece during the 5-year economic crisis,” PLoS One, vol. 12, no. 5, pp. 1–26, 2017, doi:
[32] H. Biderci and B. Canbaz, “Ergonomic Room Selection with Intuitive Fuzzy TOPSIS Method,” Procedia
Comput. Sci., vol. 158, pp. 58–67, 2019, doi: 10.1016/j.procs.2019.09.153.
[33] X. Ziquan, Y. Jiaqi, M. H. Naseem, and X. Zuquan, “Occupational Health and Safety Risk Assessment of Cruise
Ship Construction Based on Improved Intuitionistic Fuzzy TOPSIS Decision Model,” Math. Probl. Eng., vol.
2021, 2021, doi: 10.1155/2021/5966711.
[34] F. E. Boran, S. Genç, M. Kurt, and D. Akay, “A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method,” Expert Syst. Appl., vol. 36, no. 8, pp. 11363–11368, 2009, doi:
[35] G. Altuntas and B. F. Yildirim, “Logistics specialist selection with intuitionistic fuzzy TOPSIS method,” Int. J.
Logist. Syst. Manag., vol. 42, no. 1, pp. 1–34, 2022, doi: 10.1504/IJLSM.2022.123513.
[36] D. S. Costa, H. S. Mamede, and M. M. da Silva, “A method for selecting processes for automation with AHP
and TOPSIS,” Heliyon, vol. 9, no. 3, p. e13683, 2023, doi: 10.1016/j.heliyon.2023.e13683.
[37] K. Tone, A slacks-based measure of efficiency in data envelopment analysis, vol. 130, no. 3. 2001.