Intuitionistic Fuzzy Best-Worst Method for Multi-Criteria Decision Making with Application in Health Care Resource Allocation

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DOI: https://doi.org/10.28924/2291-8639-24-2026-51
Published: Feb 26, 2026
Cite as:
Sajida Kousar, Tania Hussain Satti, Nasreen Kausar, Dragan Pamucar, Intuitionistic Fuzzy Best-Worst Method for Multi-Criteria Decision Making with Application in Health Care Resource Allocation, Int. J. Anal. Appl., 24 (2026), 51.

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Sajida Kousar, Tania Hussain Satti, Nasreen Kausar, Dragan Pamucar

Abstract

In the health care industry, decision-making is critical for determining the most efficient use of limited resources. Multi-criteria decision-making is a significant area that has been used to solve complex problems. To construct an accurate, adaptable, and sustainable framework for decision-making, an intuitionistic fuzzy best-worst method for multi-criteria decision-making in healthcare resource allocation is being developed. To understand the resource allocation mechanisms in different hospitals, the proposed methods employ a pairwise comparison of seven main criteria: infrastructure, consultancy time, paramedics, hospital stay, healthcare resource allocation, healthcare professionals’ satisfaction, and improvements in resource allocation. The weights calculated from the intuitionistic fuzzy best-worst method indicate that health professional satisfaction is the best criterion, whereas the consultancy time is the worst. The goal of this approach is to effectively handle the inherent ambiguity, complexity, and uncertainty that define problems with healthcare resource allocation. This methodology has a wide range of applications, including: hospital resource management, prioritizing patient care during peak times or emergencies such as pandemics, budgeting and financial planning, evaluating the cost-effectiveness of new treatments or technologies, public health planning, planning and executing community health interventions, strategic planning, and policy making.

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References

  1. I.L. Janis, L. Mann, Decision Making: A Psychological Analysis of Conflict, Choice, and Commitment, Free Press, New York, 1979.
  2. S. Plous, The Psychology of Judgment and Decision Making, McGraw-Hill Education, New York, 1993.
  3. J. Rezaei, Best-Worst Multi-Criteria Decision-Making Method, Omega 53 (2015), 49–57. https://doi.org/10.1016/j.omega.2014.11.009.
  4. S. Guo, H. Zhao, Fuzzy Best-Worst Multi-Criteria Decision-Making Method and Its Applications, Knowledge-Based Syst. 121 (2017), 23–31. https://doi.org/10.1016/j.knosys.2017.01.010.
  5. A.R. Torbati, M.K. Sayadi, A New Approach to Investigate the Performance of Insurance Branches in Iran Using Best-Worst Method and Fuzzy Inference System, J. Soft Comput. Decis. Support Syst. 5 (2018), 13–18.
  6. J. Dong, S. Wan, S. Chen, Fuzzy Best-Worst Method Based on Triangular Fuzzy Numbers for Multi-Criteria Decision-Making, Inf. Sci. 547 (2021), 1080–1104. https://doi.org/10.1016/j.ins.2020.09.014.
  7. E. Khanmohammadi, M. Zandieh, T. Tayebi, Drawing a Strategy Canvas Using the Fuzzy Best–Worst Method, Glob. J. Flex. Syst. Manag. 20 (2018), 57–75. https://doi.org/10.1007/s40171-018-0202-z.
  8. J. Li, J. Wang, J. Hu, Multi-Criteria Decision-Making Method Based on Dominance Degree and BWM with Probabilistic Hesitant Fuzzy Information, Int. J. Mach. Learn. Cybern. 10 (2018), 1671–1685. https://doi.org/10.1007/s13042-018-0845-2.
  9. H. Aboutorab, M. Saberi, M.R. Asadabadi, O. Hussain, E. Chang, ZBWM: The Z-Number Extension of Best Worst Method and Its Application for Supplier Development, Expert Syst. Appl. 107 (2018), 115–125. https://doi.org/10.1016/j.eswa.2018.04.015.
  10. D. Pamučar, I. Petrović, G. Ćirović, Modification of the Best–Worst and MABAC Methods: A Novel Approach Based on Interval-Valued Fuzzy-Rough Numbers, Expert Syst. Appl. 91 (2018), 89–106. https://doi.org/10.1016/j.eswa.2017.08.042.
  11. D. Pamucar, K. Chatterjee, E.K. Zavadskas, Assessment of Third-Party Logistics Provider Using Multi-Criteria Decision-Making Approach Based on Interval Rough Numbers, Comput. Ind. Eng. 127 (2019), 383–407. https://doi.org/10.1016/j.cie.2018.10.023.
  12. Q. Mou, Z. Xu, H. Liao, An Intuitionistic Fuzzy Multiplicative Best-Worst Method for Multi-Criteria Group Decision Making, Inf. Sci. 374 (2016), 224–239. https://doi.org/10.1016/j.ins.2016.08.074.
  13. Q. Mou, Z. Xu, H. Liao, A Graph Based Group Decision Making Approach with Intuitionistic Fuzzy Preference Relations, Comput. Ind. Eng. 110 (2017), 138–150. https://doi.org/10.1016/j.cie.2017.05.033.
  14. H. Aboutorab, M. Saberi, M.R. Asadabadi, O. Hussain, E. Chang, ZBWM: The Z-Number Extension of Best Worst Method and Its Application for Supplier Development, Expert Syst. Appl. 107 (2018), 115–125. https://doi.org/10.1016/j.eswa.2018.04.015.
  15. Meimei Xia, Zeshui Xu, Huchang Liao, Preference Relations Based on Intuitionistic Multiplicative Information, IEEE Trans. Fuzzy Syst. 21 (2013), 113–133. https://doi.org/10.1109/TFUZZ.2012.2202907.
  16. S. Wan, J. Dong, A Novel Extension of Best-Worst Method with Intuitionistic Fuzzy Reference Comparisons, IEEE Trans. Fuzzy Syst. 30 (2022), 1698–1711. https://doi.org/10.1109/TFUZZ.2021.3064695.
  17. X. Luo, X. Wang, Extended VIKOR Method for Intuitionistic Fuzzy Multiattribute Decision‐Making Based on a New Distance Measure, Math. Probl. Eng. 2017 (2017), 4072486. https://doi.org/10.1155/2017/4072486.
  18. E.K. Zavadskas, J. Antucheviciene, S.H. Razavi Hajiagha, S.S. Hashemi, The Interval-Valued Intuitionistic Fuzzy MULTIMOORA Method for Group Decision Making in Engineering, Math. Probl. Eng. 2015 (2015), 560690. https://doi.org/10.1155/2015/560690.
  19. M.D. Krstić, S.R. Tadić, N. Brnjac, S. Zečević, Intermodal Terminal Handling Equipment Selection Using a Fuzzy Multi-Criteria Decision-Making Model, Promet - TrafficandTransportation 31 (2019), 89–100. https://doi.org/10.7307/ptt.v31i1.2949.
  20. K.T. Atanassov, On Intuitionistic Fuzzy Sets Theory, Studies in Fuzziness and Soft Computing, Springer, Berlin, 2012. https://doi.org/10.1007/978-3-642-29127-2.
  21. V.J. Clemente-Suárez, E. Navarro-Jiménez, P. Ruisoto, A.A. Dalamitros, A.I. Beltran-Velasco, et al., Performance of Fuzzy Multi-Criteria Decision Analysis of Emergency System in COVID-19 Pandemic. an Extensive Narrative Review, Int. J. Environ. Res. Public Health 18 (2021), 5208. https://doi.org/10.3390/ijerph18105208.
  22. C. López, A. Ishizaka, M. Gul, M. Yücesan, D. Valencia, A Calibrated Fuzzy Best-Worst-Method to Reinforce Supply Chain Resilience during the COVID 19 Pandemic, J. Oper. Res. Soc. 74 (2022), 1968–1991. https://doi.org/10.1080/01605682.2022.2122739.
  23. S.M. Hosseini, M.M. Paydar, M. Hajiaghaei-Keshteli, Recovery Solutions for Ecotourism Centers during the COVID-19 Pandemic: Utilizing Fuzzy DEMATEL and Fuzzy VIKOR Methods, Expert Syst. Appl. 185 (2021), 115594. https://doi.org/10.1016/j.eswa.2021.115594.
  24. B. Rathore, R. Gupta, A Fuzzy Based Hybrid Decision-Making Framework to Examine the Safety Risk Factors of Healthcare Workers during COVID-19 Outbreak, J. Decis. Syst. 31 (2020), 68–101. https://doi.org/10.1080/12460125.2020.1862988.
  25. F. Ecer, D. Pamucar, MARCOS Technique Under Intuitionistic Fuzzy Environment for Determining the COVID-19 Pandemic Performance of Insurance Companies in Terms of Healthcare Services, Appl. Soft Comput. 104 (2021), 107199. https://doi.org/10.1016/j.asoc.2021.107199.
  26. A. Hafezalkotob, A. Hafezalkotob, A Novel Approach for Combination of Individual and Group Decisions Based on Fuzzy Best-Worst Method, Appl. Soft Comput. 59 (2017), 316–325. https://doi.org/10.1016/j.asoc.2017.05.036.
  27. H. Karimi, M. Sadeghi-Dastaki, M. Javan, A Fully Fuzzy Best–Worst Multi Attribute Decision Making Method with Triangular Fuzzy Number: A Case Study of Maintenance Assessment in the Hospitals, Appl. Soft Comput. 86 (2020), 105882. https://doi.org/10.1016/j.asoc.2019.105882.
  28. Q. Mou, Z. Xu, H. Liao, An Intuitionistic Fuzzy Multiplicative Best-Worst Method for Multi-Criteria Group Decision Making, Inf. Sci. 374 (2016), 224–239. https://doi.org/10.1016/j.ins.2016.08.074.
  29. J. Rezaei, J. Wang, L. Tavasszy, Linking Supplier Development to Supplier Segmentation Using Best Worst Method, Expert Syst. Appl. 42 (2015), 9152–9164. https://doi.org/10.1016/j.eswa.2015.07.073.
  30. S. Sadaghiani, K.W. Ahmad, J. Rezaei, L. Tavasszy, Evaluation of External Forces Affecting Supply Chain Sustainability in Oil and Gas Industry Using Best Worst Method, in: 2015 International Mediterranean Gas and Oil Conference (MedGO), IEEE, 2015. https://doi.org/10.1109/MedGO.2015.7330322.
  31. J. Rezaei, T. Nispeling, J. Sarkis, L. Tavasszy, A Supplier Selection Life Cycle Approach Integrating Traditional and Environmental Criteria Using the Best Worst Method, J. Clean. Prod. 135 (2016), 577–588. https://doi.org/10.1016/j.jclepro.2016.06.125.
  32. N.J. Vickers, Animal Communication: When I’m Calling You, Will You Answer Too?, Curr. Biol. 27 (2017), R713–R715. https://doi.org/10.1016/j.cub.2017.05.064.
  33. A. Hafezalkotob, A. Hafezalkotob, A Novel Approach for Combination of Individual and Group Decisions Based on Fuzzy Best-Worst Method, Appl. Soft Comput. 59 (2017), 316–325. https://doi.org/10.1016/j.asoc.2017.05.036.
  34. H. Karimi, M. Sadeghi-Dastaki, M. Javan, A Fully Fuzzy Best–Worst Multi Attribute Decision Making Method with Triangular Fuzzy Number: A Case Study of Maintenance Assessment in the Hospitals, Appl. Soft Comput. 86 (2020), 105882. https://doi.org/10.1016/j.asoc.2019.105882.
  35. Q. Mou, Z. Xu, H. Liao, An Intuitionistic Fuzzy Multiplicative Best-Worst Method for Multi-Criteria Group Decision Making, Inf. Sci. 374 (2016), 224–239. https://doi.org/10.1016/j.ins.2016.08.074.
  36. S.A. Seyfi-Shishavan, F.K. Gündoğdu, E. Farrokhizadeh, An Assessment of the Banking Industry Performance Based on Intuitionistic Fuzzy Best-Worst Method and Fuzzy Inference System, Appl. Soft Comput. 113 (2021), 107990. https://doi.org/10.1016/j.asoc.2021.107990.
  37. P. Amenta, A. Lucadamo, G. Marcarelli, Approximate Thresholds for Salo-Hamalainen Index, IFAC-PapersOnLine 51 (2018), 1655–1659. https://doi.org/10.1016/j.ifacol.2018.08.219.
  38. P. Amenta, A. Lucadamo, G. Marcarelli, On the Transitivity and Consistency Approximated Thresholds of Some Consistency Indices for Pairwise Comparison Matrices, Inf. Sci. 507 (2020), 274–287. https://doi.org/10.1016/j.ins.2019.08.042.
  39. Z. Alam, K.U. Khan, A. Khan, F. Atlas, Navigating the Digital Road: Unveiling and Prioritizing Barriers to Digital Transformation in Pakistani Courier Supply Chains, Spectr. Decis. Mak. Appl. 2 (2025), 297–313. https://doi.org/10.31181/sdmap21202521.
  40. H.R. Kolour, V. Momayezi, F. Momayezi, Enhancing Supplier Selection in Public Manufacturing: A Hybrid Multi-Criteria Decision-Making Approach, Spectr. Decis. Mak. Appl. 3 (2026), 1–20. https://doi.org/10.31181/sdmap31202629.
  41. M. Şahin, D. Deliktaş, Multi-Criteria Evaluation of Wind Turbines Using Entropy-Based-Topsis and CoCoSo Methods: Insights from a Turkish Case Study, Spectr. Oper. Res. 3 (2025), 339–351. https://doi.org/10.31181/sor31202650.
  42. R. Anjum, M.U. Mirza, N. Kausar, R. Ali, Decision-Making Framework for Urban Transportation Using Linear Diophantine Fuzzy Z-Numbers with Dombi Aggregation, TOPSIS and VIKOR Methods, Spectr. Oper. Res. 5 (2025), 13–18. https://doi.org/10.31181/sor4155.