Application of fuzzy TOPSIS for prioritization of patients on elective surgeries waiting list - A novel multi-criteria decision-making approach

Authors

DOI:

https://doi.org/10.31181/dmame060127022023r

Keywords:

Elective surgery prioritization, TOPSIS, modified MeNTS scoring system, multi-criteria decision-making, surgical decision-support system, wait-list management.

Abstract

Prioritizing patients is a growing concern in healthcare. Once resources are limited, prioritization is considered an effective and viable solution in provision of healthcare treatment to awaiting patients. Prioritization is a preferred approach that helps clinicians to apportion scarce resources fairly and transparently. In this study, a novel methodology of prioritizing the patient is formulated using fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The objective is based on actual hospital conditions in Pakistan. The proposed methodology has two contributions: objective scoring mechanism that translates the patient’s condition given in human linguistic terms; and second methodology to prioritize patients according to corresponding scores. To validate the proposed methodology, simulation was carried out on actual data collected in real-time by surgeons, while providing consultations to their patients. The proposed methodology outperforms the traditional methodology by reducing average waiting time by 34% (from 4.246 to 2.810 days), minimize wait time and delays by 46.7% (from 15 to 8 days), and number of surgery days by 18%. The majority of the previously presented researched methodologies prioritize the patients subjectively. This study presents an objective methodology to prioritize the patients and decrease wait-times while ensuring transparency and equity.

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References

Ab Kadir, W. N., Abdullah, N. S., & Mustapha, I. R. (2019). The Application Of The Fuzzy Delphi Technique On A Component Of Development Of Form Four STEM-Based Physics Interactive Laboratory (ILab). International journal of scientific & technology research, 8, 12.

Abastante, F., Corrente, S., Greco, S., Ishizaka, A., & Lami, I. M. (2019). A new parsimonious AHP methodology: Assigning priorities to many objects by comparing pairwise few reference objects. Expert Systems with Applications, 127, 109-120. doi:10.1016/j.eswa.2019.02.036

Abbasinia, M., & Mohammadfam, I. (2022). Identifying, evaluating and prioritizing the causes of occupational accidents in the construction industry using fuzzy AHP and fuzzy TOPSIS. WOR, 72, 933-940. doi:10.3233/WOR-210024

Abbassinia, M., Kalatpour, O., Motamedzade, M., Soltanian, A., & Mohammadfam, I. (2020). A fuzzy analytic hierarchy process-TOPSIS framework for prioritizing emergency in a petrochemical industry. Arch Trauma Res, 9, 35. doi:10.4103/atr.atr_85_19

Alaoui, M. E., Yassini, K. E., & azza, H. B. (2019). Type 2 fuzzy TOPSIS for agriculture MCDM problems. IJSAMI, 5, 112. doi:10.1504/IJSAMI.2019.101672

Almeida, J. R., Noel, C. W., Forner, D., Zhang, H., Nichols, A. C., Cohen, M. A., . . . Gilbert, R. (2020). Development and validation of a Surgical Prioritization and Ranking Tool and Navigation Aid for Head and Neck Cancer (SPARTAN‐HN) in a scarce resource setting: Response to the COVID‐19 pandemic. Cancer, 126, 4895-4904. doi:10.1002/cncr.33114

Ammirato, S., Fattoruso, G., & Violi, A. (2022). Parsimonious AHP-DEA Integrated Approach for Efficiency Evaluation of Production Processes. JRFM, 15, 293. doi:10.3390/jrfm15070293

Ayub, E., Mohamad, S. N., Wei, G. W., & Luaran, J. (2020). A Learning Design Strategy Framework for Content Transformation Using Fuzzy Delphi Method. IJIET, 10, 882-888. doi:10.18178/ijiet.2020.10.12.1474

Baccour, L. (2018). Amended fused TOPSIS-VIKOR for classification (ATOVIC) applied to some UCI data sets. Expert Systems with Applications, 99, 115-125. doi:10.1016/j.eswa.2018.01.025

Badi, I., & Abdulshahed, A. (2019). Ranking the Libyan airlines by using Full Consistency Method (FUCOM) and Analytical Hierarchy Process (AHP). Oper. Res. Eng. Sci. Theor. Appl., 2, 1-14. doi:10.31181/oresta1901001b

Baghapour, M. A., Shooshtarian, M. R., Javaheri, M. R., Dehghanifard, S., Sefidkar, R., & Nobandegani, A. F. (2018). A computer-based approach for data analyzing in hospital’s health-care waste management sector by developing an index using consensus-based fuzzy multi-criteria group decision-making models. International Journal of Medical Informatics, 118, 5-15. doi:10.1016/j.ijmedinf.2018.07.001

Büyüközkan, G., & Çifçi, G. (2012). A combined fuzzy AHP and fuzzy TOPSIS based strategic analysis of electronic service quality in healthcare industry. Expert systems with applications, 39, 2341-2354.

Chakraborty, S., & Saha, A. K. (2022a). A framework of LR fuzzy AHP and fuzzy WASPAS for health care waste recycling technology. Applied Soft Computing, 127, 109388. doi:10.1016/j.asoc.2022.109388

Chakraborty, S., & Saha, A. K. (2022b). Selection of Forklift unit for transport handling using integrated MCDM under neutrosophic environment. Facta Universitatis, Series: Mechanical Engineering.

Chakraborty, S., & Saha, A. K. (2023). Novel Fermatean Fuzzy Bonferroni Mean aggregation operators for selecting optimal health care waste treatment technology. Engineering Applications of Artificial Intelligence, 119, 105752. doi:10.1016/j.engappai.2022.105752

Chaurasiya, R., & Jain, D. (2022). Pythagorean fuzzy entropy measure-based complex proportional assessment technique for solving multi-criteria healthcare waste treatment problem. Granul. Comput., 7, 917-930. doi:10.1007/s41066-021-00304-z

Chen, T.-Y. (2015). The inclusion-based TOPSIS method with interval-valued intuitionistic fuzzy sets for multiple criteria group decision making. Applied Soft Computing, 26, 57-73.

Chu, T.-C., & Lin, Y.-C. (2003). A fuzzy TOPSIS method for robot selection. The International Journal of Advanced Manufacturing Technology, 21, 284-290.

Davodabadi, A., Daneshian, B., Saati, S., & Razavyan, S. (2021). Prioritization of Patients in ICU: Composite Approach of Multiple-Criteria Decision-Making and Discrete Event Simulation. BJO&PM, 18, 1-21. doi:10.14488/BJOPM.2021.008

De Nardo, P., Gentilotti, E., Mazzaferri, F., Cremonini, E., Hansen, P., Goossens, H., . . . Malerba, G. (2020). Multi-Criteria Decision Analysis to prioritize hospital admission of patients affected by COVID-19 in low-resource settings with hospital-bed shortage. International Journal of Infectious Diseases, 98, 494-500. doi:10.1016/j.ijid.2020.06.082

De Silva, C. W. (2018). Intelligent control: Fuzzy Logic applications. Boca, Raton, FL.

Déry, J., Ruiz, A., Routhier, F., Bélanger, V., Côté, A., Ait-Kadi, D., . . . Redondo, E. (2020). A systematic review of patient prioritization tools in non-emergency healthcare services. Systematic reviews, 9, 1-14.

Déry, J., Ruiz, A., Routhier, F., Gagnon, M.-P., Côté, A., Ait-Kadi, D., . . . Lamontagne, M.-E. (2019). Patient prioritization tools and their effectiveness in non-emergency healthcare services: a systematic review protocol. Syst Rev, 8, 78. doi:10.1186/s13643-019-0992-x

El Alaoui, M. (2021). Fuzzy TOPSIS: logic, approaches, and case studies (First edition ed.). Boca Raton.

El Alaoui, M., Ben-Azza, H., El Yassini, K., & Ezziyyani, M. (2019). Fuzzy TOPSIS with Coherent Measure: Applied to a Closed Loop Agriculture Supply Chain. In M. Ezziyyani (Ed.), Advanced Intelligent Systems for Sustainable Development (AI2SD’2018) (Vol. 911, pp. 106-117). Cham. Retrieved from http://link.springer.com/10.1007/978-3-030-11878-5_12

Gebre, S. L., Cattrysse, D., Alemayehu, E., & Van Orshoven, J. (2021). Multi-criteria decision making methods to address rural land allocation problems: A systematic review. International Soil and Water Conservation Research, 9, 490-501. doi:10.1016/j.iswcr.2021.04.005

Ghorbani, A., Davoodi, F., & Zamanifar, K. (2023). Using type-2 fuzzy ontology to improve semantic interoperability for healthcare and diagnosis of depression. Artificial Intelligence in Medicine, 135, 102452. doi:10.1016/j.artmed.2022.102452

Globerman, S., Esmail, N., Day, B., & Henderson, D. (2013). Reducing wait times for health care: what Canada can learn from theory and international experience. Fraser Institute, October.

Gupta, S., Gupta, S., Mathew, M., & Sama, H. R. (2021). Prioritizing intentions behind investment in cryptocurrency: a fuzzy analytical framework. JES, 48, 1442-1459. doi:10.1108/JES-06-2020-0285

Gürsel, G. (2016). Healthcare, uncertainty, and fuzzy logic. Digit Med, 2, 101. doi:10.4103/2226-8561.194697

Hassan, U., Rana, H. S., & Hassan, R. (2021). Factors affecting scheduling of elective surgeries in a Private Hospital, Islamabad, Pakistan. Rawal Medical Journal, 46, 940-943.

Hsieh, M.-c., Wang, E. M.-y., Lee, W.-c., Li, L.-w., Hsieh, C.-y., Tsai, W., . . . Liu, T.-c. (2018). Application of HFACS, fuzzy TOPSIS, and AHP for identifying important human error factors in emergency departments in Taiwan. International Journal of Industrial Ergonomics, 67, 171-179. doi:10.1016/j.ergon.2018.05.004

Hwang, C. L., & Yoon, K. (1981). Multiple Attribute Decision Making: Methods and Applications A State-of-the-Art Survey. Berlin, Heidelberg. Retrieved from https://doi.org/10.1007/978-3-642-48318-9

IBM SPSS Statistics for Windows, Version 28.0. (2021). IBM SPSS Statistics for Windows, Version 28.0. Armonk, NY. Retrieved from www.ibm.com/analytics/spss-statistics-software

Ismail, N. K., Mohamed, S., & Hamzah, M. I. (2019). The Application of the Fuzzy Delphi Technique to the Required Aspect of Parental Involvement in the Effort to Inculcate Positive Attitude among Preschool Children. CE, 10, 2907-2921. doi:10.4236/ce.2019.1012216

Kiani, M., Bagheri, M., Ebrahimi, A., & Alimohammadlou, M. (2022). A model for prioritizing outsourceable activities in universities through an integrated fuzzy-MCDM method. International Journal of Construction Management, 22, 784-800. doi:10.1080/15623599.2019.1645264

Kirkire, M. S., Rane, S. B., & Singh, S. P. (2018). Integrated SEM-FTOPSIS framework for modeling and prioritization of risk sources in medical device development process. BIJ, 25, 178-200. doi:10.1108/BIJ-07-2016-0112

Kore, N. B., Ravi, K., & Patil, S. B. (2018). A simplified description of fuzzy TOPSIS method for multi criteria decision making. International Research Journal of Engineering and Technology (IRJET), 4, 2047-2050.

Krishankumar, R., Raj Mishra, A., Rani, P., Zavadskas, E. K., Ravichandran, K. S., & Kar, S. (2022). A new decision model with integrated approach for healthcare waste treatment technology selection with generalized orthopair fuzzy information. Information Sciences, 610, 1010-1028. doi:10.1016/j.ins.2022.08.022

Lauerer, M., & Nagel, E. (2016). Prioritization in Medicine: An International Dialogue (1st ed. 2016 ed.). (M. Lauerer, & E. Nagel, Eds.) Cham.

Law, T. J., Stephens, D., & Wright, J. G. (2022). Surgical wait times and socioeconomic status in a public healthcare system: a retrospective analysis. BMC Health Serv Res, 22, 579. doi:10.1186/s12913-022-07976-6

Li, J., Luo, L., Wu, X., Liao, C., Liao, H., & Shen, W. (2019). Prioritizing the elective surgery patient admission in a Chinese public tertiary hospital using the hesitant fuzzy linguistic ORESTE method. Applied Soft Computing, 78, 407-419. doi:10.1016/j.asoc.2019.02.001

Liu, S., Zhang, J., Niu, B., Liu, L., & He, X. (2022). A novel hybrid multi-criteria group decision-making approach with intuitionistic fuzzy sets to design reverse supply chains for COVID-19 medical waste recycling channels. Computers & Industrial Engineering, 169, 108228. doi:10.1016/j.cie.2022.108228

Malekpoor, H., Mishra, N., & Kumar, S. (2022). A novel TOPSIS-CBR goal programming approach to sustainable healthcare treatment. Ann Oper Res, 312, 1403-1425. doi:10.1007/s10479-018-2992-y

Malik, D. A., Yusof, Y., & Khalif, K. M. (2021). A view of MCDM application in education. J. Phys.: Conf. Ser., 1988, 012063. doi:10.1088/1742-6596/1988/1/012063

Mardani, A., Hooker, R. E., Ozkul, S., Yifan, S., Nilashi, M., Sabzi, H. Z., & Fei, G. C. (2019). Application of decision making and fuzzy sets theory to evaluate the healthcare and medical problems: a review of three decades of research with recent developments. Expert Systems with Applications, 137, 202-231. doi:10.1016/j.eswa.2019.07.002

Marqués, A. I., García, V., & Sánchez, J. S. (2020). Ranking-based MCDM models in financial management applications: analysis and emerging challenges. Prog Artif Intell, 9, 171-193. doi:10.1007/s13748-020-00207-1

Martino Neto, J., Salomon, V. A., Ortiz-Barrios, M. A., & Petrillo, A. (2022). Compatibility and correlation of multi-attribute decision making: a case of industrial relocation. Ann Oper Res. doi:10.1007/s10479-022-04603-9

Mathew, M., Chakrabortty, R. K., & Ryan, M. J. (2020). A novel approach integrating AHP and TOPSIS under spherical fuzzy sets for advanced manufacturing system selection. Engineering Applications of Artificial Intelligence, 96, 103988. doi:10.1016/j.engappai.2020.103988

McIntyre, D., & Chow, C. K. (2020). Waiting Time as an Indicator for Health Services Under Strain: A Narrative Review. INQUIRY, 57, 004695802091030. doi:10.1177/0046958020910305

Mishra, A. R., Mardani, A., Rani, P., & Zavadskas, E. K. (2020). A novel EDAS approach on intuitionistic fuzzy set for assessment of health-care waste disposal technology using new parametric divergence measures. Journal of Cleaner Production, 272, 122807. doi:10.1016/j.jclepro.2020.122807

Moslem, S. (2023). A Novel Parsimonious Best Worst Method for Evaluating Travel Mode Choice. IEEE Access, 1-1. doi:10.1109/ACCESS.2023.3242120

Mousavi, S. M., Abbasi, M., Yazdanirad, S., Yazdanirad, M., & Khatooni, E. (2019). Fuzzy AHP-TOPSIS method as a technique for prioritizing noise control solutions. noise cont engng j, 67, 415-421. doi:10.3397/1/376738

Mudashiru, R. B., Sabtu, N., & Abustan, I. (2021). Quantitative and semi-quantitative methods in flood hazard/susceptibility mapping: a review. Arab J Geosci, 14, 941. doi:10.1007/s12517-021-07263-4

Mukhametzyanov, I., Pamučar, D., & University of defence in Belgrade, D. o. (2018). A Sensitivity analysis in MCDM problems: A statistical approach. Decis. Mak. Appl. Manag. Eng., 1. doi:10.31181/dmame1802050m

Naseem, M. H., Yang, J., & Xiang, Z. (2021). Prioritizing the Solutions to Reverse Logistics Barriers for the E-Commerce Industry in Pakistan Based on a Fuzzy AHP-TOPSIS Approach. Sustainability, 13, 12743. doi:10.3390/su132212743

OECD. (2020). Waiting times for health services: next in line. (OECD, Ed.) Paris.

Olugu, E. U., Mammedov, Y. D., Young, J. C., & Yeap, P. S. (2021). Integrating spherical fuzzy Delphi and TOPSIS technique to identify indicators for sustainable maintenance management in the oil and gas industry. Journal of King Saud University - Engineering Sciences, S1018363921001598. doi:10.1016/j.jksues.2021.11.003

Omoregbe, N. A., Ndaman, I. O., Misra, S., Abayomi-Alli, O. O., Damaševičius, R., & Dogra, A. (2020). Text Messaging-Based Medical Diagnosis Using Natural Language Processing and Fuzzy Logic. (A. Dogra, Ed.) Journal of Healthcare Engineering, 2020, 1-14. doi:10.1155/2020/8839524

Oudhoff, J., Timmermans, D., Knol, D., Bijnen, A., & van der Wal, G. (2007). Waiting for elective general surgery: impact on health related quality of life and psychosocial consequences. BMC Public Health, 7, 164. doi:10.1186/1471-2458-7-164

Palczewski, K., & Salabun, W. (2019). The fuzzy TOPSIS applications in the last decade. Procedia Computer Science, 159, 2294-2303.

Pamučar, D., Ecer, F., Cirovic, G., & Arlasheedi, M. A. (2020). Application of Improved Best Worst Method (BWM) in Real-World Problems. Mathematics, 8, 1342. doi:10.3390/math8081342

Perwira, Y., & Apriani, W. (2020). Application of Weighted Sum Model (WSM) for Determining Development Priorities in Rural. Jurnal Teknik Informatika CIT Medicom, 12, 72-87.

Prachand, V. N., Milner, R., Angelos, P., Posner, M. C., Fung, J. J., Agrawal, N., . . . Matthews, J. B. (2020). Medically Necessary, Time-Sensitive Procedures: Scoring System to Ethically and Efficiently Manage Resource Scarcity and Provider Risk During the COVID-19 Pandemic. Journal of the American College of Surgeons, 231, 281-288. doi:10.1016/j.jamcollsurg.2020.04.011

Prentice, J. C., & Pizer, S. D. (2007). Delayed access to health care and mortality. Health services research, 42, 644-662.

PyCharm. (2021). PyCharm. Prague, Czech, Republic. Retrieved from www.jetbrains.com/pycharm

Quasim, M. T., Shaikh, A., Shuaib, M., Sulaiman, A., Alam, S., & Asiri, Y. (2023). Fuzzy Decision-Making Method based Evaluation of Smart Healthcare Management. Tech. rep. Retrieved from https://www.researchsquare.com/article/rs-1504815/v1

Rahimi, S. A., Dery, J., Lamontagne, M.-E., Jamshidi, A., Lacroix, E., Ruiz, A., . . . Routhier, F. (2022). Prioritization of patients access to outpatient augmentative and alternative communication services in Quebec: a decision tool. Disability and Rehabilitation: Assistive Technology, 17, 8-15. doi:10.1080/17483107.2020.1751314

Rahimi, S. A., Jamshidi, A., Ruiz, A., & Ait-kadi, D. (2016). A new dynamic integrated framework for surgical patients' prioritization considering risks and uncertainties. Decision Support Systems, 88, 112-120. doi:10.1016/j.dss.2016.06.003

Rahimi, S. A., Jamshidi, A., Ruiz, A., Ait-Kadi, D., Matta, A., Sahin, E., . . . Vandaele, N. J. (2017). Multi-Criteria Decision-Making Approaches to Prioritize Surgical Patients. Multi-Criteria Decision-Making Approaches to Prioritize Surgical Patients, 169, 25-34. (A. Matta, E. Sahin, J. Li, A. Guinet, & N. J. Vandaele, Eds.) Cham. Retrieved from http://link.springer.com/10.1007/978-3-319-35132-2_3

Rajak, M., & Shaw, K. (2019). Technology in Society, 59, 101186. doi:10.1016/j.techsoc.2019.101186

Rana, H. S., Umer, M., Hassan, U., & Asgher, U. (2022). A novel multi-criteria decision-making approach for prioritization of elective surgeries through formulation of weighted MeNTS scoring system. Heliyon, 8. doi:10.1016/j.heliyon.2022.e10339

Rathnayake, D., Clarke, M., & Jayasinghe, V. (2021). Patient prioritisation methods to shorten waiting times for elective surgery: a systematic review of how to improve access to surgery. Tech. rep. Retrieved from http://medrxiv.org/lookup/doi/ 10.1101/2021.02.18.21252033

Reyes-García, C. A., & Torres-García, A. A. (2022). Fuzzy logic and fuzzy systems. In Biosignal Processing and Classification Using Computational Learning and Intelligence (pp. 153-176). Retrieved from https://linkinghub.elsevier.com/retrieve/pii/B9780128201251000208

Salimian, S., & Mousavi, S. M. (2022). The selection of healthcare waste treatment technologies by a multi-criteria group decision-making method with intuitionistic fuzzy sets. Journal of Industrial and Systems Engineering, 14, 205-220.

Saltelli, A., Aleksankina, K., Becker, W., Fennell, P., Ferretti, F., Holst, N., . . . Wu, Q. (2019). Why so many published sensitivity analyses are false: A systematic review of sensitivity analysis practices. Environmental Modelling & Software, 114, 29-39. doi:10.1016/j.envsoft.2019.01.012

Silva-Aravena, F., & Morales, J. (2022). Dynamic Surgical Waiting List Methodology: A Networking Approach. Mathematics, 10, 2307. doi:10.3390/math10132307

Silva-Aravena, F., Álvarez-Miranda, E., Astudillo, C. A., González-Martínez, L., & Ledezma, J. G. (2021). Patients’ Prioritization on Surgical Waiting Lists: A Decision Support System. Mathematics, 9, 1097. doi:10.3390/math9101097

Silva-Aravena, F., Delafuente, H. N., & Astudillo, C. A. (2022). A Novel Strategy to Classify Chronic Patients at Risk: A Hybrid Machine Learning Approach. Mathematics, 10, 3053. doi:10.3390/math10173053

Silveira, P. S. (2022). Agreement coefficients in 2x2 tables. Agreement coefficients in 2x2 tables. Retrieved from https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/HMYTCK

Sofuoğlu, M. A. (2019). Fuzzy Applications of FUCOM Method in Manufacturing Environment. Journal of Polytechnic. doi:10.2339/politeknik.586036

Solangi, Y. A., Tan, Q., Mirjat, N. H., Valasai, G. D., Khan, M. W., & Ikram, M. (2019). An Integrated Delphi-AHP and Fuzzy TOPSIS Approach toward Ranking and Selection of Renewable Energy Resources in Pakistan. Processes, 7, 118. doi:10.3390/pr7020118

Taylan, O., Alamoudi, R., Kabli, M., AlJifri, A., Ramzi, F., & Herrera-Viedma, E. (2020). Assessment of Energy Systems Using Extended Fuzzy AHP, Fuzzy VIKOR, and TOPSIS Approaches to Manage Non-Cooperative Opinions. Sustainability, 12, 2745. doi:10.3390/su12072745

Testi, A., Tanfani, E., Valente, R., Ansaldo, G. L., & Torre, G. C. (2008). Prioritizing surgical waiting lists. Journal of Evaluation in Clinical Practice, 14, 59-64.

Uzun Ozsahin, D., Uzun, B., Ozsahin, I., Mustapha, M. T., & Musa, M. S. (2020). Fuzzy Logic in Medicine. In Biomedical Signal Processing and Artificial Intelligence in Healthcare (pp. 153-182). Retrieved from https://linkinghub.elsevier.com/retrieve/pii/B9780128189467000068

Valente, R., Domenico, S. D., Mascherini, M., Santori, G., Papadia, F., Orengo, G., . . . Cian, F. D. (2020). A new model to prioritize and optimize access to elective surgery throughout the COVID-19 pandemic: A feasibility & pilot study. Tech. rep. Retrieved from http://medrxiv.org/lookup/doi/10.1101/2020.07.21.20157719

Widianta, M. M., Rizaldi, T., Setyohadi, D. P., & Riskiawan, H. Y. (2018). Comparison of Multi-Criteria Decision Support Methods (AHP, TOPSIS, SAW & PROMENTHEE) for Employee Placement. J. Phys.: Conf. Ser., 953, 012116. doi:10.1088/1742-6596/953/1/012116

Yaakob, M. F., Nawi, A., Yusof, M. R., Fauzee, M. S., Awang, H., Khun-Inkeeree, H., . . . Habibi, A. (2020). A Quest for Experts' Consensus on the Geo-Education Module Using Fuzzy Delphi Analysis. ujer, 8, 3189-3203. doi:10.13189/ujer.2020.080748

Yuan, Z., Wen, B., He, C., Zhou, J., Zhou, Z., & Xu, F. (2022). Application of Multi-Criteria Decision-Making Analysis to Rural Spatial Sustainability Evaluation: A Systematic Review. IJERPH, 19, 6572. doi:10.3390/ijerph19116572

Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338-353. doi:10.1016/S0019-9958(65)90241-X

Zhang, K., Zhan, J., & Yao, Y. (2019). TOPSIS method based on a fuzzy covering approximation space: An application to biological nano-materials selection. Information Sciences, 502, 297-329. doi:10.1016/j.ins.2019.06.043

Zhang, X., Zou, G., Liang, H., & Carroll, R. J. (2020). Parsimonious Model Averaging With a Diverging Number of Parameters. Journal of the American Statistical Association, 115, 972-984. doi:10.1080/01621459.2019.1604363

Published

2023-04-09

How to Cite

Rana, H., Umer, M., Hassan, U., Asgher, U. ., Silva-Aravena, F., & Ehsan, N. (2023). Application of fuzzy TOPSIS for prioritization of patients on elective surgeries waiting list - A novel multi-criteria decision-making approach. Decision Making: Applications in Management and Engineering, 6(1), 603–630. https://doi.org/10.31181/dmame060127022023r