Research Article
BibTex RIS Cite
Year 2024, Volume: 7 Issue: 3, 127 - 144
https://doi.org/10.33187/jmsm.1587499

Abstract

References

  • [1] H. Yu, X. Sun, W.D. Solvang, X. Zhao, Reverse logistics network design for effective management of medical waste in epidemic outbreaks: Insights from the coronavirus disease 2019 (covid-19) outbreak in Wuhan (china), Int. J. Environ. Res. Public Health, 17 (2020), 1770.
  • [2] A. Menekse, H.C. Akdag, Medical waste disposal planning for healthcare units using spherical fuzzy CRITIC-WASPAS, Appl Soft Comput., 144 (2023), 110480.
  • [3] R.R. Yager, Pythagorean fuzzy subsets, Proc. Joint IFSA World Congress and NAFIPS Annual Meeting, Edmonton, Canada, 2013.
  • [4] T. Senapati, R.R. Yager, Fermatean fuzzy weighted averaging/geometric operators and its application in multi-criteria decision-making methods, Engineering Applications of Artificial Intelligence, 85 (2019), 112–121.
  • [5] L.A. Zadeh, Fuzzy sets, Inf Comp 8 (1965), 338–353.
  • [6] K. Atanassov, Intuitionistic fuzzy sets, Fuzzy Sets and Systems, 20 (1986), 87–96.
  • [7] M. Kirişci, Fibonacci statistical convergence on intuitionistic fuzzy normed spaces, Journal of Intelligent & Fuzzy Systems, 36 (2019), 5597–5604.
  • [8] R.R. Yager, Pythagorean membership grades in multicriteria decision-making, IEEE Transactions on Fuzzy Systems 22(4) (2014), 958–965.
  • [9] T. Senapati, R.R. Yager, Fermatean fuzzy sets, J. Ambient Intell. Hum. Comput., 11 (2020), 663–674.
  • [10] T. Senapati, R.R. Yager, Some new operations over Fermatean fuzzy numbers and application of Fermatean fuzzy WPM in multiple criteria decision making, Informatica 30(2) (2019), 391–412.
  • [11] P. A. Ejegwa, I. Onyeke, Medical diagnostic analysis on some selected patients based on modified Thao et al.’s correlation coefficient of intuitionistic fuzzy sets via an algorithmic approach, Journal of Fuzzy Extension and Applications, 1(2) (2020), 122—132, doi:10.22105/jfea.2020.250108.1014
  • [12] P. A. Ejegwa, I. Onyeke, B.T. Terhemen, M.P. Onoja, A. Ogiji, C.U. Opeh, Modified Szmidt and Kacprzyk’s Intuitionistic Fuzzy Distances and their Applications in Decision-making, Journal of the Nigerian Society of Physical Sciences, 4(2) (2022), 174–182, doi:10.46481/jnsps.2022.530
  • [13] P. A. Ejegwa, Y. Zhang, H. Li, Y. Feng, Novel measuring techniques with applications in pattern classification and diagnostic process under Pythagorean fuzzy environment, International Conference on New Trends in Computational Intelligence (NTCI 2023), 30 (2023), 28–35, doi:10.1109/NTCI60157.2023.10403717
  • [14] P. A. Ejegwa, S. Wen, Y. Feng, Y. W. Zhang, J. Liu, A three-way Pythagorean fuzzy correlation coefficient approach and its applications in deciding some real-life problems, Appl Intell, 53 (2023), 226–237, doi:10.1007/s10489-022-03415-5
  • [15] P. A. Ejegwa, D. Zuakwagh, Fermatean fuzzy modified composite relation and its application in pattern recognition, Journal of Fuzzy Extension and Applications, 3(2) (2022), 140–151, doi: 10.22105/jfea.2022.335251.1210
  • [16] P. A. Ejegwa, N. Kausar, N. Aydin, Y. Feng, O. A. Olanrewaju, A new Fermatean fuzzy Spearman-like correlation coefficient and its application in evaluating insecurity problem via multi-criteria decision-making approach, Heliyon, 10(22) (2024), e40403.
  • [17] H. Garg, G. Shahzadi, M. Akram, Decision-making analysis based on Fermatean Fuzzy Yager aggregation operators with application in COVID-19 testing facility, Math. Probl. Eng., (2020), Article ID 7279027, doi:10.1155/2020/7279027
  • [18] M. Kirişci, Interval-valued fermatean fuzzy based risk assessment for self-driving vehicles, Applied Soft Computing, 152 (2024), 111265.
  • [19] M. Kirişci, Data analysis for panoramic X-ray selection: Fermatean fuzzy type correlation coefficients approach, Engineering Applications of Artificial Intelligence 126 (2023), 106824.
  • [20] M. Kirişci, Fermatean fuzzy type a three-way correlation coefficients. In: Gayoso Mart´ınez, V., Yilmaz, F., Queiruga-Dios, A., Rasteiro, D.M., Mart´ın-Vaquero, J., Mierlus¸-Mazilu, I. (eds) Mathematical Methods for Engineering Applications, ICMASE 2023. Springer Proceedings in Mathematics & Statistics, 499 (2024), 325–338, doi: 10.1007/978−3−031−49218−124.
  • [21] M. Kiris¸ci, New cosine similarity and distance measures for Fermatean fuzzy sets and TOPSIS approach, Knowledge and Information Systems, 65(2) (2023), 855–868.
  • [22] M. Kirişci, Correlation Coefficients of Fermatean Fuzzy Sets with Their Application, J. Math. Sci. Model. 5(2) (2022), 16–23. doi: 10.33187/jmsm.1039613
  • [23] M. Kirişci, I. Demir, N. Şimsek, Fermatean fuzzy ELECTRE multi-criteria group decision-making and most suitable biomedical material selection, Artificial Intelligence in Medicine, 127 (2022), 102278, doi:10.1016/j.artmed.2022.102278.
  • [24] M. Kirişci, Data analysis for lung cancer: Fermatean Hesitant fuzzy sets approach, Applied Mathematics, Modeling and Computer Simulation, 30 (2022), 701–710, doi:10.3233/ATDE221087
  • [25] M. Kiris¸ci, N. Simsek, A novel kernel principal component analysis with application disaster preparedness of hospital: interval-valued Fermatean fuzzy set approach, The Journal of Supercomputing, 79 (2023), 19848–19878.
  • [26] M. Kirişci, Measures of distance and entropy based on the Fermatean fuzzy-type soft sets approach, Univ. J. Math. Appl., 7 (2024), 12–29.
  • [27] M. Riaz, H. M. A. Farid, H. M. Shakeel, D. Arif, Cost effective indoor HVAC energy efficiency monitoring based on intelligent decision support system under Fermatean fuzzy framework, Scientia Iranica, 30(6) (2023), 2143–2161.
  • [28] N. Şimşek, M. Kirişci, Incomplete Fermatean Fuzzy Preference Relations and Group Decision Making, Topol. Algebra Appl., 11(1) (2023), 20220125. [29] N. Şimşek, M. Kirişci, A new risk assessment method for autonomous vehicle driving systems: Fermatean fuzzy AHP Approach, Istanbul Commerce University Journal of Science, 22(44) (2023), 292–309.
  • [30] K. Atanassov, G. Gargov, Interval valued intuitionistic fuzzy sets, Fuzzy Sets and Systems, 31(3) (1989), 343–349.
  • [31] X. Peng, Y. Yang, Fundamental properties of interval-valued Pythagorean fuzzy aggregation operators, Int. J. Intell. Syst., 31 (2016), 444–487.
  • [32] S. Jeevaraj, Ordering of interval-valued Fermatean fuzzy sets and their applications, Expert Systems with Applications, 185 (2021), 115613.
  • [33] D. Stanujkic, E.K. Zavadskas, D. Karabasevic, F. Smarandache, Z. Turskis, The use of the PIvot Pairwise RElative Criteria Importance Assessment method for determining the weights of criteria, Romanian Journal of Economic Forecasting, 20 (2017), 116–133.
  • [34] Z. Stevic, Z. Stjepanovic, Z. Bozickovic, D.K. Das, D. Stanujkic, Assessment of conditions for implementing information technology in a warehouse system: A novel fuzzy PIPRECIA method, Symmetry, 10 (2018), 1–28.
  • [35] G. Demir, M. Riaz, M. Deveci, Wind farm site selection using geographic information system and fuzzy decision making model, Expert System and Applications, 255 (2024), 124772.
  • [36] Z. Stevic, D. Pamucar, A. Puska, P. Chatterjee, Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement Alternatives and Ranking according to COmpromise Solution (MARCOS), Computers and Industrial Engineering, 140 (2020), 1–33.
  • [37] G. Demir, P. Chatterjee, S. Kadry, A. Abdelhadi, D. Pamucar, Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) Method: A Comprehensive Bibliometric Analysis, Decision-Making Applications in Management and Engineering, 7 (2024), 313–336.
  • [38] A.R. Mishra, P. Rani, D. Pamucar, A. Saha, An integrated Pythagorean fuzzy fairly operator-based MARCOS method for solving the sustainable circular supplier selection problem, Ann. Oper. Res., 342 (2024), 523–564.
  • [39] H. M. A. Farid, S. Dabic-Miletic, M. Riaz, V. Simic, D. Pamucar, Prioritization of sustainable approaches for smart waste management of automotive fuel cells of road freight vehicles using the q-rung orthopair fuzzy CRITIC-EDAS method, Information Sci., 661 (2024), 120162
  • [40] R. Kausar, M. Riaz, V. Simic, K. Akmal, M. U. Farooq, Enhancing solid waste management sustainability with cubic m-polar fuzzy cosine similarity, Soft Comput., (2023), 1-21, doi:10.1007/s00500-023-08801-w
  • [41] P. Rani, A.R. Mishra, Interval-valued Fermatean fuzzy sets with multi-criteria weighted aggregated sum product assessment-based decision analysis framework, Neural Computing and Applications, 34 (2022), 8051–8067.
  • [42] M. H. Mateen, I. Al-Dayel, T. Alsuraiheed, Fermatean fuzzy fairly aggregation operators with multi-criteria decision-making, Axioms, 12 (2023), 865.
  • [43] A.R. Mishra, P. Rani, A.F. Alrasheedi, R. Dwivedi, Evaluating the blockchain-based healthcare supply chain using interval-valued Pythagorean fuzzy entropy-based decision support system, Engineering Applications of Artificial Intelligence, 126 (2023), 107112.
  • [44] X. Peng, W. Li, Algorithms for Interval-Valued Pythagorean Fuzzy Sets in Emergency Decision Making Based on Multiparametric Similarity Measures and WDBA, IEEE Access, 7 (2019), 7419–7441.
  • [45] Medical waste, WHO, https://www.who.int/news-room/fact-sheets/detail/health-care-waste (Access: November 2024).
  • [46] M. Ghodrat, M. Rashidi, B. Samali, Life cycle assessments of incineration treatment for sharp medical waste, Energy Technology 2017 Conference Paper, (2017), 131–143.
  • [47] C. C. Lee, G. L. Huffman, Medical waste management/incineration, J. Hazard Mater., 48 (1996), 1–30.
  • [48] Z. Sapuric, D. Dimitrovski, M. Dimitrovski, F. Ivanovski, Medical waste incineration in skopje. regulation and standards, J. Environ. Protect. Ecol., 17 (2016), 805–812.
  • [49] A. Wu, X: Huang, R.E. Gong, J. Li, Y. Lu, M. Wang, Effectiveness of immersion disinfectant on medical waste, Chin. J. Nosoconmiol., 15 (2005), 51-–52.
  • [50] S. M. Rao, P. Mamatha, R. P. Shanta, B. V. V. Reddy, Encapsulation of fluoride sludge in stabilised mud blocks, Proceedings of the Institution of Civil Engineers Waste and Resource Management, R4, (2007), 167–174.
  • [51] M. Dursun, E.E. Karsak, M.A. Karadayi, Assessment of health-care waste treatment alternatives using fuzzy multi-criteria decision making approaches, Resour. Conserv. Recycl., 57 (2011), 98e107.
  • [52] M. R. Seikh, P. Chatterjee, Determination of best renewable energy sources in India using SWARA-ARAS in confidence level based interval-valued Fermatean fuzzy environment, Applied Soft Computing, 155 (2024), 111495.
  • [53] M. Alrasheedi, A. Mardani, A.R. Mishra, P. Rani, P., L. Nanthakumar, An extended framework to evaluate sustainable suppliers in manufacturing companies using a new pythagorean fuzzy entropy-SWARA-WASPAS decisionmaking approach, J. Enterp. Inf. Manag., 35 (2022), 333–357.

Medical Waste Management Based on an Interval-Valued Fermatean Fuzzy Decision-Making Method

Year 2024, Volume: 7 Issue: 3, 127 - 144
https://doi.org/10.33187/jmsm.1587499

Abstract

Due to its infectious and hazardous nature, medical waste poses risks to people and the environment. For patients to receive medical attention and recover in a safe environment, waste must be disposed of correctly. Improper medical waste disposal poses a severe risk to society, which can accelerate the development of various pandemics and epidemics. In this case, medical waste disposal should be handled appropriately. This study presents an integrated multi-criteria decision-making method consisting of entropy, the Pivot Pairwise Relative Criteria Importance Assessment, and Measurement of Alternatives and Ranking according to Compromise Solution methods based on an interval-valued Fermatean fuzzy set. This method can guarantee high safety and security for health practitioners and society through effective modeling and ranking of risks associated with medical waste disposal. Five alternatives and eight criteria were determined. According to the results, incineration is the most suitable disposal process for medical waste. The performance was then assessed and validated using a sensitivity analysis. A sensitivity analysis has been conducted across the range of values for the $\alpha$ parameter. It was examined whether the rankings of the alternatives changed when the $\alpha$ values in the integrated weight determination model for sensitivity analysis were altered. When the different $\alpha$ values were reviewed with the selected $\alpha$ value in the application example, it was seen that incineration was the first alternative. In addition, the study's findings and their consequences for lawmakers, businesspeople, technologists, and practitioners are examined. In the future, these stakeholders can concentrate on these deficiencies and provide long-term remedies.

References

  • [1] H. Yu, X. Sun, W.D. Solvang, X. Zhao, Reverse logistics network design for effective management of medical waste in epidemic outbreaks: Insights from the coronavirus disease 2019 (covid-19) outbreak in Wuhan (china), Int. J. Environ. Res. Public Health, 17 (2020), 1770.
  • [2] A. Menekse, H.C. Akdag, Medical waste disposal planning for healthcare units using spherical fuzzy CRITIC-WASPAS, Appl Soft Comput., 144 (2023), 110480.
  • [3] R.R. Yager, Pythagorean fuzzy subsets, Proc. Joint IFSA World Congress and NAFIPS Annual Meeting, Edmonton, Canada, 2013.
  • [4] T. Senapati, R.R. Yager, Fermatean fuzzy weighted averaging/geometric operators and its application in multi-criteria decision-making methods, Engineering Applications of Artificial Intelligence, 85 (2019), 112–121.
  • [5] L.A. Zadeh, Fuzzy sets, Inf Comp 8 (1965), 338–353.
  • [6] K. Atanassov, Intuitionistic fuzzy sets, Fuzzy Sets and Systems, 20 (1986), 87–96.
  • [7] M. Kirişci, Fibonacci statistical convergence on intuitionistic fuzzy normed spaces, Journal of Intelligent & Fuzzy Systems, 36 (2019), 5597–5604.
  • [8] R.R. Yager, Pythagorean membership grades in multicriteria decision-making, IEEE Transactions on Fuzzy Systems 22(4) (2014), 958–965.
  • [9] T. Senapati, R.R. Yager, Fermatean fuzzy sets, J. Ambient Intell. Hum. Comput., 11 (2020), 663–674.
  • [10] T. Senapati, R.R. Yager, Some new operations over Fermatean fuzzy numbers and application of Fermatean fuzzy WPM in multiple criteria decision making, Informatica 30(2) (2019), 391–412.
  • [11] P. A. Ejegwa, I. Onyeke, Medical diagnostic analysis on some selected patients based on modified Thao et al.’s correlation coefficient of intuitionistic fuzzy sets via an algorithmic approach, Journal of Fuzzy Extension and Applications, 1(2) (2020), 122—132, doi:10.22105/jfea.2020.250108.1014
  • [12] P. A. Ejegwa, I. Onyeke, B.T. Terhemen, M.P. Onoja, A. Ogiji, C.U. Opeh, Modified Szmidt and Kacprzyk’s Intuitionistic Fuzzy Distances and their Applications in Decision-making, Journal of the Nigerian Society of Physical Sciences, 4(2) (2022), 174–182, doi:10.46481/jnsps.2022.530
  • [13] P. A. Ejegwa, Y. Zhang, H. Li, Y. Feng, Novel measuring techniques with applications in pattern classification and diagnostic process under Pythagorean fuzzy environment, International Conference on New Trends in Computational Intelligence (NTCI 2023), 30 (2023), 28–35, doi:10.1109/NTCI60157.2023.10403717
  • [14] P. A. Ejegwa, S. Wen, Y. Feng, Y. W. Zhang, J. Liu, A three-way Pythagorean fuzzy correlation coefficient approach and its applications in deciding some real-life problems, Appl Intell, 53 (2023), 226–237, doi:10.1007/s10489-022-03415-5
  • [15] P. A. Ejegwa, D. Zuakwagh, Fermatean fuzzy modified composite relation and its application in pattern recognition, Journal of Fuzzy Extension and Applications, 3(2) (2022), 140–151, doi: 10.22105/jfea.2022.335251.1210
  • [16] P. A. Ejegwa, N. Kausar, N. Aydin, Y. Feng, O. A. Olanrewaju, A new Fermatean fuzzy Spearman-like correlation coefficient and its application in evaluating insecurity problem via multi-criteria decision-making approach, Heliyon, 10(22) (2024), e40403.
  • [17] H. Garg, G. Shahzadi, M. Akram, Decision-making analysis based on Fermatean Fuzzy Yager aggregation operators with application in COVID-19 testing facility, Math. Probl. Eng., (2020), Article ID 7279027, doi:10.1155/2020/7279027
  • [18] M. Kirişci, Interval-valued fermatean fuzzy based risk assessment for self-driving vehicles, Applied Soft Computing, 152 (2024), 111265.
  • [19] M. Kirişci, Data analysis for panoramic X-ray selection: Fermatean fuzzy type correlation coefficients approach, Engineering Applications of Artificial Intelligence 126 (2023), 106824.
  • [20] M. Kirişci, Fermatean fuzzy type a three-way correlation coefficients. In: Gayoso Mart´ınez, V., Yilmaz, F., Queiruga-Dios, A., Rasteiro, D.M., Mart´ın-Vaquero, J., Mierlus¸-Mazilu, I. (eds) Mathematical Methods for Engineering Applications, ICMASE 2023. Springer Proceedings in Mathematics & Statistics, 499 (2024), 325–338, doi: 10.1007/978−3−031−49218−124.
  • [21] M. Kiris¸ci, New cosine similarity and distance measures for Fermatean fuzzy sets and TOPSIS approach, Knowledge and Information Systems, 65(2) (2023), 855–868.
  • [22] M. Kirişci, Correlation Coefficients of Fermatean Fuzzy Sets with Their Application, J. Math. Sci. Model. 5(2) (2022), 16–23. doi: 10.33187/jmsm.1039613
  • [23] M. Kirişci, I. Demir, N. Şimsek, Fermatean fuzzy ELECTRE multi-criteria group decision-making and most suitable biomedical material selection, Artificial Intelligence in Medicine, 127 (2022), 102278, doi:10.1016/j.artmed.2022.102278.
  • [24] M. Kirişci, Data analysis for lung cancer: Fermatean Hesitant fuzzy sets approach, Applied Mathematics, Modeling and Computer Simulation, 30 (2022), 701–710, doi:10.3233/ATDE221087
  • [25] M. Kiris¸ci, N. Simsek, A novel kernel principal component analysis with application disaster preparedness of hospital: interval-valued Fermatean fuzzy set approach, The Journal of Supercomputing, 79 (2023), 19848–19878.
  • [26] M. Kirişci, Measures of distance and entropy based on the Fermatean fuzzy-type soft sets approach, Univ. J. Math. Appl., 7 (2024), 12–29.
  • [27] M. Riaz, H. M. A. Farid, H. M. Shakeel, D. Arif, Cost effective indoor HVAC energy efficiency monitoring based on intelligent decision support system under Fermatean fuzzy framework, Scientia Iranica, 30(6) (2023), 2143–2161.
  • [28] N. Şimşek, M. Kirişci, Incomplete Fermatean Fuzzy Preference Relations and Group Decision Making, Topol. Algebra Appl., 11(1) (2023), 20220125. [29] N. Şimşek, M. Kirişci, A new risk assessment method for autonomous vehicle driving systems: Fermatean fuzzy AHP Approach, Istanbul Commerce University Journal of Science, 22(44) (2023), 292–309.
  • [30] K. Atanassov, G. Gargov, Interval valued intuitionistic fuzzy sets, Fuzzy Sets and Systems, 31(3) (1989), 343–349.
  • [31] X. Peng, Y. Yang, Fundamental properties of interval-valued Pythagorean fuzzy aggregation operators, Int. J. Intell. Syst., 31 (2016), 444–487.
  • [32] S. Jeevaraj, Ordering of interval-valued Fermatean fuzzy sets and their applications, Expert Systems with Applications, 185 (2021), 115613.
  • [33] D. Stanujkic, E.K. Zavadskas, D. Karabasevic, F. Smarandache, Z. Turskis, The use of the PIvot Pairwise RElative Criteria Importance Assessment method for determining the weights of criteria, Romanian Journal of Economic Forecasting, 20 (2017), 116–133.
  • [34] Z. Stevic, Z. Stjepanovic, Z. Bozickovic, D.K. Das, D. Stanujkic, Assessment of conditions for implementing information technology in a warehouse system: A novel fuzzy PIPRECIA method, Symmetry, 10 (2018), 1–28.
  • [35] G. Demir, M. Riaz, M. Deveci, Wind farm site selection using geographic information system and fuzzy decision making model, Expert System and Applications, 255 (2024), 124772.
  • [36] Z. Stevic, D. Pamucar, A. Puska, P. Chatterjee, Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement Alternatives and Ranking according to COmpromise Solution (MARCOS), Computers and Industrial Engineering, 140 (2020), 1–33.
  • [37] G. Demir, P. Chatterjee, S. Kadry, A. Abdelhadi, D. Pamucar, Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) Method: A Comprehensive Bibliometric Analysis, Decision-Making Applications in Management and Engineering, 7 (2024), 313–336.
  • [38] A.R. Mishra, P. Rani, D. Pamucar, A. Saha, An integrated Pythagorean fuzzy fairly operator-based MARCOS method for solving the sustainable circular supplier selection problem, Ann. Oper. Res., 342 (2024), 523–564.
  • [39] H. M. A. Farid, S. Dabic-Miletic, M. Riaz, V. Simic, D. Pamucar, Prioritization of sustainable approaches for smart waste management of automotive fuel cells of road freight vehicles using the q-rung orthopair fuzzy CRITIC-EDAS method, Information Sci., 661 (2024), 120162
  • [40] R. Kausar, M. Riaz, V. Simic, K. Akmal, M. U. Farooq, Enhancing solid waste management sustainability with cubic m-polar fuzzy cosine similarity, Soft Comput., (2023), 1-21, doi:10.1007/s00500-023-08801-w
  • [41] P. Rani, A.R. Mishra, Interval-valued Fermatean fuzzy sets with multi-criteria weighted aggregated sum product assessment-based decision analysis framework, Neural Computing and Applications, 34 (2022), 8051–8067.
  • [42] M. H. Mateen, I. Al-Dayel, T. Alsuraiheed, Fermatean fuzzy fairly aggregation operators with multi-criteria decision-making, Axioms, 12 (2023), 865.
  • [43] A.R. Mishra, P. Rani, A.F. Alrasheedi, R. Dwivedi, Evaluating the blockchain-based healthcare supply chain using interval-valued Pythagorean fuzzy entropy-based decision support system, Engineering Applications of Artificial Intelligence, 126 (2023), 107112.
  • [44] X. Peng, W. Li, Algorithms for Interval-Valued Pythagorean Fuzzy Sets in Emergency Decision Making Based on Multiparametric Similarity Measures and WDBA, IEEE Access, 7 (2019), 7419–7441.
  • [45] Medical waste, WHO, https://www.who.int/news-room/fact-sheets/detail/health-care-waste (Access: November 2024).
  • [46] M. Ghodrat, M. Rashidi, B. Samali, Life cycle assessments of incineration treatment for sharp medical waste, Energy Technology 2017 Conference Paper, (2017), 131–143.
  • [47] C. C. Lee, G. L. Huffman, Medical waste management/incineration, J. Hazard Mater., 48 (1996), 1–30.
  • [48] Z. Sapuric, D. Dimitrovski, M. Dimitrovski, F. Ivanovski, Medical waste incineration in skopje. regulation and standards, J. Environ. Protect. Ecol., 17 (2016), 805–812.
  • [49] A. Wu, X: Huang, R.E. Gong, J. Li, Y. Lu, M. Wang, Effectiveness of immersion disinfectant on medical waste, Chin. J. Nosoconmiol., 15 (2005), 51-–52.
  • [50] S. M. Rao, P. Mamatha, R. P. Shanta, B. V. V. Reddy, Encapsulation of fluoride sludge in stabilised mud blocks, Proceedings of the Institution of Civil Engineers Waste and Resource Management, R4, (2007), 167–174.
  • [51] M. Dursun, E.E. Karsak, M.A. Karadayi, Assessment of health-care waste treatment alternatives using fuzzy multi-criteria decision making approaches, Resour. Conserv. Recycl., 57 (2011), 98e107.
  • [52] M. R. Seikh, P. Chatterjee, Determination of best renewable energy sources in India using SWARA-ARAS in confidence level based interval-valued Fermatean fuzzy environment, Applied Soft Computing, 155 (2024), 111495.
  • [53] M. Alrasheedi, A. Mardani, A.R. Mishra, P. Rani, P., L. Nanthakumar, An extended framework to evaluate sustainable suppliers in manufacturing companies using a new pythagorean fuzzy entropy-SWARA-WASPAS decisionmaking approach, J. Enterp. Inf. Manag., 35 (2022), 333–357.
There are 52 citations in total.

Details

Primary Language English
Subjects Applied Mathematics (Other)
Journal Section Articles
Authors

Murat Kirisci 0000-0003-4938-5207

Early Pub Date December 17, 2024
Publication Date
Submission Date November 18, 2024
Acceptance Date December 10, 2024
Published in Issue Year 2024 Volume: 7 Issue: 3

Cite

APA Kirisci, M. (2024). Medical Waste Management Based on an Interval-Valued Fermatean Fuzzy Decision-Making Method. Journal of Mathematical Sciences and Modelling, 7(3), 127-144. https://doi.org/10.33187/jmsm.1587499
AMA Kirisci M. Medical Waste Management Based on an Interval-Valued Fermatean Fuzzy Decision-Making Method. Journal of Mathematical Sciences and Modelling. December 2024;7(3):127-144. doi:10.33187/jmsm.1587499
Chicago Kirisci, Murat. “Medical Waste Management Based on an Interval-Valued Fermatean Fuzzy Decision-Making Method”. Journal of Mathematical Sciences and Modelling 7, no. 3 (December 2024): 127-44. https://doi.org/10.33187/jmsm.1587499.
EndNote Kirisci M (December 1, 2024) Medical Waste Management Based on an Interval-Valued Fermatean Fuzzy Decision-Making Method. Journal of Mathematical Sciences and Modelling 7 3 127–144.
IEEE M. Kirisci, “Medical Waste Management Based on an Interval-Valued Fermatean Fuzzy Decision-Making Method”, Journal of Mathematical Sciences and Modelling, vol. 7, no. 3, pp. 127–144, 2024, doi: 10.33187/jmsm.1587499.
ISNAD Kirisci, Murat. “Medical Waste Management Based on an Interval-Valued Fermatean Fuzzy Decision-Making Method”. Journal of Mathematical Sciences and Modelling 7/3 (December 2024), 127-144. https://doi.org/10.33187/jmsm.1587499.
JAMA Kirisci M. Medical Waste Management Based on an Interval-Valued Fermatean Fuzzy Decision-Making Method. Journal of Mathematical Sciences and Modelling. 2024;7:127–144.
MLA Kirisci, Murat. “Medical Waste Management Based on an Interval-Valued Fermatean Fuzzy Decision-Making Method”. Journal of Mathematical Sciences and Modelling, vol. 7, no. 3, 2024, pp. 127-44, doi:10.33187/jmsm.1587499.
Vancouver Kirisci M. Medical Waste Management Based on an Interval-Valued Fermatean Fuzzy Decision-Making Method. Journal of Mathematical Sciences and Modelling. 2024;7(3):127-44.

29237    Journal of Mathematical Sciences and Modelling 29238

                   29233

Creative Commons License The published articles in JMSM are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.