A consensus-based Fermatean fuzzy WASPAS methodology for selection of healthcare waste treatment technology selection
DOI:
https://doi.org/10.31181/dmame622023621Keywords:
Fermatean fuzzy numbers, consensus reaching, WASPAS, healthcare waste treatment technology selectionAbstract
Healthcare waste (HCW) management is a complex issue influenced by many factors, including technological, economic, environmental, and social factors. It is possible to regard the evaluation of the best treatment technique for HCW management as a challenging case of MCDM (multi-criteria decision-making), where various alternatives and evaluation criteria must be considered. The presentation and handling of the shaky data are crucial to choosing the HCW treatment technology. In order to address the issue of MCDM issues with Fermatean fuzzy (FF) data, we first build a consensus-based WASPAS approach in this study. In the suggested integrated methodology, the rank of the alternatives is determined using the WASPAS method in an FF environment, and the attribute weights are estimated using the entropy measure technique. In the preceding, an HCW treatment technology assessment issue is considered to make the proposed structure's applicability more transparent. In this study, four HCW treatment methods—chemical disinfection, microwave disinfection, cremation, and autoclaving—are considered options. According to the study's findings, autoclaving is the most effective HCW treatment method. Additionally, we demonstrate a sensitivity assessment using several criteria weight sets to test the stability of our intriguing proposed approach. We also call attention to a contrast between our suggested approach to decision-making and the practices now in use.
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Alagöz, A. Z., & Kocasoy, G. (2008). Determination of the best appropriate management methods for the healthcare wastes in İstanbul. Waste Management, 28(7), 1227-1235. https://doi.org/10.1016/j.wasman.2007.05.018
Alagöz, B. A. Z., & Kocasoy, G. (2007). Treatment and disposal alternatives for healthcare waste in developing countries - A case study in Istanbul, Turkey. Waste Management and Research, 25(1), 83-89. https://doi.org/10.1177/0734242X07069497
ASSOCHAM. (2022). Knowledge Architect of India. https://www.assocham.org/
Aung, T. S., Luan, S., & Xu, Q. (2019). Application of multi-criteria-decision approach for the analysis of medical waste management systems in Myanmar. Journal of Cleaner Production, 222, 733-745. https://doi.org/10.1016/j.jclepro.2019.03.049
Aydemir, S. B., & Yilmaz Gunduz, S. (2020). Fermatean fuzzy TOPSIS method with Dombi aggregation operators and its application in multi-criteria decision making. Journal of Intelligent and Fuzzy Systems, 39(1), 851-869. https://doi.org/10.3233/JIFS-191763
Aytekin, A., Görçün, Ö. F., Ecer, F., Pamucar, D., & Karamaşa, Ç. (2022). Evaluation of the pharmaceutical distribution and warehousing companies through an integrated Fermatean fuzzy entropy-WASPAS approach. Kybernetes. https://doi.org/10.1108/K-04-2022-0508
Badalpur, M., & Nurbakhsh, E. (2021). An application of WASPAS method in risk qualitative analysis: a case study of a road construction project in Iran. International Journal of Construction Management, 21(9), 910-918. https://doi.org/10.1080/15623599.2019.1595354
Badi, I., Shetwan, A., & Hemeda, A. (2019). A grey-based assessment model to evaluate healthcare waste treatment alternatives in Libya. Operational Research in Engineering Sciences: Theory and Applications, 2(3), 92–106. https://doi.org/10.31181/oresta1903092b
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 healthcare 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. https://doi.org/10.1016/j.ijmedinf.2018.07.001
Bid, S., & Siddique, G. (2019). Human risk assessment of Panchet Dam in India using TOPSIS and WASPAS Multi-Criteria Decision-Making (MCDM) methods. Heliyon, 5(6), e01956. https://doi.org/10.1016/j.heliyon.2019.e01956
Birpinar, M. E., Bilgili, M. S., & Erdoǧan, T. (2009). Medical waste management in Turkey: A case study of Istanbul. Waste Management, 29(1), 445-448. https://doi.org/10.1016/j.wasman.2008.03.015
Brent, A. C., Rogers, D. E. C., Ramabitsa-Siimane, T. S. M., & Rohwer, M. B. (2007). Application of the analytical hierarchy process to establish health care waste management systems that minimize infection risks in developing countries. European Journal of Operational Research, 181(1), 403-424. https://doi.org/10.1016/j.ejor.2006.06.015
Chen, H., Li, Q., Kaufman, J. S., Wang, J., Copes, R., Su, Y., & Benmarhnia, T. (2018). Effect of air quality alerts on human health: a regression discontinuity analysis in Toronto, Canada. The Lancet Planetary Health, 2(1), 23-45. https://doi.org/10.1016/S2542-5196(17)30185-7
Davoudabadi, R., Mousavi, S. M., & Mohagheghi, V. (2020). A new last aggregation method of multi-attributes group decision making based on concepts of TODIM, WASPAS and TOPSIS under interval-valued intuitionistic fuzzy uncertainty. Knowledge and Information Systems, 62(4), 1371–1391. https://doi.org/10.1007/s10115-019-01390-x
Deveci, M., Canıtez, F., & Gökaşar, I. (2018). WASPAS and TOPSIS based interval type-2 fuzzy MCDM method for a selection of a car sharing station. Sustainable Cities and Society, 41, 777-791. https://doi.org/10.1016/j.scs.2018.05.034
Dorfeshan, Y., & Mousavi, S. M. (2020). A novel interval type-2 fuzzy decision model based on two new versions of relative preference relation-based MABAC and WASPAS methods (with an application in aircraft maintenance planning). Neural Computing and Applications, 32(8), 3367–3385. https://doi.org/10.1007/s00521-019-04184-y
Dursun, M., Karsak, E. E., & Karadayi, M. A. (2011a). A fuzzy multi-criteria group decision making framework for evaluating healthcare waste disposal alternatives. Expert Systems with Applications, 38(9), 11453-11462. https://doi.org/10.1016/j.eswa.2011.03.019
Dursun, M., Karsak, E. E., & Karadayi, M. A. (2011b). Assessment of healthcare waste treatment alternatives using fuzzy multi-criteria decision making approaches. Resources, Conservation and Recycling, 57, 98-107. https://doi.org/10.1016/j.resconrec.2011.09.012
Ghram, M., & Frikha, H. M. (2020). Assessing healthcare waste treatment technologies in Tunisia within fuzzy ARAS-H. 2020 13th International Colloquium of Logistics and Supply Chain Management, LOGISTIQUA 2020. https://doi.org/10.1109/LOGISTIQUA49782.2020.9353873
Görçün, Ö. F., Aytekin, A., Selçuk Korucuk, & Tirkolaee, E. B. (2023a). Evaluating and selecting sustainable logistics service providers for medical waste disposal treatment in the healthcare industry. Journal of Cleaner Production, 408, 137194. https://doi.org/10.1016/j.jclepro.2023.137194
Görçün, Ö. F., Pamucar, D., & Biswas, S. (2023). The blockchain technology selection in the logistics industry using a novel MCDM framework based on Fermatean fuzzy sets and Dombi aggregation. Information Sciences, 635, 345–374. https://doi.org/10.1016/j.ins.2023.03.113
Gul, M., Lo, H.-W., & Yucesan, M. (2021). Fermatean fuzzy TOPSIS-based approach for occupational risk assessment in manufacturing. Complex & Intelligent Systems, 7(5), 2635–2653. https://doi.org/10.1007/s40747-021-00417-7
Gül, S. (2021). Fermatean fuzzy set extensions of SAW, ARAS, and VIKOR with applications in COVID-19 testing laboratory selection problem. Expert Systems, 38(8), e12769. https://doi.org/10.1111/exsy.12769
Gusca, J., Kuznecova, I., & Kalnins, S. N. (2017). Algorithm for Life Cycle Inventory of Medical Waste Treatment Technologies Emphasizing the Role of Treatment Efficiency. Energy Procedia, 113, 423-427. https://doi.org/10.1016/j.egypro.2017.04.028
Hadi, A., Khan, W., & Khan, A. (2021). A novel approach to MADM problems using Fermatean fuzzy Hamacher aggregation operators. International Journal of Intelligent Systems, 36(7), 3464-3499. https://doi.org/10.1002/int.22423
Hinduja, A., & Pandey, M. (2018). Assessment of healthcare waste treatment alternatives using an integrated decision support framework. International Journal of Computational Intelligence Systems, 12(1), 98-107. https://doi.org/10.2991/ijcis.2018.125905685
Ho, C. C. (2011). Optimal evaluation of infectious medical waste disposal companies using the fuzzy analytic hierarchy process. Waste Management, 31(7), 1553-1559. https://doi.org/10.1016/j.wasman.2011.02.020
Kahraman, C., Cevik Onar, S., Oztaysi, B., & Ilbahar, E. (2019). Selection among GSM operators using Pythagorean fuzzy waspas method. Journal of Multiple-Valued Logic and Soft Computing, 33(4–5).
Karagiannidis, A., Papageorgiou, A., Perkoulidis, G., Sanida, G., & Samaras, P. (2010). A multi-criteria assessment of scenarios on thermal processing of infectious hospital wastes: A case study for Central Macedonia. Waste Management, 30(2), 251-262. https://doi.org/10.1016/j.wasman.2009.08.015
Karamouz, M., Zahraie, B., Kerachian, R., Jaafarzadeh, N., & Mahjouri, N. (2007). Developing a master plan for hospital solid waste management: A case study. Waste Management, 27(5), 626-638. https://doi.org/10.1016/j.wasman.2006.03.018
Keshavarz-Ghorabaee, M., Amiri, M., Hashemi-Tabatabaei, M., Zavadskas, E. K., & Kaklauskas, A. (2020). A new decision-making approach based on fermatean fuzzy sets and waspas for green construction supplier evaluation. Mathematics, 8(12), 1-23. https://doi.org/10.3390/math8122202
Keshavarz-Ghorabaee, M., Govindan, K., Amiri, M., Zavadskas, E. K., & Antuchevičienė, J. (2019). An integrated type-2 fuzzy decision model based on waspas and seca for evaluation of sustainable manufacturing strategies. Journal of Environmental Engineering and Landscape Management, 27(4), 187-200. https://doi.org/10.3846/jeelm.2019.11367
Krishankumar, R., Subrajaa, L. S., Ravichandran, K. S., Kar, S., & Saeid, A. B. (2019). A Framework for Multi-Attribute Group Decision-Making Using Double Hierarchy Hesitant Fuzzy Linguistic Term Set. International Journal of Fuzzy Systems, 21(4), 1130–1143. https://doi.org/10.1007/s40815-019-00618-w
Kundu, P., Görçün, Ö. F., & Küçükönder, H. (2021). Medical device selection in private hospitals by integrated fuzzy MCGDM methods: A case study in choosing MRI (Magnetic Resonance Imaging) system. Journal of the Operational Research Society, 73(9), 2059-2079. https://doi.org/10.1080/01605682.2021.1960910
Kutlu Gundogdu, F., & Kahraman, C. (2019). Extension of WASPAS with spherical fuzzy sets. Informatica (Netherlands), 30(2), 269–292. https://doi.org/10.15388/Informatica.2019.206
Liu, D., & Huang, A. (2020). Consensus reaching process for fuzzy behavioral TOPSIS method with probabilistic linguistic q-rung orthopair fuzzy set based on correlation measure. International Journal of Intelligent Systems, 35(3), 494-528. https://doi.org/10.1002/int.22215
Liu, P., Rani, P., & Mishra, A. R. (2021). A novel Pythagorean fuzzy combined compromise solution framework for the assessment of medical waste treatment technology. Journal of Cleaner Production, 292, 126047. https://doi.org/10.1016/j.jclepro.2021.126047
Liu, P., Saha, A., Mishra, A. R., Rani, P., Dutta, D., & Baidya, J. (2022). A BCF–CRITIC–WASPAS method for green supplier selection with cross-entropy and Archimedean aggregation operators. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-022-03745-9
Liu, Y. B., Liu, L., Li, Y. F., & Chen, Y. L. (2015). Relationship between health literacy, health-related behaviors and health status: A survey of elderly Chinese. International Journal of Environmental Research and Public Health, 12(8), 9714-9725. https://doi.org/10.3390/ijerph120809714
Lu, C., You, J. X., Liu, H. C., & Li, P. (2016). Health-carewaste treatment technology selection using the interval 2-Tuple induced TOPSIS method. International Journal of Environmental Research and Public Health, 13(6), 562. https://doi.org/10.3390/ijerph13060562
Mahmood, T., Ullah, K., Khan, Q., & Jan, N. (2019). An approach toward decision-making and medical diagnosis problems using the concept of spherical fuzzy sets. Neural Computing and Applications, 31(11), 7041-7053. https://doi.org/10.1007/s00521-018-3521-2
Mahmood, T., & ur Rehman, U. (2022). A novel approach towards bipolar complex fuzzy sets and their applications in generalized similarity measures. International Journal of Intelligent Systems, 37(1), 535-567. https://doi.org/10.1002/int.22639
Mahmood, T., ur Rehman, U., & Ali, Z. (2023). Analysis and application of Aczel-Alsina aggregation operators based on bipolar complex fuzzy information in multiple attribute decision making. Information Sciences, 619, 817-833. https://doi.org/10.1016/j.ins.2022.11.067
Mishra, A. R., & Rani, P. (2018). Interval-Valued Intuitionistic Fuzzy WASPAS Method: Application in Reservoir Flood Control Management Policy. Group Decision and Negotiation, 27(6), 1047-1078. https://doi.org/10.1007/s10726-018-9593-7
Mishra, A. R., & Rani, P. (2021). Multi-criteria healthcare waste disposal location selection based on Fermatean fuzzy WASPAS method. Complex & Intelligent Systems, 7(5), 2469-2484. https://doi.org/10.1007/s40747-021-00407-9
Mishra, A. R., Rani, P., & Pandey, K. (2022). Fermatean fuzzy CRITIC-EDAS approach for the selection of sustainable third-party reverse logistics providers using improved generalized score function. Journal of Ambient Intelligence and Humanized Computing, 13(1), 1-17. https://doi.org/10.1007/s12652-021-02902-w
Mishra, A. R., Rani, P., Pardasani, K. R., & Mardani, A. (2019). A novel hesitant fuzzy WASPAS method for assessment of green supplier problem based on exponential information measures. Journal of Cleaner Production, 238, 117901. https://doi.org/10.1016/j.jclepro.2019.117901
Mishra, A. R., Singh, R. K., & Motwani, D. (2019). Multi-criteria assessment of cellular mobile telephone service providers using intuitionistic fuzzy WASPAS method with similarity measures. Granular Computing, 4(3), 511–529. https://doi.org/10.1007/s41066-018-0114-5
Mohagheghi, V., & Mousavi, S. M. (2020). D-WASPAS: Addressing Social Cognition in Uncertain Decision-Making with an Application to a Sustainable Project Portfolio Problem. Cognitive Computation, 12(3), 619-641. https://doi.org/10.1007/s12559-019-09679-3
Ouyang, L., Zhu, Y., Zheng, W., & Yan, L. (2021). An information fusion FMEA method to assess the risk of healthcare waste. Journal of Management Science and Engineering, 6(1), 111-124. https://doi.org/10.1016/j.jmse.2021.01.001
Özkan, A. (2013). Evaluation of healthcare waste treatment/disposal alternatives by using multi-criteria decision-making techniques. Waste Management and Research, 31(2), 141-149. https://doi.org/10.1177/0734242X12471578
Pamučar, D., Puška, A., Stević, Ž., & Ćirović, G. (2021). A new intelligent MCDM model for HCW management: The integrated BWM–MABAC model based on D numbers. Expert Systems with Applications, 175, 114862. https://doi.org/10.1016/j.eswa.2021.114862
Qian, W., Wang, Z. J., & Li, K. W. (2016). Medical waste disposal method selection based on a hierarchical decision model with intuitionistic fuzzy relations. International Journal of Environmental Research and Public Health, 13(9), 896. https://doi.org/10.3390/ijerph13090896
Rani, P., & Mishra, A. R. (2020). Multi-criteria weighted aggregated sum product assessment framework for fuel technology selection using q-rung orthopair fuzzy sets. Sustainable Production and Consumption, 24, 90-104. https://doi.org/10.1016/j.spc.2020.06.015
Rani, P., & Mishra, A. R. (2021). Fermatean fuzzy Einstein aggregation operators-based MULTIMOORA method for electric vehicle charging station selection. Expert Systems with Applications, 182, 115267. https://doi.org/10.1016/j.eswa.2021.115267
Rani, P., Mishra, A. R., Saha, A., Hezam, I. M., & Pamucar, D. (2022). Fermatean fuzzy Heronian mean operators and MEREC‐based additive ratio assessment method: An application to food waste treatment technology selection. International Journal of Intelligent Systems, 37(3), 2612–2647.
Rudnik, K., Bocewicz, G., Kucińska-Landwójtowicz, A., & Czabak-Górska, I. D. (2021). Ordered fuzzy WASPAS method for selection of improvement projects. Expert Systems with Applications, 169, 114471. https://doi.org/10.1016/j.eswa.2020.114471
Saha, A., Pamucar, D., Gorcun, O. F., & Raj Mishra, A. (2023). Warehouse site selection for the automotive industry using a fermatean fuzzy-based decision-making approach. Expert Systems with Applications, 211, 118497. https://doi.org/10.1016/j.eswa.2022.118497
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(1), 205-220.
Saraji, M. K., Streimikiene, D., & Kyriakopoulos, G. L. (2021). Fermatean fuzzy critic‐copras method for evaluating the challenges to industry 4.0 adoption for a sustainable digital transformation. Sustainability (Switzerland), 13(17), 1-23. https://doi.org/10.3390/su13179577
Schitea, D., Deveci, M., Iordache, M., Bilgili, K., Akyurt, İ. Z., & Iordache, I. (2019). Hydrogen mobility roll-up site selection using intuitionistic fuzzy sets based WASPAS, COPRAS and EDAS. International Journal of Hydrogen Energy, 44(16), 8585–8600. https://doi.org/10.1016/J.IJHYDENE.2019.02.011
Senapati, T., & Yager, R. R. (2019). Fermatean fuzzy weighted averaging/geometric operators and its application in multi-criteria decision-making methods. Engineering Applications of Artificial Intelligence, 85, 112-121. https://doi.org/10.1016/j.engappai.2019.05.012
Senapati, T., & Yager, R. R. (2020). Fermatean fuzzy sets. Journal of Ambient Intelligence and Humanized Computing, 11(2), 663–674. https://doi.org/10.1007/s12652-019-01377-0
Sharma, R., & Pradhan, S. (2020). Investigation of machinability criteria during micro-abrasive finishing of SUS-304L steel using fuzzy combined with WASPAS approach. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 42(3), 112. https://doi.org/10.1007/s40430-020-2198-5
Simic, V., Gokasar, I., Deveci, M., & Isik, M. (2021). Fermatean Fuzzy Group Decision-Making Based CODAS Approach for Taxation of Public Transit Investments. IEEE Transactions on Engineering Management. https://doi.org/10.1109/TEM.2021.3109038
Simić, V., Lazarević, D., & Dobrodolac, M. (2021). Picture fuzzy WASPAS method for selecting last-mile delivery mode: a case study of Belgrade. European Transport Research Review, 13(1), 13–43.
Stanujkić, D., & Karabašević, D. (2018). An extension of the WASPAS method for decision-making problems with intuitionistic fuzzy numbers: A case of website evaluation. Operational Research in Engineering Sciences: Theory and Applications, 1(1), 29–39. https://doi.org/10.31181/oresta19012010129s
Torkayesh, A. E., Malmir, B., & Rajabi Asadabadi, M. (2021). Sustainable waste disposal technology selection: The stratified best-worst multi-criteria decision-making method. Waste Management, 122, 100-112. https://doi.org/10.1016/j.wasman.2020.12.040
Tudor, T. L., Townend, W. K., Cheeseman, C. R., & Edgar, J. E. (2009). An overview of arisings and large-scale treatment technologies for healthcare waste in the United Kingdom. In Waste Management and Research (Vol. 27, Issue 4). https://doi.org/10.1177/0734242X09336244
Ucal Sari, I., Kuchta, D., & Sergi, D. (2021). Analysis of intelligent software implementations in air cargo using fermatean fuzzy CODAS method. In Intelligent and Fuzzy Techniques in Aviation 4.0: Theory and Applications (pp. 147-173). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-75067-1_7
Ucal Sari, I., & Sargin, S. N. (2022). Prioritization of R&D Projects Using Fermatean Fuzzy MARCOS Method. In Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation: Proceedings of the INFUS 2021 Conference, held August 24-26, 2021. Volume 2 (pp. 569-577). Springer International Publishing.. https://doi.org/10.1007/978-3-030-85577-2_67
Voudrias, E. A. (2016). Technology selection for infectious medical waste treatment using the analytic hierarchy process. Journal of the Air and Waste Management Association, 66(7), 663-672. https://doi.org/10.1080/10962247.2016.1162226
Yager, R. R. (2014). Pythagorean membership grades in multi-criteria decision making. IEEE Transactions on Fuzzy Systems, 22(4), 958-965. https://doi.org/10.1109/TFUZZ.2013.2278989
Yazdani, M., Tavana, M., Pamučar, D., & Chatterjee, P. (2020). A rough based multi-criteria evaluation method for healthcare waste disposal location decisions. Computers and Industrial Engineering, 143, 106394. https://doi.org/10.1016/j.cie.2020.106394
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353.
Zavadskas, E. K., Turskis, Z., Antucheviciene, J., & Zakarevicius, A. (2012). Optimization of weighted aggregated sum product assessment. Elektronika Ir Elektrotechnika, 122(6), 3–6.
Zhao, W., Van Der Voet, E., Huppes, G., & Zhang, Y. (2009). Comparative life cycle assessments of incineration and non-incineration treatments for medical waste. International Journal of Life Cycle Assessment, 14(2), 114-121. https://doi.org/10.1007/s11367-008-0049-1
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