Review of Alternative Ranking Methods in Multi-Criteria Decision Analysis Based on WASPAS and CoCoSo Methodologies
Abstract
Along with crisp data, one of the most popular methods in decision-making under uncertainty is the multi-criteria decision-making (MCDM) method. Over time, the MCDM process has incorporated numerous decision-making methods. Among these methods, WeightedAggregatedSumProductAssessment(WASPAS)andCombinedCompromise Solution (CoCoSo) are lauded for their effectiveness in handling complex, uncertain decision situations. Using these methods, numerous researchers have discussed in their research papers how to solve various problems observed in different areas of real life. Based on multiple previous research findings, this review paper provides a thorough description of the above MCDM methods. It explains in detail their theoretical basis, mathematical framework, computational methods, pseudocode, their use in various realworld problems, etc. in uncertain environments. A comparative discussion between the two methods is given which focuses on the similarities, contrasts, strengths, and weaknesses between them. Further, two real-world applications of these two methods were added and results were discussed in a comparative manner. Additionally, this review discusses the applicability of these methods in real-world decision-making contexts and identifies avenues for future research in this dynamic field.
References
M. G. Logsdon, “Traditional decision making in urban neighborhoods,” Indonesia, vol. 26, pp. 95–110, 1978. doi: 10.2307/3350837
M.W.Merkhofer, “Comparative analysis of formal decision-making approaches,” Risk Evaluation and Management, vol. 1, p. 183–219, 1986. doi: 10.1007/978-1-4613-2103-3 7
G. F. Pitz and N. J. Sachs, “Judgment and decision: Theory and application,” Annual Review of Psychology, vol. 35, pp. 139–163, 1984. doi: 10.1146/annurev.ps.35.020184.001035
A. Tversky and D. Kahneman, “Judgment under uncertainty: Heuristics and biases: Biases in judgments reveal some heuristics of thinking under uncertainty,” Science, vol. 185, no. 4157, pp. 1124–1131, 1974. doi: 10.1126/science.185.4157.1124
S. Mandal, K. H. Gazi, S. Salahshour, S. P. Mondal, P. Bhattacharya, and A. K. Saha, “Application of interval valued intuitionistic fuzzy uncertain mcdm methodology for ph. d supervisor selection problem,” Results in Control and Optimization, vol. 15, no. 100411, pp. 1–33, 2024. doi: 10.1016/j.rico.2024.100411
C. C¸alikoˇglu and A. Łuczak, “Multidimensional assessment of sdi and hdi using topsis and bilinear ordering,” International Journal of Economic Sciences, vol. 13, no. 2, pp. 116–128, 2024. doi: 10.52950/ES.2024.13.2.007
H.Wang,W.Zhao,andJ.Zheng, “Improved q-rung orthopair fuzzy waspas method based on softmax function and frank operations for investment decision of community group-buying platform,” Journal of Soft Computing and Decision Analytics, vol. 2, no. 1, pp. 188–212, 2024. doi: 10.31181/jscda21202442
E. K. Zavadskas, Z. Turskis, J. Antucheviciene, and A. Zakarevicius, “Optimization of weighted aggregated sum product assessment,” Elektronika Ir Elektrotechnika, vol. 122, no. 6, pp. 3–6, 2012. doi: 10.5755/j01.eee.122.6.1810
A. E. Ionasc¸u, S. S. Goswam, A. D˘anil˘a, M.-G. Horga, C. A. Barbu, and A. S¸erbanCom˘anescu, “Analyzing primary sector selection for economic activity in romania: An interval-valued fuzzy multi-criteria approach,” Mathematics, vol. 12, no. 1157, pp. 1–40, 2024. doi: 10.3390/math12081157
P. Rani, A. R. Mishra, and K. R. Pardasani, “A novel waspas approach for multi-criteria physician selection problem with intuitionistic fuzzy type-2 sets,” Soft Computing, vol. 24, p. 2355–2367, 2020. doi: 10.1007/s00500-019-04065-5
A. R. Mishra and P. Rani, “Multi-criteria healthcare waste disposal location selection based on fermatean fuzzy waspas method,” Complex & Intelligent Systems, vol. 7, pp. 2469–2484, 2021. doi: 10.1007/s40747-021-00407-9
S. Chakraborty and A. K. Saha, “A framework of lr fuzzy ahp and fuzzy waspas for health care waste recycling technology,” Applied Soft Computing, vol. 127, no. 109388, 2022. doi: 10.1016/j.asoc.2022.109388
A. Meneks¸e and H. C. oz Akda˘g, “Medical waste disposal planning for healthcare units using spherical fuzzy critic-waspas,” Applied Soft Computing, vol. 144, no. 110480, 2023. doi: 10.1016/j.asoc.2023.110480
C. N. Rao and M. Sujatha, “A consensus-based fermatean fuzzy waspas methodology for selection of healthcare waste treatment technology selection,” Decision Making: Applications in Management and Engineering, vol. 6, no. 2, pp. 600–619, 2023. doi: 10.31181/dmame622023621
C¸. Sıcaky¨uz, “Analyzing healthcare and wellness products’ quality embedded in online customer reviews: Assessment with a hybrid fuzzy lmaw and fermatean fuzzy waspas method,” Sustainability, vol. 15, no. 3428, pp. 1–41, 2023. doi: 10.3390/su15043428
E. Tumsekcali, E. Ayyildiz, and A. Taskin, “Interval valued intuitionistic fuzzy ahp-waspas based public transportation service quality evaluation by a new extension of servqual model: P-servqual 4.0,” Expert Systems with Applications, vol. 186, no. 115757, 2021. doi: 10.1016/j.eswa.2021.115757
S. Koma, A. O. kusakci, and M. H. Amiri, “A pythagorean fuzzy ahp and waspas methods for airline new route selection: Case study of turkish airlines,” SSRN, pp. 1–37, 2023. doi: 10.2139/ssrn.4594277
D. Andjelkovi´c, G. Stoji´c, N. Nikoli´c, D. K. Das, M. Suboti´c, and ˇ Z. Stevi´c, “A novel dataenvelopment analysis interval-valued fuzzy-rough-number multi-criteria decision-making (dea-ifrn mcdm) model for determining the efficiency of road sections based on headway analysis,” Mathematics, vol. 12, no. 976, pp. 1–19, 2024. doi: 10.3390/math12070976
R. Verma and E. ´ Alvarez Miranda, “Multiple-attribute group decision-making approach using power aggregation operators with critic-waspas method under 2-dimensional linguistic intuitionistic fuzzy framework,” Applied Soft Computing, vol. 157, no. 111466, 2024. doi: 10.1016/j.asoc.2024.111466
H. Dhumras and R. K. Bajaj, “On potential strategic framework for green supply chain management in the energy sector using q−rung picture fuzzy ahp & waspas decisionmaking model,” Expert Systems with Applications, vol. 237, no. 121550, 2024. doi: 10.1016/j.eswa.2023.121550
E. Ilbahar, S. Cebi, and C. Kahraman, “Prioritization of renewable energy sources using multi-experts pythagorean fuzzy waspas,” Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6407–6417, 2020. doi: 10.3233/JIFS-189106
C.-N. Wang, Y.-T. Chen, and C.-C. Tung, “Evaluation of wave energy location by using an integrated mcdm approach,” Energies, vol. 14, no. 1840, pp. 1–14, 2021. doi: 10.3390/en14071840
C. Is¸ık, M. T¨urkkan, S. Marbou, and S. G¨ul, “Stock market performance evaluation of listed food and beverage companies in istanbul stock exchange with mcdm methods,” Decision Making: Applications in Management and Engineering, vol. 7, no. 2, pp. 35–64, 2024. doi: 10.31181/dmame722024692
˙ I. Yel, A. Sarucan, and M. E. Baysal, “An application of fuzzy ahp, edas and waspas for the selection of process method in software projects,” In: Kahraman, C., Cebi, S., Cevik Onar, S., Oztaysi, B., Tolga, A.C., Sari, I.U. (eds) Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation. INFUS 2021. Lecture Notes in Networks and Systems, vol. 307, 2021. doi: 10.1007/978-3-030-85626-7 42
A.AktasandM.Kabak,“Anapplicationofintervalvaluedpythagoreanfuzzywaspasmethod for drone selection to last mile delivery operations,” In: Erdebilli, B., Weber, GW. (eds) Multiple Criteria Decision Making with Fuzzy Sets. Multiple Criteria Decision Making, p. 179–191, 2022. doi: 10.1007/978-3-030-98872-2 12
S. Sampathkumar, F. Augustin, M. K. Kaabar, and X.-G. Yue, “An integrated intuitionistic dense fuzzy entropy-copras-waspas approach for manufacturing robot selection,” Advances in Mechanical Engineering, vol. 15, no. 3, pp. 1–18, 2023. doi: 10.1177/16878132231160265
S. A. Shanthi and R. Preethi, “Analysis of electrochemical discharge machining by bipolar fuzzy waspas method,” 2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT), pp. 1–4, 2023. doi: 10.1109/ICECCT56650.2023.10179780
M. Yazdani, P. Zarate, E. K. Zavadskas, and Z. Turskis, “A combined compromise solution (cocoso) method for multi-criteria decision-making problems,” Management Decision, vol. 57, no. 9, pp. 2501–2519, 2019. doi: 10.1108/MD-05-2017-0458 45
J. Bhanutej and V. K. Rao, “Evaluating healthcare efficacy: An exploration of grey relational analysis and cocoso in mcdm framework,” Educational Administration: Theory and Practice, vol. 30, no. 4, pp. 7261–7276, 2024. doi: 10.53555/kuey.v30i4.2539
L. ˘ Svadlenka, V. Simi ’c, M. Dobrodolac, D. Lazarevi ’c, and G. Todorovi ’c, “Picture fuzzy decision-making approach for sustainable last-mile delivery,” IEEE Access, vol. 8, pp. 209393–209414, 2020. doi: 10.1109/ACCESS.2020.3039010
P. T. Kieu, V. T. Nguyen, V. T. Nguyen, and T. P. Ho, “A spherical fuzzy analytic hierarchy process (sf-ahp) and combined compromise solution (cocoso) algorithm in distribution center location selection: A case study in agricultural supply chain,” Axioms, vol. 10, no. 53, pp. 1–13, 2021. doi: 10.3390/axioms10020053
R. G. Rasoanaivo, M. Yazdani, P. Zarat´e, and A. Fateh, “Combined compromise for ideal solution (cocofiso): A multi-criteria decision-making based on the cocoso method algorithm,” Expert Systems with Applications, vol. 251, no. 124079, 2024. doi: 10.1016/j.eswa.2024.124079
X. Peng and H. Huang, “Fuzzy decision making method based on cocoso with critic for f inancial risk evaluation,” Technological and Economic Development of Economy, vol. 26, no. 4, p. 695–724, 2020. doi: 10.3846/tede.2020.11920
˘ Z. Erceg, V. Star˘cevi´c, D. Pamu˘car, G. Mitrovi´c, ˘ Z. Stevi´c, and S. ˘ziki´c, “A new model for stock management in order to rationalize costs: Abc-fucom-interval rough cocoso model,” Symmetry, vol. 11, no. 1527, pp. 1–29, 2019. doi: 10.3390/sym11121527
P. P. Dwivedi and D. K. Sharma, “Application of shannon entropy and cocoso methods in selection of the most appropriate engineering sustainability components,” Cleaner Materials, vol. 5, no. 100118, 2022. doi: 10.1016/j.clema.2022.100118
S. Petchimuthu, F. B. M, C. Mahendiran, and T. Premala, “Power and energy transformation: Multi-criteria decision-making utilizing complex q-rung picture fuzzy generalized power prioritized yager operators,” Spectrum of Operational Research, vol. 2, no. 1, pp. 219–258, 2025. doi: 10.31181/sor21202525
A. F. Momena, K. H. Gazi, M. Rahaman, A. Sobczak, S. Salahshour, S. P. Mondal, and A. Ghosh, “Ranking and challenges of supply chain companies using mcdm methodology,” Logistics, vol. 8, no. 3, p. 87, 2024. doi: 10.3390/logistics8030087
A. Biswas, K. H. Gazi, P. Bhaduri, and S. P. Mondal, “Site selection for girls hostel in a university campus by mcdm based strategy,” Spectrum of Decision Making and Applications, vol. 2, no. 1, pp. 68–93, 2024. doi: 10.31181/sdmap21202511
K.R.MacCrimmon,“Decisionmakingamongmulti-attribute alternatives: A survey and consolidated approach,” Santa Monica, Calif.: Rand Corp, no. RM-4823-ARPA, pp. 1–78, 1968.
S. Zionts and J. Wallenius, “An interactive programming method for solving the multiple criteria problem,” Management Science, vol. 22, no. 6, pp. 652–663, 1976.
K. H. Gazi, N. Raisa, A. Biswas, F. Azizzadeh, and S. P. Mondal, “Finding the most important criteria in women’s empowerment for sports sector by pentagonal fuzzy dematel methodology,” Spectrum of Decision Making and Applications, vol. 2, no. 1, pp. 28–52, 2024. doi: 10.31181/sdmap21202510
A. Charnes, W. Cooper, and E. Rhodes, “Measuring the efficiency of decision makingunits,” European Journal of Operational Research, vol. 2, pp. 429–444., 1978. doi: 10.1016/03772217(78)90138-8
R. L. Keeney, H. Raiffa, and D. W. Rajala, “Decisions with multiple objectives: Preferences and value trade-offs,” IEEE Transactions on Systems Man and Cybernetics, vol. 9, p. 403–403, 11979. doi: 10.1109/TSMC.1979.4310245
C.-L. Hwang and A. S. M. Masud, “Multiple objective decision making — methods and applications: A state-of-the-art survey,” In Lecture Notes in Economics and Mathematical Systems. Springer Berlin, Heidelberg, vol. 164, pp. 1–366, 1979. doi: 10.1007/978-3-64245511-7
J. Wie¸ckowski and W. Sałabun, “Comparative sensitivity analysis in composite material selection: Evaluating oat and comsam methods in multi-criteria decision-making,” Spectrum of Mechanical Engineering and Operational Research, vol. 2, no. 1, pp. 1–12, 2025. doi: 10.31181/smeor21202524
T. L. Saaty, “The analytic hierarchy process: Planning, priority setting, resources allocation,” McGraw Hill, 1980.
C.-L. Hwang and K. Yoon, “Methods for multiple attribute decision making,” In: Multiple Attribute Decision Making. Lecture Notes in Economics and Mathematical Systems, vol. 186, pp. 1–134, 1981. doi: 10.1007/978-3-642-48318-9 3
J. P. Brans, “L’ing´enierie de la d´ecision: ´elaboration d’instruments d’aide `a la d´ecision. la m´ethode promethee.” Presses de l’Universit´e Laval, 1982.
T. L. Saaty, “The analytic hierarchy process: Decision making in complex environments,” In: Avenhaus, R., Huber, R.K. (eds) Quantitative Assessment in Arms Control. Springer, Boston, pp. 285–308, 1984. doi: 10.1007/978-1-4613-2805-6 12
——, “What is the analytic hierarchy process?” In: Mitra, G., Greenberg, H.J., Lootsma, F.A., Rijkaert, M.J., Zimmermann, H.J. (eds) Mathematical Models for Decision Support. NATO ASI Series, vol. 48, 1988. doi: 10.1007/978-3-642-83555-1 5
——, “Decision making with dependence and feedback: The analytic network process,” Pittsburgh: RWS publications, vol. 4922, no. 2, 1996.
——, “Fundamentals of the analytic network process — dependence and feedback in decision-making with a single network,” Journal of Systems Science and Systems Engineering, vol. 13, pp. 129–157, 2004. doi: 10.1007/s11518-006-0158-y
W. K. M. Brauers and E. K. Zavadskas, “The moora method and its application to privatization in a transition economy,” Control and Cybernetics, vol. 35, no. 2, pp. 445–469, 2006.
E. K. Zavadskas and Z. Turskis, “A new additive ratio assessment (aras) method in multicriteria decision-making,” Technological and Economic Development of Economy, vol. 16, no. 2, p. 159–172, 2010. doi: 10.3846/tede.2010.10
V. Ker˘ sulien˙e, E. K. Zavadskas, and Z. Turskis, “Selection of rational dispute resolution method by applying new step-wise weight assessment ratio analysis (swara),” Journal of Business Economics and Management, vol. 11, no. 2, pp. 243–258, 2010. doi: 10.3846/jbem.2010.12
A. Krylovas, E. K. Zavadskas, and N. Kosareva, “Multiple criteria decision-making kemirammethodforsolution oflocation alternatives,” Economic Research-Ekonomska Istra˘zivanja, vol. 29, no. 1, pp. 50–65, 2016. doi: 10.1080/1331677X.2016.1152560
M.K.Ghorabaee, M. Amiri, E. K. Zavadskas, Z. Turskis, and J. Antucheviciene, “Stochastic edas method for multi-criteria decision-making with normally distributed data,” Journal of Intelligent & Fuzzy Systems, vol. 33, no. 3, pp. 1627–1638, 2017. doi: 10.3233/JIFS-17184
H. A. Daˇgıstanlı and K. G. Kurtay, “Facility location selection for ammunition depots based on gis and pythagorean fuzzy waspas,” Journal of Operations Intelligence, vol. 2, no. 1, pp. 36–49, 2024. doi: 10.31181/jopi2120247
E. Triantaphyllou and S. H. Mann, “An examination of the effectiveness of multi–dimensional decision–making methods: A decision–making paradox,” Decision Support Systems, vol. 5, no. 3, pp. 303–312, 1989. doi: 10.1016/0167-9236(89)90037-7
E. K. Zavadskas, J. Antucheviciene, J. ˇ Saparauskas, and Z. Turskis, “Multi-criteria assessment of facades’ alternatives: Peculiarities of ranking methodology,” Procedia Engineering, vol. 57, pp. 107–112, 2013. doi: 10.1016/j.proeng.2013.04.016 47
V. Bago˘cius, K. E. Zavadskas, and Z. Turskis, “Multi-criteria selection of a deepwater port in klaipeda,” Procedia Engineering, vol. 57, pp. 144–148, 2013. doi: 10.1016/j.proeng.2013.04.021
M. Stani¯unas, M. Medineckien˙e, E. Zavadskas, and D. Kalibatas, “To modernize or not: Ecological–economical assessment of multi-dwelling houses modernization,” Archives of Civil and Mechanical Engineering, vol. 13, no. 1, pp. 88–98, 2013. doi: 10.1016/j.acme.2012.11.003
E. ˘ sio˘zinyt˙e and J. Antuchevi˘cien˙e, “Solving the problems of daylighting and tradition continuity in a reconstructed vernacular building,” Journal of Civil Engineering and Management, vol. 19, no. 6, pp. 873–882, 2013. doi: 10.3846/13923730.2013.851113
A. Biswas, K. H. Gazi, P. Bhaduri, and S. P. Mondal, “Neutrosophic fuzzy decision-making framework for site selection,” Journal of Decision Analytics and Intelligent Computing, vol. 4, no. 1, pp. 187–215, 2024. doi: 10.31181/jdaic10004122024b
S. H. Zolfani, M. H. Aghdaie, A. Derakhti, E. K. Zavadskas, and M. H. M. Varzandeh, “Decision making on business issues with foresight perspective; an application of new hybrid mcdm model in shopping mall locating,” Expert Systems with Applications, vol. 40, no. 17, pp. 7111–7121, 2013. doi: 10.1016/j.eswa.2013.06.040
A.R.MishraandP.Rani, “Evaluating and prioritizing blockchain networks using intuitionistic fuzzy multi-criteria decision-making method,” Spectrum of Mechanical Engineering and Operational Research, vol. 2, no. 1, pp. 78–92, 2025. doi: 10.31181/smeor21202527
Z. Turskis, E. K. Zavadskas, J. Antucheviciene, and N. Kosareva, “A hybrid model based on fuzzy ahp and fuzzy waspas for construction site selection,” International Journal of Computers Communications & Control, vol. 10, no. 6, pp. 873–888, 2015.
E. K. Zavadskas, R. Bauˇ sys, and M. Lazauskas, “Sustainable assessment of alternative sites for the construction of a waste incineration plant by applying waspas method with single-valued neutrosophic set,” Sustainability, vol. 7, no. 12, pp. 15923–15936, 2015. doi: 10.3390/su71215792
R. xin Nie, J. qiang Wang, and H. yu Zhang, “Solving solar-wind power station location problem using an extended weighted aggregated sum product assessment (waspas) technique with interval neutrosophic sets,” Symmetry, vol. 9, no. 106, pp. 1–20, 2017. doi: 10.3390/sym9070106
N. Y. Pehlivan, A. S¸ahin, E. K. Zavadskas, and Z. Turskis, “A comparative study of integrated fmcdm methods for evaluation of organizational strategy development,” Journal of Business Economics and Management, vol. 19, no. 2, pp. 360–381, 2018. doi: 10.3846/jbem.2018.5683
C. Kahraman, S. S. Onar, B. Oztaysi, and E. Ilbahar, “Selection among gsm operators using pythagorean fuzzy waspas method,” Journal of Multiple-Valued Logic & Soft Computing, vol. 33, no. 4/5, 2019.
D. Stanujki´c and D. Karaba˘ sevi´c, “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, vol. 1, no. 1, pp. 29–39, 2018. doi: 10.31181/oresta19012010129s
M. B. Bouraima, B. Ibrahim, Y. Qiu, M. Kridish, and M. Dantonka, “Integrated spherical decision-making model for managing climate change risks in africa,” Journal of Soft Computing and Decision Analytics, vol. 2, no. 1, pp. 71–85, 2024. doi: 10.31181/jscda21202435
F. M. Khan, A. Munir, M. Albaity, and T. Mahmood, “Parameter selection impacting software reliability by utilizing waspas technique based on tangent trigonometric complex fuzzy aggregation operators,” IEEE Access, pp. 1–14, 2024. doi: 10.1109/ACCESS.2024.3396908
S. Chakraborty, R. D. Raut, T. M. Rofin, and S. Chakraborty, “On solving a healthcare supplier selection problem using mcdm methods in intuitionistic fuzzy environment,” OPSEARCH, pp. 1–29, 2024. doi: 10.1007/s12597-023-00733-1
C. Kahraman and E. Haktanır, “Fuzzy multi-criteria investment decision making,” Fuzzy Investment Decision Making with Examples, p. 223–244, 2024. doi: 10.1007/978-3-03154660-0 13
D. Stanujki´c and D. Karabaˇ sevi´c, “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, vol. 1, no. 1, pp. 29–39, 2018. doi: 10.31181/oresta19012010129s
M. Deveci, S. C. ¨ Oner, M. E. Ciftci, E. ¨ Ozcan, and D. Pamucar, “Interval type-2 hesitant fuzzy entropy-based waspas approach for aircraft type selection,” Applied Soft Computing, vol. 114, no. 108076, 2022. doi: 10.1016/j.asoc.2021.108076
M. Keshavarz-Ghorabaee, K. Govindan, M. Amiri, E. K. Zavadskas, and J. Antucheviˇcien˙e, “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, vol. 27, no. 4, pp. 187–200, 2019. doi: 10.3846/jeelm.2019.11367
S. K. Kaya, P. Kundu, and ¨ O. F. G¨ Orc¸¨un, “Evaluation of container port sustainability using waspas technique using on type-2 neutrosophic fuzzy numbers,” Marine Pollution Bulletin, vol. 190, no. 114849, 2023. doi: 10.1016/j.marpolbul.2023.114849
M. Yazdani, Z. Wen, H. Liao, A. Banaitis, and Z. Turskis, “A grey combined compromise solution (cocoso-g) method for supplier selection in construction management,” Journal of Civil Engineering and Management, vol. 25, no. 8, pp. 858–874, 2019. doi: 10.3846/jcem.2019.11309
S. H. Zolfani, P. Chatterjee, and M. Yazdani, “A structured framework for sustainable supplier selection using a combined bwm-cocoso model,” International Scientific Conference: Contemporary Issues in Business, Management and Economics Engineering’ 2019, pp. 797804, 2019. doi: 10.3846/cibmee.2019.081
Z. Wen, H. Liao, E. K. Zavadskas, and A. Al-Barakati, “Selection third-party logistics service providers in supply chain finance by a hesitant fuzzy linguistic combined compromise solution method,” Economic research- Ekonomska istraˇzivanja, vol. 32, no. 1, p. 4033–4058, 2019. doi: 10.1080/1331677X.2019.1678502
Z. Wen, H. Liao, R. Ren, C. Bai, E. K. Zavadskas, J. Antucheviciene, and A. Al-Barakati, “Cold chain logistics management of medicine with an integrated multi-criteria decisionmaking method,” Int. J. Environ. Res. Public Health, vol. 16, no. 23, p. 4843, 2019. doi: 10.3390/ijerph16234843
H. Lai, H. Liao, Z. Wen, E. K. Zavadskas, and A. Al-Barakati, “An improved cocoso method with a maximum variance optimization model for cloud service provider selection,” Inzinerine Ekonomika-Engineering Economics, vol. 31, no. 4, pp. 411–424, 2020. doi: 10.5755/j01.ee.31.4.24990
N. Ersoy, “Normalization procedures for cocoso method: A comparative analysis under different scenarios,” Dokuz Eyl¨ul University Journal of the Faculty of Business, vol. 22, no. 2, pp. 217–234, 2021. doi: 10.24889/ifede.974252
Z. Turskis, R. Bausys, F. Smarandache, G. Kazakeviciute-Januskeviciene, and E. K. Zavadskas, “m−generalised q− neutrosophic extension of cocoso method,” International Journal of Computers Communications and Control, vol. 17, no. 4646, pp. 1–12, 2022. doi: 10.15837/ijccc.2022.1.4646
X.-G. Xu, L. Zhang, L.-X. Mao, and K. Li, “New approach for quality function deployment using an extended cocoso method with spherical fuzzy sets,” Systems, vol. 10, no. 253, pp. 1–17, 2022. doi: 10.3390/systems10060253 49
H. Wang, T. Mahmood, and K. Ullah, “Improved cocoso method based on frank softmax aggregation operators for t-spherical fuzzy multiple attribute group decision-making,” Int. J. Fuzzy Syst., vol. 25, no. 3, pp. 1275–1310, 2023. doi: 10.1007/s40815-022-01442-5
H. Liu, “Enhanced cocoso method for intuitionistic fuzzy magdm and application to financial risk evaluation of high-tech enterprises,” Informatica, vol. 48, pp. 1–14, 2024. doi: 10.31449/inf.v48i5.5169
S. Chakraborty, R. D. Raut, T. M. Rofin, and S. Chakraborty, “On solving a healthcare supplier selection problem using mcdm methods in intuitionistic fuzzy environment,” OPSEARCH, pp. 1–29, 2024. doi: 10.1007/s12597-023-00733-1
A. Alnoor, Y. R. Muhsen, N. A. Husin, X. Chew, M. B. Zolkepli, and N. Manshor, “Z-cloud rough fuzzy-based piprecia and cocoso integration to assess agriculture decision support tools,” International Journal of Fuzzy Systems, 2024. doi: 10.1007/s40815-024-01771-7
M.Narang,M.C.Joshi, K.Bisht, andA.Pal, “Stockportfolio selection using a new decisionmaking approach based on the integration of fuzzy cocoso with heronian mean operator,” Decision Making: Applications in Management and Engineering, vol. 5, no. 1, pp. 90–112, 2022. doi: 10.31181/dmame0310022022n
S. Karami, S. M. Mousavi, and J. Antucheviciene, “Enhancing contractor selection process by anewinterval-valued fuzzy decision-making model based on swara and cocoso methods,” Axioms, vol. 12, no. 729, 2023. doi: 10.3390/axioms12080729
H. Erdal, K. G. Kurtay, H. A. Dagistanli, and A. Altundas, “Evaluation of anti-tank guided missiles: An integrated fuzzy entropy and fuzzy cocoso multi criteria methodology using technical and simulation data,” Applied Soft Computing, vol. 137, no. 110145, 2023. doi: 10.1016/j.asoc.2023.110145
T. E. Saputro, T. Y. Rosiani, A. Mubin, S. K. Dewi, and T. Baroto, “Green supplier selection under supply risks using novel integrated fuzzy multi-criteria decision making techniques,” Journal of Cleaner Production, vol. 449, no. 141788, 2024. doi: 10.1016/j.jclepro.2024.141788
T. N. Parthasarathy, S. Narayanamoorthy, C. S. Thilagasree, P. R. Marimuthu, S. Salahshour, M. Ferrara, and A. Ahmadian, “An enhanced fuzzy idocriw-cocoso multi-attribute decision making algorithm for decisive electric vehicle battery recycling method,” Results in Engineering, vol. 22, no. 102272, pp. 1–12, 2024. doi: 10.1016/j.rineng.2024.102272
I. Badi, ˇ Z. Stevi´c, and M. B. Bouraima, “Overcoming obstacles to renewable energy development in libya: An mcdm approach towards effective strategy formulation,” Decision Making Advances, vol. 1, no. 1, pp. 17–24, 2023. doi: 10.31181/v120234
L. A. Fern´andez-Portillo, M. Yazdani, L. Estepa-Mohedano, and R. Sisto, “Prioritisation of strategies for the adoption of organic agriculture using bwm and fuzzy cocoso,” Soft Computing, pp. 1–3, 2023. doi: 10.1007/s00500-023-09431-y
T. M. H. Nguyen, V. P. Nguyen, and D. T. Nguyen, “A new hybrid pythagorean fuzzy ahp and cocoso mcdm based approach by adopting artificial intelligence technologies,” Journal of Experimental & Theoretical Artificial Intelligence, pp. 1–27, 2022. doi: 10.1080/0952813X.2022.2143908
A. Łuczak and S. Kalinowski, “A multidimensional comparative analysis of poverty statuses in european union countries,” International Journal of Economic Sciences, vol. 11, no. 1, pp. 146–160, 2022. doi: 10.52950/ES.2022.11.1.009
R. Gul, “An extension of vikor approach for mcdm using bipolar fuzzy preference δcovering based bipolar fuzzy rough set model,” Spectrum of Operational Research, vol. 2, no. 1, pp. 72–91, 2025. doi: 10.31181/sor21202511
M. K. Ghorabaee, M. Amiri, E. K. Zavadskas, and J. Antucheviˇcien˙e, “Assessment of thirdparty logistics providers using a critic–waspas approach with interval type-2 fuzzy sets,” TRANSPORT, vol. 32, no. 1, pp. 66–78, 2017. doi: 10.3846/16484142.2017.1282381
D. Pamucar, A. E. Torkayesh, M. Deveci, and V. Simic, “Recovery center selection for endof-life automotive lithium-ion batteries using an integrated fuzzy waspas approach,” Expert Systems with Applications, vol. 206, no. 117827, 2022. doi: 10.1016/j.eswa.2022.117827
N. V. Thanh and N. T. K. Lan, “Solar energy deployment for the sustainable future of vietnam: Hybrid swoc-fahp-waspas analysis,” Energies, vol. 15, no. 2798, pp. 1–11, 2022. doi: 10.3390/en15082798
M. Das, M. Rahaman, S. Alam, and D. K. Jana, “Developing sustainable and efficient customer-centric strategies for a three-stage dual-channel green supply chain: a gametheoretic approach with two-part tariff contract,” Environment, Development and Sustainability, vol. 2024, 2024. doi: 10.1007/s10668-024-04883-0
M. K. Ghorabaee, E. K. Zavadskas, M. Amiri, and A. Esmaeili, “Multi-criteria evaluation of green suppliers using an extended waspas method with interval type-2 fuzzy sets,” Journal of Cleaner Production, vol. 137, pp. 213–229, 2016. doi: 10.1016/j.jclepro.2016.07.031
S. Gupta, U. Soni, and G. Kumar, “Green supplier selection using multi-criterion decision making under fuzzy environment: A case study in automotive industry,” Computers & Industrial Engineering, vol. 136, pp. 663–680, 2019. doi: 10.1016/j.cie.2019.07.038
L.Xiong, S. Zhong, S. Liu, X.Zhang, andY.Li, “Anapproach for resilient-green supplier selection based on waspas, bwm, and topsis under intuitionistic fuzzy sets,” Data-driven Fuzzy Multiple Criteria Decision Making and its Potential Applications, vol. 2020, no. 1761893, pp. 1–18, 2020. doi: 10.1155/2020/1761893
S. Agarwal, R. Kant, and R. Shankar, “Evaluating solutions to overcome humanitarian supply chain management barriers: A hybrid fuzzy swara– fuzzy waspas approach,” International Journal of Disaster Risk Reduction, vol. 51, no. 101838, pp. 1–18, 2020. doi: 10.1016/j.ijdrr.2020.101838
A. Aytekin, ¨ O. F. G¨ Orc¸¨un, F. Ecer, D. Pamucar, and C¸. Karamas¸a, “Evaluation of the pharmaceutical distribution and warehousing companies through an integrated fermatean fuzzy entropy-waspas approach,” Kybernetes, vol. 52, no. 11, pp. 5561–5592, 2023. doi: 10.1108/K-04-2022-0508
V. Mohagheghi and S. M. Mousavi, “A new framework for high-technology project evaluation and project portfolio selection based on pythagorean fuzzy waspas, moora and mathematical modeling,” Iranian Journal of Fuzzy Systems, vol. 16, no. 6, pp. 89–106, 2019. doi: 10.22111/ijfs.2019.5022
S. Maja, “Comparative analysis of multimoora, waspas and wisp methods: The case of candidate selection,” Journal of Process Management and New Technologies, vol. 11, no. 3-4, pp. 79–88, 2023. doi: 10.5937/jpmnt11-47703
O. Gireesha, N. Somu, K. Krithivasan, and S. S. V.S., “Iivifs-waspas: An integrated multicriteria decision-making perspective for cloud service provider selection,” Future Generation Computer Systems, vol. 103, pp. 91–110, 2020. doi: 10.1016/j.future.2019.09.053
K. A. Alam, R. Ahmed, F. S. Butt, S.-G. Kim, and K.-M. Ko, “An uncertainty-aware integrated fuzzy ahp-waspas model to evaluate public cloud computing services,” Procedia Computer Science, vol. 130, pp. 504–509, 2018. doi: 10.1016/j.procs.2018.04.068
Z. Turskis, N. Goranin, A. Nurusheva, and S. Boranbayev, “A fuzzy waspas-based approach to determine critical information infrastructures of eu sustainable development,” Sustainability, vol. 11, no. 424, pp. 1–25, 2019. doi: 10.3390/su11020424
S. Salimian, M. M. Einehvarzani, and J. Antucheviciene, “Evaluation of infrastructure projects by a decision model based on rpr, mabac, and waspas methods with interval-valued intuitionistic fuzzy sets,” International Journal of Strategic Property Management, vol. 26, no. 2, pp. 106–18, 2022. doi: 10.3846/ijspm.2022.16476
M. Alimohammadlou and Z. Khoshsepehr, “Investigating organizational sustainable development through an integrated method of interval-valued intuitionistic fuzzy ahp and waspas,” Environment, Development and Sustainability, vol. 24, p. 2193–2224, 2022. doi: 10.1007/s10668-021-01525-7
S. J. Ghoushchi, S. R. Bonab, A. M. Ghiaci, G. Haseli, H. Tomaskova, , and M. HajiaghaeiKeshteli, “Landfill site selection for medical waste using an integrated swara-waspas framework based on spherical fuzzy set,” Sustainability, vol. 13, no. 13950, pp. 1–19, 2021. doi: 10.3390/su132413950
Komal, “Archimedean t−norm and t−conorm based intuitionistic fuzzy waspas method to evaluate health-care waste disposal alternatives with unknown weight information,” Applied Soft Computing, vol. 146, no. 110751, 2023. doi: 10.1016/j.asoc.2023.110751
E. Bolt¨urk and F. K. G¨undoˆgdu, “Prioritizing manufacturing challenges of a contract manufacturing company for personal auto by using spherical waspas method,” In: Kahraman, C., Kutlu G¨undoˆgdu, F. (eds) Decision Making with Spherical Fuzzy Sets. Studies in Fuzziness and Soft Computing, vol. 392, pp. 259–275, 2021. doi: 10.1007/978-3-030-45461-6 11
P. Trivedi, R. Vansjalia, S. Erra, S. Narayanan, and D. Nagaraju, “A fuzzy critic and fuzzy waspas-based integrated approach for wire arc additive manufacturing (waam) technique selection,” Arabian Journal for Science and Engineering, vol. 48, no. 3269-3288, 2023. doi: 10.1007/s13369-022-07127-3
N. Pavlov, D. Durdjevi´c, and M. Andreji´c, “A novel two-stage methodological approach for storage technology selection: An engineering–fahp–waspas approach,” Sustainability, vol. 15, no. 13037, pp. 1–20, 2023. doi: 10.3390/su151713037
E. Ayyildiz and A. T. Gumus, “A novel spherical fuzzy ahp-integrated spherical waspas methodology for petrol station location selection problem: a real case study for ˙ Istanbul,” Environmental Science and Pollution Research, vol. 27, p. 36109–36120, 2020. doi: 10.1007/s11356-020-09640-0
N. Aydin and S. Seker, “Waspas based multimoora method under ivif environment for the selection of hub location,” Journal of Enterprise Information Management, vol. 33, no. 5, pp. 1233–1256, 2020. doi: 10.1108/JEIM-09-2019-0277
E. Ayyildiz, M. Erdogan, and A. T. Gumus, “A pythagorean fuzzy number-based integration of ahp and waspas methods for refugee camp location selection problem: a real case study for istanbul, turkey,” Neural Computing and Applications, vol. 33, p. 15751–15768, 2021. doi: 10.1007/s00521-021-06195-0
E. K. Zavadskas, J. Antucheviciene, S. H. R. Hajiagha, and S. S. Hashemi, “Extension of weighted aggregated sum product assessment with interval-valued intuitionistic fuzzy numbers (waspas-ivif),” Applied Soft Computing, vol. 24, pp. 1013–1021, 2014. doi: 10.1016/j.asoc.2014.08.031
R. Bauˇ sys and B. Juodagalvien˙e, “Garage location selection for residential house by waspassvns method,” Journal of Civil Engineering and Management, vol. 23, no. 3, pp. 421–419, 2017. doi: 10.3846/13923730.2016.1268645
M. Deveci, F. Canıtez, and I. G¨okas¸ar, “Waspas and topsis based interval type-2 fuzzy mcdm method for a selection of a car sharing station,” Sustainable Cities and Society, vol. 41, pp. 777–791, 2018. doi: 10.1016/j.scs.2018.05.034
E. Ilbahar and C. Kahraman, “Retail store performance measurement using a novel intervalvalued pythagorean fuzzy waspas method,” Journal of Intelligent & Fuzzy Systems, vol. 35, no. 3, pp. 3835–3846, 2018. doi: 10.3233/JIFS-18730
A. R. Mishra, P. Rani, K. R. Pardasani, and A. Mardani, “A novel hesitant fuzzy waspas method for assessment of green supplier problem based on exponential information measures,” Journal of Cleaner Production, vol. 238, no. 117901, pp. 1–16, 2019. doi: 10.1016/j.jclepro.2019.117901
R. Davoudabadi, S. M. Mousavi, and V. Mohagheghi, “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, vol. 62, p. 1371–1391, 2020. doi: 10.1007/s10115-019-01390-x
I. Otay and S. Atik, “Multi-criteria oil station location evaluation using spherical ahp & waspas: A real-life case study,” In: Kahraman, C., Cevik Onar, S., Oztaysi, B., Sari, I., Cebi, S., Tolga, A. (eds) Intelligent and Fuzzy Techniques: Smart and Innovative Solutions. INFUS 2020. Advances in Intelligent Systems and Computing, vol. 1197, pp. 591–598, 2020. doi: 10.1007/978-3-030-51156-2 68
P.-H. Nguyen, J.-F. Tsai, T.-T. Dang, M.-H. Lin, H.-A. Pham, and K.-A. Nguyen, “A hybrid spherical fuzzy mcdm approach to prioritize governmental intervention strategies against the covid-19 pandemic: A case study from vietnam,” Mathematics, vol. 9, no. 2626, pp. 1–26, 2021. doi: 10.3390/math9202626
Z. Lin, H. Ayed, B. Bouallegue, H. Tomaskova, S. J. Ghoushchi, and G. Haseli, “An integrated mathematical attitude utilizing fully fuzzy bwm and fuzzy waspas for risk evaluation in a sofc,” Mathematics, vol. 9, no. 2328, pp. 1–18, 2021. doi: 10.3390/math9182328
M. Eghbali-Zarch, R. Tavakkoli-Moghaddam, K. Dehghan-Sanej, and A. Kaboli, “Prioritizing the effective strategies for construction and demolition waste management using fuzzy idocriw and waspas methods,” Engineering, Construction and Architectural Management, vol. 29, no. 3, pp. 1109–1138, 2022. doi: 10.1108/ECAM-08-2020-0617
M. Deveci, I. Gokasar, D. Pamucar, D. Coffman, and E. Papadonikolaki, “Safe escooter operation alternative prioritization using a q-rung orthopair fuzzy einstein based waspas approach,” Journal of Cleaner Production, vol. 347, no. 131239, 2022. doi: 10.1016/j.jclepro.2022.131239
S. K. Vaid, G. Vaid, S. Kaur, R. Kumar, and M. S. Sidhu, “Application of multicriteria decision-making theory with vikor-waspas-entropy methods: A case study of silent genset,” Materials Today: Proceedings, vol. 50, no. 5, pp. 2416–2423, 2022. doi: 10.1016/j.matpr.2021.10.259
S. H. Zolfani, ¨ O. F. G¨ Orc¸¨un, and H. K¨uc¸”uk”onder, “Evaluation of the special warehouse handling equipment (turret trucks) using integrated fucom and waspas techniques based on intuitionistic fuzzy dombi aggregation operators,” Arabian Journal for Science and Engineering, vol. 48, p. 15561–15595, 2023. doi: 10.1007/s13369-023-07615-0
N. Handayani, N. Heriyani, F. Septian, and A. D. Alexander, “Multi-criteria decision making using the waspas method for online english course selection,” Jurnal Teknoinfo, vol. 17, no. 1, pp. 260–270, 2023. doi: 10.33365/jti.v17i1.2371
A. A. Khan, D. S. Mashat, and K. Dong, “Evaluating sustainable urban development strategies through spherical critic-waspas analysis,” Journal of Urban Development and Management, vol. 3, no. 1, pp. 1–17, 2024. doi: 10.56578/judm030101
M. Anjum, V. Simic, M. Alrasheedi, and S. Shahab, “T-spherical fuzzy-critic-waspas model for the evaluation of cooperative intelligent transportation system scenarios,” IEEE Access, vol. 4, pp. 1–16, 2024. doi: 10.1109/ACCESS.2024.3392019
A. Thilagavathy and S. Mohanaselvi, “Hamacher maclaurin symmetric mean aggregation operators and waspas method for multiple criteria group decision making under spherical fuzzy environment,” Results in Control and Optimization, vol. 14, no. 100378, 2024. doi: 10.1016/j.rico.2024.100378
A. Eisa, M. Fattouh, and A. A. ElShabshery, “Single-valued neutrosophic sets based score function and waspas method for plant location selection problem,” Journal of Advanced Research in Applied Sciences and Engineering Technology, vol. 41, no. 2, pp. 139–151, 2024. doi: 10.37934/araset.41.2.139151
S. J. H. Dehshiri, M. Amiri, and A. Mostafaeipour, “Evaluation of renewable energy projects based on sustainability goals using a hybrid pythagorean fuzzy-based decision approach,” Energy, vol. 297, no. 131272, 2024. doi: 10.1016/j.energy.2024.131272
H. A. Da˘gıstanlı and K. G. Kurtay, “Facility location selection for ammunition depots based on gis and pythagorean fuzzy waspas,” Journal of Operations Intelligenc, vol. 2, no. 1, pp. 36–49, 2024. doi: 10.31181/jopi2120247
A. F. Momena, K. H. Gazi, A. K. Mukherjee, S. Salahshour, A. Ghosh, and S. P. Mondal, “Adaptation challenges of edge computing model in educational institute,” Journal of Intelligent & Fuzzy Systems, pp. 1–18, 2024. doi: https://doi.org/10.3233/JIFS-239887
E. T¨umsekc¸alı and A. Taskin, “Sustainable and smart public transportation service quality assessment by a hybrid picture fuzzy-waspas methodology: A real case in izmir, turkiye,” SSRN, pp. 1–43, 2024. doi: 10.2139/ssrn.4786507
J. Ali, M. I. Syam, and W. K. Mashwani, “q− rung orthopair fuzzy 2-tuple linguistic waspas algorithm for patients’ prioritization based on prioritized maclaurin symmetric mean aggregation operators,” Scientific Reports, vol. 14, no. 10659, pp. 1–31, 2024. doi: 10.1038/s41598-024-57452-w
B. Ayvaz, V. Tatar, Z. Sa˘gır, and D. Pamucar, “An integrated fine-kinney risk assessment model utilizing fermatean fuzzy ahp-waspas for occupational hazards in the aquaculture sector,” Process Safety and Environmental Protection, vol. 186, pp. 232–251, 2024. doi: 10.1016/j.psep.2024.04.025
A. Abdelhafeez, N. A. Khalil, M. Eassa, and M. Elkholy, “Selection optimal livestock location under multi-criteria decision making fuzzy framework,” Precision Livestock, vol. 1, pp. 58–65, 2024. doi: 10.61356/j.pl.2024.1225
P.-D. Hoang, L.-T. Nguyen, and B.-Q. Tran, “Assessing environmental, social and governance (esg) performance of global electronics industry: an integrated mcdm approachbased spherical fuzzy sets,” Cogent Engineering, vol. 11, no. 1, pp. 1–21, 2024. doi: 10.1080/23311916.2023.2297509
S. Goyal and P. Rani, “Multi-criteria decision-making based on einstein operators, waspas method and quadripartitioned single-valued neutrosophic sets,” Granular Computing, vol. 9, no. 54, 2024. doi: 10.1007/s41066-024-00482-6
N. K. Tays¸ir, B. ¨ulgen, N. ˘ O. ˙ Iyig¨un, and A. G¨orener, “A framework to overcome barriers to social entrepreneurship using a combined fuzzy mcdm approach,” Soft Computing, vol. 28, p. 2325–2351, 2024. doi: 10.1007/s00500-023-09293-4
J. Jeon, T. Manirathinam, S. Geetha, S. Narayanamoorthy, M. Salimi, and A. Ahmadian, “An identification of optimal waste disposal method for dumpsite remediation using the fermatean fuzzy multi-criteria decision-making method,” Environmental Science and Pollution Research, pp. 1–22, 2024. doi: 10.1007/s11356-024-32366-2
M. Das, M. Rahaman, S. Alam, D. K. Jana, A. A. Salameh, S. A. Sulaie, H. Yin, and A. Ahmadian, “Pareto improvement in a dual-channel closed-loop supply chain system by integrating product sustainability and consumer focus,” Socio-Economic Planning Sciences, vol. 99, no. 102182, 2025. doi: 10.1016/j.seps.2025.102182
M. Hossain, M. Das, M. Rahaman, and S. Alam, “A profit-cost ratio maximization approach for a manufacturing inventory model having stock-dependent production rate and stock and price-dependent demand rate,” Results in Control and Optimization, vol. 15, no. 100408, 2024. doi: 10.1016/j.rico.2024.100408
A. Ulutas¸, C. ¨ B. Karakus¸, and A. Topal, “Location selection for logistics center with fuzzy swara and cocoso methods,” Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 46934709, 2020. doi: 10.3233/JIFS-191400
H. Liao, R. Qin, D. Wu, M. Yazdani, and E. K. Zavadskas, “Pythagorean fuzzy combined compromise solution method integrating the cumulative prospect theory and combined weights for cold chain logistics distribution center selection,” International Journal of Intelligent Systems, vol. 35, pp. 2009–2031, 2020. doi: 10.1002/int.22281
P. T. Kieu, V. T. Nguyen, V. T. Nguyen, and T. P. Ho, “A spherical fuzzy analytic hierarchy process (sf-ahp) and combined compromise solution (cocoso) algorithm in distribution center location selection: A case study in agricultural supply chain,” Axioms, vol. 10, no. 53, pp. 1–13, 2021. doi: 10.3390/axioms10020053
S. Korucuk, A. Aytekin, F. Ecer, D. S. S. Pamucar, and C¸. Karamas¸a, “Assessment of ideal smart network strategies for logistics companies using an integrated picture fuzzy lbwa–cocoso framework,” Management Decision, vol. 61, no. 5, pp. 1434–62, 2023. doi: 10.1108/MD-12-2021-1621
D.Pamucarand ˘ O.F.G¨orc¸¨un, “Evaluation of the european container ports using a new hybrid fuzzy lbwa-cocoso’b techniques,” Expert Systems with Applications, vol. 203, no. 117463, 2022. doi: 10.1016/j.eswa.2022.117463
N. Metawa and N. Mourad, “Neutrosophic-based multi-objectives model for financial risk management,” International Journal of Neutrosophic Science (IJNS), vol. 19, no. 1, pp. 188199, 2022. doi: 10.54216/IJNS.190114
Y. Xia, H. Long, Z. Li, and J. Wang, “Farmers’ credit risk assessment based on sustainable supply chain finance for green agriculture,” Sustainability, vol. 14, no. 12836, pp. 1–20, 2022. doi: 10.3390/su141912836
A. Ulutas¸, G. Popovic, P. Radanov, D. Stanujkic, and D. Karabasevic, “A new hybrid fuzzy psi-piprecia-cocoso mcdm based approach to solving the transportation company selection problem,” Technological and Economic Development of Econom, vol. 27, no. 5, pp. 12271249, 2021. doi: 10.3846/tede.2021.15058
F. Ecer, H. K¨uc¸¨uk¨onder, S. K. Kaya, and ”Omer Faruk G”orc¸¨un, “Sustainability performanceanalysis of micro-mobility solutions in urban transportation with a novel ivfnn-delphilopcow-cocoso framework,” Transportation Research Part A: Policy and Practice, vol. 172, no. 103667, 2023. doi: 10.1016/j.tra.2023.103667
O. F. Gorcun, S. Senthil, and H. K¨uc¸¨u¨onder, “Evaluation of tanker vehicle selection using a novel hybrid fuzzy mcdm technique,” Applications in Management and Engineering, vol. 4, no. 2, pp. 140–162, 2021. doi: 10.31181/dmame210402140g
M. Ort´ız-Barrios, N. Jaramillo-Rueda, M. Gul, M. Yucesan, G. Jim´enez-Delgado, and J.J. Alfaro-Sa´ız, “A fuzzy hybrid mcdm approach for assessing the emergency department performance during the covid-19 outbreak,” Int. J. Environ. Res. Public Health, vol. 20, no. 4591, pp. 1–39, 2023. doi: 10.3390/ijerph20054591
X. Peng, R. Krishankumar, and K. S. Ravichandran, “A novel interval-valued fuzzy soft decision-making method based on cocoso and critic for intelligent healthcare management evaluation,” Soft Computing, vol. 25, p. 4213–424, 2021. doi: 10.1007/s00500-020-05437-y
C. Zhang and J. Tian, “An integrated framework for community medical and health services evaluation with fuzzy number intuitionistic fuzzy sets,” Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7519–7531, 2023. doi: 10.3233/JIFS-231700
M.Ortiz-Barrios, A. Espeleta-Aris, G. Jim´enez-Delgado, H. J. C.-D. Souza, J. S. de Oliveira, A. Konios, L. Campis-Freyle, and E. Navarro-Jimenez, “A hybrid multi-criteria framework for evaluating the performance of clinical labs during the covid-19 pandemic,” In: Duffy, V.G. (eds) Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management, vol. 14029, pp. 104–122, 2023. doi: 10.1007/978-3-031-35748-0 8
N. V. Thanh, “Optimal waste-to-energy strategy assisted by fuzzy mcdm model for sustainable solid waste management,” Sustainability, vol. 14, no. 6565, pp. 1–13, 2022. doi: 10.3390/su14116565
S. J. H. Dehshiri and M. Amiri, “Evaluating the risks of the internet of things in renewable energy systems using a hybrid fuzzy decision approach,” Energy, vol. 285, no. 129493, 2023. doi: 10.1016/j.energy.2023.129493
P. Rani, J. Ali, R. Krishankumar, A. R. Mishra, F. Cavallaro, and K. S. Ravichandran, “An integrated single-valued neutrosophic combined compromise solution methodology for renewable energy resource selection problem,” Energies, vol. 14, no. 4594, pp. 1–23, 2021. doi: 10.3390/en14154594
M. A. Alao, O. M. Popoola, and T. R. Ayodele, “Sustainable prime movers selection for biogas-based combined heat and power for a community microgrid: A hybrid fuzzy multi criteria decision-making approach with consolidated ranking strategies,” Energy Conversion and Management: X, vol. 16, no. 100281, pp. 1–20, 2022. doi: 10.1016/j.ecmx.2022.100281
T. Ali, K. Aghaloo, Y.-R. Chiu, and M. Ahmad, “Lessons learned from the covid-19 pandemic in planning the future energy systems of developing countries using an integrated mcdm approach in the off-grid areas of bangladesh,” Renewable Energy, vol. 189, pp. 2528, 2022. doi: 10.1016/j.renene.2022.02.099
R. Rajalakshmi and D. K. J. R. Mary, “Site selection of solar plant based on normal wiggly hesitant bipolar-valued fuzzy set,” Mathematical Statistician and Engineering Applications, vol. 71, no. 4, pp. 4558– 4583, 2022. doi: 10.17762/msea.v71i4.1054
X. Peng and Z. Luo, “Decision-making model for china’s stock market bubble warning: the cocoso with picture fuzzy information,” Artificial Intelligence Review, vol. 54, p. 5675–5697, 2021. doi: 10.1007/s10462-021-09954-6
S. A. Banihashemi and M. Khalilzadeh, “Application of fuzzy bwm-cocoso to time–cost–environmental impact trade-off construction project scheduling problem,” International Journal of Environmental Science and Technology, vol. 20, p. 1199–1214, 2020. doi: 10.1007/s13762-022-04075-1
E. M. Abdelkader, T. Zayed, H. E. Fathali, G. Alfalah, A. Al-Sakkaf, and O. Moselh, “An integrated multi-criteria decision making model for the assessment of public private partnerships in transportation projects,” Mathematics, vol. 11, no. 3559, pp. 1–41, 2023. doi: 10.3390/math11163559
Z. Zhang, H. Liao, A. Al-Barakati, E. K. Zavadskas, and J. Antuchevi˘cien˙e, “Supplier selection for housing development by an integrated method with interval rough boundaries,” International Journal of Strategic Property Management, vol. 24, no. 4, 2020. doi: 10.3846/ijspm.2020.12434
M. A. Ort´ız-Barrios, S. L. Madrid-Sierra, A. Petrillo, and L. E. Quezada, “A novel approach integrating if-ahp, if-dematel and cocoso methods for sustainability management in food digital manufacturing supply chain systems,” Journal of Enterprise Information Management, 2023. doi: 10.1108/JEIM-04-2023-0199
X. Han and P. Rani, “Evaluate the barriers of blockchain technology adoption in sustainable supply chain management in the manufacturing sector using a novel pythagorean fuzzycritic-cocoso approach,” Operations Management Research, vol. 15, pp. 725–742, 2022. doi: 10.1007/s12063-021-00245-5
F. Ecer and D. Pamucar, “Sustainable supplier selection: A novel integrated fuzzy best worst method (f-bwm) and fuzzy cocoso with bonferroni (cocoso’b) multi-criteria model,” Journal of Cleaner Production, vol. 266, no. 121981, pp. 1–18, 2020. doi: 10.1016/j.jclepro.2020.121981
F. Zhang and W. Song, “Sustainability risk assessment of blockchain adoption in sustainable supply chain: An integrated method,” Computers & Industrial Engineering, vol. 171, no. 108378, 2022. doi: 10.1016/j.cie.2022.108378
D. Wei, D. Meng, Y. Rong, Y. Liu, H. Garg, and D. Pamucar, “Fermatean fuzzy schweizer–sklar operators and bwm-entropy-based combined compromise solution approach: An application to green supplier selection,” Entropy, vol. 24, no. 776, pp. 1–32, 2022. doi: 10.3390/e24060776
S. Lahane and R. Kant, “A hybrid pythagorean fuzzy ahp– cocoso framework to rank the performance outcomes of circular supply chain due to adoption of its enablers,” Waste Management, vol. 130, pp. 48–60, 2021. doi: 10.1016/j.wasman.2021.05.013
A. R. Mishra, P. Rani, R. Krishankumar, E. K. Zavadskas, F. Cavallaro, and K. S. Ravichandran, “A hesitant fuzzy combined compromise solution framework-based on discrimination measure for ranking sustainable third-party reverse logistic providers,” Sustainability, vol. 13, no. 2064, pp. 1–24, 2021. doi: 10.3390/su13042064
X. Peng, X. Zhang, and Z. Luo, “Pythagorean fuzzy mcdm method based on cocoso and critic with score function for 5g industry evaluation,” Artificial Intelligence Review, vol. 53, no. 3813–3847, 2020. doi: 10.1007/s10462-019-09780-x
X. Peng and F. Smarandache, “A decision-making framework for china’s rare earth industry security evaluation by neutrosophic soft cocoso method,” Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7571–7585, 2020. doi: 10.3233/JIFS-200847
Z. Zhang, H. Liao, A. Al-Barakati, E. K. Zavadskas, and J. Antuchevi˘cien˙e, “Supplier selection for housing development by an integrated method with interval rough boundaries,” International Journal of Strategic Property Management, vol. 24, no. 4, pp. 269–284, 2020. doi: 10.3846/ijspm.2020.12434
A. I. Maghsoodi, S. Soudian, L. Mart ’inez, E. Herrera-Viedma, and E. K. Zavadskas, “A phase change material selection using the interval-valued target-based bwm-cocomultimoora approach: A case-study on interior building applications,” Applied Soft Computing Journal, vol. 95, no. 106508, pp. 1–20, 2020. doi: 10.1016/j.asoc.2020.106508
M. Qiyas, M. Naeem, S. Khan, S. Abdullah, T. Botmart, and T. Shah, “Decision support system based oncocosomethodwiththepicturefuzzyinformation,” Journal of Mathematics, vol. 2022, no. 476233,, pp. 1–11, 2022. doi: 10.1155/2022/1476233
H. Lai, H. Liao, Y. Long, and E. K. Zavadskas, “A hesitant fermatean fuzzy cocoso method for group decision-making and an application to blockchain platform evaluation,” International Journal of Fuzzy Systems, vol. 24, p. 2643–2661, 2022. doi: 10.1007/s40815-02201319-7
D.K.Tripathi, S. K. Nigam, P. Rani, and A. R. Shah, “Newintuitionistic fuzzy parametric divergence measures and score function-based cocoso method for decision-making problems,” Decision Making: Applications in Management and Engineering, vol. 6, no. 1, pp. 535–563, 2023. doi: 10.31181/dmame0318102022t
S. O. Ogundoyin and I. A. Kamil, “An integrated fuzzy-bwm, fuzzy-lbwa and v-fuzzycocoso-ld model for gateway selection in fog-bolstered internet of things,” Applied Soft Computing, vol. 143, p. 110393, 2023. doi: 10.1016/j.asoc.2023.110393
M. Tavana, A. Shaabani, D. D. Caprio, and A. Bonyani, “A novel interval type-2 fuzzy best-worst method and combined compromise solution for evaluating eco-friendly packaging alternatives,” Expert Systems with Applications, vol. 200, no. 117188, 2022. doi: 10.1016/j.eswa.2022.117188
G. Haseli, S. R. Bonab, M. Hajiaghaei-Keshteli, S. J. Ghoushchi, and M. Deveci, “Fuzzy ze-numbers framework in group decision-making using the bcm and cocoso to address sustainable urban transportation,” Information Sciences, vol. 653, no. 119809, pp. 1–26, 2024. doi: 10.1016/j.ins.2023.119809 57
J. Wang, L. Yu, and Y. Rong, “A new cocoso ranking-based qfd approach in pythagorean fuzzy environment and its application on evaluating design attributes of mobile medical app,” Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3677–3700, 2024. doi: 10.3233/JIFS-233229
S. Chatterjee and S. Chakraborty, “A comparative study on combined compromise solution (cocoso)-based optimization of drilling of aluminium metal matrix composites in fuzzy environments,” International Journal on Interactive Design and Manufacturing (IJIDeM), pp. 1–27, 2024. doi: 10.1007/s12008-024-01743-z
A. P. Rad, M. Khalilzadeh, S. A. Banihashemi, D. Bo˘zani´c, A. Mili´c, and G. ´ Cirovi´c, “Supplier selection in downstream oil and gas and petrochemicals with the fuzzy bwm and gray cocoso methods considering sustanainability criteria and uncertainty conditions,” Sustainability, vol. 16, no. 880, pp. 1–18, 2024. doi: 10.3390/su16020880
Q. A. Ahmad, S. Ashraf, M. S. Chohan, B. Batool, , and M. L. Qiang, “Extended csf-cocoso method: A novel approach for optimizing logistics in the oil and gas supply chain,” IEEE Access, vol. 12, pp. 75678–75688, 2024. doi: 10.1109/ACCESS.2024.3390938
Y. Zheng, H. Qin, and X. Ma, “A novel group decision making method based on cocoso and interval-valued q-rung orthopair fuzzy sets,” Scientific Reports, vol. 14, no. 6562, pp. 1–18, 2024. doi: 10.1038/s41598-024-56922-5
R. Kumar and S. Kumar, “An extended combined compromise solution framework based on novel intuitionistic fuzzy distance measure and score function with applications in sustainable biomass crop selection,” Expert Systems with Applications, vol. 239, no. 122345, 2024. doi: 10.1016/j.eswa.2023.122345
M. Joshi, “Motor insurance policy selection: A joint spherical fuzzy analytic hierarchy process (sf-ahp) and combined compromise solution (cocoso) approach,” Journal of Scientific and Industrial Research (JSIR), vol. 83, no. 2, 2024. doi: 10.56042/jsir.v83i2.4302
S. Dhruba, R. Krishankumar, E. K. Zavadskas, K. S. Ravichandran, and A. H. Gandomi, “Selection of suitable cloud vendors for health centre: A personalized decision framework with fermatean fuzzy set, lopcow, and cocoso,” INFORMATICA, vol. 35, no. 1, pp. 65–98, 2024. doi: 10.15388/23-INFOR537
S. Sampathkumar and F. Augustin, “Optimizing robot deployment in hazardous environment: Mcdm approach using field performers under intuitionistic dense fuzzy set,” International Journal of Fuzzy Systems, 2024. doi: 10.1007/s40815-024-01688-1
N. A. Nabeeh and K. M. Sallam, “A combined compromise solution (cocoso) of mcdm problems for selection of medical best bearing ring,” Neutrosophic Opt. Int. Syst., vol. 1, pp. 1–13, 2024. doi: 10.61356/j.nois.2024.16089
K. H. Gazi, S. P. Mondal, B. Chatterjee, N. Ghorui, A. Ghosh, and D. De, “A new synergistic strategy for ranking restaurant locations: A decision-making approach based on the hexagonal fuzzy numbers,” RAIRO Operations Research, vol. 57, no. 2, pp. 571–608, 2023. doi: 10.1051/ro/2023025
F. A. Alzahrani, N. Ghorui, K. H. Gazi, B. C. Giri, A. Ghosh, and S. P. Mondal, “Optimal site selection for women university using neutrosophic multi-criteria decision making approach,” Buildings, vol. 13, no. 152, pp. 1–36, 2023. doi: 10.3390/buildings13010152
A. F. Momena, S. Mandal, K. H. Gazi, B. C. Giri, and S. P. Mondal, “Prediagnosis of disease based on symptoms by generalized dual hesitant hexagonal fuzzy multi-criteria decision-making techniques,” Systems, vol. 11, no. 231, pp. 1–38, 2023. doi: 10.3390/systems11050231
Hamdani and R. Wardoyo, “The complexity calculation for group decision making using topsis algorithm,” AIP Conference Proceedings 1755, no. 070007, pp. 1–7, 2016. doi: 10.1063/1.4958502
A. M. Ghaleb, H. Kaid, A. Alsamhan, S. H. Mian, and L. Hidri, “Assessment and comparison of variousmcdmapproaches in the selection of manufacturing process,” Advances in Materials Science and Engineering, vol. 2020, no. 4039253, pp. 1–16, 2020. doi: 10.1155/2020/4039253
K. H. Gazi, A. F. Momena, S. Salahshour, S. P. Mondal, and A. Ghosh, “Synergistic strategy of sustainable hospital site selection in saudi arabia using spherical fuzzy mcdm methodology,” Journal of Uncertain Systems, vol. 17, no. 3, pp. 1–64, 2024. doi: 10.1142/S1752890924500041
D. Adhikari, K. H. Gazi, A. Sobczak, B. C. Giri, S. Salahshour, and S. P. Mondal, “Ranking of different states in india based on sustainable women empowerment using mcdm methodology under uncertain environment,” Journal of Uncertain Systems, no. 2450010, pp. 1–52, 2024. doi: 10.1142/S1752890924500107
C. E. Shannon, “A mathematical theory of communication,” Bell System Technical Journal, vol. 27, no. 3, pp. 379–423, 1948. doi: 10.1002/j.1538-7305.1948.tb01338.x

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.