2-tuple linguistic Fermatean fuzzy MAGDM based on the WASPAS method for selection of solid waste disposal location

Muhammad Akram; Usman Ali; Gustavo Santos-García; Zohra Niaz; Muhammad Akram; Usman Ali; Gustavo Santos-García; Zohra Niaz

doi:10.3934/mbe.2023179

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 2: 3811-3837. doi: 10.3934/mbe.2023179

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Research article Special Issues

2-tuple linguistic Fermatean fuzzy MAGDM based on the WASPAS method for selection of solid waste disposal location

1.
Department of Mathematics, University of the Punjab, New Campus, Lahore 54590, Pakistan
2.
Centre for Advanced Studies in Pure & Applied Mathematics, Bahauddin Zakariya University, Multan, Pakistan
3.
IME, Universidad de Salamanca, 37007 Salamanca, Spain

Received: 19 August 2022 Revised: 22 September 2022 Accepted: 18 October 2022 Published: 12 December 2022

Manufacturing plants generate toxic waste that can be harmful to workers, the population and the atmosphere. Solid waste disposal location selection (SWDLS) for manufacturing plants is one of the fastest growing challenges in many countries. The weighted aggregated sum product assessment (WASPAS) is a unique combination of the weighted sum model and the weighted product model. The purpose of this research paper is to introduce a WASPAS method with a 2-tuple linguistic Fermatean fuzzy (2TLFF) set for the SWDLS problem by using the Hamacher aggregation operators. As it is based on simple and sound mathematics, being quite comprehensive in nature, it can be successfully applied to any decision-making problem. First, we briefly introduce the definition, operational laws and some aggregation operators of 2-tuple linguistic Fermatean fuzzy numbers. Thereafter, we extend the WASPAS model to the 2TLFF environment to build the 2TLFF-WASPAS model. Then, the calculation steps for the proposed WASPAS model are presented in a simplified form. Our proposed method, which is more reasonable and scientific in terms of considering the subjectivity of the decision maker's behaviors and the dominance of each alternative over others. Finally, a numerical example for SWDLS is proposed to illustrate the new method, and some comparisons are also conducted to further illustrate the advantages of the new method. The analysis shows that the results of the proposed method are stable and consistent with the results of some existing methods.

Keywords:

Citation: Muhammad Akram, Usman Ali, Gustavo Santos-García, Zohra Niaz. 2-tuple linguistic Fermatean fuzzy MAGDM based on the WASPAS method for selection of solid waste disposal location[J]. Mathematical Biosciences and Engineering, 2023, 20(2): 3811-3837. doi: 10.3934/mbe.2023179

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Abstract

References

[1]	W. Doaemo, S. Dhiman, A. Borovskis, W. Zhang, S. Bhat, S. Jaipuria, et al., Assessment of municipal solid waste management system in Lae City, Papua New Guinea in the context of sustainable development, Environ. Develop. Sustain., 23 (2021), 18509–18539. https://doi.org/10.1007/s10668-021-01465-2 doi: 10.1007/s10668-021-01465-2
[2]	M. Eskandari, M. Homaee, A. Falamaki, Landfill site selection for municipal solid wastes in mountainous areas with landslide susceptibility, Environ. Sci. Pollut. Res., 23 (2016), 12423–12434. https://doi.org/10.1007/s11356-016-6459-x doi: 10.1007/s11356-016-6459-x
[3]	L. A. Zadeh, Fuzzy sets, Inform. Control, 8 (1965), 338–353. https://doi.org/10.1142/9789814261302_0021 doi: 10.1142/9789814261302_0021
[4]	K. T. Atanassov, Intuitionistic fuzzy sets, Fuzzy Sets Syst., 20 (1986), 87–96.
[5]	E. Szmidt, J. Kacprzyk, Intuitionistic fuzzy sets in some medical applications, in International Conference on Computational Intelligence, Springer, Berlin, Heidelberg, (2001), 148–151.
[6]	R. R. Yager, Pythagorean membership grades in multicriteria decision making, IEEE Trans. Fuzzy Syst., 22(2014), 958–965.
[7]	T. Senapati, R. R. Yager, Fermatean fuzzy sets, J. Amb. Intell. Human. Comput., 11 (2020), 663–674. https://doi.org/10.1007/s12652-019-01377-0 doi: 10.1007/s12652-019-01377-0
[8]	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 (2019), 391–412.
[9]	T. Senapati, R. R. Yager, Fermatean fuzzy weighted averaging/geometric operators and its application in multi-criteria decision-making methods, Eng. Appl. Artif. Intell., 85 (2019), 112–121. https://doi.org/10.1016/j.engappai.2019.05.012 doi: 10.1016/j.engappai.2019.05.012
[10]	M. Akram, N. Ramzan, F. Feng, Extending COPRAS method with linguistic Fermatean fuzzy sets and Hamy mean operators, J. Math., (2022), Article ID 8239263. https://doi.org/10.1155/2022/8239263
[11]	H. Garg, M. Akram, G. Shahzadi, Decision-making analysis based on Fermatean fuzzy Yager aggregation operators with application in COVID $-$ 19 testing facility, Math. Problems Eng., (2020), Article ID 7279027. https://doi.org/10.1155/2020/7279027
[12]	P. Liu, Y. Li, X. Zhang, W. Pedrycz, A multiattribute group decision-making method with probabilistic linguistic information based on an adaptive consensus reaching model and evidential reasoning, IEEE Trans. Cybern., (2022). https://doi.org/10.1109/TCYB.2022.3165030
[13]	P. Liu, Y. Li, P. Wang, Opinion dynamics and minimum adjustment-driven consensus model for multi-criteria large-scale group decision making under a novel social trust propagation mechanism, IEEE Trans. Fuzzy Syst., (2022). https://doi.org/10.1109/TFUZZ.2022.3186172
[14]	P. Liu, S. M. Chen, G. Tang, Multicriteria decision making with incomplete weights based on 2-D uncertain linguistic Choquet integral operators, IEEE Trans. Cybern., 51 (2019), 1860–1874.
[15]	F. Herrera, L. Martínez, An approach for combining linguistic and numerical information based on the 2 $-$ tuple fuzzy linguistic representation model in decision-making, Int. J. Uncert. Fuzz. Knowl. Syst., 8 (2000), 539–562. https://doi.org/10.1142/S0218488500000381 doi: 10.1142/S0218488500000381
[16]	F. Herrera, L. Martínez, A 2 $-$ tuple fuzzy linguistic representation model for computing with words, IEEE Trans. Fuzzy Syst., 8 (2000), 746–752.
[17]	S. Faizi, W. Sałabun, S. Nawaz, A. Rehman, J. Watróbski, Best-worst method and Hamacher aggregation operations for intuitionistic 2 $-$ tuple linguistic sets, Expert Syst. Appl., 181 (2021), 115088. https://doi.org/10.1016/j.eswa.2021.115088 doi: 10.1016/j.eswa.2021.115088
[18]	F. Herrera, E. Herrera-Viedma, Linguistic decision analysis: Steps for solving decision problems under linguistic information, Fuzzy Sets Syst., 115 (2000), 67–82. https://doi.org/10.1016/S0165-0114(99)00024-X doi: 10.1016/S0165-0114(99)00024-X
[19]	X. M. Deng, H. Gao, TODIM method for multiple attribute decision making with 2 $-$ tuple linguistic Pythagorean fuzzy information, J. Intell. Fuzzy Syst., 37 (2019), 1769–1780.
[20]	P. Liu, S. Naz, M. Akram, M. Muzammal, Group decision-making analysis based on linguistic q-rung orthopair fuzzy generalized point weighted aggregation operators, Int. J. Mach. Learn. Cybern., 13 (2022), 883–906. https://doi.org/10.1007/s13042-021-01425-2 doi: 10.1007/s13042-021-01425-2
[21]	E. K. Zavadskas, Z. Turskis, J. Antucheviciene, A. Zakarevicius, Optimization of weighted aggregated sum product assessment, Elektronika ir elektrotechnika, 122 (2012), 3–6.
[22]	E. K. Zavadskas, R. Bausys, 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, 7 (2015), 15923–15936. https://doi.org/10.3390/su71215792 doi: 10.3390/su71215792
[23]	A. R. Mishra, P. Rani, K. R. Pardasani, A. Mardani, A novel hesitant fuzzy WASPAS method for assessment of green supplier problem based on exponential information measures, J. Clean. Product., 238 (2019), 117901. https://doi.org/10.1016/j.jclepro.2019.117901 doi: 10.1016/j.jclepro.2019.117901
[24]	D. Schitea, M. Deveci, M. Iordache, K. Bilgili, I. Z. Akyurt, I. Iordache, Hydrogen mobility roll-up site selection using intuitionistic fuzzy sets based WASPAS, COPRAS and EDAS, Int. J. Hyd. Energy, 44 (2019), 8585–8600. https://doi.org/10.1016/j.ijhydene.2019.02.011 doi: 10.1016/j.ijhydene.2019.02.011
[25]	A. Mardani, M. K. Saraji, A. R. Mishra, P. Rani, A novel extended approach under hesitant fuzzy sets to design a framework for assessing the key challenges of digital health interventions adoption during the COVID $-$ 19 outbreak, Appl. Soft Comput., 142 (2017), 403–412. https://doi.org/10.1016/j.asoc.2020.106613 doi: 10.1016/j.asoc.2020.106613
[26]	M. Akram, Z. Niaz, 2-Tuple linguistic Fermatean fuzzy decision-making method based on COCOSO with CRITIC for drip irrigation system analysis, J. Comput. Cognit. Eng., (2022). https://doi.org/10.47852/bonviewJCCE2202356
[27]	P. Rani, A. R. Mishra, K. R. Pardasani, A novel WASPAS approach for multi criteria physician selection problem with intuitionistic fuzzy type-2 sets, Soft Comput., 24 (2020), 2355–2367. https://doi.org/10.1007/s00500-019-04065-5 doi: 10.1007/s00500-019-04065-5
[28]	J. Antucheviciene, J. Saparauskas, MCDM methods WASPAS and MULTIMOORA: Verification of robustness of methods when assessing alternative solutions, Econ. Comput. Econ. Cybern. Stud. Res., 47 (2013), 5–20.
[29]	S. Lashgari, J. Antucheviien, A. Delavari, O. Kheirkhah, Using QSPM and WASPAS methods for determining outsourcing strategies, J. Business Econ. Manag., 15 (2014), 729–743. https://doi.org/10.3846/16111699.2014.908789 doi: 10.3846/16111699.2014.908789
[30]	E. K. Zavadskas, J. Antucheviciene, S. H. R. Hajiagha, S. S. Hashemi, Extension of weighted aggregated sum product assessment with interval-valued intuitionistic fuzzy numbers (WASPAS-IVIF), Appl. Soft Comput., 24(2014), 1013–1021. https://doi.org/10.1016/j.asoc.2014.08.031 doi: 10.1016/j.asoc.2014.08.031
[31]	S. Chakraborty, E. K. Zavadskas, Applications of WASPAS method in manufacturing decision making, Informatica, 25 (2014), 1–20.
[32]	S. Chakraborty, E. K. Zavadskas, J. Antucheviciene, Applications of WASPAS method as a multi-criteria decision-making tool, Econ. Comput. Econ. Cyber. Stud. Res., 49 (2015), 5–22.
[33]	E. K. Zavadskas, S. Chakraborty, O. Bhattacharyya, J. Antucheviciene, Application of WASPAS method as an optimization tool in non-traditional machining processes, Inform. Technol. Control, 44 (2015), 77–88. https://doi.org/10.5755/j01.itc.44.1.7124 doi: 10.5755/j01.itc.44.1.7124
[34]	D. Karabašević, D. Stanujkić, S. Urošević, M. Maksimović, An approach to personnel selection based on SWARA and WASPAS methods, Bizinfo (Blace) J. Econ. Manag. Inform., 7 (2016), 1–11. https://doi.org/10.5937/bizinfo1601001K doi: 10.5937/bizinfo1601001K
[35]	E. K. Zavadskas, D. Kalibatas, D. Kalibatiene, A multi-attribute assessment using WASPAS for choosing an optimal indoor environment, Arch. Civil Mechan. Eng., 16(2016), 76–85.
[36]	A. Mardani, M. Nilashi, N. Zakuan, N. Loganathan, S. Soheilirad, M. Z. M. Saman, et al., A systematic review and meta-analysis of SWARA and WASPAS methods: Theory and applications with recent fuzzy developments, Appl. Soft Comput., 57 (2017), 265–292. https://doi.org/10.1016/j.asoc.2017.03.045 doi: 10.1016/j.asoc.2017.03.045
[37]	R. Bausys, B. Juodagalvien, Garage location selection for residential house by WASPAS-SVNS method, J. Civil Eng. Manag., 23 (2017), 421–429. https://doi.org/10.3846/13923730.2016.1268645 doi: 10.3846/13923730.2016.1268645
[38]	D. Stanujki, D. Karabasevi, An extension of the WASPAS method for decision-making problems with intuitionistic fuzzy numbers: A case of website evaluation, Oper. Res. Eng. Sci. Theory Appl., 1 (2018), 29–39. https://doi.org/10.31181/oresta19012010129s doi: 10.31181/oresta19012010129s
[39]	Z. Turskis, N. Goranin, A. Nurusheva, S. Boranbayev, A fuzzy WASPAS-based approach to determine critical information infrastructures of EU sustainable development, Sustainability, 11 (2019), 424. https://doi.org/10.3390/su11020424 doi: 10.3390/su11020424
[40]	F. K. Gündoğdu, C. Kahraman, Extension of WASPAS with spherical fuzzy sets, Informatica, 30 (2019), 269–292. https://doi.org/10.15388/Informatica.2019.206 doi: 10.15388/Informatica.2019.206
[41]	S. J. H. Dehshiri, M. Aghaei, Identifying and prioritizing solutions to overcome obstacles of the implementation of reverse logistics with a hybrid approach: Fuzzy Delphi, SWARA and WASPAS in the paper industry, Iranian J. Supply Chain Manag., 21 (2019), 85–98.
[42]	P. Rani, A. R. Mishra, Multi-criteria weighted aggregated sum product assessment framework for fuel technology selection using $q$ -rung orthopair fuzzy sets, Sustain. Product. Consumpt., 24 (2020), 90–104. https://doi.org/10.1016/j.spc.2020.06.015 doi: 10.1016/j.spc.2020.06.015
[43]	V. Mohagheghi, S. M. Mousavi, D $-$ WASPAS: Addressing social cognition in uncertain decision-making with an application to a sustainable project portfolio problem, Cognit. Comput., 12 (2020), 619–641. https://doi.org/10.1007/s12559-019-09679-3
[44]	D. Sergi, I. Ucal Sari, Prioritization of public services for digitalization using fuzzy Z-AHP and fuzzy Z-WASPAS, Complex Intell. Syst., 7 (2021), 841–856. https://doi.org/10.1007/s40747-020-00239-z doi: 10.1007/s40747-020-00239-z
[45]	K. Rudnik, G. Bocewicz, A. Kucińska-Landwójtowicz, & I. D. Czabak-Górska, Ordered fuzzy WASPAS method for selection of improvement projects, Expert Syst. Appl., 169 (2021), 114471. https://doi.org/10.1016/j.eswa.2020.114471 doi: 10.1016/j.eswa.2020.114471
[46]	M. Badalpur, E. Nurbakhsh, An application of WASPAS method in risk qualitative analysis: A case study of a road construction project in Iran, Int. J. Construct. Manag., 21 (2021), 910–918. https://doi.org/10.1080/15623599.2019.1595354 doi: 10.1080/15623599.2019.1595354
[47]	D. Pamucar, M. Deveci, I. Gokasar, M. Popovic, Fuzzy Hamacher WASPAS decision-making model for advantage prioritization of sustainable supply chain of electric ferry implementation in public transportation, Environ. Develop. Sustain., 24 (2022), 7138–7177. https://doi.org/10.1007/s10668-021-01742-0 doi: 10.1007/s10668-021-01742-0
[48]	M. Yazdani, M. Tavana, D. Pamucar, P. Chatterjee, A rough based multi-criteria evaluation method for healthcare waste disposal location decisions, Comput. Indust. Eng., 143 (2020), 106394. https://doi.org/10.1016/j.cie.2020.106394 doi: 10.1016/j.cie.2020.106394
[49]	A. R. Mishra, A. Mardani, P. Rani, E. K. Zavadskas, A novel EDAS approach on intuitionistic fuzzy set for assessment of health-care waste disposal technology using new parametric divergence measures, J. Clean. Product., 272 (2020), 122807. https://doi.org/10.1016/j.jclepro.2020.122807 doi: 10.1016/j.jclepro.2020.122807
[50]	M. N. Yahya, H. Gokceku, D. U. Ozsahin, B. Uzun, Evaluation of wastewater treatment technologies using TOPSIS, Desalin Water Treat, 177 (2020), 416–422.
[51]	S. Suntrayuth, X. Yu and J. Su, A comprehensive evaluation method for industrial sewage treatment projects based on the improved entropy-TOPSIS, Sustainability, 12 (2020), 6734. https://doi.org/10.3390/su12176734 doi: 10.3390/su12176734
[52]	P. Liu, P. Rani, A. R. Mishra, A novel Pythagorean fuzzy combined compromise solution framework for the assessment of medical waste treatment technology, J. Clean. Product., 292 (2021), 126047. https://doi.org/10.1016/j.jclepro.2021.126047 doi: 10.1016/j.jclepro.2021.126047
[53]	A. Mussa, K. Y. Suryabhagavan, Solid waste dumping site selection using GIS-based multi-criteria spatial modeling: A case study in Logia town, Afar region, Ethiopia, Geol. Ecol. Landscapes, 5 (2021), 186–198. https://doi.org/10.1080/24749508.2019.1703311 doi: 10.1080/24749508.2019.1703311
[54]	B. Aslam, A. Maqsoom, M. D. Tahir, F. Ullah, M. S. U. Rehman, M. Albattah, Identifying and ranking landfill sites for municipal solid waste management: An integrated remote sensing and GIS approach, Buildings, 12 (2022), 605. https://doi.org/10.3390/buildings12050605 doi: 10.3390/buildings12050605
[55]	T. D. Bui, J. W. Tseng, M. L. Tseng, M. K. Lim, Opportunities and challenges for solid waste reuse and recycling in emerging economies: A hybrid analysis, Resour. Conserv. Recycl., 177 (2022), 105968. https://doi.org/10.1016/j.resconrec.2021.105968 doi: 10.1016/j.resconrec.2021.105968
[56]	X. Deng, J. Wang, G. Wei, Some 2 $-$ tuple linguistic Pythagorean Heronian mean operators and their application to multiple attribute decision-making, J. Exper. Theor. Artif. Intell., 31 (2019), 555–574. https://doi.org/10.1080/0952813X.2019.1579258 doi: 10.1080/0952813X.2019.1579258
[57]	X. Deng, G. Wei, H. Gao, J. Wang, Models for safety assessment of construction project with some 2-tuple linguistic Pythagorean fuzzy Bonferroni mean operators, IEEE Access, 6 (2018), 52105–52137.
[58]	M. Akram, R. Bibi, M. A. Al $-$ Shamiri, A decision-making framework based on 2 $-$ tuple linguistic Fermatean fuzzy Hamy mean operators, Math. Problems Eng., (2022), Article ID 1501880. https://doi.org/10.1155/2022/1501880
[59]	M. Akram, Z. Niaz, F. Feng, Extended CODAS method for multi attribute group decision making based on 2-tuple linguistic Fermatean fuzzy Hamacher aggregation operators, Gran. Comput., (2022). https://doi.org/10.1007/s41066-022-00332-3
[60]	N. H. Zardari, K. Ahmed, S. M. Shirazi, Z. B. Yusop, Weighting methods and their effects on multi-criteria decision making model outcomes in water resources management, Springer, (2015).
[61]	T. He, S. Zhang, G. Wei, R. Wang, J. Wu, C. Wei, CODAS method for 2-tuple linguistic Pythagorean fuzzy multiple attribute group decision making and its application to financial management performance assessment, Technol. Econ. Develop. Econ., 26 (2020), 920–932. https://doi.org/10.3846/tede.2020.11970

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