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Top1. Introduction
Decision-making as an integral part of decision science where identification and selection of choices based on the preferences of decision-makers are considered via relevant assessment criteria. Decision-making could be multi-criteria, multi-attributes or multi-objectives in nature. Multi-criteria decision-making (MCDM) is an operational technique for curbing complex engineering problems in machine language, artificial intelligence, etc. Multi-attributes decision-making (MADM) has to do with making choices among a finite number of decision alternates with respect to multiple, usually conflicting, attributes. Whereas in multi-objectives decision-making (MODM) problems, the number of alternates is infinite, and the trade-offs among the considered criteria are described by continuous functions. One of the challenge of decision-making is the uncertainty of fuzzy data or variables. To cope with such fuzziness, Zadeh (1965) introduced fuzzy set to model the fuzziness in human knowledge. It suffices to state that fuzzy models are not dependable because it considered only the membership degree (MD) of the concerned data.
Atanassov (1986) introduced intuitionistic fuzzy sets (IFSs) to amend the setback of the ordinary fuzzy sets, to better model real-life problems. IFS constituents membership degree (MD) µ and non-membership degree (NMD) ν with the possibility of the existent of hesitation margin (HM) π where their sum is always one and µ + ν ≤ 1. Due to the importance of the idea of IFSs, it has been used to solved many practical decision-making problems via different tools like distance measures, similarity measures, etc. (Atanassov, 1999; Boran and Akay, 2014; De et al., 2001; Ejegwa et al., 2014a,b; Ejegwa and Onasanya, 2019 ; Ejegwa and Onyeke, 2018; Hatzimichailidis et al., 2012; Szmidt and Kacprzyk, 2001, 2004; Wang and Xin, 2005; Xu et al., 2008, Li, 2004; 2005; Ejegwa, 2015; 2021; Ejegwa and Adamu, 2019; Ejegwa and Alabaa, 2014; Ejegwa and Modom, 2015; Ejegwa et al., 2014c, 2014d, 2014e, 2014f, 2016). Some applications of interval-valued IFSs have been discussed (Li, 2011; Wei et al., 2019, 2021).