Multi-criteria decision making in robotic agri-farming with q-rung orthopair m-polar fuzzy sets

Abstract

q-Rung orthopair fuzzy set (qROFS) and m-polar fuzzy set (mPFS) are rudimentary concepts in the computational intelligence, which have diverse applications in fuzzy modeling and decision making under uncertainty. The aim of this paper is to introduce the hybrid concept of q-rung orthopair m-polar fuzzy set (qROmPFS) as a hybrid model of q-rung orthopair fuzzy set and m-polar fuzzy set. A qROmPFS has the ability to deal with real life situations when decision experts are interested to deal with multi-polarity as well as membership and non-membership grades to the alternatives in an extended domain with q-ROF environment. Certain operations on qROmPFSs and several new notions like support, core, height, concentration, dilation, α-cut and (α, β)-cut of qROmPFS are defined. Additionally, grey relational analysis (GRA) and choice value method (CVM) are presented under qROmPFSs for multi-criteria decision making (MCDM) in robotic agri-farming. The proposed methods are suitable to find out an appropriate mode of farming among several kinds of agri-farming. The applications of proposed MCDM approaches are illustrated by respective numerical examples. To justify the feasibility, superiority and reliability of proposed techniques, the comparison analysis of the final ranking in the robotic agri-farming computed by the proposed techniques with some existing MCDM methods is also given.

A novel MADM algorithm for landfill site selection based on q-rung orthopair probabilistic hesitant fuzzy power Muirhead mean operator

“…At the same time, the operation of q-ROPHFE can be improved by combining T norm and T co-norm, and the distance measure can also be improved. In addition, the power Muirhead average operator can also be extended to complex q-rung orthopair fuzzy set [ 51 ], q-rung orthopair m-polar fuzzy set [ 52 ] and spherical and T-spherical fuzzy set [ 53 ], and its special properties under different environments are discussed.…”

Soft Rough q-Rung Orthopair m-Polar Fuzzy Sets and q-Rung Orthopair m-Polar Fuzzy Soft Rough Sets and Their Applications

“…[47] to q-rung orthopair form below. Definition 2.8: [51] If Ξ is the origin set. For every Ĥ ∈ Ξ, if we have a membership grade ϑ r E : Ξ → [0, 1] and a non-membership grade κ r E : Ξ → [0, 1].…”

“…By merging the concepts of SRS, PFS, and m P S, Riaz et al [50] investigated the Pythagorean mpolar fuzzy sets (P m P F S), soft rough Pythagorean m-polar fuzzy sets (SRP m P F S) and Pythagorean m-polar fuzzy soft rough sets (P m P F SRS). The concept of q-rung orthopair mpolar fuzzy sets ( q RO m P F S) was then defined by Riaz et al [51].…”

Integration of fuzzy-weighted zero-inconsistency and fuzzy decision by opinion score methods under a q-rung orthopair environment: A distribution case study of COVID-19 vaccine doses

“…Moreover, a q-rung orthopair shadowed set was suggested to represent attribute values and extends the vlsekriterijumska optimizcija i kaompromisno resenje (VIKOR) [50] . The authors in [64] introduced the hybrid concept of q-rung orthopair m-polar fuzzy set (qROmPFS) and developed a robust MCDM approach where several uncertainties are measured by the proposed concept. Additionally, a decision-making model has been developed and used for hybrid q-ROFSs with notions of covering rough sets and techniques for the order of preference by similarity to ideal solution (TOPSIS) [55] .…”