Department of Mathematics, University of the Punjab, Lahore, Lahore, Pakistan.
Department of Mathematics & Statistics, The University of Lahore, Lahore, Pakistan.
PLoS One. 2021 Feb 25;16(2):e0246485. doi: 10.1371/journal.pone.0246485. eCollection 2021.
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.
q-阶正交对模糊集(qROFS)和 m-极性模糊集(mPFS)是计算智能中的基本概念,它们在不确定性下的模糊建模和决策中有广泛的应用。本文的目的是引入 q-阶正交对 m-极性模糊集(qROmPFS)的混合概念,作为 q-阶正交模糊集和 m-极性模糊集的混合模型。qROmPFS 具有在决策专家有兴趣处理多极性以及对扩展域中替代方案的成员和非成员等级进行处理的实际情况的能力,该扩展域具有 q-ROF 环境。定义了 qROmPFS 上的某些运算以及一些新的概念,如支持、核心、高度、浓度、扩张、α-切割和 qROmPFS 的(α,β)-切割。此外,在 qROmPFS 下提出了灰色关联分析(GRA)和选择值方法(CVM),用于机器人农业中的多准则决策(MCDM)。所提出的方法适用于在多种农业中找到合适的农业模式。通过各自的数值示例说明了所提出的 MCDM 方法的应用。为了验证所提出技术的可行性、优越性和可靠性,还给出了通过所提出技术计算的机器人农业中最终排名与一些现有 MCDM 方法的比较分析。