q-Rung orthopair fuzzy hypersoft ordered aggregation operators and their application towards green supplier

Abstract

Green Supply Chain Management (GSCM) is essential to ensure environmental compliance and commercial growth in the current climate. Businesses constantly look for fresh concepts and techniques for ensuring environmental sustainability. To keep up with the new trends in environmental concerns related to company management and procedures, Green Supplier Selection (GSS) criteria are added to the traditional supplier selection processes. This study aims to identify general and environmental supplier selection criteria to provide a framework that can assist decision-makers in choosing and prioritizing appropriate green supplier selection. The development and implementation of decision support systems aimed to solve these difficulties at a rapid rate. In order to manage inaccurate data and simulate decision-making problems. Fuzzy sets introduced by Zadeh, are a useful technique to handle the imperfectness and uncertainty in different problems. Although fuzzy sets can handle incomplete information in different real worlds problems, but its cannot handle all type of uncertainty such as incomplete and indeterminate data. Therefore different extensions of fuzzy sets such as intuitionistic fuzzy, pythagorean fuzzy and q-rung orthopair fuzzy sets introduced to address the problems of uncertainty by considering the membership and non-membership grade. However, these concepts have some shortcomings in the handling uncertainty with sub-attributes. To overcome this difficulties Khan et al. developed the structure of q-rung orthopair fuzzy hypersoft sets by combining q-rung orthopair fuzzy sets with hypersoft sets. A remarkable and beneficial research work is done in the field of q-rung orthopair fuzzy hypersoft sets, and then we think about the application. In this paper, we use the structure of q-rung orthopair fuzzy hypersoft in multi-criteria supplier selection problems. For this, we present aggregation operator to solve multi-criteria decision-making (MCDM) problems with q-rung orthopair fuzzy hypersoft (q-ROFH) information, known as ordered weighted geometric aggregation operator. Since the uncertainty and vagueness is an unavoidable feature of multi-criteria decision-making problems, the proposed structure can be a useful tool for decision making in an uncertain environment. Further, the expert opinions were investigated using the multi-criteria decision-making (MCDM) technique, which helped identify interrelationship and causal preference of green supplier evaluation aspects that used aggregation operators. Finally, a numerical example of the proposed method for the task of Green Supplier Selection is presented.

A Novel Method for Determining Tourism Carrying Capacity in a Decision-Making Context Using <i>q</i>−Rung Orthopair Fuzzy Hypersoft Environment

“…Definition 2.4. [37] The score function of the q−ROFHNs is defined as S e ij = ϕ e ij (m ij ) q − ψ e ij (m ij ) q .…”

“…Therefore, q−ROFHS can provide a more complete and realistic way to model complex and dynamic situations. The concept of q−Rung orthopair fuzzy hypersoft sets was introduced by Khan et al [37,38], which utilizes specific operational rules and aggregation operators (AOs) to address various interactions among input arguments. Subsequently, Gurmani et al [39] proposed different AOs-based basic operational laws.…”

An innovative approach to passport quality assessment based on the possibility q-rung ortho-pair fuzzy hypersoft set

“…Maji et al [43] 2001 Fuzzy Soft set Yes No No Yes Yes No P.K. Maji et al [44] 2001 Intuitionistic Fuzzy Soft Set Yes Yes No Yes Yes No Peng et al [45] 2015 Pythagorean Fuzzy Soft Set Yes Yes No Yes Yes No M.Riaz et al [46] 2020 q-rung orthopair fuzzy Soft set Yes Yes No Yes Yes No Smarandache [25] 2018 Hypersoft set Yes No No Yes Yes Yes Smarandache [47] 2018 Fuzzy Hypersoft Set Yes No No Yes Yes Yes Smarandache [48] 2018 Intuitionistic Fuzzy Hyperaoft Set Yes Yes No Yes Yes Yes Zulqarnain et al [49] 2021 Pythagorean Fuzzy Hypersoft Set Yes Yes No Yes Yes Yes S. Khan et al [50] 2021 q-rung orthopair fuzzy hypersoft set Yes Yes No Yes Yes Yes Atiqe et al ...…”

Evaluation of Cryptocurrency Markets Based on q-Rung Orthopair Fuzzy Hypersoft Frank Approach

“…The definition of a pair(ℶ, ∆) = {(𝜛, ∆(𝜛))|𝜛 ∈ Δ, Δ(𝜛) ∈ 𝑝(𝑢)}. Definition 3: [39] Let ℉ be a set with universal and that 𝑏 1 , 𝑏 2 , ⋯ 𝑏 𝑛 be 𝑛 different characteristics pertaining to ℉ respectively, whose correspondence characteristics values the sets 𝜔 1 , 𝜔 2 , ⋯ 𝜔 𝑛 such that 𝜔 𝑖 ∩ 𝜔 𝑗 = 𝜙 where 𝑖 = 𝑗 for each 𝑛 > 1 𝑎𝑛𝑑 𝑖, 𝑗 = {1,2, ⋯ 𝑛}. A pair (ℶ, ∆) is referred to as qROFHSS, where 𝜔 1 , 𝜔 2 , ⋯ 𝜔 𝑛 = ∆= {𝑑 1 , 𝑑 2 , ⋯ 𝑑 𝑛 } is a collection of sub parameters and ℶ is a mapping ℶ: ∆→ 𝑞𝑅𝑂𝐹𝑆 ℉ for qROFS.…”