Multi-attribute decision-making for electronic waste recycling using interval-valued Fermatean fuzzy Hamacher aggregation operators

Abstract

The utilization of electrical and electronics equipments in waste recycling has become a paramount for various countries. The waste electrical and electronics equipment (WEEE) recyclers own a crucial position in the environmental growth of a country as they help to minimize the carbon emissions during the recycling of WEEE in the most eco-friendly way. Therefore, the selection and assessment of an appropriate WEEE recycling partner has become a most important part of DM (decision-making) applications. The collusion of numerous quantitative and qualitative factors makes the recycling partner selection problem, a multifaced and significant decision for the managerial experts. The main objective of this work is to propose MADM (multi-attribute decision-making) techniques to evaluate the WEEE recycling partners under interval-valued Fermatean fuzzy (IVFF) information. In this regard, certain Hamacher AOs (aggregation operators) are proposed to develop the required DM method. These AOs include Hamacher weighted averaging, ordered weighted averaging, weighted geometric, ordered weighted geometric, generalized Einstein weighted averaging, generalized Einstein ordered weighted averaging, generalized Einstein weighted geometric, etc. Then, these averaging operators are utilized to come up with a MADM techniques under IVFF environment. Furthermore, the constructed technique is applied to a case study in China to incorporate with the e-waste recycling partner selection problem. Moreover, a brief comparison of the proposed with is presented with various existing techniques to manifest the productivity and coherence of the proposed model. Finally, the accuracy and consistency of results shows that the proposed technique is fully compatible and applicable to handle any MADM problem.

Optimizing E-waste management through neutrosophic applications: A comprehensive analysis of factors based on Superiority and Inferiority Ranking (SIR)

“…Notably, cost and return quality emerged as pivotal criteria, among other considerations. [17] The primary goal of this study is to suggest Multi-Attribute Decision-Making (MADM) techniques for assessing WEEE recycling partners within the context of interval-valued Fermatean fuzzy (IVFF) information. To achieve this, spe-ciĄc Hamacher Aggregation Operators (AOs) are introduced as part of the decision-making method needed for the evaluation.…”

Enhancing breast cancer treatment selection through 2TLIVq-ROFS-based multi-attribute group decision making

“…Gurmani et al (2023) aimed to establish a novel methodology for determining expert weights through distance and similarity measures by using IVq-ROFNs. Luqman and Shahzadi (2023) developed the IVq-ROF superiority and inferiority technique, incorporating sine-trigonometric operational laws. Xu (2023) proposed a two-stage MADM method that used the IVq-ROF technique to address complex problems, such as the selection of a bike-sharing recycling supplier.…”

Multi-attribute decision-making based on novel Fermatean fuzzy similarity measure and entropy measure

“…Some FF aggregating functions based on Einstein’s norm were proposed by Akram et al ( 2020 ). There are some investigations of FFSs and their real-world applications in the literature (Salsabeela and John 2021 ; Aydın 2021 ; Gul et al 2021 ; Hadi et al 2021 ; Shit and Ghorai 2021 ; Rani and Mishra 2021 ; Akram et al 2022 , 2023d ; Mishra et al 2022a , b , 2023 ; Ali and Ansari 2022 ; Zhou et al 2022 ; Luqman and Shahzadi 2023 ). The creation of several FF similarity and entropy measurements is discussed in this study.…”