An enhanced fuzzy IDOCRIW-COCOSO multi-attribute decision making algorithm for decisive electric vehicle battery recycling method

An adaptation to electric mobility quickens waste management tasks for recyclers to end-to-end processing of marketed electric vehicle batteries. Especially lithium-ion batteries play a prominent role in electrifying the world for e-transport technology innovation. This research offers a multi-attri...

Full description

Saved in:
Bibliographic Details
Published in:Results in engineering 2024-06, Vol.22, p.102272, Article 102272
Main Authors: Parthasarathy, Thirumalai Nallasivan, Narayanamoorthy, Samayan, Thilagasree, Chakkarapani Sumathi, Marimuthu, Palanivel Rubavathi, Salahshour, Soheil, Ferrara, Massimiliano, Ahmadian, Ali
Format: Article
Language:English
Subjects:
CILOS
CoCoSo
Electric vehicle
Entropy
q-rung picture fuzzy
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:An adaptation to electric mobility quickens waste management tasks for recyclers to end-to-end processing of marketed electric vehicle batteries. Especially lithium-ion batteries play a prominent role in electrifying the world for e-transport technology innovation. This research offers a multi-attribute decision-making (MADM) structure for finding the best performance e-vehicle recycling techniques. The structured algorithm combines an advanced stratified MADM strategy with e-transportation recycling techniques. The optimal algorithm evaluates the results of qualitative attributes and alternatives using a weighted-ranking MADM approach. The importance of attributes is calculated using a blending of dual objective-weighted approaches: entropy and CILOS methods, viz., the aggregated IDOCRIW approach. The ranking of alternatives is determined through the COCOSO method in a hesitation environment. The q-rung orthopair picture fuzzy set (q-ROPFS) is used to cope with uncertainty and vagueness in decision analysis. The feasibility and robustness of the suggested algorithm were validated through different MADM methods and by altering crucial ranking-dependent parameters in the problem. •An extended q-rung orthopair picture fuzzy set is used for data acquisition.•An emerging electric vehicle battery recycling selection application has been utilized.•An objective IDOCRIW method is utilized for attribute weight priority.•An extended COCOSO method is developed to address the ranking of the application.•The proposed algorithm has been comprehensively validated in nine cases.
ISSN:2590-1230
2590-1230
DOI:10.1016/j.rineng.2024.102272