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Title Multi-objective artificial bee colony algorithm for reducing carbon emission and maximizing profit in a circular supply chain network
ID_Doc 22245
Authors Sakthivel, AR; Vimal, KEK; Kandasamy, J; Shiva, SN; Jayanth, G; Nishanth, AS; Kumar, DV
Title Multi-objective artificial bee colony algorithm for reducing carbon emission and maximizing profit in a circular supply chain network
Year 2024
Published
Abstract With increased product demand, companies produce more, resulting in higher greenhouse gas emissions. Circular Supply Chain (CSC) integrates Circular Economy (CE) principles, emphasizing Reduce, Reuse, and Recycle. Algorithms optimize CSC, yet limited research focuses on emission control. The study aims to fill research gaps, demonstrating MOABC's effectiveness in optimizing CSC for profitability and carbon emission reduction, contributing to manufacturing carbon neutrality. Comparison with Linear Supply Chain (LSC) and stability tests using varying inputs were conducted using data from an Indian automobile radiator manufacturer. It highlights CSC's advantages over LSC, serving as a basis for future research on logistics network optimization. Objective functions aim to reduce emission levels and improve profit margins for automotive companies, offering insights into sustainable manufacturing practices. CSC's superior performance over LSC was consistent across simulated iterations. Stability tests showed expected variations, affirming the research's reliability. This research informs CSC parameter optimization, potentially benefiting other industries. In all three case scenarios of 15, 30, and 50 iterations that were simulated, the CSC outperformed the LSC significantly in all three scenarios of them. The objective functions and constraints were subjected to a stability test, by varying the inputs. The stability test also reveals that there was no significant variation. This research can be used to optimize the various parameters of a CSC and can be extrapolated to other industries.
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