Sustainability, Год журнала: 2024, Номер 16(15), С. 6491 - 6491

Опубликована: Июль 29, 2024

Purpose: E-waste management (EWM) refers to the operation of discarded electronic devices, a challenge exacerbated due overindulgent urbanization. The main purpose this paper is amalgamate production engineering, statistical methods, mathematical modelling, supported with Machine Learning develop dynamic e-waste supply chain model. Method Used: This article presents multidimensional, cost function-based analysis EWM framework structured on three modules including environmental, economic, and social uncertainties in material recovery from an (MREW) plant, production–delivery–utilization process. Each module ranked using (ML) protocols—Analytical Hierarchical Process (AHP) combined AHP-Principal Component Analysis (PCA). Findings: model identifies probabilistically ranks two key sustainability contributors chain: energy consumption carbon dioxide emission. Additionally, precise time window 400–600 days start identified for policy resurrection. Novelty: Ours data-intensive that founded sustainable product designing line SDG requirements. AHP-PCA consistently outperformed traditional tools, second novelty. Model ratification real plant data third Implications: embeds powerful probabilistic prediction algorithm based data-based decision making future sustained roadmaps.

Язык: Английский