An Economic Optimization Model of an E-Waste Supply Chain Network: Machine Learned Kinetic Modelling for Sustainable Production DOI Open Access
Biswajit Debnath, Amit K. Chattopadhyay,

T. Krishna Kumar

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(15), P. 6491 - 6491

Published: July 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.

Language: Английский

Resilience-based complex system early design using dynamic Copula Bayesian network: Heave compensation hydraulic system design as a case study DOI
Chao Zhang, Yaohui Lu, Rentong Chen

et al.

Ocean Engineering, Journal Year: 2025, Volume and Issue: 320, P. 120314 - 120314

Published: Jan. 10, 2025

Language: Английский

Citations

8
Resilient socio-technical systems for adaptive consumer e-waste management DOI
Chun‐Hung Lee, Chun‐Hung Lee, I Wayan Koko Suryawan

et al.

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: unknown, P. 106026 - 106026

Published: Dec. 1, 2024

Language: Английский

Citations

5
An Economic Optimization Model of an E-Waste Supply Chain Network: Machine Learned Kinetic Modelling for Sustainable Production DOI Open Access
Biswajit Debnath, Amit K. Chattopadhyay,

T. Krishna Kumar

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(15), P. 6491 - 6491

Published: July 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.

Language: Английский

Citations

1