Abstract
Purpose: E-waste management (EWM) refers to the operation-management of discarded electronic devices, a challenge exacerbated due to overindulgent urbanization. The main purpose of this paper is to amalgamate production engineering, statistical methods, mathematical modelling, supported with machine learning to develop a dynamic e-waste supply chain model.
Method Used: This article presents a multidimensional, cost-function-based analysis of the EWM framework structured on three modules - environmental, economic, and social uncertainties in an material recovery from e-waste (MREW) plant, including the production-delivery-utilization process. Each module is ranked using Machine Learning (ML) protocols - Analytical Hierarchical Process (AHP) and combined AHP-Principal Component Analysis (PCA).
Findings: The model identifies and probabilistically ranks two key sustainability contributors to the EWM supply chain: energy consumption and carbon-dioxide emission. Additionally, the precise time window of 400 – 600 days from the start of operation is identified for policy resurrection.
Novelty: Ours is a data-intensive model that is founded on sustainable product designing in line with SDG requirements. The combined AHP-PCA consistently outperformed traditional statistical tools, is the second novelty. Model ratification using real e-waste plant data is the third novelty.
Implications: The Machine Learning framework embeds a powerful probabilistic prediction algorithm based on data-based decision-making in future E-waste sustained roadmaps.
Method Used: This article presents a multidimensional, cost-function-based analysis of the EWM framework structured on three modules - environmental, economic, and social uncertainties in an material recovery from e-waste (MREW) plant, including the production-delivery-utilization process. Each module is ranked using Machine Learning (ML) protocols - Analytical Hierarchical Process (AHP) and combined AHP-Principal Component Analysis (PCA).
Findings: The model identifies and probabilistically ranks two key sustainability contributors to the EWM supply chain: energy consumption and carbon-dioxide emission. Additionally, the precise time window of 400 – 600 days from the start of operation is identified for policy resurrection.
Novelty: Ours is a data-intensive model that is founded on sustainable product designing in line with SDG requirements. The combined AHP-PCA consistently outperformed traditional statistical tools, is the second novelty. Model ratification using real e-waste plant data is the third novelty.
Implications: The Machine Learning framework embeds a powerful probabilistic prediction algorithm based on data-based decision-making in future E-waste sustained roadmaps.
| Original language | English |
|---|---|
| Article number | 6491 |
| Number of pages | 23 |
| Journal | Sustainability |
| Volume | 16 |
| Issue number | 15 |
| Early online date | 29 Jul 2024 |
| DOIs | |
| Publication status | Published - Aug 2024 |
Bibliographical note
Copyright © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).Data Access Statement
The original contributions presented in this study are included in the article, further inquiries can be directed to the corresponding author.Keywords
- supply chain sustainability
- e-waste management
- sustainable production
- machine learning
- kinetic modeling
- global optimization