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.Funding
Both B.D. and A.K.C. acknowledge support received from Aston University. This research was funded by the Commonwealth Scholarships Commission (Reference: INCN-2018-52).
| Funders | Funder number |
|---|---|
| Aston University | |
| Commonwealth Scholarship Commission | INCN-2018-52 |
| Commonwealth Scholarship Commission |
Keywords
- supply chain sustainability
- e-waste management
- sustainable production
- machine learning
- kinetic modeling
- global optimization