Version 1
: Received: 12 May 2024 / Approved: 13 May 2024 / Online: 13 May 2024 (08:27:03 CEST)
How to cite:
Chattopadhyay, A.; Debnath, B.; Krishna Kumar, T. Optimization of a Dynamic Supply Chain Network: Kinetic Modeling of E-Waste Plants. Preprints2024, 2024050774. https://doi.org/10.20944/preprints202405.0774.v1
Chattopadhyay, A.; Debnath, B.; Krishna Kumar, T. Optimization of a Dynamic Supply Chain Network: Kinetic Modeling of E-Waste Plants. Preprints 2024, 2024050774. https://doi.org/10.20944/preprints202405.0774.v1
Chattopadhyay, A.; Debnath, B.; Krishna Kumar, T. Optimization of a Dynamic Supply Chain Network: Kinetic Modeling of E-Waste Plants. Preprints2024, 2024050774. https://doi.org/10.20944/preprints202405.0774.v1
APA Style
Chattopadhyay, A., Debnath, B., & Krishna Kumar, T. (2024). Optimization of a Dynamic Supply Chain Network: Kinetic Modeling of E-Waste Plants. Preprints. https://doi.org/10.20944/preprints202405.0774.v1
Chicago/Turabian Style
Chattopadhyay, A., Biswajit Debnath and T Krishna Kumar. 2024 "Optimization of a Dynamic Supply Chain Network: Kinetic Modeling of E-Waste Plants" Preprints. https://doi.org/10.20944/preprints202405.0774.v1
Abstract
E-waste management (EWM) refers to the operation-management of discarded or unproductive electronic devices and components, a challenge exacerbated due to overindulgent urbanization. This article presents a multidimensional cost-function-based analysis of the EWM framework structured on three modules - environmental, economic, and social uncertainties - which contemplate the 3 pillars of sustainability in an e-waste recycling plant, including the production-delivery-utilization process. The framework incorporates material recovery from a single e-waste facility provisioning for chemical and mechanical recycling. Each module is ranked using independent Machine Learning (ML) protocols: a) Analytical Hierarchical Process (AHP) and b) combined AHP and Principal Component Analysis (PCA). From a long list of possible contributors, the model identifies and ranks two key sustainability contributors to the EWM supply chain: overall energy consumption and volume of carbon dioxide generated. Another key finding is a precise time window for policy resurrection, which for the data considered, happens to be 400-600 days from the start of operation. Another interesting outcome is the quality of prediction using a combination of AHP and PCA, which consistently produced better results than any of these ML methods individually implemented. Model outcomes have been verified using a case study to outline a future E-waste sustained roadmap.
Business, Economics and Management, Econometrics and Statistics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
