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Article

An Economic Optimization Model of an E-Waste Supply Chain Network: Machine Learned Kinetic Modelling for Sustainable Production

by
Biswajit Debnath
1,2,
Amit K. Chattopadhyay
1,* and
T. Krishna Kumar
3
1
Aston Centre for Artificial Intelligence Research & Applications (ACAIRA), Department of Applied Mathematics and Data Science, College of Engineering and Physical Sciences, Aston University, Birmingham B4 7ET, UK
2
Department of Chemical Engineering, Jadavpur University, Kolkata 700032, India
3
Rockville-Analytics, Rockville, MD 20850, USA
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(15), 6491; https://doi.org/10.3390/su16156491
Submission received: 10 May 2024 / Revised: 11 June 2024 / Accepted: 16 July 2024 / Published: 29 July 2024
(This article belongs to the Section Economic and Business Aspects of Sustainability)

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 including environmental, economic, and social uncertainties in material recovery from an 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: This 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 the 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, and 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.
Keywords: supply chain sustainability; e-waste management; sustainable production; machine learning; kinetic modeling; global optimization supply chain sustainability; e-waste management; sustainable production; machine learning; kinetic modeling; global optimization

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MDPI and ACS Style

Debnath, B.; Chattopadhyay, A.K.; Kumar, T.K. An Economic Optimization Model of an E-Waste Supply Chain Network: Machine Learned Kinetic Modelling for Sustainable Production. Sustainability 2024, 16, 6491. https://doi.org/10.3390/su16156491

AMA Style

Debnath B, Chattopadhyay AK, Kumar TK. An Economic Optimization Model of an E-Waste Supply Chain Network: Machine Learned Kinetic Modelling for Sustainable Production. Sustainability. 2024; 16(15):6491. https://doi.org/10.3390/su16156491

Chicago/Turabian Style

Debnath, Biswajit, Amit K. Chattopadhyay, and T. Krishna Kumar. 2024. "An Economic Optimization Model of an E-Waste Supply Chain Network: Machine Learned Kinetic Modelling for Sustainable Production" Sustainability 16, no. 15: 6491. https://doi.org/10.3390/su16156491

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