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

Biswajit Debnath, Amit K. Chattopadhyay () and T. Krishna Kumar
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Biswajit Debnath: 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
Amit K. Chattopadhyay: 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
T. Krishna Kumar: Rockville-Analytics, Rockville, MD 20850, USA

Sustainability, 2024, vol. 16, issue 15, 1-25

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 (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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