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

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.