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

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Listed:
  • 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
    Department of Chemical Engineering, Jadavpur University, Kolkata 700032, India)

  • 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)

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.

Suggested Citation

  • Biswajit Debnath & Amit K. Chattopadhyay & T. Krishna Kumar, 2024. "An Economic Optimization Model of an E-Waste Supply Chain Network: Machine Learned Kinetic Modelling for Sustainable Production," Sustainability, MDPI, vol. 16(15), pages 1-25, July.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:15:p:6491-:d:1445559
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    References listed on IDEAS

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    3. Biswajit Debnath & Rihab El-Hassani & Amit K Chattopadhyay & T Krishna Kumar & Sadhan K Ghosh & Rahul Baidya, 2022. "Time Evolution of a Supply Chain Network: Kinetic Modeling," Papers 2209.01138, arXiv.org.
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