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October 3, 2025SustainabilityOpen Access

Machine Learning-Aided Supply Chain Analysis of Waste Management Systems: System Optimization for Sustainable Production

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Authors

ZNZhe Wee NgBDBiswajit DebnathACAmit K. Chattopadhyay

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Overview

Analysis of machine learning techniques in e-waste supply chains shows resilience against market changes, indicating pathways for sustainability.

Key Points

  • The Feedforward Neural Network model outperforms the Random Forest model in predicting economic arbitrage for e-waste management.
  • Monte Carlo Simulation effectively addresses data scarcity in supply chain modeling, revealing nonlinear variable relationships.
  • This analysis highlights the three pillars of sustainability—environmental, economic, and social—within e-waste management systems.
  • A comprehensive data-driven toolkit is proposed for enhancing smart urban engineering solutions in managing electronic waste.

Cite This Study

Ng et al. (2025) studied this question.

synapsesocial.com/papers/68e034fdf0e39f13e7fa359chttps://doi.org/10.3390/su17198848
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