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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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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Optimizing a Closed-Loop Supply Chain for Electronic Waste Management2024 · 1 citations
  2. 2Optimization of E-Waste Sorting Process Using Deep Learning2025
  3. 3Optimization of Selective Disassembly Sequence Planning for Waste Electrical and Electronic Equipment Using a Hybrid Dual‐Advantage Reinforcement Learning Approach2025
  4. 4Intelligent Waste Management Systems: A Review of IoT, Deep Learning, and Optimization Techniques for Sustainable E-Waste and Solid Waste Handling2025
  5. 5A Comprehensive Study of Machine Learning for Waste-to-Energy Process Modeling and Optimization2025