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An Intelligent Multi-Task Supply Chain Model Based on Bio-Inspired Networks.

Mehdi Khaleghi1, Sobhan Sheykhivand2, Nastaran Khaleghi3

  • 1Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran 15847-43311, Iran.

Biomimetics (Basel, Switzerland)
|February 26, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel bio-inspired deep graph network for intelligent supply chains, enhancing sustainability and risk management. The Chebyshev ensemble graph network (Ch-EGN) achieves 98.95% accuracy in delivery predictions.

Keywords:
DataCoSupplyGraphbio-inspired neural networksensemble deep learningintelligent supply chainsupply chain managementsustainability

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Area of Science:

  • Artificial Intelligence
  • Supply Chain Management
  • Computational Neuroscience

Background:

  • Deep neural networks are inspired by biological systems, with convolutional neural networks (CNNs) mimicking visual cortex processing and graph neural networks (GNNs) simulating neuronal communication.
  • Intelligent supply chains require agile, resilient, and sustainable systems, with network sustainability being crucial for overall performance.

Purpose of the Study:

  • To propose a novel bio-inspired deep ensemble network, the Chebyshev ensemble graph network (Ch-EGN), for creating an intelligent supply chain model.
  • To enhance supply chain sustainability, improve risk administration, identify hidden risks, and increase transparency.
  • To evaluate the Ch-EGN's functionality on real-world supply chain datasets.

Main Methods:

  • Developed a hybrid learning approach using a novel deep ensemble network, the bio-inspired Chebyshev ensemble graph network (Ch-EGN).
  • Leveraged principles from both CNNs and GNNs, inspired by biological neural processing.
  • Assessed the network's performance on the SupplyGraph and DataCo databases for various supply chain tasks.

Main Results:

  • Achieved an average accuracy of 98.95% for automatic delivery status prediction.
  • Demonstrated significant improvements in risk administration, supply chain sustainability, and transparency.
  • Validated the Ch-EGN's efficiency in multi-class categorization scenarios for intelligent supply chains.

Conclusions:

  • The proposed bio-inspired Chebyshev ensemble graph network (Ch-EGN) is an effective hybrid learning model for intelligent supply chains.
  • The Ch-EGN significantly enhances supply chain sustainability and risk management capabilities.
  • The approach offers a promising direction for developing more agile, resilient, and transparent supply chain systems.