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  1. Home
  3. Smart Logistics Model For Supply Chain Management Via Brain-inspired Geometric Deep Networks.
  1. Home
  3. Smart Logistics Model For Supply Chain Management Via Brain-inspired Geometric Deep Networks.

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Related Experiment Video

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Smart Logistics Model for Supply Chain Management via Brain-Inspired Geometric Deep Networks.

Mehdi Khaleghi1, Farshad Pashootanizadeh2, Nastaran Khaleghi3

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

Biomimetics (Basel, Switzerland)
|June 25, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a novel hybrid deep learning model for smart supply chain logistics. The biomimetic approach enhances prediction accuracy, leading to more agile, sustainable, and resilient supply chains.

Keywords:
brain-inspired networksgeometric deep learninghealthcare supply chainhybrid networksparticle swarm optimizationsmart logisticssupply chain logisticssupply chain management

Related Experiment Videos

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Area of Science:

  • Supply Chain Management
  • Artificial Intelligence
  • Biomimetic Computing

Background:

  • Intelligent logistics models are crucial for agile, sustainable, and resilient supply chains.
  • Brain-inspired deep learning architectures like LSTM, GNN, and CNN offer advanced decision-making capabilities.
  • These models are biomimetically inspired by biological information processing.

Purpose of the Study:

  • To propose a novel hybrid deep learning strategy for smart supply chain logistics management.
  • To leverage biomimetic computational principles for enhanced logistics decision-making.
  • To improve supply chain agility, sustainability, and resilience through intelligent management.

Main Methods:

  • A hybrid deep learning strategy combining LSTM, convolutional layers, and GraphSAGE geometric layers.
  • Utilizing biomimetic particle swarm optimizer and Adam (PSO-Adam) for sequential optimization.
  • Leveraging GraphSAGE for scalable graph learning and enhanced predictive accuracy on unseen data.

Main Results:

  • Achieved high average accuracies (96.6%–100%) across five diverse logistics datasets.
  • Demonstrated effectiveness in multi-category logistics parameter forecasting.
  • Confirmed the model's potential for complex supply chain decision-making.

Conclusions:

  • The proposed hybrid deep learning model offers a cost-efficient solution for intelligent logistics.
  • The model enhances supply chain visibility, customer satisfaction, and industry reputation.
  • Biomimetic geometric networks show significant potential for optimizing complex supply chain operations.