基于生物灵感网络的智能多任务供应链模型
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
概括
本研究介绍了一种新的生物灵感深度图形网络,用于智能供应链,增强可持续性和风险管理. 切比舍夫集体图形网络 (Ch-EGN) 在交付预测中达到98.95%的准确性.
科学领域:
- 人工智能的人工智能
- 供应链管理 供应链管理
- 计算神经科学是一种神经科学.
背景情况:
- 深度神经网络受到生物系统的启发,卷积神经网络 (CNN) 模仿视觉皮层处理和图形神经网络 (GNN) 模拟神经通信.
- 智能供应链需要敏捷,弹性和可持续的系统,网络的可持续性对整体性能至关重要.
研究的目的:
- 为创建一个智能供应链模型,提出一个新的生物灵感深层集体网络,切比舍夫集体图形网络 (Ch-EGN).
- 提高供应链的可持续性,改善风险管理,识别隐藏风险,提高透明度.
- 在真实世界供应链数据集上评估Ch-EGN的功能.
主要方法:
- 开发了一种混合学习方法,使用一种新的深层集体网络,生物灵感的切比舍夫集体图形网络 (Ch-EGN).
- 从CNN和GNN中利用原则,灵感来自生物神经处理.
- 评估网络在各种供应链任务的 SupplyGraph 和 DataCo 数据库上的性能.
主要成果:
- 实现了98.95%的平均准确度,用于自动交付状态预测.
- 在风险管理,供应链可持续性和透明度方面取得显著改进.
- 在智能供应链的多类分类场景中验证了Ch-EGN的效率.
结论:
- 拟议的生物启发的切比舍夫集体图形网络 (Ch-EGN) 是智能供应链的有效混合学习模型.
- Ch-EGN显著提高了供应链的可持续性和风险管理能力.
- 该方法为开发更灵活,更有弹性和更透明的供应链系统提供了一个有希望的方向.
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