通过因果AI框架构建全球货运转发中的弹性
Lu Wang1, Yunfeng Wang2, Na Li3
1School of Civil Aviation Transportation, Shanghai Civil Aviation College.
Journal of visualized experiments : JoVE
|December 8, 2025
概括
本研究介绍了一个因果人工智能 (AI) 框架,使用贝叶斯网络 (BNs) 进行货运代理系统风险管理. 人工智能模型将市场供需确定为核心风险驱动因素,从而实现积极的缓解策略.
科学领域:
- 人工智能的人工智能
- 运营研究 运营研究
- 供应链管理 供应链管理
背景情况:
- 全球货运运输业面临着复杂的,级联的风险.
- 传统的风险管理方法往往是不足的.
- 需要先进的分析工具来应对物流中的系统性风险.
研究的目的:
- 为系统风险管理提出因果AI框架的协议.
- 为货运代理商开发一个可解释的AI工具.
- 实现从反应性降低风险转变为主动性降低风险.
主要方法:
- 构建一个层次化的贝叶斯网络 (BN) 作为因果知识图.
- 综合领域专业知识和行业报告,用于BN参数化.
- 使用Noisy-MAX模型进行不确定性管理和预测模拟.
主要成果:
- 市场供需被确定为系统性风险的中心节点.
- 对于成本 (商业因素),时间 (物流中断) 和可靠性 (网络安全威胁) 存在明显的风险指纹.
- 敏感性分析确定了高杆性干预点.
结论:
- 因果AI框架提供了一个可解释的"如果"引擎.
- 允许精确引导的风险减轻在货运转运.
- 通过人工智能驱动的洞察力推进系统风险管理.
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