基于GAN生成中断场景的多式联运货运网络数据驱动压力测试
Data-Driven Stress Testing of Intermodal Freight Networks Using GAN-Generated Disruption Scenarios
- Dept. of Industrial and Systems Eng University of Tennessee（田纳西大学工业与系统工程系）
- Amazon（亚马逊）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
本文提出结合GAN与多式联运优化模型的数据驱动压力测试框架，用于评估复合中断下的网络韧性，发现GAN场景使成本增加超25%，年预期成本达511万美元，并识别关键风险节点以指导韧性投资。
中文摘要 AI 辅助
多式联运货运网络日益面临相关的多模式中断，然而韧性评估往往依赖于历史或非相关的场景，这些场景低估了系统性风险。本文开发了一个数据驱动的压力测试框架，将生成对抗网络（GAN）与多式联运优化模型相结合，以评估在现实复合中断下的性能。案例研究考察了田纳西河谷走廊的天气相关中断。每个GAN生成的场景被用作仿真输入，并求解由此产生的路径规划问题以获得系统成本。汇总结果能够估计预期成本并识别主要风险驱动因素。结果表明，历史中断使总成本增加约3%，而GAN生成的场景使成本增加超过25%，产生511万美元的预期年度成本。风险集中在相关的多节点故障和关键节点（如诺克斯维尔港）。该框架有助于识别脆弱性并优先安排韧性投资。
英文摘要
Intermodal freight networks are increasingly exposed to correlated, multi-mode disruptions, yet resilience assessments often rely on historical or uncorrelated scenarios that understate systemic risk. This paper develops a data-driven stress-testing framework integrating generative adversarial networks (GANs) with an intermodal optimization model to evaluate performance under realistic compound disruptions. The case study examines weather-related disruptions in the Tennessee Valley corridor. Each GAN-generated scenario is used as a simulation input, and the resulting routing problem is solved to obtain system costs. Aggregating outcomes enables estimation of expected costs and identification of major risk drivers. Results show that historical disruptions increase total cost by about 3%, whereas GAN-generated scenarios raise costs by over 25%, producing an expected annual cost of $5.11 million. Risk is concentrated in correlated multi-node failures and critical nodes such as the Port of Knoxville. The framework helps identify vulnerabilities and prioritize resilience investments.
