运输基础设施在不确定性条件下的基于风险和弹性的恢复优化
Juanjuan Lin1,2, Qizhou Hu1, Wangbing Lin2
1School of Automation, Nanjing University of Science and Technology, Nanjing, China.
PloS one
|August 1, 2024
概括
这项研究优化了运输基础设施恢复计划,以增强弹性和减少破坏性事件带来的风险. 它引入了一种使用有条件的风险值与遗憾 (CVaR-R) 的随机双级模型来管理恢复中的不确定性.
科学领域:
- 运输工程 运输工程
- 运营研究 运营研究
- 风险管理 风险管理
背景情况:
- 破坏性事件严重影响运输基础设施的功能,并造成重大财务损失.
- 优化恢复规划对于提高运输系统的弹性和降低风险至关重要.
- 修复活动固有的不确定性需要先进的风险评估方法.
研究的目的:
- 开发一个随机双级优化模型,用于不确定性下的运输基础设施恢复规划.
- 为了进行可靠的风险评估,将有条件的风险值与遗憾 (CVaR-R) 措施纳入.
- 提出一个集成的算法框架来解决复杂的随机优化问题.
主要方法:
- 使用总旅行时间作为弹性度量的双级优化模型的制定.
- 通过使用拉丁式超立方体技术生成场景来表示不确定性.
- 开发一个综合框架,将遗传算法和弗兰克-沃尔夫算法结合起来,用于随机模型解决方案.
主要成果:
- 拟议的随机双级编程模型有效地解决了运输恢复中的不确定性.
- 综合基因-弗兰克-沃尔夫算法在解决随机模型方面表现出高效的性能.
- 数字实验验证模型的属性和算法的有效性.
结论:
- 开发的方法提供了一个强大的方法,在不确定性下优化运输恢复.
- 该研究为运输基础设施管理中的紧急决策提供了宝贵的见解.
- 该CVaR-R措施通过在修复规划中考虑最坏情景来提高风险评估.
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