4.5 Article

Optimizing Disruption Tolerance for Rail Transit Networks Under Uncertainty

期刊

TRANSPORTATION SCIENCE
卷 55, 期 5, 页码 1206-1225

出版社

INFORMS
DOI: 10.1287/trsc.2021.1040

关键词

rail transit networks; resilience; disruption tolerance; planning strategies; distributionally robust optimization

资金

  1. National Research Foundation, Prime Minister's Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme

向作者/读者索取更多资源

This paper develops a distributionally robust optimization model for rail transit tactical planning strategies and disruption tolerance enhancement, proposing a novel performance function to evaluate rail transit disruption tolerance under downtime uncertainty. The model is applied to optimize platform downtime protection and bus-bridging services design within budget constraints, with identified optimality conditions leading to a computationally tractable linear mixed-integer reformulation for efficient solutions. Insights are showcased based on the Singapore Mass Rapid Transit Network.
In this paper, we develop a distributionally robust optimization model for the design of rail transit tactical planning strategies and disruption tolerance enhancement under downtime uncertainty. First, a novel performance function evaluating the rail transit disruption tolerance is proposed. Specifically, the performance function maximizes the worst-case expected downtime that can be tolerated by rail transit networks over a family of probability distributions of random disruption events given a threshold commuter outflow. This tolerance function is then applied to an optimization problem for the planning design of platform downtime protection and bus-bridging services given budget constraints. In particular, our implementation of platform downtime protection strategy relaxes standard assumptions of robust protection made in network fortification and interdiction literature. The resulting optimization problem can be regarded as a special variation of a two-stage distributionally robust optimization model. In order to achieve computational tractability, optimality conditions of the model are identified. This allows us to obtain a linear mixed-integer reformulation that can be solved efficiently by solvers like CPLEX. Finally, we show some insightful results based on the core part of Singapore Mass Rapid Transit Network.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.5
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据