Optimizing Disruption Tolerance for Rail Transit Networks Under Uncertainty

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.

Automated summary

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.