A Robust Mixed-Integer Linear Programming Model for Mitigating Rail Transit Disruptions Under Uncertainty

Abstract

In this paper, we propose a two-stage robust optimization framework to evaluate widely controllable commuter flow strategies that can help mitigate uncertain disruption impacts on rail transit commuter flows. In the operation-as-usual stage, preemptive control strategies including timetable adjustment, commuter path choice diversion, and commuter trip starting time diversion are planned within budget and other requirement constraints. In the disruption stage, optimal contingency routing is derived to minimize disruption impacts. To this end, a linear programming model that simulates the commuter movement logic under train service and platform disruption events is developed, which is then integrated in a mixed-integer max-min optimization model to evaluate the worst-case impacts on commuter flows arising from a set of uncertain disruption scenarios. We show that the two-stage robust optimization framework can be solved by a cutting plane algorithm efficiently using high performance and parallel computing platforms. Finally, we demonstrate the application of the model in mitigating disruption impacts through computational studies based on an actual rail transit network.