Integrated Reinforcement Learning and Optimization for Railway Timetable Rescheduling
Hengkai Zhang (Student TU Delft)
Xiaoyu Liu (TU Delft - Team Bart De Schutter)
Dingshan Sun (TU Delft - Traffic Systems Engineering)
Azita Dabiri (TU Delft - Team Azita Dabiri)
B. Schutter (TU Delft - Delft Center for Systems and Control)
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Abstract
The railway timetable rescheduling problem is regarded as an efficient way to handle disturbances. Typically, it is tackled using a mixed integer linear programming (MILP) formulation. In this paper, an algorithm that combines both reinforcement learning and optimization is proposed to solve the railway timetable rescheduling problem. Specifically, a value-based reinforcement learning algorithm is implemented to determine the independent integer variables of the MILP problem. Then, the values of all the integer variables can be derived from these independent integer variables. With the solution for the integer variables, the MILP problem can be transformed into a linear programming problem, which can be solved efficiently. The simulation results show that the proposed method can reduce passenger delays compared with the baseline, while also reducing the solution time.
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