**Reference:**

H. Zhang,
X. Liu,
D. Sun,
A. Dabiri, and
B. De Schutter,
"Integrated reinforcement learning and optimization for railway
timetable rescheduling," *Proceedings of the 17th IFAC Symposium on
Control in Transportation Systems (CTS 2024)*, Ayia Napa, Cyprus,
pp. 310-315, July 2024.

**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.

**Bibtex entry**:

author={H. Zhang and X. Liu and D. Sun and A. Dabiri and B. {D}e Schutter},

title={Integrated reinforcement learning and optimization for railway timetable rescheduling},

booktitle={Proceedings of the 17th IFAC Symposium on Control in Transportation Systems (CTS 2024)},

address={Ayia Napa, Cyprus},

pages={310--315},

month=jul,

year={2024},

doi={10.1016/j.ifacol.2024.07.358}

}

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