An Integrated Framework Integrating Monte Carlo Tree Search and Supervised Learning for Train Timetabling Problem

Understand

The single-track railway train timetabling problem (TTP) is an important and complex problem.

• This article proposes an integrated Monte Carlo Tree Search (MCTS) computing framework that combines heuristic methods, unsupervised learning methods, and supervised learning methods for solving TTP in discrete action spaces.
• This article first describes the mathematical model and simulation system dynamics of TTP, analyzes the characteristics of the solution from the perspective of MCTS, and proposes some heuristic methods to improve MCTS.
• This article considers these methods as planners in the proposed framework.