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

arXiv: https://arxiv.org/abs/2311.00971 · HTML: https://mlsanity.com/paper/2311.00971/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 55 references that are in the map.
- First posted on arXiv 2 Nov 2023.

## Builds on (most cited first, 3 of 3)

- [Monte Carlo Tree Search: A Review of Recent Modifications and Applications](https://mlsanity.com/paper/2103.04931/) (2021)
- [DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning](https://mlsanity.com/paper/2106.06135/) (2021)
- [Combining Monte-Carlo and Hyper-heuristic methods for the Multi-mode Resource-constrained Multi-project Scheduling Problem](https://mlsanity.com/paper/1511.04387/) (2015)

## Built on by (most cited first, 5 of 5)

- [Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Search](https://mlsanity.com/paper/2412.18319/) (2024)
- [Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B](https://mlsanity.com/paper/2406.07394/) (2024)
- [DeepSearch: Overcome the Bottleneck of Reinforcement Learning with Verifiable Rewards via Monte Carlo Tree Search](https://mlsanity.com/paper/2509.25454/) (2025)
- [MC-NEST: Enhancing Mathematical Reasoning in Large Language Models leveraging a Monte Carlo Self-Refine Tree](https://mlsanity.com/paper/2411.15645/) (2024)
- [MCTSr-Zero: Self-Reflective Psychological Counseling Dialogues Generation via Principles and Adaptive Exploration](https://mlsanity.com/paper/2505.23229/) (2025)

## Abstract (arXiv)

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. Secondly, this article utilizes deep convolutional neural networks to approximate the value of nodes and further applies them to the MCTS search process, referred to as learners. The experiment shows that the proposed heuristic MCTS method is beneficial for solving TTP; The algorithm framework that integrates planners and learners can improve the data efficiency of solving TTP; The proposed method provides a new paradigm for solving TTP.