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A 3D Monte Carlo tree search method for railway alignment optimization
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AI
DOI：10.1016/j.asoc.2023.111158.png)
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Abstract
En 中文
Railway alignment design is an important process, which fundamentally affects the construction, operation and maintenance of a railway. However, optimizing an alignment is challenging due to, e.g., the usually huge search space, infinite number of possible alternatives and numerous constraints. To address this problem, we propose a three-dimensional Monte Carlo Tree Search (3D-MCTS) method for alignment optimization. Specifically, a timevarying selection approach is first designed for efficiently exploring the search space. Then, the feasible search space is dynamically delineated with a customized tree expansion operator to accelerate the search process. In addition, a simulation strategy with global reward estimation is proposed to balance global exploration and local exploitation during optimization, which contributes to enhancing the quality of the optimized alignment. Finally, the proposed 3D-MCTS is applied to a complex real-world railway case. It shows that the 3D-MCTS can find better solutions compared to the best alignments that are manually designed by experienced engineers or produced by a previous distance transform algorithm. Two sensitivity analyses also reveal the 3D-MCTS's performance and robustness with respect to cost optimization and search efficiency.
Keywords:
Railway design
Alignment optimization
Monte Carlo tree search
Time-varying selection approach
Global reward estimations
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