The efficiency of shunting operations in flat yards is crucial to the performance of railway networks, especially in single-wagon systems. Inefficiencies in these operations can lead to significant delays and increased operational costs. This impacts the overall reliability of rail systems. Despite the critical nature of this challenge, existing methods often rely on manual interventions.
We propose a novel algorithm, HEROS, tailored for optimizing shunting operations in flat yards. HEROS integrates elements of reinforcement learning and evolutionary algorithms, offering a sophisticated combination of AI concepts. Our algorithm is highly configurable, allowing it to be adapted to the specific needs of different flat rail yards.
Results show that HEROS consistently improves the efficiency of shunting operations, with the objective value converging and the standard deviation decreasing as the time budget increases. This indicates increased robustness and reliability over time. The algorithm offers substantial benefits in terms of cost savings, reliability, and scalability, making it a valuable tool for rail operators facing operational complexity.