Stochastic multi-objective optimization for dynamic timetable and track allocation at high-speed railway hubs

Train scheduling and track allocation are crucial for minimizing passenger flow conflicts, ensuring safety, and enhancing travel experience at railway hubs. This study presents a collaborative optimization model for railway hub operations, focusing on improving passenger transfer efficiency and minimizing arrival-departure track (ADT) utilization costs. A multi-objective hybrid model, referred to as COTADT, is developed to address spatiotemporal and stochastic constraints while balancing these two objectives. To solve this model efficiently, a customized augmented epsilon-constraint algorithm (CAEC) is introduced, utilizing augmented constraints and interval partitioning to generate Pareto-optimal solutions. The approach is validated with real-world data from the Hangzhou Hub, yielding significant improvements, including a 23.149% reduction in passenger transfer costs and a 7.101% reduction in ADT utilization costs. Comparative experiments demonstrate that CAEC outperforms heuristic algorithms in both solution quality and computational efficiency. This research provides a robust, scalable framework for enhancing operational performance and passenger experience at railway hubs.