Stochastic multi-objective optimization for dynamic timetable and track allocation at high-speed railway hubs - Yan, Bing; Huang, Lu; Wan, Chen; Qu, Siyuan; Fan, Xiaodong; Zou, Xiaolei (International Journal of Transportation Science and Technology, 2025)
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.