Train Scheduling in High-Speed Railway Freight Transportation Using a Hyper-Heuristic Algorithm

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The rapidly growing demand for high-quality logistics services, coupled with the expansion of high-speed rail networks, has presented both challenges and opportunities in the realm of high-speed rail freight transportation. Because of the intricacies of transportation logistics and the inherent complexities within passenger-train operations, addressing market demands proves to be challenging. This study focuses on optimizing passenger–freight collaboration within train timetables. Express cargo is typically transported either by incorporating it into planned passenger-train schedules or by introducing additional freight trains, both of which can disrupt the original passenger transport arrangements. To mitigate such disruptions, a buffer time is allocated to compensate for any disturbances, thereby transforming the delivery timeframe into a streamlined car-flow transit deadline. To address these challenges, we developed a passenger–freight collaboration optimization model for train timetables. This model was then solved using a bacterial foraging-based hyper-heuristic algorithm known for its global optimization capabilities and distribution-centric approach. In a comparison with existing studies and algorithms in transportation modeling, and considering parameters such as average fitness and running time, the proposed model and algorithm demonstrated superior efficiency.