Major notations.

To mitigate empty seat loss caused by random passenger no-show behavior, this study extends seat allocation to joint optimization of overbooking and seat allocation for high-speed railways (HSR). Assuming that stochastic passenger demand follows a specific distribution and considering various constraints, including train capacity, demand, and denied boarding rate constraints, a nonlinear stochastic programming model for joint optimization of overbooking and seat allocation for HSR is constructed with the aim of maximizing railway expected revenue. To solve this optimization model, a multi-level optimization algorithm is designed. Based on the sampling averaging approximation method, demand scenarios and passenger no-show scenarios are generated and the optimization problem is decomposed, including the joint optimization of overbooking and seat allocation under a single demand scenario, and the ticket adjustment under other demand scenarios. For the former, it is further divided into two sub-problems according to the stochastic nature of passenger no-show behavior, which is optimized iteratively. Finally, the effectiveness of the proposed model and algorithm is evaluated through numerical studies. The results demonstrate that the proposed joint optimization method effectively addresses the randomness of passenger demand and no-show behavior, thereby improving HSR expected revenue and making up for the empty seat loss resulting from passenger no-show behavior.为缓解旅客随机未登车（no-show）行为引发的空座损失，本研究将座位分配问题拓展至高速铁路（High-Speed Railways, HSR）超售与座位分配的联合优化范畴。假设随机旅客需求服从特定分布，同时综合考量列车运力、需求规模及拒载率约束等各类限制条件，本研究以最大化铁路运营预期收益为目标，构建了高速铁路超售与座位分配联合优化的非线性随机规划模型。为求解该优化模型，本研究设计了多级优化算法。基于采样平均近似（Sampling Averaging Approximation）方法生成需求场景与旅客未登车场景，并对优化问题进行分解：涵盖单需求场景下的超售与座位分配联合优化，以及其余需求场景下的票务调整。针对前者，结合旅客未登车行为的随机性特征，将其进一步拆分为两个子问题并开展迭代优化。最后，通过数值实验对所提模型与算法的有效性进行验证。结果表明，所提出的联合优化方法可有效应对旅客需求与未登车行为的随机性，进而提升高速铁路预期收益，弥补旅客未登车行为造成的空座损失。