需求响应公交作为共享公共交通新模式的典型代表，正面临着高效处理出行需求与实时规划车辆路径的挑战，而传统的需求响应公交动态调度方法侧重于需求已知后对车辆路径的动态调整，往往难以全面适应出行需求的变化。因此，该研究通过引入模型预测控制（MPC）方法，构建了基于MPC多周期滚动优化框架的需求响应公交动态调度模型。该模型利用未来阶段的先验客流信息，为当前阶段的调度决策提供优化条件，并及时根据系统最新披露的信息重新规划，以应对需求的不确定性和动态变化。求解方法上，研究结合自适应大邻域搜索（ALNS）策略，设计了MPC-ALNS算法，通过两阶段启发式方法对车辆调度序列进行迭代优化。数值实验结果显示：在无预测偏差的理想场景下，相较于传统动态调度方法，该方法能够使系统总成本显著降低14.54%；即便在预测偏差为30%的悲观场景下，仍然能够实现5.27%的成本优化，并且各项乘客服务指标均表现出了更优异的性能，验证了其在不同随机环境下的普适性。同时，实验进一步验证了该方法在应对不同订单和车辆规模时的稳定优化性能，并对拒单成本进行了敏感性分析，提出了适用于不同运营场景的最优拒单成本设置思路。

As a typical representative of the new mode of shared public transport, demand responsive transit (DRT) systems are facing the challenge of efficiently processing travel demand and real-time planning of vehicle routes. Traditional dynamic scheduling methods for DRT primarily focus on adjusting vehicle routes after demand has been realized, which often limits their ability to effectively respond to dynamic fluctuations in travel demand. Therefore, this study introduced a Model Predictive Control (MPC) approach and develops a dynamic scheduling model for DRT based on a multi-period rolling optimization framework. The model used potential future stage passenger flow information to optimize current stage scheduling decisions and timely re-planning according to the latest disclosed information to cope with the uncertainty and dynamic changes of demand. In terms of solution methods, this study integrated the adaptive large neighborhood search (ALNS) strategy to design the MPC-ALNS algorithm. It iteratively optimized the vehicle scheduling sequence through a two-phase heuristic approach. Numerical experimental results demonstrate that in ideal scenarios without prediction deviation, compared to traditional dynamic scheduling methods, the proposed method significantly reduces the total cost of the system by 14.54%. Even in a pessimistic scenario with a 30% prediction deviation, it still achieves a cost optimization of 5.27%, and various passenger service indicators show superior performance, indicating strong universal applicability in different stochastic environments. At the same time, the experiment further verified the stable optimization performance of the method in dealing with different orders and vehicle scales, and analyzed the sensitivity of the rejection cost and proposed the setting idea of the optimal rejection cost suitable for different operating scenarios.