Optimizing service schedules is pivotal to the reliable, efficient, and inclusive on-demand mobility. This pressing challenge is further exacerbated by the increasing needs of an aging population, the over-subscription of existing services, and the lack of effective solution methods. This study addresses the intricacies of service scheduling, by jointly optimizing rider trip planning and crew scheduling for a complex dynamic mobility service. The resulting optimization problems are extremely challenging computationally for state-of-the-art methods. To address this fundamental gap, this paper introduces the Joint Rider Trip Planning and Crew Shift Scheduling Problem (JRTPCSSP) and a novel solution method, called AGGNNI-CG (Attention and Gated GNN- Informed Column Generation), that hybridizes column generation and machine learning to obtain near-optimal solutions to the JRTPCSSP with the real-time constraints of the application. The key idea of the machine-learning component is to dramatically reduce the number of paths to explore in the pricing component, accelerating the most time-consuming component of the column generation. The machine learning component is a graph neural network with an attention mechanism and a gated architecture, that is particularly suited to cater for the different input sizes coming from daily operations. AGGNNI-CG has been applied to a challenging, real-world dataset from the Paratransit system of Chatham County in Georgia. It produces dramatic improvements compared to the baseline column generation approach, which typically cannot produce feasible solutions in reasonable time on both medium-sized and large-scale complex instances. AGGNNI-CG also produces significant improvements in service compared to the existing system.

翻译：优化服务排程对于实现可靠、高效且包容的按需出行至关重要。这一紧迫挑战因人口老龄化带来的需求增长、现有服务资源超负荷以及缺乏有效解决方案而进一步加剧。本研究通过联合优化复杂动态出行服务中的乘客行程规划与司乘排班，深入探讨服务排程的复杂性。由此产生的优化问题对现有最先进方法而言在计算上极具挑战性。为弥补这一根本性不足，本文提出了联合乘客行程规划与司乘班次安排问题（JRTPCSSP），并创新性地设计了一种名为AGGNNI-CG（注意力与门控图神经网络增强的列生成）的求解方法，该方法将列生成与机器学习相结合，在满足应用实时性约束的前提下获取JRTPCSSP的近最优解。机器学习组件的核心思路是大幅减少定价子问题中需探索的路径数量，从而加速列生成中最耗时的环节。该机器学习组件采用具备注意力机制和门控架构的图神经网络，特别适用于处理日常运营中不同规模的输入。AGGNNI-CG已在佐治亚州查塔姆县辅助公交系统的真实复杂数据集上完成应用验证。与基准列生成方法相比，该方法实现了显著性能提升——基准方法在中等规模和大规模复杂实例上通常无法在合理时间内生成可行解。相较于现有系统，AGGNNI-CG在服务质量方面也带来了重大改善。