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Optimizing service schedules is pivotal to the reliable, efficient, andinclusive on-demand mobility. This pressing challenge is further exacerbated bythe increasing needs of an aging population, the over-subscription of existingservices, and the lack of effective solution methods. This study addresses theintricacies of service scheduling, by jointly optimizing rider trip planningand crew scheduling for a complex dynamic mobility service. The resultingoptimization problems are extremely challenging computationally forstate-of-the-art methods. To address this fundamental gap, this paperintroduces the Joint Rider Trip Planning and Crew Shift Scheduling Problem(JRTPCSSP) and a novel solution method, called AGGNNI-CG (Attention and GatedGNN- Informed Column Generation), that hybridizes column generation and machinelearning to obtain near-optimal solutions to the JRTPCSSP with the real-timeconstraints of the application. The key idea of the machine-learning componentis to dramatically reduce the number of paths to explore in the pricingcomponent, accelerating the most time-consuming component of the columngeneration. The machine learning component is a graph neural network with anattention mechanism and a gated architecture, that is particularly suited tocater for the different input sizes coming from daily operations. AGGNNI-CG hasbeen applied to a challenging, real-world dataset from the Paratransit systemof Chatham County in Georgia. It produces dramatic improvements compared to thebaseline column generation approach, which typically cannot produce feasiblesolutions in reasonable time on both medium-sized and large-scale complexinstances. AGGNNI-CG also produces significant improvements in service comparedto the existing system.

Boosting Column Generation with Graph Neural Networks for Joint Rider Trip Planning and Crew Shift Scheduling