<ul><li>This study addresses the complexities of service scheduling by jointly optimizing rider trip planning and crew scheduling for a dynamic mobility service.</li><li>The paper introduces the Joint Rider Trip Planning and Crew Shift Scheduling Problem (JRTPCSSP) and a solution method called Attention and Gated GNN-Informed Column Generation (AGGNNI-CG).</li><li>AGGNNI-CG hybridizes column generation and machine learning to obtain near-optimal solutions with real-life constraints.</li><li>With its graph neural network and attention mechanism, AGGNNI-CG significantly improves service quality and produces substantial improvements compared to baseline approaches.</li></ul>

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

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