Boosting column generation with graph neural networks for joint rider trip planning and crew shift scheduling

Jiawei Lu, Tinghan Ye, Wenbo Chen and Pascal Van Hentenryck

Transportation Research Part E: Logistics and Transportation Review, 2025, vol. 202, issue C

Abstract: 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 oversubscription 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.

Keywords: Service scheduling; Paratransit; Column generation; Graph neural network (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.tre.2025.104281

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