Abstract
Mobility on demand has been gaining more attention from the research community as a way to offer smart and efficient transportation services to people. Despite the advancements in vehicular technologies, vehicle breakdown (VB) remains one of the major contributors to the disruption of fleet operations, which may inflict large recovery costs and damage the service provider?s reputation. However, modeling such dynamic events for dial-a-ride (DAR) systems has not been addressed until now; this is the research gap we attempt to address in this article. The following are the contributions of our work: 1) the formulation of a disruptive DAR problem (DDARP) with VB (DDARP-VB), 2) a GPU-based adaptive large neighborhood search (G-ALNS) algorithm to solve the DDARP-VB, and 3) a fleet size minimization (FSM) strategy that leads to reduced operational costs under disruptions. From our simulations, the proposed G-ALNS algorithm performs up to 52 times faster than its CPU counterpart and produces better solutions when compared to the existing GPU approach for DARPs. Overall, our FSM strategy leads to a 59% reduction in fleet idle time under normal operations and a 15% reduction in operational cost under disruption.
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