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- W3202305212 abstract "In this paper, we envision an emerging Mixed Operation Zone (MOZ) where both autonomous vehicles (AVs) and human-driven vehicles (HVs) are present for on-demand ride services. This paper aims to size and operate a fleet of AVs and HVs in the presence of MOZs and to investigate the impact of MOZs on on-demand ride services. Considering the demand uncertainty, we propose a demand-oriented robust minimum fleet problem (RMFP) and employ a two-stage robust optimization (RO) to model the decision-making. Fluctuant demand is bounded by distribution-free uncertainty sets. For the convenience of solving RO models, we reformulate the second-stage recourse problem with an equivalent mathematical programming formulation. A tailored column-and-constraint generation algorithm is developed to solve the RMFP exactly. The algorithm is proved to converge in a finite number of iterations. Extensive experiments are conducted on the instances based on a real-world on-demand ride service in Chengdu. The developed algorithm performs better than the state-of-the-art Benders decomposition approach. Numerical results imply huge potential benefits from MOZs on improving service performance for ride service platforms." @default.
- W3202305212 created "2021-10-11" @default.
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- W3202305212 date "2021-11-01" @default.
- W3202305212 modified "2023-10-04" @default.
- W3202305212 title "Robust minimum fleet problem for autonomous and human-driven vehicles in on-demand ride services considering mixed operation zones" @default.
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- W3202305212 doi "https://doi.org/10.1016/j.trc.2021.103390" @default.
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