XH
Xiao Huang
7 records found
1
Vehicle proactive guidance strategies are used by ride-hailing platforms to mitigate supply–demand imbalance across regions by directing idle vehicles to high-demand regions before the demands are realized. This article presents a data-driven stochastic optimization framework for
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This paper proposes an integrated dispatching framework for matching drivers with riders in ride-hailing systems. The goal is to compute matching solutions that maximize social welfare and benefit both sides of the market, such that the sustainable growth of the ride-hailing syst
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Freelance drivers in ride-hailing systems may strategically accept or reject ride requests based on their projection of the profitability of the assigned rides. This driver acceptance uncertainty is mainly caused by the flat rate payment and the blind ride acceptance rule adopted
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In ride-sharing services, travel time uncertainty significantly impacts the quality of matching solutions for both the drivers and the riders. This paper studies a one-to-many ride-sharing matching problem where travel time between locations is uncertain. The goal is to generate
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To reduce the vehicle relocation rate considering relieving disequilibrium of the supply-demand ratios across regions for car-sharing systems, in this paper, we propose a data-driven optimization framework by integrating the non-parametric learning algorithm and two-stage stochas
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In this paper, we propose a data-driven robust optimization model to reduce total travel cost in ride-sharing systems under travel time uncertainty. Instead of using a pre-defined uncertainty set, we study a data-driven robust optimization approach that integrates gated recurrent
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We propose a learning-based approach for open driver guidance and rebalancing in ride-hailing platforms. The objective is to further enhance the wait time reduction benefit of batched matching by incorporating learning-based open driver guidance and rebalancing. By leveraging the
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