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Nov 1, 2023 · This study proposes a Markov decision process to jointly optimize the charging and order-dispatching schemes for an e-hailing EV fleet.
Dec 11, 2023 · This paper introduces the ReinforceRouting model, a novel approach to optimizing evacuation routes using reinforcement learning (RL).
Dec 11, 2023 · This paper introduces the ReinforceRouting model, a novel approach to optimizing evacuation routes using reinforcement learning (RL). The model incorporates a ...
Missing: Optimising | Show results with:Optimising
Jan 27, 2024 · We introduce a stochastic share-a-ride problem in which a fleet of electric vehicles (EV) in a ride-hailing system are dynamically dispatched to serve ...
Missing: Optimising | Show results with:Optimising
Oct 24, 2023 · To better match drivers to riders in our ridesharing application, we revised Lyft's core matching algorithm. We use a novel online reinforcement learning ...
Missing: Optimising Enhanced
Sep 11, 2023 · As shown in Figure 1, this framework consists of two main components: the charging scheduling management model and the order dispatching model. The charging ...
May 15, 2024 · This paper proposes the formulation of a novel deep reinforcement learning framework to solve a dynamic and uncertain vehicle routing problem (DU-VRP)
Missing: Optimising Enhanced
Jun 10, 2024 · The results indicate that the multi-agent dispatch algorithm can closely approximate the optimal dispatch policy with significantly less computational expenses ...
Missing: Optimising Enhanced
Jul 20, 2024 · Machine learning (ML)-based methods have been applied to a variety of combinatorial optimization problems, specifically VRPs.
A deep reinforcement learning with dynamic spatio-temporal graph ...
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Jul 10, 2024 · This research proposes a novel deep reinforcement learning model from the dynamic spatial and temporal graph perspective (DRLDSTG) to solve urban logistics ...
Missing: Optimising | Show results with:Optimising
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