An online reinforcement learning approach to charging and order-dispatching optimization for an e-hailing electric vehicle fleet
From MaRDI portal
Publication:6168573
DOI10.1016/j.ejor.2023.03.039MaRDI QIDQ6168573
Kaize Yu, Zhibin Chen, Pengyu Yan, Xiuli Chao
Publication date: 11 July 2023
Published in: European Journal of Operational Research (Search for Journal in Brave)
transportationMarkov decision processreinforcement learningelectric vehiclecharging and dispatching decision
Cites Work
- Unnamed Item
- An optimization framework for the development of efficient one-way car-sharing systems
- Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy
- Service network design in freight transportation
- Bootstrap confidence intervals. With comments and a rejoinder by the authors
- Robust sample average approximation
- Optimisation model for multi-item multi-echelon supply chains with nested multi-level products
- Optimal routing for electric vehicle charging systems with stochastic demand: a heavy traffic approximation approach
- Approximate dynamic programming for planning a ride-hailing system using autonomous fleets of electric vehicles
- Recovery management for a dial-a-ride system with real-time disruptions
- On sample size control in sample average approximations for solving smooth stochastic programs
- Column generation based heuristic for tactical planning in multi-period vehicle routing
- The consistent electric-vehicle routing problem with backhauls and charging management
- An Adaptive Dynamic Programming Algorithm for Dynamic Fleet Management, I: Single Period Travel Times
- An Adaptive Dynamic Programming Algorithm for Dynamic Fleet Management, II: Multiperiod Travel Times
- Solving large-scale dynamic vehicle routing problems with stochastic requests
- Recent advances in vehicle routing with stochastic demands: Bayesian learning for correlated demands and elementary branch-price-and-cut