mlsanityData to 7 Oct 2026

Learning-based Online Optimization for Autonomous Mobility-on-Demand Fleet Control

arXiv 2302.03963 ↗First posted 8 Feb 2023math.OC cs.LGcited by 14

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Abstract (arXiv)

Autonomous mobility-on-demand systems are a viable alternative to mitigate many transportation-related externalities in cities, such as rising vehicle volumes in urban areas and transportation-related pollution. However, the success of these systems heavily depends on efficient and effective fleet control strategies. In this context, we study online control algorithms for autonomous mobility-on-demand systems and develop a novel hybrid combinatorial optimization enriched machine learning pipeline which learns online dispatching and rebalancing policies from optimal full-information solutions. We test our hybrid pipeline on large-scale real-world scenarios with different vehicle fleet sizes and various request densities. We show that our approach outperforms state-of-the-art greedy, and model-predictive control approaches with respect to various KPIs, e.g., by up to 17.1% and on average by 6.3% in terms of realized profit.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 18 of its 41 references that are in the map. First posted on arXiv 8 Feb 2023.

Builds on

18 of 41 references.

References · 18

  1. 2018Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizoncited by 612
  2. 2017Smart "Predict, then Optimize"cited by 332
  3. 2021End-to-End Constrained Optimization Learning: A Surveycited by 154
  4. 2019Efficient Ridesharing Order Dispatching with Mean Field Multi-Agent Reinforcement Learningcited by 57
  5. 2021A Deep Value-network Based Approach for Multi-Driver Order Dispatchingcited by 45
  6. 2022Learning with Combinatorial Optimization Layers: a Probabilistic Approachcited by 28
  7. 2021Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand Systemscited by 27
  8. 2019Multi-Agent Reinforcement Learning for Order-dispatching via Order-Vehicle Distribution Matchingcited by 26
  9. 2014Control of Robotic Mobility-On-Demand Systems: a Queueing-Theoretical Perspectivecited by 24
  10. 2017Data-Driven Model Predictive Control of Autonomous Mobility-on-Demand Systemscited by 23
  11. 2021Real-world Ride-hailing Vehicle Repositioning using Deep Reinforcement Learningcited by 22
  12. 2019A concise guide to existing and emerging vehicle routing problem variantscited by 17
6 more
  1. 2022Hybrid Multi-agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systemscited by 17
  2. 2022Reinforcement Learning in the Wild: Scalable RL Dispatching Algorithm Deployed in Ridehailing Marketplacecited by 11
  3. 2021A Modular and Transferable Reinforcement Learning Framework for the Fleet Rebalancing Problemcited by 6
  4. 2021Analysis and Control of Autonomous Mobility-on-Demand Systemscited by 6
  5. 2018Stochastic Model Predictive Control for Autonomous Mobility on Demandcited by 2
  6. 2019Proactive rebalancing and speed-up techniques for on-demand high capacity ridesourcing servicescited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 5; 2025: 3; 2026: 4.

14 papers.

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  1. 2023Combinatorial Optimization enriched Machine Learning to solve the Dynamic Vehicle Routing Problem with Time Windowscited by 10
  2. 2023Global Rewards in Multi-Agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systemscited by 8
  3. 2025Structured Reinforcement Learning for Combinatorial Decision-Makingcited by 7
  4. 2024Multi-Agent Soft Actor-Critic with Coordinated Loss for Autonomous Mobility-on-Demand Fleet Controlcited by 3
  5. 2024Generalization Bounds of Surrogate Policies for Combinatorial Optimization Problemscited by 3
  6. 2024WardropNet: Traffic Flow Predictions via Equilibrium-Augmented Learningcited by 3
  7. 2026Combinatorial Optimization Augmented Machine Learningcited by 3
  8. 2024Single- vs. Dual-Policy Reinforcement Learning for Dynamic Bike Rebalancingcited by 2
  9. 2024Dual Policy Reinforcement Learning for Real-time Rebalancing in Bike-sharing Systemscited by 2
  10. 2025Sobolev Training of End-to-End Optimization Proxiescited by 2
  11. 2025Optimization-Augmented Machine Learning for Vehicle Operations in Emergency Medical Servicescited by 1
  12. 2026Imitation Learning for Combinatorial Optimisation under Uncertaintycited by 0
2 more
  1. 2026Dynamic Vehicle Routing Problem with Prompt Confirmation of Advance Requestscited by 0
  2. 2026Learning Optimization Proxies for Sequential Contextual Stochastic Programs: An Order Fulfillment Applicationcited by 0