Fangqi Liu earned her Ph.D. in Networked Systems from the University of California, Irvine, in 2023. She holds bachelor’s and master’s degrees from Jilin University, China. Her research focuses on mobile vehicular ad hoc networks, motion planning, scheduling optimization for mobile vehicles, as well as IoT and CPS applications for emergency monitoring.

Fangqi Liu Publications

1. A. Khanna, F. Liu, S. Gupta, S. Pavia, A. Mukhopadhyay, and A. Dubey, PDPTW-DB: MILP-Based Offline Route Planning for PDPTW with Driver Breaks, in Proceedings of the 26th International Conference on Distributed Computing and Networking, New York, NY, USA, 2025, pp. 73–83.
   Bibtex Abstract DOI PDF

   @inproceedings{khanna2025driverbreaks,
     author = {Khanna, Agrima and Liu, Fangqi and Gupta, Samir and Pavia, Sophie and Mukhopadhyay, Ayan and Dubey, Abhishek},
     booktitle = {Proceedings of the 26th International Conference on Distributed Computing and Networking},
     title = {PDPTW-DB: MILP-Based Offline Route Planning for PDPTW with Driver Breaks},
     year = {2025},
     address = {New York, NY, USA},
     pages = {73--83},
     acceptance = {32},
     publisher = {Association for Computing Machinery},
     series = {ICDCN '25},
     category = {other},
     contribution = {lead},
     doi = {10.1145/3700838.3700854},
     isbn = {9798400710629},
     keywords = {Microtransit, Vehicular Networks, Pickup and Delivery Problem with Time Windows, Driver Breaks, MILP, Optimization, Microtransit, Vehicular Networks, Pickup and Delivery Problem with Time Windows, Driver Breaks, MILP, Optimization},
     numpages = {11},
     url = {https://doi.org/10.1145/3700838.3700854}
   }
   

   The Pickup and Delivery Problem with Time Windows (PDPTW) involves optimizing routes for vehicles to meet pickup and delivery requests within specific time constraints, a challenge commonly faced in logistics and transportation. Microtransit, a flexible and demand-responsive service using smaller vehicles within defined zones, can be effectively modeled as a PDPTW. Yet, the need for driver breaks—a key human constraint—is frequently overlooked in PDPTW solutions, despite being necessary for regulatory compliance. This study presents a novel mixed-integer linear programming formulation for the Pickup and Delivery Problem with Time Windows and Driver Breaks (PDPTW-DB). To the best of our knowledge this formulation is the first to consider mandatory periodic driver breaks within optimized Microtransit routes. The proposed model incorporates regulatory compliant break scheduling directly within the vehicle routing optimization framework. By considering driver break requirements as an integral component of the optimization process, rather than as a post-processing step, the model enables the generation of routes that respect hours of service regulations while minimizing operational costs. This integrated approach facilitates the generation of schedules that are operationally efficient and prioritize driver welfare through driver breaks. We work with a public transit agency from the southern USA, and highlight the specific nuances of driver break optimization, and present a Pickup and Delivery Problem with Time Windows formulation for optimizing Microtransit operations and scheduling driver breaks. We validate our approach using real-world data from the transit agency. Our results validate our formulation in producing cost-effective, and regulation-compliant solutions.

2. F. Liu, R. Sen, J. Talusan, A. Pettet, A. Kandel, Y. Suzue, A. Mukhopadhyay, and A. Dubey, Reinforcement Learning-based Approach for Vehicle-to-Building Charging with Heterogeneous Agents and Long Term Rewards, in Proceedings of the 23rd Conference on Autonomous Agents and MultiAgent Systems, AAMAS 2025, Detroit, Michigan, Richland, SC, 2025.
   Bibtex Abstract PDF

   @inproceedings{liu2024reinforcement,
     author = {Liu, Fangqi and Sen, Rishav and Talusan, Jose and Pettet, Ava and Kandel, Aaron and Suzue, Yoshinori and Mukhopadhyay, Ayan and Dubey, Abhishek},
     booktitle = {Proceedings of the 23rd Conference on Autonomous Agents and MultiAgent Systems, {AAMAS} 2025, Detroit, Michigan},
     title = {Reinforcement Learning-based Approach for Vehicle-to-Building Charging with Heterogeneous Agents and Long Term Rewards},
     year = {2025},
     address = {Richland, SC},
     note = {nominated for best paper},
     organization = {International Conference on Autonomous Agents and Multi-Agent Systems},
     publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
     series = {AAMAS '25},
     acceptance = {24.5},
     category = {selective},
     contribution = {lead},
     location = {Detroit, Michigan}
   }
   

   Strategic aggregation of electric vehicle batteries as energy reservoirs can optimize power grid demand, benefiting smart and connected communities, especially large office buildings that offer workplace charging. This involves optimizing charging and discharging to reduce peak energy costs and net peak demand, monitored over extended periods (e.g., a month), which involves making sequential decisions under uncertainty and delayed and sparse rewards, a continuous action space, and the complexity of ensuring generalization across diverse conditions. Existing algorithmic approaches, e.g., heuristic-based strategies, fall short in addressing real-time decision-making under dynamic conditions, and traditional reinforcement learning (RL) models struggle with large stateaction spaces, multi-agent settings, and the need for long-term reward optimization. To address these challenges, we introduce a novel RL framework that combines the Deep Deterministic Policy Gradient approach (DDPG) with action masking and efficient MILP-driven policy guidance. Our approach balances the exploration of continuous action spaces to meet user charging demands. Using real-world data from a major electric vehicle manufacturer, we show that our approach comprehensively outperforms many well-established baselines and several scalable heuristic approaches, achieving significant cost savings while meeting all charging requirements. Our results show that the proposed approach is one of the first scalable and general approaches to solving the V2B energy management challenge.

3. R. Sen, Y. Zhang, F. Liu, J. P. Talusan, A. Pettet, Y. Suzue, A. Mukhopadhyay, and A. Dubey, Online Decision-Making Under Uncertainty for Vehicle-to-Building Systems, in Proceedings of the ACM/IEEE 16th International Conference on Cyber-Physical Systems (ICCPS), New York, NY, USA, 2025.
   Bibtex Abstract PDF

   @inproceedings{sen2025iccps,
     author = {Sen, Rishav and Zhang, Yunuo and Liu, Fangqi and Talusan, Jose Paolo and Pettet, Ava and Suzue, Yoshinori and Mukhopadhyay, Ayan and Dubey, Abhishek},
     booktitle = {Proceedings of the ACM/IEEE 16th International Conference on Cyber-Physical Systems (ICCPS)},
     title = {Online Decision-Making Under Uncertainty for Vehicle-to-Building Systems},
     year = {2025},
     address = {New York, NY, USA},
     publisher = {Association for Computing Machinery},
     series = {ICCPS '25},
     acceptance = {28.4},
     category = {selective},
     contribution = {lead},
     location = {California, USA},
     numpages = {10},
     ranking = {rank1}
   }
   

   Vehicle-to-building (V2B) systems combine physical infrastructure such as smart buildings and electric vehicles (EVs) connected to chargers at the building, with digital control mechanisms to manage energy use. By utilizing EVs as flexible energy reservoirs, buildings can dynamically charge and discharge EVs to effectively manage energy usage, and reduce costs under time-variable pricing and demand charge policies. This setup leads to the V2B optimization problem, where buildings coordinate EV charging and discharging to minimize total electricity costs while meeting users’ charging requirements. However, the V2B optimization problem is difficult due to: 1) fluctuating electricity pricing, which includes both energy charges (/kWh) and demand charges (/kW); 2) long planning horizons (usually over 30 days); 3) heterogeneous chargers with differing charging rates, controllability, and directionality (unidirectional or bidirectional); and 4) user-specific battery levels at departure to ensure user requirements are met. While existing approaches often model this setting as a single-shot combinatorial optimization problem, we highlight critical limitations in prior work and instead model the V2B optimization problem as a Markov decision process, i.e., a stochastic control process. Solving the resulting MDP is challenging due to the large state and action spaces. To address the challenges of the large state space, we leverage online search, and we counter the action space by using domain-specific heuristics to prune unpromising actions. We validate our approach in collaboration with an EV manufacturer and a smart building operator in California, United States, showing that the proposed framework significantly outperforms state-of-the-art methods.