Research
My research focuses on operations research, game theory, machine learning with applications to resource allocation, operational, and management problems in the smart green infrastructure.
Intelligent Transportation System
EV charging scheduling in urban areas
Highway EV charging planning
Smart Grid & Microgrid
Dynamic pricing-driven demand response for charging stations
V2G-based energy management
Smart City
Logistics vehicle operations
AnyLogic: EV dynamic charging scheduling and simulation
Intelligent Manufacturing system
AGV system layout design
RFID sensor deployment
Real-time scheduling system for AGV
Low-Carbon Infrastructure Design, Planning and Management Vancouver, Canada
MéridaLabs (Dr. Walter Mérida), Department of Mechanical Engineering Jan. 2021- Present
Integrated system planning, market design and optimization framework development for the next generation of hydrogen energies and charging/refueling infrastructure
Autonomous vehicle scheduling and operations, demand response programs, grid-interactive transportation in the smart city
Hydrogen supply chain planning and policy evaluation for heavy-duty transport in British Columbia
Energetic modeling of the car-sharing fleet, and collection and management of real time or offline data
Machina Economicus paradigm and practices involve operations research, reinforcement learning-based mechanism design, machine Learning
Electric Vehicle (EV) Charging Scheduling in Smart Infrastructure Montréal, Canada
Lab of Distributed System Engineering (Dr. Chun Wang)
Lab of Cyber-Physical Systems & Security (Dr. Jun Yan) Sep. 2016 - Dec. 2020
Operations research, game theory, and reinforcement learning with applications to market-based EV charging scheduling
Mathematical modelling for EV charging scheduling problems by CPLEX: mixed-integer linear programs
Auction mechanism design for charging scheduling: iterative bidding; incentive-compatible auction, simultaneous multi-round auction
Dynamic auction design and scheduling algorithm for EV charging: Java CPLEX API
Reinforcement learning-based mechanism design for pricing-driven demand response of EVs in microgrids: reinforcement mechanism design, multi-agent reinforcement mechanism design
AnyLogic simulation: multiple EVs charging scheduling in urban areas
Coordinated Planning and Scheduling for Intelligent Manufacturing Systems Dalian, China
Institute for Engineering Machinery (Dr. Yanjun Shi) Sep. 2013 - Jun. 2016
Design, optimization, and coordination of manufacturing system in multi-agent environments
Mathematical programming for layout and planning and RFID sensor deployment of tandem Automatic Guided Vehicle (AGV) systems and plant logistics systems
Methodology: co-evolutionary optimization framework, exact optimization, meta-heuristic algorithms, and surrogate model
iSight united simulation: integration of MATLAB and ANSYS for multi-objective optimization on parameters of gas turbine
AGV system: data management for integrated navigation control, real-time scheduling for AGVs