RESEARCH
RESEARCH INTEREST
My research interests include large-scale optimization and decision analysis, stochastic networks and simulation modeling, and artificial intelligence and machine learning, with applications to smart and sustainable systems. I am particularly interested in improving the resilience of large-scale transportation and supply chain or logistics systems, developing emergency management planning under uncertainty, and designing sustainable systems that matter to society.
SELECTED GRANTS
PI, Enhancing AIT-based Passenger Screening Processes and Security Through Integrated Stochastic Optimization and Artificial Intelligence Strategies, Department of Homeland Security (DHS), 2025 - 2027.
PI, Integrated Deep Reinforcement Learning and Large-Scale Optimization for Enhancing Last-Mile Logistics with Future Mobility Technologies, Jungseok Logistics Foundation (JLF), 2025 - 2026.
Co-PI, Texas 9-1-1 Funding Analysis and Public Opinion Survey, Texas 9-1-1 Alliance Rapid Grant, 2024 - 2025.
Co-PI, Data-driven Disruptive Event Modeling to Improve Operational Resilience in Modernized and Decarbonized Smart Grids, Mizzou College of Engineering Seed Grant, 2024 - 2025.
PI, Optimal Deployment of Capacitated Fast Charging Stations for Electric Vehicles on a Directed Highway Network, Mizzou Research Council Grant, 2024 - 2025.
PI, Disaster Assessment from Satellite Imagery using Improved Generative Adversarial Network and UNet, National Aeronautics and Space Administration (NASA) EPSCoR Research Initiation Grant (RIG), 2022 - 2024.
PI, An Integrated Simulation-based Optimization Model for Solving Multi-Objective Dynamic Facility Layout Problems, Black Hills Stock Show Foundation, 2022 - 2023.
PI, Collaborative Research: Leveraging the Resilience and Recovery through the Analysis of Spatial-Temporal Data-Driven Aerial Imagery, National Aeronautics and Space Administration (NASA) EPSCoR Research Grant, 2020 - 2023.
Eco-routing for Electric Vehicles
Eco-routing, which aims at finding the most ecological transportation route, is attracting more and more attention from academia and industry due to the rising price of fuel and the mission of achieving environmental sustainability nowadays. Recent developments in alternative and clean energy for vehicles (e.g. electric vehicles (EVs)) have brought new challenges and opportunities to this topic. In particular, due to the unique characteristics of EVs (e.g. limited battery capacity, ability to regenerate energy from wind and solar power, and design of regenerative braking), eco-routing for EVs is different and more important than other regular routing schemes (e.g. shortest path, fastest path, etc.). We aim to study eco-routing so that EVs can use the new sustainable technology more efficiently.
Traffic Flow Modeling, Estimation, and Control
We investigate fundamental questions regarding the modeling, estimation, and network design under uncertainty for complex transportation service systems. The rapid development of mobile internet (such as GPS-equipped vehicles and smartphones) has significantly improved the capabilities of the engineering community to monitor and control traffic. Importantly, mobile sensing has brought many opportunities and challenges for large-scale infrastructure systems. We propose tractable and scalable approaches (front tracking and robust optimization) to address traffic dynamics and uncertainties and to directly handle mobile sensing data.
Evacuation Transportation Planning
Large-scale evacuation in events is of critical importance since it bears significant infeasibility costs resulting from the potential loss of life and property. It is challenging to model the problem mathematically due to the inherent complexity and uncertainty. We used cell transmission model (CTM) based dynamic traffic assignment (DTA) to model the evacuation transportation problem and applied robust optimization and chance-constrained optimization to model demand uncertainty. Tractable formulations were developed and numerical experiments were conducted. Results showed that our approach leads to less total social cost than the deterministic approach. We plan to extend the study by considering more sources of uncertainty (e.g. road capacity uncertainty, evacuees’ behavior uncertainty).
Urban Freight Transportation
Growing demand for urban freight transportation services and the recognition of the value of sustainable transport make efficiency and ecology the two main issues for the urban freight transportation system of the 21st century. Considering that the private vehicles transporting people are also a main component of the urban transportation system and their interactions with vehicles transporting freight were often ignored in the study, we investigated their interactions by formulating the urban freight transportation model as a mathematical program with equilibrium constraints (MPEC) and develop accurate measures of the interactions. We solved the problem numerically and discussed some managerial insights for truck companies and central planners. The study will be extended by modeling freight transportation as vehicle routing problems and considering more realistic issues like policies, restrictions, and uncertainties.
Complex Transportation Network Design
Recently, we proposed to develop a new complex network design theory that is dynamic and considers the influence of transportation, social, and data networks on one another. In particular, we will consider multiple time scales and human behaviors when modeling traffic flows and demand formation. Network science and evolutionary game theory will be used to model demand dynamics. Dynamic arc and path flows will be modeled based on hydrodynamic traffic flow theory.