Network Optimization Research / Methodologies Network Optimization — how we research Mathematical optimization is the backbone of INSPIRE Lab. We develop stochastic, multi-agent, and network-based optimization models to plan and operate large-scale infrastructure systems under uncertainty, competition, and decentralized decision-making. Key Approaches — four core directions Theme 01 Stochastic Multi-Agent Optimization Theme 02 Network Equilibrium & Game Theory Theme 03 Bi-level & Decentralized Optimization Theme 04 Mixed-Integer Programming Featured Publications — recent highlights TR-C · 2026 Optimal Information Sharing Locations in Locally Connected Mobility Guo, Z. TR-E · 2026 Optimizing On-Site Green H₂ Consumption with Heavy-Duty FCEVs Ghorbanali Zadegan & Guo IEEE TTE · 2021 Stochastic Multi-Agent Framework for Transportation–Power Analyses Guo, Afifah, Qi & Baghali PUB FIG 4IEEE T-PWRS 2022 IEEE T-PWRS · 2022 EVs for Distribution System Load Pickup under Stressed Conditions Baghali, Guo, Wei & Shahidehpour TR-C · 2023 Intermediate Service Facility Planning in a Stochastic and Competitive Market Baghali, Guo, Deride & Fan TR-C · 2016 Infrastructure Planning for Fast Charging Stations in a Competitive Market Guo, Deride & Fan Full Publication List → Highlighted Projects PI NSF Decentralized Optimization for Next-Gen Transportation & Power Systems Co-PI NSF Northeast US SH2INE: Sustainable, Holistic Hydrogen INtegration Evaluation Models and analyses of human, environmental, and policy dimensions PI NSF Optimizing Information Value in Heterogeneous Multi-agent Transportation Systems (OPTIMA)
