Research

Our research focuses on advancing data-driven decision-making and optimization techniques for large-scale, complex systems. We apply cutting-edge methods, including reinforcement learning, and develop innovative models and algorithms to tackle challenges in areas such as supply chain management, urban mobility, smart city services, and healthcare systems. Our work is committed to driving efficiency, sustainability, and innovation across diverse industries and urban environments.

Bike sharing systems

Healthcare planning

Facility location

Supply chain management

Recent Publications:

• Liu, S., & Salari, N., "Fairness in accessibility of public service facilities", Operations Research Letters, 55, 107141 (2024).
• Carlsson, J. G., Liu, S., Salari, N., & Yu, H., "Provably good region partitioning for on-time last-mile delivery", Operations Research, 72(1), 91-109 (2024).
• Azizi, F., & Salari, N., "A novel condition-based maintenance framework for parallel manufacturing systems based on bivariate birth/birth–death processes", Reliability Engineering & System Safety, 229, 108798 (2023).
• Salari, N., Liu, S., & Shen, Z. J. M.,"Real-time delivery time forecasting and promising in online retailing: When will your package arrive?", Manufacturing & Service Operations Management, 24(3), 1421-1436 (2022).
Complete list of publications @Google Scholar

Recent Conference Presentations:

• M. Eslamipirharati, J. Doucette, N. Salari, “Dynamic Optimization for Smart Bike-Sharing: Demand-Driven Rebalancing”, CORS 2025.
• M. Kanani Torshizi, A. Rastpour, J. Doucette, S. Samiedaluie, N. Salari, “Optimizing EMS Ambulance Repositioning: A Threshold-Driven Simulation Approach”, CORS 2025.
• M. Motamedi, J. Doucette, N. Salari, “Optimizing Dynamic Bike Repositioning in Station-Based Bike-Sharing Systems Using Q-Learning”, CORS 2025.

Our Team

Teaching