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:

• Eslamipirharati, M., Motamedi, M., Doucette, J., & Salari, N., "What if rebalancing fleets could adapt? A two-stage stochastic model for dynamic bike redistribution", Transportation Research Part E: Logistics and Transportation Review, 208 (2026): 104640
• 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 -->

Our Team

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