Industrial and Systems Engineering
Biography
Omid Shahvari is an associate teaching professor in the Department of Industrial and Systems Engineering at the University of Missouri, where he has been a faculty member since 2020. Prior to joining Mizzou, he was a Visiting Assistant Professor at Worcester Polytechnic Institute (WPI) and a Postdoctoral Associate at Mississippi State University.
His expertise is in operations research, optimization, supply chain and operations management, and data-driven decision making. His research interests include scheduling, logistics and transportation, smart and additive manufacturing, and infrastructure resilience, using methodologies such as mathematical and stochastic modeling, mixed-integer optimization, heuristics, simulation, and machine learning. His research has appeared in journals including Journal of Scheduling, IISE Transactions, International Journal of Production Economics, and Computers & Operations Research. Shahvari has extensive teaching experience across undergraduate and graduate programs, with courses spanning operations research and optimization, supply chain and operations management, engineering economy, stochastic modeling, and computational optimization and analytics. His teaching emphasizes the integration of mathematical foundations, computational tools, and real-world applications to develop students’ analytical reasoning and decision-making skills. He is also interested in the responsible integration of artificial intelligence and emerging technologies into engineering education to enhance active learning, critical thinking, and independent problem-solving. He also has more than eight years of industry experience applying optimization and analytics to production planning, logistics, transportation, and supply chain decision-making. Shahvari received the Outstanding IMSE Instructor Award for outstanding teaching performance in 2021 and was recognized at the ISE Hall of Fame in 2022.
Education
PhD from Oregon State University
Technical Focus
Operations management data-driven decision making