Dr. Uta Mohring is a postdoctoral researcher at the Rotman School of Management, University of Toronto, Canada. She received her doctoral degree from the Karlsruhe Institute of Technology (KIT), Germany, in 2022.
Her research is at the interface between management science, industrial engineering, and data science and focuses on prescriptive models for decision-making in service operations, logistics, and transportation. Within these fields, she is particularly interested in the design and control of stochastic systems and networks with strategic agents in logistics and shared-mobility and on-demand transportation applications. Her scientific ambition is to make valuable contributions to improving resource efficiency and eco-efficiency of on-demand transportation services and order fulfillment operations in logistics that advance society's transition towards smart cities and a sustainable economy.
In her research, she applies a broad range of mostly quantitative methods from the fields of operations research and business analytics including, specifically, stochastic, game-theoretical, data-driven, and simulation-based modeling and optimization techniques.
Her profile is distinguished by her substantial international research experience and network, the interdisciplinarity of her research at the interface between management science, industrial engineering, and data science, and her strong experience in third-party funding.
She serves as an advisory board member of the German Academic International Network (GAIN).
Her research is at the interface between management science, industrial engineering, and data science and focuses on prescriptive models for decision-making in service operations, logistics, and transportation. Within these fields, she is particularly interested in the design and control of stochastic systems and networks with strategic agents in logistics and shared-mobility and on-demand transportation applications. Her scientific ambition is to make valuable contributions to improving resource efficiency and eco-efficiency of on-demand transportation services and order fulfillment operations in logistics that advance society's transition towards smart cities and a sustainable economy.
In her research, she applies a broad range of mostly quantitative methods from the fields of operations research and business analytics including, specifically, stochastic, game-theoretical, data-driven, and simulation-based modeling and optimization techniques.
Her profile is distinguished by her substantial international research experience and network, the interdisciplinarity of her research at the interface between management science, industrial engineering, and data science, and her strong experience in third-party funding.
She serves as an advisory board member of the German Academic International Network (GAIN).
Techniques/Methodologies (alphabetical order)
Analytics, data-driven modeling, game theory, machine learning, network flow theory, optimization, queueing theory, simulation, statistics, stochastic modeling
Applications (alphabetical order)
E-commerce, last-mile logistics, platform economics, platform operations, retail logistics, revenue management and pricing, shared mobility, sharing economy, smart cities, supply chain management, sustainability, urban logistics, urban mobility, warehouse logistics
E-commerce, last-mile logistics, platform economics, platform operations, retail logistics, revenue management and pricing, shared mobility, sharing economy, smart cities, supply chain management, sustainability, urban logistics, urban mobility, warehouse logistics