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Martijn Mes

University of Twente


Martijn Mes is a full professor of Transportation and Logistics Management (TLM) and chair of the Industrial Engineering and Business Information Systems (IEBIS) section within the High Tech Business and Entrepreneurship (HBE) department at the University of Twente (Enschede, The Netherlands). He holds a master’s degree in Applied Mathematics (2002) and did his PhD at the School of Management and Governance, University of Twente (2008). After finishing his PhD, Martijn did his postdoc at Princeton University, Department of Operations Research and Financial Engineering, where he did research on the topics of Ranking and Selection (R&S), Bayesian Global Optimization (BGO), and Optimal Learning. In general, Martijn's research involves optimization and artificial intelligence for transportation and logistics management. Three application areas can be distinguished within this domain: (i) emergency logistics, (ii) urban logistics, and (iii) sustainable logistics. Within these application areas, Martijn focusses on (i) the use of AI for logistics management (supporting strategic, tactical and operational logistics decision-making) and (ii) the use of autonomous or electric vehicles (e.g., drones, delivery robots, AGVs, autonomous trucks). More specifically, Martijn uses quantitative modelling techniques, from the Artificial Intelligence and Operations Research domains, such as stochastic optimization (Approximate Dynamic Programming, Optimal Learning, Machine Learning, Deep Reinforcement Learning), simulation (discrete-event simulation, simulation optimization), multi-agent systems, and serious gaming. Martijn participated in various research and implementation projects (national as well as European) on the topics of sustainable logistics, urban logistics, city distribution, port logistics, and intermodal/synchromodal transport. Within the program Industrial Engineering and Management, Martijn provides various BSc and MSc courses related to simulation, queueing theory, Markov chains, dynamic programming, approximate dynamic programming, reinforcement learning, transportation management, and management of technology.

Key Achievements and Outputs

Research Interests

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Artificial Intelligence, Deep Reinforcement Learning, Optimal Learning, Stochastic Optimisation, Simulation Optimisation, Discrete-event Simulation, Freight Transport, Dynamic Vehicle Routing, Drones/UAVs, Electric Vehicles, Green Logistics, Multi-agent Systems, Pricing and Auctions in Freight Transport.

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