I am a PhD candidate in the section of Industrial Engineering and Business Information Systems (IEBIS) and obtained a master in Industrial Engineering and Management from the University of Twente in 2021.

I work in the general areas of operations management and machine learning. More specifically, my focus is on sequential decision-making under uncertainty for transportation and logistics management problems. My main methodological focus is on reinforcement learning and approximate dynamic programming.

My research involves the development of DynaPlex; an artificial intelligence toolbox containing various (deep) reinforcement learning algorithms that can be applied to a variety of dynamic data-driven logistics challenges. The DynaPlex project is in collaboration with the Eindhoven University of Technology and a wide range of companies.


  • Economics, Econometrics and Finance

    • Learning
    • Benefits
    • Cost Function
    • Regression Model
    • Costs
  • Computer Science

    • Reinforcement Learning
    • Vehicle Routing
    • Real World



Distance Approximation for Dynamic Waste Collection PlanningIn Computational Logistics - 11th International Conference, ICCL 2020, Proceedings (pp. 356-370). Springer. Akkerman, F., Mes, M. & Heijnen, W.https://doi.org/10.1007/978-3-030-59747-4_23Distance Approximation for Dynamic Waste Collection Planning. Mes, M., Heijnen, W. & Akkerman, F.

Research profiles

Courses academic year 2023/2024

Courses in the current academic year are added at the moment they are finalised in the Osiris system. Therefore it is possible that the list is not yet complete for the whole academic year.

Courses academic year 2022/2023

Current projects


Deep Reinforcement Learning for Data-Driven Logistics

In December 2018, a team of researchers of DeepMind (owned by Google) published a paper in the journal Science, demonstrating the ability of their newly developed AlphaZero algorithm to beat the best game engines in Chess, Go, and Shogi. What is more, instead of relying on hand-crafted evaluation functions of board states, the AlphaZero algorithm contains no expert information on any of the games played: it autonomously learns to play each game only by playing the game many times against itself. This AI breakthrough is exciting because Go and Chess are games where it is crucial to anticipate unknown moves of the opponent. When making logistics decisions, it is equally important to anticipate the arrival of new data (e.g., orders, delays, disruptions, etc.). For various dynamic data-driven decision problems, Deep Reinforcement Learning (DRL) algorithms like AlphaZero have been demonstrated to be game-changers. The logistics sector recognizes the opportunities and is eager to adopt AI for decision automation. However, companies struggle to translate the abstract possibilities of AI into the tangible project plans, and employing DRL-based decision making requires expert algorithmic knowledge that is difficult to source. To overcome these challenges, we started to develop the DynaPlex toolbox. In a similar fashion as AlphaZero was designed as a generic tool to solve various games, we created the DynaPlex toolbox to support the rapid development of automated decision making based on DRL. DynaPlex focuses on dynamic data-driven logistics challenges, with a focus on planning, scheduling, allocation and routing decisions, for example in transportation management, inventory management, and warehouse management.


University of Twente

Ravelijn (building no. 10), room 3402
Hallenweg 17
7522 NH Enschede

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