dr.ir. F.R. Akkerman (Fabian)

I am assistant professor in the section of Industrial Engineering and Management Science at the University of Twente. I earned my PhD in 2025 (cum laude), advised by Martijn Mes, Maria Iacob, and Willem van Jaarsveld, following an MSc in Industrial Engineering and Management in 2021. I was a visiting researcher at Polytechnique Montréal from September to December 2024, hosted by Thibaut Vidal.

I am a machine learning researcher working at the intersection of decision making and interpretability. In my applied work, I use reinforcement learning and related methods to address sequential decisions under uncertainty in transportation and logistics, including dynamic routing, time slot selection and pricing, and other resource allocation tasks. In my methodological work, I develop mathematically grounded approaches to interpretable AI, with an emphasis on improving accuracy and scalability.

My research received the INFORMS TSL Society Dissertation Award in 2025. The DynaPlex project, that includes my PhD and collaborations, received the TKI Dinalog Impact Award in 2025. I interned at ORTEC from September 2020 to March 2021, researching revenue management for attended home delivery. My work has been discussed in the media, including Dutch Radio 1.

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Courses academic year 2025/2026

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 2024/2025

Current projects

EnerLogiX

Integrated Energy and Logistics Planning for Electric Trucking

Electric freight transport is a game-changer for the sustainability of the logistics sector. Major truck manufacturers and government policies at the European, national, and local levels are supporting this development. However, the Dutch electricity grid is reaching its limits, making the integration of electric trucks challenging. In addition, the range of electric trucks remains uncertain, and electricity prices are highly volatile and sometimes unpredictable. As a result, the logistics sector must adapt its business models, network design, fleet composition, and operations to the dynamics of future power grids. The EnerLogiX project focuses on validating smart (local) energy solutions in combination with logistics planning solutions. The project will develop publicly available tools to accelerate the transition to electric freight transport and reduce CO2 emissions.

Finished projects

DynaPlex

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.

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University of Twente

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

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