F.R. Akkerman | People Pages: Find employees & contact details

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.

Organisations

Publications

Jump to: 2025 | 2024 | 2023 | 2022 | 2020

2025

Machine Learning for Sequential Decisions in Logistics (2025)[Thesis › PhD Thesis - Research UT, graduation UT]. University of Twente. Akkerman, F. R.https://doi.org/10.3990/1.9789036565349Dynamic reordering and inspection for the multi-item Inventory Record Inaccuracy problem (2025)European journal of operational research, 321(2), 428-444. Akkerman, F., Prak, D. & Mes, M.https://doi.org/10.1016/j.ejor.2024.09.033A comparison of reinforcement learning policies for dynamic vehicle routing problems with stochastic customer requests (2025)Computers & industrial engineering, 200. Article 110747. Akkerman, F., Mes, M. & van Jaarsveld, W.https://doi.org/10.1016/j.cie.2024.110747

2024

Learning Dynamic Selection and Pricing of Out-of-Home Deliveries (2024)Transportation science (E-pub ahead of print/First online). Akkerman, F., Dieter, P. & Mes, M.https://doi.org/10.1287/trsc.2023.0434Dynamic Neighborhood Construction for Structured Large Discrete Action Spaces (2024)[Contribution to conference › Paper] 12th International Conference on Learning Representations, ICLR 2024. Akkerman, F., Luy, J., van Heeswijk, W. & Schiffer, M.

2023

Handling Large Discrete Action Spaces via Dynamic Neighborhood Construction (2023)[Working paper › Preprint]. ArXiv.org. Akkerman, F., Luy, J., Heeswijk, W. v. & Schiffer, M.https://arxiv.org/abs/2305.19891

2022

A Comparison of Reinforcement Learning Policies for Dynamic Vehicle Routing Problems with Stochastic Customer Requests (2022)[Working paper › Working paper]. Akkerman, F., Mes, M. & van Jaarsveld, W.Cross-Docking: Current Research Versus Industry Practice and Industry 4.0 Adoption (2022)In Smart Industry - Better Management (pp. 69-104) (Advanced Series in Management; Vol. 28). Emerald. Akkerman, F., Lalla-Ruiz, E., Mes, M. & Spitters, T.https://doi.org/10.1108/S1877-636120220000028007Distance approximation to support customer selection in vehicle routing problems (2022)Annals of operations research (E-pub ahead of print/First online). Akkerman, F. & Mes, M.https://doi.org/10.1007/s10479-022-04674-8Dynamic Time Slot Pricing Using Delivery Costs Approximations (2022)In Computational Logistics: 13th International Conference, ICCL 2022, Barcelona, Spain, September 21–23, 2022, Proceedings (pp. 214–230) (Lecture Notes in Computer Science; Vol. 13557). Springer. Akkerman, F., Mes, M. & Lalla-Ruiz, E.https://doi.org/10.1007/978-3-031-16579-5_15

2020

Distance Approximation for Dynamic Waste Collection Planning (2020)In Computational Logistics: 11th International Conference, ICCL 2020, Enschede, The Netherlands, September 28–30, 2020, Proceedings (pp. 356-370) (Lecture Notes in Computer Science; Vol. 12433). Springer. Akkerman, F., Mes, M. & Heijnen, W.https://doi.org/10.1007/978-3-030-59747-4_23Distance Approximation for Dynamic Waste Collection Planning (2020)[Working paper › Working paper]. Mes, M., Heijnen, W. & Akkerman, F.

Research profiles

Courses academic year 2024/2025

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 2023/2024

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.

Address

University of Twente

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

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