While real-time data and sophisticated Reinforcement Learning (RL) approaches are
emerging, logistic organizations, in particular Small and Medium-sized Enterprises (SMEs) …

A logistics drone routing game utilising modelled flight risk and energy consumption data is
presented, and its design elements are discussed. The game has been developed as part of …

This research introduces a supervised learning modelin the existing planning software of a
large logistic service provider. The goal of this research is to decrease the total replanning …

Intelligence and complexity are increasingly demanded requirements during decision-
making. In our former research a software framework has been developed, which enables …

This thesis researches the benefits of reinforcement learning in operational spare part
management. Because reinforcement learning is not often used in a (semi) closed-loop …

We researched the potential of reinforcement learning for the modality planning of a
hinterland logistic service provider. The RL-method outperformed the benchmark heuristic …

In Reinforcement Learning (RL), agents operate in a (potentially stochastic) environment,
repeatedly making and evaluating sequential decisions to learn suitable decision policies …