The main objective of the study is to present the latest trends and research directions
in the field of optimization of logistics systems with Discrete-Event Simulation (DES)
and Deep Learning (DL). This research area is highly relevant from several aspects:
on the one hand, in the modern Industry 4.0 concept, simulation tools, especially
Discrete-Event Simulations, are increasingly used for the modelling of material flow
processes; on the other hand, the use of Artificial Intelligence (AI)—especially Deep
Neural Networks (DNNs)—to evaluate the results of the former significantly enhances
the potential applicability and effectiveness of such simulations. At the same time,
the results obtained from Discrete-Event Simulations can also be used as synthetic
datasets for the training of DNNs, which creates entirely new opportunities for both
scientific research and practical applications. As a result, the interest in the combination
of Discrete-Event Simulation with Deep Learning in the field of logistics has significantly
increased in the recent period, giving rise to multiple different approaches. The
main contribution of the current paper is that, through a review of the relevant literature,
it provides an overview and systematization of the state-of-the-art methods and approaches
in this developing field. Based on the results of the literature review, the study
also presents the evolution of the research trends and identifies the most important
research gaps in the field.
