Background: The capacity planning of production systems is one of the most fundamental strategic problems in the creation of a production plant. However, the implementation of increasingly complex production systems combined with sophisticated automated material handling justifies the development of novel approaches to solve the combined capacity planning and material handling problem, which is also the objective of the current study. Methods: The presented approach combines the use of capacity planning formulas and discrete event simulation for optimizing extensive automated guided vehicle (AGV) systems from the aspect of the number of required vehicles. Extensive series of simulation experiments are applied in the case of each model variant for optimal results and to account for machine failures in the system. Results: The application of the proposed method is demonstrated through a realistic sample problem in a plastic industry setting with the use of the Siemens Tecnomatix Plant Simulation software (version 2302.0003, Educational license). Conclusions: The results from the sample problem demonstrate the usefulness of the approach, as a non-intuitive solution proved to be the most efficient. Additionally, the main advantage of the method is that it provides a standardized framework for the simulation-based optimization of AGV systems starting out from the comprehensive production capacity parameters.

The use of automated guided vehicles (AGVs) in manufacturing systems has become particularly widespread in the latter period, thanks to the intensification of automation and to the adoption of the Industry 4.0 principle. Modern AGVs can also have greater autonomy then before, thanks to the advancement of the related technologies which further improve their applicability in numerous complex settings. In the Industry 4.0 principle, the use of predictive simulations in relation to manufacturing logistics systems also plays an important role, which applies to the modelling of the operation of AGVs as well. In light of these developments, the current paper introduces an initial model with the aim to demonstrate a possible approach for modelling the operation of freely moving AGVs in a discrete event simulation environment. The model is made with Plant Simulation, a widely applied discrete event simulation software. The presented approach opens the door for multiple future research avenues and could also serve as a basis for a further developed general model with wider utilization possibilities.

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.

Urban freight transport presents significant challenges to the goals of sustainable mobility and energy efficiency. The aim of this paper is to provide an approach utilizing center search and employment data for selecting the optimal location of a distribution center designed for electrified urban freight transport. The application of the proposed method is demonstrated through a case study, in which a potential site for a logistics center in and around Miskolc was identified, using multiple center search methods such as area grids and radial models. The paper also presents additional practical results, including the required fleet size and the characteristics of the supply area. The findings highlight that with thorough planning and appropriate data collection, a logistics center can significantly improve the efficiency and environmental performance of freight transport.

The aim of the paper is to present the creation process of a digital twin based on discrete-event simulation for a production logistics model system. The given system is located in the Logistics 4.0 Laboratory of the Institute of Logistics of the University of Miskolc. It consists of an automated, PLC-controlled material handling system based on roller conveyors and turntables, which is supplemented by a gravity flow rack. It also has multiple locations along the conveyor track that could serve as storage or assembly stations. A real-scale model of the entire system was created with the aid of Siemens Tecnomatix Plant Simulation. In the developed model, proprietary methods control the transfer of raw materials, congestion management and the operation of the turntables. Based on the operation of the complete system, the simulation examines machine utilization, human resources and bottlenecks in the material flow. The results demonstrate the advantages of using discrete-event simulation based digital twins for the optimization of production logistics systems.