The expected growth of electric vehicle (EV) usage will not only increase the energy demand but also bring the requirement to provide the necessary electrical infrastructure to handle the load. While charging infrastructure is becoming increasingly present in urban areas, special attention is required for transit traffic, not just for passengers but also for freight transport. Differences in the nature of battery charging compared to that of classical refueling require careful planning in order to provide a resilient electrical infrastructure that will supply enough energy at critical locations during peak hours. This paper presents a hybrid simulation model for analyzing fast-charging demand based on traffic flow, projected EV adoption, battery characteristics, and environmental conditions. The model integrates a probabilistic model for evaluating the charging requirements based on traffic flows with a discrete-event simulation (DES) framework to analyze charger utilization, waiting queues, and energy demand. The presented case of traffic flow on Slovenian motorways explored the expected power demands at various seasonal traffic intensities. The findings provide valuable insight for planning the charging infrastructure, the electrical grid, and also the layout by anticipating the number of vehicles seeking charging services. The modular design of the model allowed replacing key parameters with different traffic projections, supporting a robust scenario analysis and adaptive infrastructure planning. Replacing the parameters with real-time data opens the path for integration into a digital twin framework of individual EV charging hubs, providing the basis for development of an EV charging hub network digital twin.

This research presents an overview of transportation mode choice, emphasizing key influencing factors and a range of methodological approaches from traditional Random Utility Theory (RUT) models to modern Machine Learning (ML) techniques. A comprehensive review covered 875 papers, which were screened for relevance. The search was conducted on ScienceDirect and Google Scholar between October and November 2024 using the keywords transport and choice model. Search results were reviewed until several consecutive entries no longer contained content relevant to the topic. After the screening and exclusion process, 106 papers remained for analysis. The review reveals that the Multinomial Logit (MNL) model remains the most widely used approach for modeling transportation mode choice, despite a growing interest in ML methods. Cars and buses dominate in passenger transport studies, while trucks, trains, and ships are most common in freight research. Data is typically collected through surveys (for passenger transport) and interviews (for freight), though some studies use secondary sources. Geographically, Asia and Europe are most represented, with regions like South America underrepresented. Travel time and cost are key variables, with increasing attention to the built environment in passenger studies and service reliability in freight studies. Overall, most studies aim to address real-world transport challenges. The review highlights the persistent gap between theoretical advancements and real-world applicability. To support this analysis, it examines the specific research objectives and findings of each study.

Due to an ever-increasing movement of containers across the globe in line with the economic boom, the trade imbalance and issues related to empty containers have become inevitable. Empty containers accumulated at specific ports cannot only generate profit but also increase the environmental footprint. This research proposes a Maritime Empty Container Reposition Modelling Framework by integrating the agent-based modelling (ABM) paradigm to model the global movements of empty containers. An agent-based maritime logistic empty container redistribution model was developed to help minimize the total relevant costs for empty container movement in the planning horizon. In the system, ports, shipping companies, customers, and empty containers were identified as critical agents. Using simulated annealing (SA), shipping line agents were able to optimise empty container repositioning to determine the best sequence for moving containers. The model was applied to the Asia–Middle East region to simulate global empty containers repositioning in the region. The results comparison shows that the proposed optimised empty container repositioning framework can significantly reduce the shipping line’s costs and make full use of empty containers.

This paper deals with the optimization of the flow of sterile Reusable Medical Devices (RMD) in a general hospital which takes place between the centralized sterilization department and the operating theatres, clinics, hospital...

This paper presents a discrete-event simulation (DES) approach for simulation of distributed energy resources (DER). The DES approach provides means to adapt the energy workloads of the system according to any variations of demands that can occur due to temperature regulation requirements or operational demands. Along with a basic model based on a concrete DER configuration, additional models are presented and analysed in order to demonstrate the adaptability of the DES approach to analyse alternative configurations. The models were tested on historical energy consumption data in order to provide the expected workloads and costs. Models are capable to predict the amount of three different energies and cost for the company based on the inside and outside temperatures.