Automated Guided Vehicles (AGVs) are increasingly used in modern production and logistics systems to automate internal material handling processes. They enable flexible material flows between production stations, warehouses, and transfer points and thus represent a key component of modern intralogistics systems.
A central aspect of operating such systems is the energy management of the vehicles. Due to their limited battery capacities, AGVs must regularly visit charging stations. The number and placement of these charging stations have a significant impact on overall system performance. Too few or poorly positioned charging stations can lead to waiting times or congestion, while an oversized charging infrastructure may result in inefficient resource utilization. At the same time, the energy demand of the vehicles is influenced by various dynamic and stochastic factors, such as traffic intensity, waiting times, transport orders, and vehicle load conditions.
Due to the interactions between vehicles, transport orders, and charging infrastructure, discrete-event simulation is particularly well suited for analyzing such systems. In combination with optimization methods, this approach is referred to as Simulation-Optimization: the simulation evaluates the performance of a given system configuration, while a metaheuristic systematically generates new configurations of the charging infrastructure and searches for high-performing solutions.
Against this background, this thesis focuses on the Simulation-Optimization of charging infrastructure for AGV systems. The task is to implement a material handling system with AGVs and to perform a Simulation-Optimization of the charging infrastructure. The objective is to systematically determine suitable numbers and locations of charging stations and to analyze the impact of different configurations on system performance. To this end, simulation experiments are conducted and various charging infrastructure configurations are evaluated.
The thesis provides practical insights into the simulation and optimization of modern intralogistics systems, as well as into algorithmic approaches for designing complex logistics systems.
If you are interested, please contact Judith Schulze.