In modern production and logistics systems, various vehicles such as Automated Guided Vehicles (AGVs), tow trains, and forklifts operate on shared transportation networks within a production facility. Particularly on highly utilized main routes, congestion and delays may occur. As a result, the shortest path between two points is not necessarily the fastest.
In many simulation models, vehicle routing is determined using classical shortest-path algorithms. However, these approaches typically consider only static criteria such as travel distances. In real-world systems, travel times are strongly influenced by dynamic factors such as traffic intensity, waiting times, and local bottlenecks within the network. Consequently, traffic flows may concentrate on certain routes, leading to congestion.
One promising approach is to use simulation results to iteratively adjust routing parameters. Based on simulation outcomes, heavily utilized network segments can be identified, and edge weights within the network can be modified to encourage the use of alternative routes. Such an iterative procedure enables a more balanced distribution of traffic flows and helps to reduce congestion within the system.
The objective of this thesis is to model an intralogistics transportation network and to develop and analyze a simulation-based approach for the iterative adjustment of routing weights. To this end, simulation experiments are conducted to analyze traffic loads within the network, identify bottlenecks, and systematically adjust edge weights. Subsequently, the impact of this approach on key performance indicators such as travel times, waiting times, and congestion is evaluated.
The thesis provides practical insights into the simulation of intralogistics systems as well as into algorithmic approaches for analyzing and controlling traffic flows in complex transportation networks.
If you are interested, please contact Judith Schulze.