The growing complexity of manufacturing and logistics operations, driven by increasing product diversity and labor shortages, requires flexible and scalable transportation solutions. Cellular Automated Guided Vehicles (AGVs) offer a promising alternative to conventional transport systems by enabling autonomous and collaborative transport of goods. This research evaluates the performance of cellular AGVs compared to classical AGVs and tow tractors based on key logistics metrics, including transport throughput time, machine utilization, and number of products transported. An event-driven simulation environment was developed in Python to model logistics and production processes, incorporating transportation constraints and optimization models. The results show that cellular AGVs significantly reduce throughput time in complex logistics environments. For the same transport capacity, their average throughput time can be up to 55% lower than that of classic AGVs and tow tractors. In production environments with ten recurring delivery relationships, cellular AGVs reduce transport time by up to 37% and achieve the highest machine utilization (87%). Their modularity increases efficiency by dynamically adapting to changing transport requirements. This study demonstrates the benefits of cellular AGVs in improving transportation performance, particularly in complex, high-throughput environments. The results support their adoption over traditional transportation systems.