The widespread adoption of Industry 4.0 technologies is resulting in a wide availability of real-time data gathered on the shop floor. This data, once properly elaborated, can be used to support dynamic decision-making, im-proving manufacturing companies’ capability to deal with uncertainty and thus leading to potential benefits in their performance. This paper presents a simulation model to assess the changes in manufacturing systems perfor-mance resulting from the use of real-time data in the dynamic scheduling of in-plant logistics activities. The model was developed considering a general factory layout and implemented in Python, a widely used open-source pro-gramming language. Therefore, the model can be used and extended by a wide community of researchers, serving as a base for future studies, and adapted to be applied to a large number of factories, thus favoring a more widespread adoption of dynamic scheduling systems in practice. In this study, the model was applied to the setting of a factory in the food industry in which a fleet of mobile robots supply materials to production stations and retrieve finished goods, carrying them to the factory warehouse. Results show that a dynamic scheduling system, in which in-plant logistics activities are scheduled considering real-time data on the status of shop floor re-sources, leads to better performance, in terms of production stations uptime, compared with the static system currently adopted by the company.

Automated Guided Vehicles (AGVs) and Autonomous Mobile Robots (AMRs) are flexible and reliable options for material handling automation. The integration level with the roduction/logistic systems is crucial for performance and investment costs. Proper design of loading/unloading points is essential as they impact the number, level of automation, sorting/buffering level, and vehicle requirements. This paper presents an innovative approach combining virtual-interactive simulation and mathematical modeling to optimize loading/unloading points for maximum operational and economic performance. This approach simulates different scenarios and identifies the best loading/unloading points configurations optimizing the whole system’s performance. A numerical analysis is reported to demonstrate the practical implications.

The integration of Autonomous Guided Vehicles (AGVs) into smart facto-ries is transforming modern manufacturing, creating coexistence between humans and robotic systems. In this evolving landscape, one critical aspect is the seamless coordination of AGVs and human workers within factory set-tings. To achieve this, our research presents a portable indoor localization system that utilizes ESP32 microcontrollers as compact access points. Using Wi-Fi Fine Time Measurement (FTM) with smartphones, the system esti-mates worker positions through multi-lateration techniques in conjunction with advanced filtering methods. This localization system serves as a pivotal bridge, ensuring that AGVs can interact with and respond to the movements of shop floor workers. A field study in an actual warehouse environment val-idates the system's performance, demonstrating 1.13 meters accuracy in lat-eral movements. Furthermore, its localization capabilities within specific warehouse areas showcase its potential to enhance order picking processes and optimize human-AGV interaction.