Innovative models and advanced solutions for the automation of material handling and storage systems

The rapid evolution of global supply chains in the era of digitalization and e-commerce has transformed industrial logistics, elevating warehousing from a passive storage function to a strategic driver of responsiveness, efficiency, and competitiveness. Among advanced automation technologies, shuttle-based storage and retrieval systems (SBS/RS), particularly four-way shuttle architectures, have emerged as a leading solution for next-generation warehouses due to their modularity, scalability, and high-density capabilities. Despite their increasing adoption, the design and optimization of shuttle-based systems remain highly complex. The strong interdependence between structural design parameters, control policies, and stochastic operational dynamics generates a multi-dimensional decision problem that cannot be adequately addressed through purely analytical or purely simulation-based approaches. This dissertation proposes an integrated methodological framework that combines strategic optimization, hybrid analytical–simulation modeling, and robotic experimentation to support the efficient, flexible, and sustainable design of SBS/RS within the Industry 5.0 paradigm. First, a comprehensive literature review identifies research gaps and structures existing contributions across system design, modeling methodologies, and performance evaluation metrics. At the strategic level, an optimization model is developed to support long-term planning decisions, balancing storage capacity, space utilization, throughput requirements, and investment costs under varying demand conditions. To capture operational complexity, a hybrid analytical–simulation model is then introduced. Analytical formulations estimate travel times and queuing behavior, while discrete-event simulation reproduces dynamic interactions among shuttles, lifts, and buffers. The integrated model forms the core of a Decision Support System (DSS) for evaluating alternative layouts and operational strategies. A key contribution lies in the development of multi-agent path and trajectory planning algorithms for coordinating multiple four-way shuttles. These algorithms are experimentally validated using a TurtleBot3-based robotic testbed, demonstrating feasibility and robustness in realistic environments. Finally, a Life Cycle Assessment (LCA) evaluates the environmental impacts of automated case-handling systems, providing a sustainability perspective on automation decisions.