Models and decision-making tools for the optimization of the efficiency and environmental impact of logistics and production systems

This thesis provides a comprehensive analysis and design framework for shuttle-based storage systems (SBSS), addressing key challenges in supply chain efficiency. As globalization and e-commerce drive the need for agile and automated warehousing, SBSS offer high-density storage, flexibility, and scalability. However, assessing their performance remains difficult due to the limitations of existing models, unrealistic assumptions, and the complexity of real-world implementations. To bridge this gap, this research conducts a critical literature review, establishing a consistent nomenclature and proposing an original taxonomy of system configurations. A comprehensive framework is then developed, integrating analytical and simulation-based tools to estimate performance and optimize system design. The proposed hybrid model combines analytical travel time equations with simulations, improving accuracy in evaluating throughput, space efficiency, and system responsiveness under dynamic operational conditions. A key innovation is the Space Efficiency Control System (SECS), a visual dashboard that monitors and optimizes storage utilization using time-based key performance indicators (KPIs). Additionally, a warehouse material flow generator simulates storage and retrieval requests when real data is unavailable, ensuring adaptability to different operational contexts. The bay sizing optimization procedure, based on a multi-queue single-server model, further enhances throughput by identifying and reducing system bottlenecks. These tools collectively form a digital twin of SBSS, providing a user-friendly environment for designing, monitoring, and managing both new and existing systems. This digital twin allows real-time performance assessment, scenario simulation, and system reconfiguration, ensuring adaptability to evolving warehousing and material handling requirements. Finally, the thesis evaluates the sustainability impact of SBSS using Life Cycle Assessment (LCA), measuring the carbon footprint of automated storage solutions. By addressing research gaps with rigorously developed methodologies, this work advances the state of the art and contributes to the transition to Industry 5.0, where human-machine collaboration, efficiency, and sustainability drive future warehouse innovations.