<p dir="ltr">The emergence of Autonomous Mobile Robots (AMR) signifies a pivotal shift in vehicle-based material handling systems, demonstrating their effectiveness across a broad spectrum of applications. Advancing beyond the traditional Automated Guided Vehicles (AGV), AMRs offer unprecedented flexibility in movement, liberated from electromagnetic guidance constraints. Their decentralized control architecture not only enables remarkable scalability but also fortifies system resilience through advanced conflict resolution mechanisms. Nevertheless, transitioning from AGV to AMR presents intricate challenges, chiefly due to the expanded complexity in path planning and task selection, compounded by the heightened potential for conflicts from their dynamic interaction capabilities. This dissertation confronts these challenges by fully leveraging the technological advancements of AMRs. A kinematic-enabled agent-based simulator was developed to replicate AMR system behavior, enabling detailed analysis of fleet dynamics and interactions within AMR intralogistics systems and their environments. Additionally, a comprehensive fleet management protocol was formulated to enhance the throughput of AMR-based intralogistics systems from an integrated perspective. A pivotal discovery of this research is the inadequacy of existing path planning protocols to provide reliable plans throughout their execution, leading to task allocation decisions based on inaccurate plan information and resulting in false optimality. In response, a novel machine learning enhanced probabilistic Multi-Robot Path Planning (MRPP) protocol was introduced to ensure the generation of dependable path plans, laying a solid foundation for task allocation decisions. The contributions of this dissertation, including the kinematic-enabled simulator, the fleet management protocol, and the MRPP protocol, are intended to pave the way for practical enhancements in autonomous vehicle-based material handling systems, fostering the development of solutions that are both innovative and applicable in industrial practices.<br></p>