Future factories, underpinned by Industry 4.0 and Industry 5.0 frameworks, increasingly depend on Automated Guided Vehicles (AGVs) and Intelligent Autonomous Vehicles to enhance internal logistics efficiency. These vehicles are integral to achieving greater efficiency, safety, and adaptability in modern manufacturing environments. AGVs are transformative technologies that significantly enhance internal logistics operations in modern manufacturing. Despite their numerous advantages, overcoming key challenges is crucial for their successful integration and sustainable operation. This paper evaluates the operational efficiencies of AGVs in industrial settings, with a focus on optimising the sequencing of AGV movements. Traditionally, the First In, First Out (FIFO) method has been employed to decide the next job for the AGV. However, through computational experiments, we analyse various heuristic methods to enhance the throughput and reduce idle times or ‘empty’ movements of AGVs, thereby optimising internal logistics and supporting robust production processes. The FIFO approach, while straightforward, often leads to suboptimal scheduling outcomes, especially in complex and dynamic manufacturing environments where job priorities frequently shift. It is increasingly evident that FIFO is inadequate for the nuanced demands of modern AGV operations, which benefit significantly from the adaptability and efficiency provided by heuristic solutions. This study suggests that advanced heuristics, particularly those designed for solving Travelling Salesman Problem (TSP) or Vehicle Routing Problem (VRP) are better suited to these tasks. These methods not only offer more flexible and efficient route planning but also significantly improve the overall utility of AGVs in high-demand scenarios.