The paper presents a multi-agent simulation using fuzzy inference to explore in an integrated way the task allocation and battery charging management of mobile baggage conveyor robots in an airport. This simulation approach offers high adaptability thanks to a distributed system, adapting to variations in the availability of conveyor agents, their battery capacity, knowledge of the context of infrastructure resource availability, and awareness of the activity of the conveyor fleet. Dynamic factors, such as workload variations and communication between the conveyor agents and infrastructure are considered as heuristics, highlighting the importance of flexible and collaborative approaches in autonomous systems. The results highlight the effectiveness of adaptive fuzzy multi-agent models to optimize dynamic task allocation, adapt to the variation of baggage arrival flows, improve the overall operational efficiency of conveyor agents, and reduce their energy consumption.