The execution of material handling tasks using autonomous guided vehicles (AGVs) has proven a real success during the last decade. Nevertheless, the installation of AGVs is costly as it needs to modify the workshop's configuration by defining dedicated movement zones. Recently, more flexible and collaborative mobile robots known as autonomous intelligent robots (AIV) can be used in manufacturing systems. This new generation of intelligent mobile robots does not need specific zones and can interact with unexpected or mobile obstacles such as human operators. This paper focuses on AIV fleet size definition in a variable and unexpected environment with humans while keeping AIV assigned transportation tasks on time. A simulation that model the complexity of the AIV travel time estimation under the mentioned circumstances and the improvement brought by IoT, Big Data and sensors by using them as the real-time data source is developed.