Analytical models for the evaluation of deep-lane autonomous vehicle storage and retrieval system performance

In the last decades, huge efforts have been made to develop innovative solutions supporting the automation of logistics activities. Among the available technologies, autonomous vehicle storage and retrieval systems (AVS/RS) are very promising: they use light vehicles able to independently travel on different axes, and thus perform different tasks at the same time. Despite the increasing diffusion of such systems, there is still a lack of performance evaluation techniques. Furthermore, there are no standards available to provide performance evaluation criteria. In this paper, novel analytical models to assess the performance of AVS/RS pertaining to deep-lane racks. These models extend the state of the art by (i) taking into account the real criteria related to the storage and retrieval of items, (ii) considering the capability of the shuttle and the satellite to simultaneously perform different tasks, and (iii) evaluating the standard deviation of the cycle time, beside its average value. The presented models are validated through simulations performed on different warehouse layouts, in different deployment scenarios. The ultimate aim of such models is to support warehouse equipment designers in evaluating the performance of their systems, considering a multiplicity of realistic scenarios.