Impact of modular design: Performance evaluation and operating policies of a modular robotic compact storage and retrieval system

This study is motivated by a new robotic compact system, the modular robotic compact (MRC) storage and retrieval system. Unlike traditional robotic compact systems, the MRC system comprises multiple modules, each equipped with a robot movement layer and several storage layers. Although the modular design reduces vertical travel time during reshuffling, it compromises space utilisation and increases costs. This study models and analyses the system under different workstation assignment policies (dedicated versus shared) and reshuffling policies (immediate versus delayed). It also examines the impact of robot blocking on system throughput. The system is modelled using closed queuing networks under various operating policies, and the analytical models are validated through simulation. The results yield several managerial insights: First, the shared workstation assignment policy achieves higher utilisation and throughput compared with the dedicated workstation assignment policy. Second, the MRC system is less susceptible to robot congestion. Third, a threshold value, β, is identified such that immediate reshuffling (IR) is applied when the semi-perimeter of the footprint exceeds β, whereas delayed reshuffling (DR) is employed otherwise. Finally, for the same total storage capacity, the AutoStore system incurs lower costs, while the MRC system demonstrates superior throughput and reduced carbon emissions.