Conceptualizing tools for autonomous vehicle storage and retrieval systems

TLDR

Autonomous vehicle storage and retrieval systems use rail‑guided vehicles on rectilinear paths within unit load racks, with vertical movement via lifts at fixed rack peripheries, allowing fleet size and lift numbers to be matched to transaction demand as an alternative to traditional systems. The authors propose analytical conceptualizing tools to model expected performance based on key system attributes such as storage capacity, rack configuration, and fleet size. These tools model expected performance as a function of those attributes, enabling analysis of storage capacity, rack configuration, and fleet size. The models were demonstrated on a sample problem and compared favorably with existing analytical tools for automated storage and retrieval systems.

Abstract

Autonomous vehicle storage and retrieval systems utilize rail-guided vehicles moving in rectilinear paths within and between the aisles of unit load storage racks. Vertical vehicle movement is provided by lifts installed at fixed locations along the rack periphery. As an alternative to traditional automated storage and retrieval systems, autonomous vehicle systems enable users to match vehicle fleet size and the number of lifts to the level of transactions demand in a storage system. Analytical conceptualizing tools based on the features of autonomous vehicle systems are proposed for modelling expected performance as a function of key system attributes including storage capacity, rack configuration and fleet size. The models are demonstrated for a sample problem and compared with analytical conceptualizing tools used for automated storage and retrieval systems.