Charles J. Malmborg

TL;DR: In this article, 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.

TL;DR: In this paper, the authors proposed conceptualization models for automated unit load storage and retrieval systems based on autonomous vehicle technology. And they provided a practical means of predicting key aspects of system performance based on five design variables that drive the majority of system costs.

TL;DR: A network queuing approach is used to estimate performance measures for Autonomous Vehicle Storage and Retrieval (AVS/R) Systems using opportunistic interleaving and provides the additional advantage of flexibility for modeling the interfaces between a storage system and the overall material flow system in a facility.

TL;DR: In this article, the authors investigate the performance impact of vehicle assignment and configuration decisions on the performance of a multi-class semi-open queuing network with class switching in an autonomous vehicle-based storage and retrieval system.

TL;DR: In this article, the authors model variants of the two systems as an open queuing network (OQN) and use an existing tool for the analysis of OQNs, called the manufacturing system performance analyser (MPA), for analysing the performance of the AS/RS and AVS/RS.