The objective of this research is to develop design conceptualization tools for unit-load warehouses that use autonomous vehicles as storage and retrieval (S/R) devices. This research will develop analytical models that integrate the strategic and tactical level design decisions for different system configurations and derive warehouse design principles based on the unique features of autonomous vehicle based storage and retrieval systems (AVS/RSs). The research will also advance the analytical foundations of AVS/RS modeling through the development of a new class of specialized semi-open queuing network models. The models will capture the unique features of AVS/RS operations such as vehicle and lift interfacing, storage and retrieval interference effects, selective transaction dispatching, batching operations, consolidated load buffering, and explicit linkage of S/R operations with in-process material flows. These models will be integrated into conceptualization tools that would facilitate comparison of alternative material handling technologies.
If successful, the design principles derived from this research will accelerate the adoption of automation in unit load S/R systems by closing the gap between AVS/RS hardware and control system technology, and the design tools needed to assess their impact on unit-load S/R operations. Warehouses using autonomous vehicles are expected to be modular and flexible and could be reconfigured to meet changing throughput, capacity or cycle time requirements. This would make them more robust to the changing requirements of the distribution systems in a supply chain. This research would bridge the gap between academic research, university education, hardware development, and technology adoption in this area using a web based repository. The repository will have design tools, principles, tutorials, case studies and course modules for use in the classroom. Practitioners, hardware providers, system integrators, and end users could share case studies and also use design tools to conceptualize systems to best suit their application needs.
