An analytical performance model for robotic compact storage and retrieval systems (RCS/RS)

Abstract

The rapid evolution of technology, coupled with challenges like climate change, rising land costs, demographic shifts, and multiple crises such as COVID-19 and wars, has pushed companies toward installing high-density automated storage systems, including robotic compact storage and retrieval systems (RCS/RS). These systems, characterised by stacked containers in a block layout, are crucial for modern logistics due to their high throughput, storage density, scalability, and resilience. Disruptions caused by global crises have highlighted the vulnerabilities in traditional supply chains, emphasising the need for more robust and efficient storage solutions.However, easily accessible and fast-solvable analytical models for RCS/RS performance calculation have been lacking. This cumulative thesis addresses this gap by developing an analytical model for performance approximation of RCS/RS. Initially, a cycle time model is created to predict the throughput of a single robot operating on the grid. This approach is then extended to multiple robots serving a single station, considering their interactions at the picking station using an M|M|1|K queueing model. Finally, the model is expanded to multiple robots and multiple picking stations, utilising a multi-server M|M|m|K model.The models build on existing methods for performance investigation of automated storage systems and are validated through discrete event simulation. The rigorous validation process ensures the precision and reliability of the analytical approaches. The resulting models offer straightforward, closed-form expressions that can be easily implemented using standard algebraic or spreadsheet tools. Each paper in this thesis includes extensive parameter variations and optimisation examples, demonstrating the practical applications and general validity of the performance models.This analytical approach provides a versatile performance evaluation tool for RCS/RS, enabling quick performance estimates under various scenarios. This aids stakeholders - including customers, consultants, material handling suppliers, and manufacturers - in making informed decisions for warehouse design and purchasing, thereby enhancing the resilience and efficiency of supply chains in the face of multiple crises.