Minimizing Robot Digging Times to Retrieve Bins in Robotic-Based Compact Storage and Retrieval Systems

Abstract

In this thesis, we study storage policies for a robotic-based compact storage and retrieval system (RCS/RS), which provides high-density storage in distribution center and warehouse applications. In the system, items are stored in bins, and the bins are organized inside a three-dimensional grid. Robots move on top of the grid to retrieve and deliver bins. To retrieve a bin, a robot removes all the bins above one by one with its gripper, called bin digging. The closer the target bin is to the top of the grid, the less digging the robot needs to do to retrieve the bin, and the shorter the waiting time at the workstation.

In this thesis, we propose a policy to optimally arrange the bins in the grid while processing bin requests so that the most frequently accessed bins remain near the top of the grid. This improves the performance of the system and makes it responsive to changes in the bin demand. Our solution approach identifies the optimal bin arrangement in the storage facility, initiates a transition to this optimal setup, and subsequently ensures the ongoing maintenance of this arrangement for optimal performance.

We perform extensive simulations on a custom-built discrete event model of the system. Our simulation results show that under the proposed policy more than half of the bins requested are located on top of the grid, reducing bin digging compared to existing policies. Compared to existing approaches, the proposed policy results in a significant reduction in the retrieval time of the requested bins (by at least 30%) and the number of bin requests that exceed certain time thresholds (by more than 50%). Our simulation results also illustrate that the proposed bin arrangement and policy effectively reduce the working time of robots by at least 20% compared to existing policies.