Vertical or Horizontal Transport? - Comparison of robotic storage and retrieval systems

Autonomous vehicle-based storage and retrieval systems are commonly used in e-commerce fulfillment as they allow a high and flexible throughput capacity. In these systems, roaming robots transport loads between a storage location and a workstation. Two main variants exist: Horizontal, where the robots only move horizontally and use lifts for vertical transport and a new variant Vertical, where the robots can also travel vertically in the rack. This paper builds a framework to analyze the performance of the vertical system and to compare its throughput capacity with the horizontal system. We build closed-queueing network models for this that in turn are used to optimize the design. The results show that the optimal height-to-width ratio of a vertical system is around 1. As a large number of system robots may lead to blocking and delays, we compare the effect of two different robot blocking protocols on the system throughput: robot Recirculation and Wait-On-Spot. The Wait-On-Spot policy produces a higher system throughput when the number of robots in the system is small. However, for a large number of robots in the system, the Recirculation policy dominates the Wait-On-Spot policy. Finally, we compare the operational costs of the vertical and the horizontal transport system. For systems with one load/unload (L/U) point, the vertical system always produces a similar or higher system throughput, with a lower operating cost comp

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

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

Performance evaluation of warehouses with automated storage and retrieval technologies.

In this dissertation, we study the performance evaluation of two automated warehouse material handling (MH) technologies - automated storage/retrieval system (AS/RS) and autonomous vehicle storage/retrieval system (AVS/RS). AS/RS is a traditional automated warehouse MH technology and has been used for more than five decades. AVS/RS is a relatively new automated warehouse MH technology and an alternative to AS/RS. There are two possible configurations of AVS/RS: AVS/RS with tier-captive vehicles and AVS/RS with tier-to-tier vehicles. We model the AS/RS and both configurations of the AVS/RS as queueing networks. We analyze and develop approximate algorithms for these network models and use them to estimate performance of the two automated warehouse MH technologies. Chapter 2 contains two parts. The first part is a brief review of existing papers about AS/RS and AVS/RS. The second part is a methodological review of queueing network theory, which serves as a building block for our study. In Chapter 3, we model AS/RSs and AVS/RSs with tier-captive vehicles as open queueing networks (OQNs). We show how to analyze OQNs and estimate related performance measures. We then apply an existing OQN analyzer to compare the two MH technologies and answer various design questions. In Chapter 4 and Chapter 5, we present some efficient algorithms to solve SOQN. We show how to model AVS/RSs with tier-to-tier vehicles as SOQNs and evaluate performance of these designs in Chapter 6. AVS/RS is a relatively new automated warehouse design technology. Hence, there are few efficient analytical tools to evaluate performance measures of this technology. We developed some efficient algorithms based on SOQN to quickly and effectively evaluate performance of AVS/RS. Additionally, we present a tool that helps a warehouse designer during the concepting stage to determine the type of MH technology to use, analyze numerous alternate warehouse configurations and select one of these for final implementation

Performance evaluation of shuttle-based storage and retrieval systems using discrete-time queueing network models

Shuttle-based storage and retrieval systems (SBS/RSs) are an important part of today‘s warehouses. In this work, a new approach is developed that can be applied to model different configurations of SBS/RSs. The approach is based on the modeling of SBS/RSs as discrete-time open queueing networks and yields the complete probability distributions of the performance measures