Operating Policies in Robotic Compact Storage and Retrieval Systems

Abstract

Robotic compact storage and retrieval systems (RCSRS) have seen many implementations over the last few years. In such a system, the inventory items are stored in bins, organized in a grid. In each cell of the grid, a certain number of bins are stored on top of each other. Robots with transport and lifting capabilities move on the grid roof to transport bins between manual workstations and storage stacks. We estimate performance and evaluate storage policies of RCSRS, considering both dedicated and shared storage policies coupled with random and zoned storage stacks. Semi-open queuing networks (SOQNs) are built to estimate the system performance, which can handle both immediate and delayed reshuffling processes. We approximate the models by reduced SOQNs with two load-dependent service nodes and use the matrix-geometric method to solve them. Both simulations and a real case are used to validate the analytical models. Assuming a given number of stored products, our models can be used to optimize not only the length-to-width ratio of the system but also the stack height, depending on the storage strategy used. For a given inventory and optimal system configuration, we demonstrate that the dedicated storage policy outperforms the shared storage policy when the objective is to minimize dual command throughput time. However, from a cost perspective, with a maximum dual command throughput time as a constraint, we show that shared storage substantially outperforms dedicated storage. The annualized costs of dedicated storage are up to twice as large as those of shared storage, as a result of the larger number of storage positions required by dedicated storage and the relatively lower filling degree of storage stacks. The online appendix is available at https://doi.org/10.1287/trsc.2017.0786 .

On Truncation of the Matrix-Geometric Stationary Distributions

“…This Corollary 1 follows directly from Theorem 1 applied to each diagonal block of the matrices (17) and (18).…”

“…Since the first papers [14,15] on semi-open networks appeared, the use of these models has become increasingly widespread [16][17][18]. However, the exact analysis even of the simplest network, with two exponential single-server nodes, Poisson arrival process, and an infinite queue, remains a difficult task [15].…”

How to benefit from order data: correlated dispersed storage assignment in robotic warehouses

“…Research on storage assignment policies widely studies the class-based assignment that uses the turnover frequency of the products to rank them, group them into different classes and to assign these classes to storage locations (Hausman, Schwarz, and Graves 1976;Graves, Hausman, and Schwarz 1977;Zaerpour, Yu, and de Koster 2017a;Zou, De Koster, and Xu 2018). Several studies discuss the optimal number of classes in the classbased storage assignment (Van den Berg and Gademann 2000;Petersen, Aase, and Heiser 2004;Yu, de Koster, and Guo 2015).…”

MILP Model and a Rolling Horizon Algorithm for Crane Scheduling in a Hybrid Storage Container Terminal

“…Gharehgozli et al [19] show that this problem can be solved in polynomial time and develop different polynomial time algorithms for different storage and retrieval systems. Zou et al [20] consider a compact storage and retrieval system by using both dedicated and shared storage policies to minimize dual command throughput time. Considering container relocations are not addressed in single crane scheduling problems, Yuan and Tang [21] incorporate a single yard crane scheduling problem with relocations to reduce crane travel and relocations.…”