Robotized Warehouse Systems: Developments and Research Opportunities

Robotized handling systems are increasingly applied in distribution centers. They require little space, provide flexibility in managing varying demand requirements, and are able to work 24/7. This makes them particularly fit for e-commerce operations. This paper reviews new categories of robotized handling systems, such as the shuttle-based storage and retrieval systems, shuttle-based compact storage systems, and robotic mobile fulfillment systems. For each system, we categorize the literature in three groups: system analysis, design optimization, and operations planning and control. Our focus is to identify the research issue and OR modeling methodology adopted to analyze the problem. We find that many new robotic systems and applications have hardly been studied in academic literature, despite their increasing use in practice. Due to unique system features (such as autonomous control, networked and dynamic operation), new models and methods are needed to address the design and operational control challenges for such systems, in particular, for the integration of subsystems. Integrated robotized warehouse systems will form the next category of warehouses. All vital warehouse design, planning and control logic such as methods to design layout, storage and order picking system selection, storage slotting, order batching, picker routing, and picker to order assignment will have to be revisited for new robotized warehouses

A reinforcement learning approach for transaction scheduling in a shuttle-based storage and retrieval system

With recent Industry 4.0 developments, companies tend to automate their industries. Warehousing companies also take part in this trend. A shuttle-based storage and retrieval system (SBS/RS) is an automated storage and retrieval system technology experiencing recent drastic market growth. This technology is mostly utilized in large distribution centers processing mini-loads. With the recent increase in e-commerce practices, fast delivery requirements with low volume orders have increased. SBS/RS provides ultrahigh-speed load handling due to having an excess amount of shuttles in the system. However, not only the physical design of an automated warehousing technology but also the design of operational system policies would help with fast handling targets. In this work, in an effort to increase the performance of an SBS/RS, we apply a machine learning (ML) (i.e., Q-learning) approach on a newly proposed tier-to-tier SBS/RS design, redesigned from a traditional tier-captive SBS/RS. The novelty of this paper is twofold: First, we propose a novel SBS/RS design where shuttles can travel between tiers in the system; second, due to the complexity of operation of shuttles in that newly proposed design, we implement an ML-based algorithm for transaction selection in that system. The ML-based solution is compared with traditional scheduling approaches: first-in-first-out and shortest process time (i.e., travel) scheduling rules. The results indicate that in most cases, the Q-learning approach performs better than the two static scheduling approaches

A Performance Calculator for Shuttle-based Storage and Retrieval System Design

In this study, we present an analytical model based tool that can estimate critical performance measures from a pre-defined shuttle-based storage and retrieval system (SBS/RS) design. SBS/RS is relatively a new automated storage and retrieval technology and mostly used for mini-load material handling. In this study, we develop an open queuing network model based tool estimating critical performance measures: the mean travel time of lifts/shuttles, utilization of lifts/shuttles, amount of energy consumption and energy regeneration per transaction, waiting times and number of jobs waiting in queues, etc., from a pre-defined SBS/RS design. By the developed tool, one can evaluate an SBS/RS design’s performance promptly by changing the input design parameters (e.g., distance between two adjacent bays/tiers, velocity of vehicles, acceleration/deceleration of vehicles, number of tiers, number of bays, number of aisles, arrival rates, weight of totes, etc.) in these systems

A reinforcement learning approach for transaction scheduling in a shuttle-based storage and retrieval system

With recent Industry 4.0 developments, companies tend to automate their industries. Warehousing companies also take part in this trend. A shuttle-based storage and retrieval system (SBS/RS) is an automated storage and retrieval system technology experiencing recent drastic market growth. This technology is mostly utilized in large distribution centers processing mini-loads. With the recent increase in e-commerce practices, fast delivery requirements with low volume orders have increased. SBS/RS provides ultrahigh-speed load handling due to having an excess amount of shuttles in the system. However, not only the physical design of an automated warehousing technology but also the design of operational system policies would help with fast handling targets. In this work, in an effort to increase the performance of an SBS/RS, we apply a machine learning (ML) (i.e., Q-learning) approach on a newly proposed tier-to-tier SBS/RS design, redesigned from a traditional tier-captive SBS/RS. The novelty of this paper is twofold: First, we propose a novel SBS/RS design where shuttles can travel between tiers in the system; second, due to the complexity of operation of shuttles in that newly proposed design, we implement an ML-based algorithm for transaction selection in that system. The ML-based solution is compared with traditional scheduling approaches: first-in-first-out and shortest process time (i.e., travel) scheduling rules. The results indicate that in most cases, the Q-learning approach performs better than the two static scheduling approaches

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

Analytical model to estimate performances of autonomous vehicle storage and retrieval systems for product totes

In today’s competitive scenario of increasingly faster deliveries and smaller order sizes, material-handling providers are progressively developing new solutions. A recent, automated material-handling technology for unit load storage and retrieval consists of an autonomous vehicle storage and retrieval system (AVS/RS). The present paper presents an analytical model to estimate the performances (the transaction cycle time and waiting times) of AVS/RS for product tote movement. The model is based on an open queuing network approach. The model effectiveness in performance estimation is validated through simulation

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

Hybrid model for the design of a deep-lane multisatellite AVS/RS

The autonomous vehicle storage and retrieval system (AVS/RS) significantly improves the responsiveness and throughput of the traditional automated storage and retrieval system (AS/RS) in regard to handling unit loads. The AVS/RS consists of multiple tiers connected to an elevator system and is equipped with at least two autonomous vehicles, that is, a shuttle and satellite. Other necessary equipment are the lifts and input/output buffer areas. This paper aims to present and apply an original hybrid analytical-simulative model for the design of a deep-lane and multisatellite AVS-RS by evaluating and controlling the system performance. This AVS-RS is equipped with multiple free and non-free satellites for each tier. As an original contribution, this study reviews the literature on AVS/RS according to the introduction of multiple features categorized into five homogeneous groups: (1) rack configuration, (2) vehicle kinematics and configuration, (3) dispatching rules, (4) modeling approach, and (5) validation. Two of the most critical issues in existing research studies are the random arrival time of storage and retrieval transactions and the random storage policy. The proposed modeling approach is data-driven and based on realistic assumptions, filling the gap between the literature and real applications. This hybrid model is applied to a case study of the beverage industry according to a what-if comparative and competitive multiscenario analysis. This data-driven assessment supports the decision-making process on the number of satellites for each tier, while simultaneously controlling the service and waiting times, system throughput, and vehicle utilization. The analysis based on the maximum system throughput estimation demonstrates that introducing more than two satellites does not increase the productivity of the system

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