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

On the performance of robotic parts-to-picker order picking systems

Order picking is the activity in which a number of items are retrieved from a warehousing system to satisfy a number of customer orders. Automating order picking systems has become a common response to the wide variety of products and components stored in today’s warehouses and the short delivery lead times requested by today’s customers. As a result, new technical solutions have reached the market, including robotic parts-to-picker order picking systems such as robot-based compact storage and retrieval systems (RCSRSs) and robotic mobile fulfilment systems (RMFSs).\ua0Despite the increased use of robotic parts-to-picker order picking systems, knowledge about how they perform in terms of throughput, order lead time, human factors, quality, flexibility, operational efficiency, and investment and operational costs needs to be further developed, as does knowledge about how their performance is affected by the order picking system’s design and context. Accordingly, the purpose of this thesis is to expand knowledge about the performance of robotic parts-to-picker order picking systems by investigating how their design and context influence their performance. \ua0The thesis is built upon three studies: a systematic literature review study focusing on automated order picking systems, a multiple-case study on RCSRSs, and a single-case study on RMFSs. First, the systematic literature review study on the performance of automated order picking systems provides an overview of literature on order picking systems to date, aspects of their performance, and how their performance relates to their design. Second, the multiple-case study sheds light on characteristics of the performance of RCSRSs and the relationships between their performance and design. Third and last, the single-case study affords insights on how the context of RMFSs affects their performance.\ua0The thesis contributes to practice by providing guidance to decision makers within industry in terms of the performance to expect of robotic parts-to-picker OPSs depending on their design and context. In turn, such knowledge can facilitate the selection and design of an OPS or else the redesign of a current system. At the same time, the thesis contributes to theory by providing a synthesis of literature addressing the performance of automated OPSs and by outlining the relationships between their design and performance

Advanced Storage and Retrieval Policies in Automated Warehouses

Warehouses are key components in supply chain. They facilitate the product flow from production to distribution. The performance of supply chains relies on the performance of warehouses and distribution centers. Being able to realize short order delivery lead times, in retail and ecommerce particularly, is important for warehouses. Efficient and responsive storage and retrieval operations can help in realizing a short order delivery lead time. Additionally, space scarcity has brought some companies to use high-density storage systems that increase space usage in the warehouse. In such storage systems, most of the available space is used for storing products, as little space is needed for transporting loads. However, the throughput capacity of high-density storage systems is typically low. New robotic and automated technologies help warehouses to increase their throughput and responsiveness. Warehouses adapting such technologies require customized storage and retrieval policies fit for automated operations. This thesis studies storage and retrieval policies in warehouses using several common and emerging automated technologies

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

Optimizing the Performance of Robotic Mobile Fulfillment Systems

A robotic mobile fulfillment system is a novel type of automated part-to-picker material handling system. In this type of system, robots transport mobile shelves, called pods, containing items between the storage area and the workstations. It is well suited to e-commerce, due to its modularity and it's ability to adapt to changing orders patterns. Robots can nearly instantaneously switch between inbound and outbound tasks, pods can be continually repositioned to allow for automatic sorting of the inventory, pods can contain many different types of items, and unloaded robots can drive underneath pods, allowing them to use completely different routes than loaded robots. This thesis studies the performance of robotic mobile fulfillment systems by solving decision problems related to warehouse design, inventory and resource allocation, and real-time operations. For warehouse design, a new queueing network is developed that incorporates realistic robot movement, storage zones, and multi-line orders. For inventory allocation, we develop a new type of queueing network, the cross-class matching multi-class semi-open queueing network, which can be applied to other systems as well. Resource (re)allocation is modeled by combining queueing networks with Markov decision processes while including time-varying demand. This model compares benchmark policies from practice wit

JURA: a collaborative solution to Hong Kong academic libraries storage challenge

Purpose: The purpose of this paper is to discuss issues and concerns raised in a collaborative and cooperative central storage facility for Hong Kong academic libraries. Design/methodology/approach: The approach is to propose and to implement a joint storage business plan and a possibility of acting for others to consider similar storage facilities. Findings: Useful experiences have been gained while planning a central storage facility. Research limitations/implications: The proposed JURA project is for Hong Kong academic libraries. Practical implications: The sharing of JURA proposal to create a central storage will inform the libraries around the region of the benefits of having a useful facility in the long term. Originality/value: The paper will inform others wishing to set up collaborative storages on governance, storage systems, business plan, problems and issues in what is still a relatively unexplored approach to storage problems. © Emerald Group Publishing Limited.postprin

NASA space station automation: AI-based technology review

Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures

Inventory Allocation in Robotic Mobile Fulfillment Systems

A Robotic Mobile Fulfillment System is a recently developed automated, parts-to- picker material handling system. Robots can move storage shelves, also known as inventory pods, between the storage area and the workstations and can continually reposition them during operations. This paper shows how to optimize three key decision variables: (1) the number of pods per product (2) the ratio of the number of pick stations to replenishment stations, and (3) the replenishment level per pod. Our results show that throughput performance improves substantially when inventory is spread across multiple pods, when an optimum ratio between the number of pick stations to replenishment stations is achieved and when a pod is replenished before it is completely empty. This paper contributes methodologically by introducing a new type of Semi-Open Queueing Networks (SOQN): cross-class matching multi- class SOQN, by deriving necessary stability conditions, and by introducing a novel interpretation of the classes

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