A Novel Approach to Analyze Inventory Allocation Decisions in Robotic Mobile Fulfillment Systems

The Robotic Mobile Fulfillment System is a newly developed automated, parts-to-picker material handling system. Storage shelves, also known as inventory pods, are moved by robots between the storage area and the workstations, which means that they can be continually repositioned during operations. This paper develops a queueing model for optimizing three key decision variables: (1) the number of pods per product (2) the ratio of the number of pick to the number of replenishment stations, and (3) the replenishment level per pod. We show that too few or too many pods per product leads to unnecessarily long order throughput times, that the ratio of the number of pick to the number of replenishment stations can be optimized for order throughput time, and that waiting to replenish until a pod is completely empty can severely decrease throughput performance

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

Planning and control of autonomous mobile robots for intralogistics: Literature review and research agenda

Autonomous mobile robots (AMR) are currently being introduced in many intralogistics operations, like manufacturing, warehousing, cross-docks, terminals, and hospitals. Their advanced hardware and control software allow autonomous operations in dynamic environments. Compared to an automated guided vehicle (AGV) system in which a central unit takes control of scheduling, routing, and dispatching decisions for all AGVs, AMRs can communicate and negotiate independently with other resources like machines and systems and thus decentralize the decision-making process. Decentralized decision-making allows the system to react dynamically to changes in the system state and environment. These developments have influenced the traditional methods and decision-making processes for planning and control. This study identifies and classifies research related to the planning and control of AMRs in intralogistics. We provide an extended literature review that highlights how AMR technological advances affect planning and control decisions. We contribute to the literature by introducing an AMR planning and control framework t

Estimating Performance in a Robotic Mobile Fulfillment System

This paper models Robotic Mobile Fulfillment Systems and analyzes their performance. A Robotic Mobile Fulfillment System is an automated, parts-to-picker storage system where robots bring pods with products to a workstation. It is especially suited for e-commerce distribution centers with large assortments of small products, and with strong demand fluctuations. Its most important feature is the ability to automatically sort inventory and to adapt the warehouse layout in a short period of time. Queueing network models are developed for both single-line and multi-line orders, to analytically estimate maximum order throughput, average order cycle time, and robot utilization. These models can be used to quickly evaluate different warehouse layouts, or robot zoning strategies. Two main contributions are that the models include accurate driving behavior of robots and multi-line orders. The results show that: (1) the analytical models accurately estimate robot utilization, workstation utilization, and order cycle time, (2) maximum order throughput is quite insensitive to the length-to-width ratio of the storage area and (3) maximum order throughput is affected by the location of the workstations around the storage area

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