Performance Comparison of Automated Warehouses Using Simulation

The purpose of this study is to compare the performance of two types of warehouses, both of which use autonomous vehicles (AVs). One warehouse uses movable racks (MR) for storing mini-loads, whereas the other uses fixed racks (FR). In general, warehouse automation not only increases the speed of the fulfillment process but also makes the picking process more accurate. We simulate three scenarios for the MR and FR systems using Simio. Four performance measures are considered for the comparison – the average order processing time (WR), the average utilization of AVs (U), the average order processing queue length (Nq) and the average distance travelled by AVs (d). We also estimate the capital costs of both systems and use it to compare the two systems. On the basis of our assumptions and simulation results, we find that the FR system not only requires an average 56 % less capital investment than the MR system, but it also provides a more efficient warehousing automation option with relatively lower utilization of AVs, lower order processing time and lower average number of orders waiting to be processed

Comparing Industry and Academic Perspectives on Cross-docking Operations

This paper performs a comparative analysis on the industry and academic perspectives on cross-docking operations. Detailed descriptions are provided for three typical cross-dock settings by means of case illustrations. The purpose of these descriptions is to inspire break-through innovations in future cross-docking research by identifying constraints, decision problems, and performance indicators that are thoroughly anchored in current practice

Variation in Lifting Behavior During a Highly Repetitive Manual Material Task

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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