Storage Assignment with Rack-Moving Mobile Robots in KIVA Warehouses

Abstract

This paper treats an automated warehousing system, known as the KIVA system, where mobile robots lift complete racks and autonomously move them to stationary pickers. This innovative parts-to-picker system relieves the pickers from unproductive walking times without requiring high investment costs into inflexible hardware such as conveyors, storage and retrieval machines, or lifts. In this context, we treat the decision problem where to park the racks during order processing when they are consistently moved between the picking stations and the storage area. We formalize the resulting rack assignment problem as a special interval scheduling problem and introduce a new matheuristic dubbed adaptive programming. In a comprehensive computational study, we compare the results of our optimization approach with simple rule-based assignment policies. Our results reveal that the well-established rules widely applied in traditional picker-to-parts warehouses, for example, random and dedicated storage, lead to considerable optimality gaps. Adapted rules, however, which consider the peculiarities of KIVA warehouses, lead to very good results. The e-companion is available at https://doi.org/10.1287/trsc.2018.0826 .

Velocity‐Based Storage Assignment in Semi‐Automated Storage Systems

“…Our work differs from Weidinger et al. () in that in our context, we do not have a schedule of future pod visits to the picking stations. As a consequence, we examine dynamic storage policies that will rely on forecasts of when the pod will next return to the picking station.…”

Adaptive Task Planning for Multi-Robot Smart Warehouse

“…These methods proactively increase picking order throughput since they provide more efficient options to select pods. [12] focus on efficient pod alignment on the warehouse storage area. [13] prove that spreading inventory across many pods significantly decreases the time of collecting orders.…”

“…The system may limit number of replenishment items even though the pod has enough space. Several studies (such as [12], [13]) analyze effects of pod selection and inventory allocation in the replenishment process. These works show that classification of items and spreading the inventory across multiple pods will increase the pod selection options for order tasks and improve the order throughput performance.…”

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

“…Additionally, robots are free to return the storage pods to any available location after each retrieval, whereas the location of products in a manual warehouse usually does not change. Weidinger, Boysen, and Briskorn (2018) study the optimal pod assignment to storage locations to minimise robots' travel time, given that the pick stations sequence the robot has to visit, and product assignment to pods are known. The inventory and combination of products on each storage unit significantly impact the order picking performance.…”