Abstract
In robotic mobile fulfillment systems, human pickers don’t go to the inventory area to search for and pick the ordered items. Instead, robots carry shelves (called “pods”) containing ordered items from the inventory area to picking stations. At the picking stations, pickers put ordered items into totes; then these items are transported to the packing stations. This type of warehousing system relieves the human pickers and improves the picking process. In this paper, we concentrate on decisions about the assignment of pods to stations and orders to stations to fulfill picking for each incoming customer’s order. In previous research for an RMFS with multiple picking stations, these decisions are made sequentially with heuristics. Instead, we present a new MIP-model to integrate both decision problems. To improve the system performance even more, we extend our model by splitting orders. This means parts of an order are allowed to be picked at different stations. To the best of the authors’ knowledge, this is the first publication on split orders in an RMFS. And we prove the computational complexity of our models. We analyze different performance metrics, such as pile-on, pod-station visits, robot moving distance and throughput. We compare the results of our models in different instances with the sequential method in our open-source simulation framework RAWSim-O. The integration of the decisions brings better performances, and allowing split orders further improves the performances (for example: increasing throughput by 46%). In order to reduce the computational time for a real-world application, we have proposed a heuristic.
Highlights
The most important and time-consuming task in a warehouse is the collection of items from their storage locations to fufill customer orders
We introduce in this paper a new way to define the capacity of a picking station – limited by the number of items to be handled at a time – that works for both, whole orders and split orders
Compared to the sequential approach for the number of pod-station visits per order: The integrated model improves this performance by 20% to 30% for different instance sets, the split-among-stations model shows improvements of about 50%, and the split-over-time model improves on the sequential solution by 57% to 80% for different instance sets
Summary
The most important and time-consuming task in a warehouse is the collection of items from their storage locations to fufill customer orders. Robots are sent to carry storage units, so-called “pods,” from the inventory area and bring them to human operators, who work at picking stations. The items are picked according to the customers’ orders This system is called robotic mobile fulfillment system (RMFS). There are some other suppliers of such systems, such as Scallog, Swisslog (KUKA), GreyOrange and Hitachi (see Banker (2016)). We consider an efficient order picking system as a system to handle more orders within minimal time (as suggested in Van Gils et al (2018) for picker-toparts systems). In order to achieve that, we want to reduce idle time between changes of pods, so in this paper we aim at minimizing the visits by pods to stations for given sets of orders for the POA and PPS problems.
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