Item Assignment Problem in a Robotic Mobile Fulfillment System
| Autor: | Zuo-Jun Max Shen, Cristobal Pais, Hyun-Jung Kim |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Mathematical optimization Lift (data mining) business.industry Computer science Heuristic (computer science) Computation 02 engineering and technology Automation 020901 industrial engineering & automation Control and Systems Engineering Order fulfillment Robot Electrical and Electronic Engineering business Cluster analysis Assignment problem |
| Zdroj: | IEEE Transactions on Automation Science and Engineering. 17:1854-1867 |
| ISSN: | 1558-3783 1545-5955 |
| DOI: | 10.1109/tase.2020.2979897 |
| Popis: | A robotic mobile fulfillment system (RMFS) performs the order fulfillment process by bringing inventory to workers at pick-pack-and-ship warehouses. In the RMFS, robots lift and carry shelving units, called inventory pods, from storage locations to picking stations where workers pick items off the pods and put them into shipping cartons. The robots then return the pods to the storage area and transport other pods. In this article, we consider an item assignment problem in the RMFS in order to maximize the sum of similarity values of items in each pod. We especially focus on a reoptimization heuristic to address the situation where the similarity values are altered so that a good assignment solution can be obtained quickly with the changed similarity values. A constructive heuristic algorithm for the item assignment problem is developed, and then, a reoptimization heuristic is proposed based on the constructive heuristic algorithm. Then, computational results for several instances of the problem with 10–500 items are presented. We further analyze the case for which an item type can be placed into two pods. Note to Practitioners —This article proposes an efficient heuristic algorithm for assigning items to pods in a robotic mobile fulfillment system (RMFS) so that items ordered together frequently are put into the same pod. Computational results with 10–500 items show that the gaps from upper bounds are very small on average. For cases where the similarity values between items change or their estimation is not accurate due to the fluctuations in demand, a reoptimization heuristic algorithm that alters the original assignment is developed. The experimental results show that the reoptimization algorithm is robust when perturbation levels are approximately 40%–50% of the original similarity values with much less computation times. We believe that this research work can be very helpful for operating the RMFS efficiently. |
| Databáze: | OpenAIRE |
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