Decision rules for robotic mobile fulfillment systems
The Robotic Mobile Fulfillment Systems (RMFS) is a new type of robotized, parts-to-picker material handling system, designed especially for e-commerce warehouses. Robots bring movable shelves, called pods, to workstations where inventory is put on or removed from the pods. This paper simulates both...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2019-01-01
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| Series: | Operations Research Perspectives |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214716019300946 |
| _version_ | 1811270541600882688 |
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| author | M. Merschformann T. Lamballais M.B.M. de Koster L. Suhl |
| author_facet | M. Merschformann T. Lamballais M.B.M. de Koster L. Suhl |
| author_sort | M. Merschformann |
| collection | DOAJ |
| description | The Robotic Mobile Fulfillment Systems (RMFS) is a new type of robotized, parts-to-picker material handling system, designed especially for e-commerce warehouses. Robots bring movable shelves, called pods, to workstations where inventory is put on or removed from the pods. This paper simulates both the pick and replenishment process and studies the order assignment, pod selection and pod storage assignment problems by evaluating multiple decision rules per problem. The discrete event simulation uses realistic robot movements and keeps track of every unit of inventory on every pod. We analyze seven performance measures, e.g. throughput capacity and order due time, and find that the unit throughput is strongly correlated with the other performance measures. We vary the number of robots, the number of pick stations, the number of SKUs (stock keeping units), the order size and whether returns need processing or not. The decision rules for pick order assignment have a strong impact on the unit throughput rate. This is not the case for replenishment order assignment, pod selection and pod storage. Furthermore, for warehouses with a large number of SKUs, more robots are needed for a high unit throughput rate, even if the number of pods and the dimensions of the storage area remain the same. Lastly, processing return orders only affects the unit throughput rate for warehouse with a large number of SKUs and large pick orders. Keywords: Logistics, Warehouse control, Simulation, Robotic mobile fulfillment system, Parts-to-Picker system |
| first_indexed | 2024-04-12T22:03:48Z |
| format | Article |
| id | doaj.art-32673165ba0849faa328cf36f9daef70 |
| institution | Directory Open Access Journal |
| issn | 2214-7160 |
| language | English |
| last_indexed | 2024-04-12T22:03:48Z |
| publishDate | 2019-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Operations Research Perspectives |
| spelling | doaj.art-32673165ba0849faa328cf36f9daef702022-12-22T03:15:00ZengElsevierOperations Research Perspectives2214-71602019-01-016Decision rules for robotic mobile fulfillment systemsM. Merschformann0T. Lamballais1M.B.M. de Koster2L. Suhl3Corresponding author.; DS&OR Lab, Paderborn University, Warburger Str. 100, Paderborn, 33098, GermanyRotterdam School of Management, Erasmus University, PO Box 1738, Rotterdam, 3000 DR, The NetherlandsRotterdam School of Management, Erasmus University, PO Box 1738, Rotterdam, 3000 DR, The NetherlandsDS&OR Lab, Paderborn University, Warburger Str. 100, Paderborn, 33098, GermanyThe Robotic Mobile Fulfillment Systems (RMFS) is a new type of robotized, parts-to-picker material handling system, designed especially for e-commerce warehouses. Robots bring movable shelves, called pods, to workstations where inventory is put on or removed from the pods. This paper simulates both the pick and replenishment process and studies the order assignment, pod selection and pod storage assignment problems by evaluating multiple decision rules per problem. The discrete event simulation uses realistic robot movements and keeps track of every unit of inventory on every pod. We analyze seven performance measures, e.g. throughput capacity and order due time, and find that the unit throughput is strongly correlated with the other performance measures. We vary the number of robots, the number of pick stations, the number of SKUs (stock keeping units), the order size and whether returns need processing or not. The decision rules for pick order assignment have a strong impact on the unit throughput rate. This is not the case for replenishment order assignment, pod selection and pod storage. Furthermore, for warehouses with a large number of SKUs, more robots are needed for a high unit throughput rate, even if the number of pods and the dimensions of the storage area remain the same. Lastly, processing return orders only affects the unit throughput rate for warehouse with a large number of SKUs and large pick orders. Keywords: Logistics, Warehouse control, Simulation, Robotic mobile fulfillment system, Parts-to-Picker systemhttp://www.sciencedirect.com/science/article/pii/S2214716019300946 |
| spellingShingle | M. Merschformann T. Lamballais M.B.M. de Koster L. Suhl Decision rules for robotic mobile fulfillment systems Operations Research Perspectives |
| title | Decision rules for robotic mobile fulfillment systems |
| title_full | Decision rules for robotic mobile fulfillment systems |
| title_fullStr | Decision rules for robotic mobile fulfillment systems |
| title_full_unstemmed | Decision rules for robotic mobile fulfillment systems |
| title_short | Decision rules for robotic mobile fulfillment systems |
| title_sort | decision rules for robotic mobile fulfillment systems |
| url | http://www.sciencedirect.com/science/article/pii/S2214716019300946 |
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