Algorithm Selection for Allocating Pods Within Robotic Mobile Fulfillment Systems: A Hyper-Heuristic Approach
Robotic Mobile Fulfillment Systems (RMFS) are an example of warehouse automation. Nonetheless, the complexity of RMFS is such that tackling the entire problem at once is unfeasible. So, this work focuses on a component known as the Pod Allocation Problem (PAP). We analyze the performance of 20 hyper...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10843692/ |
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| author | Maria Torcoroma Benavides-Robles Jorge M. Cruz-Duarte Jose Carlos Ortiz-Bayliss Ivan Amaya |
| author_facet | Maria Torcoroma Benavides-Robles Jorge M. Cruz-Duarte Jose Carlos Ortiz-Bayliss Ivan Amaya |
| author_sort | Maria Torcoroma Benavides-Robles |
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| description | Robotic Mobile Fulfillment Systems (RMFS) are an example of warehouse automation. Nonetheless, the complexity of RMFS is such that tackling the entire problem at once is unfeasible. So, this work focuses on a component known as the Pod Allocation Problem (PAP). We analyze the performance of 20 hyper-heuristic models over 60 instances and compare them against a baseline of six low-level heuristics. Our data revealed three key insights. First, sequence-based hyper-heuristics outperformed low-level heuristics in 22% of the models we tested. Second, we noted that, under certain conditions, even the worst-performing heuristics can lead to successful hyper-heuristics. For example, when simulating for 24 hours, the best hyper-heuristic uses the worst heuristic for 80% of the time, and yields a solution with a throughput time 1.4% better than that of the best heuristic. Finally, the resulting model is affected by simulation time, sequence ordering, and heuristic subset. |
| format | Article |
| id | doaj-art-829ddc562bcb43319a4c7c970b7061e3 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-829ddc562bcb43319a4c7c970b7061e32025-01-25T00:01:18ZengIEEEIEEE Access2169-35362025-01-0113140101402810.1109/ACCESS.2025.353084210843692Algorithm Selection for Allocating Pods Within Robotic Mobile Fulfillment Systems: A Hyper-Heuristic ApproachMaria Torcoroma Benavides-Robles0https://orcid.org/0000-0003-2051-8969Jorge M. Cruz-Duarte1https://orcid.org/0000-0003-4494-7864Jose Carlos Ortiz-Bayliss2https://orcid.org/0000-0003-3408-2166Ivan Amaya3https://orcid.org/0000-0002-8821-7137School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, MexicoCNRS, Inria, Centrale Lille, UMR 9189 CRIStAL, University of Lille, Lille, FranceSchool of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, MexicoSchool of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, MexicoRobotic Mobile Fulfillment Systems (RMFS) are an example of warehouse automation. Nonetheless, the complexity of RMFS is such that tackling the entire problem at once is unfeasible. So, this work focuses on a component known as the Pod Allocation Problem (PAP). We analyze the performance of 20 hyper-heuristic models over 60 instances and compare them against a baseline of six low-level heuristics. Our data revealed three key insights. First, sequence-based hyper-heuristics outperformed low-level heuristics in 22% of the models we tested. Second, we noted that, under certain conditions, even the worst-performing heuristics can lead to successful hyper-heuristics. For example, when simulating for 24 hours, the best hyper-heuristic uses the worst heuristic for 80% of the time, and yields a solution with a throughput time 1.4% better than that of the best heuristic. Finally, the resulting model is affected by simulation time, sequence ordering, and heuristic subset.https://ieeexplore.ieee.org/document/10843692/Robotic mobile fulfillment systemhyper-heuristicsheuristicspod allocationwarehouseKiva system |
| spellingShingle | Maria Torcoroma Benavides-Robles Jorge M. Cruz-Duarte Jose Carlos Ortiz-Bayliss Ivan Amaya Algorithm Selection for Allocating Pods Within Robotic Mobile Fulfillment Systems: A Hyper-Heuristic Approach IEEE Access Robotic mobile fulfillment system hyper-heuristics heuristics pod allocation warehouse Kiva system |
| title | Algorithm Selection for Allocating Pods Within Robotic Mobile Fulfillment Systems: A Hyper-Heuristic Approach |
| title_full | Algorithm Selection for Allocating Pods Within Robotic Mobile Fulfillment Systems: A Hyper-Heuristic Approach |
| title_fullStr | Algorithm Selection for Allocating Pods Within Robotic Mobile Fulfillment Systems: A Hyper-Heuristic Approach |
| title_full_unstemmed | Algorithm Selection for Allocating Pods Within Robotic Mobile Fulfillment Systems: A Hyper-Heuristic Approach |
| title_short | Algorithm Selection for Allocating Pods Within Robotic Mobile Fulfillment Systems: A Hyper-Heuristic Approach |
| title_sort | algorithm selection for allocating pods within robotic mobile fulfillment systems a hyper heuristic approach |
| topic | Robotic mobile fulfillment system hyper-heuristics heuristics pod allocation warehouse Kiva system |
| url | https://ieeexplore.ieee.org/document/10843692/ |
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