Improving the Efficiency of Modern Warehouses Using Smart Battery Placement
In the ever-evolving landscape of warehousing, the integration of unmanned ground vehicles (UGVs) has profoundly revolutionized operational efficiency. Despite this advancement, a key determinant of UGV productivity remains its energy management and battery placement strategies. While many studies e...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Future Internet |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-5903/15/11/353 |
| _version_ | 1827639782590644224 |
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| author | Nikolaos Baras Antonios Chatzisavvas Dimitris Ziouzios Ioannis Vanidis Minas Dasygenis |
| author_facet | Nikolaos Baras Antonios Chatzisavvas Dimitris Ziouzios Ioannis Vanidis Minas Dasygenis |
| author_sort | Nikolaos Baras |
| collection | DOAJ |
| description | In the ever-evolving landscape of warehousing, the integration of unmanned ground vehicles (UGVs) has profoundly revolutionized operational efficiency. Despite this advancement, a key determinant of UGV productivity remains its energy management and battery placement strategies. While many studies explored optimizing the pathways within warehouses and determining ideal power station locales, there remains a gap in addressing the dynamic needs of energy-efficient UGVs operating in tandem. The current literature largely focuses on static designs, often overlooking the challenges of multi-UGV scenarios. This paper introduces a novel algorithm based on affinity propagation (AP) for smart battery and charging station placement in modern warehouses. The idea of the proposed algorithm is to divide the initial area into multiple sub-areas based on their traffic, and then identify the optimal battery location within each sub-area. A salient feature of this algorithm is its adeptness at determining the most strategic battery station placements, emphasizing uninterrupted operations and minimized downtimes. Through extensive evaluations in a synthesized realistic setting, our results underscore the algorithm’s proficiency in devising enhanced solutions within feasible time constraints, paving the way for more energy-efficient and cohesive UGV-driven warehouse systems. |
| first_indexed | 2024-03-09T16:48:54Z |
| format | Article |
| id | doaj.art-6dea798987bf4ad186316c0ae13f33c9 |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-03-09T16:48:54Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-6dea798987bf4ad186316c0ae13f33c92023-11-24T14:43:09ZengMDPI AGFuture Internet1999-59032023-10-01151135310.3390/fi15110353Improving the Efficiency of Modern Warehouses Using Smart Battery PlacementNikolaos Baras0Antonios Chatzisavvas1Dimitris Ziouzios2Ioannis Vanidis3Minas Dasygenis4Department of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, GreeceDepartment of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, GreeceDepartment of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, GreeceDepartment of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, GreeceDepartment of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, GreeceIn the ever-evolving landscape of warehousing, the integration of unmanned ground vehicles (UGVs) has profoundly revolutionized operational efficiency. Despite this advancement, a key determinant of UGV productivity remains its energy management and battery placement strategies. While many studies explored optimizing the pathways within warehouses and determining ideal power station locales, there remains a gap in addressing the dynamic needs of energy-efficient UGVs operating in tandem. The current literature largely focuses on static designs, often overlooking the challenges of multi-UGV scenarios. This paper introduces a novel algorithm based on affinity propagation (AP) for smart battery and charging station placement in modern warehouses. The idea of the proposed algorithm is to divide the initial area into multiple sub-areas based on their traffic, and then identify the optimal battery location within each sub-area. A salient feature of this algorithm is its adeptness at determining the most strategic battery station placements, emphasizing uninterrupted operations and minimized downtimes. Through extensive evaluations in a synthesized realistic setting, our results underscore the algorithm’s proficiency in devising enhanced solutions within feasible time constraints, paving the way for more energy-efficient and cohesive UGV-driven warehouse systems.https://www.mdpi.com/1999-5903/15/11/353autonomous vehiclesmodern warehousebattery placement |
| spellingShingle | Nikolaos Baras Antonios Chatzisavvas Dimitris Ziouzios Ioannis Vanidis Minas Dasygenis Improving the Efficiency of Modern Warehouses Using Smart Battery Placement Future Internet autonomous vehicles modern warehouse battery placement |
| title | Improving the Efficiency of Modern Warehouses Using Smart Battery Placement |
| title_full | Improving the Efficiency of Modern Warehouses Using Smart Battery Placement |
| title_fullStr | Improving the Efficiency of Modern Warehouses Using Smart Battery Placement |
| title_full_unstemmed | Improving the Efficiency of Modern Warehouses Using Smart Battery Placement |
| title_short | Improving the Efficiency of Modern Warehouses Using Smart Battery Placement |
| title_sort | improving the efficiency of modern warehouses using smart battery placement |
| topic | autonomous vehicles modern warehouse battery placement |
| url | https://www.mdpi.com/1999-5903/15/11/353 |
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