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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.08633 |
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| _version_ | 1866914869000798208 |
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| author | Shahroudnejad, Atefeh Mousavi, Payam Perepelytsia, Oleksii Sahir Staszak, David Taylor, Matthew E. Bawel, Brent |
| author_facet | Shahroudnejad, Atefeh Mousavi, Payam Perepelytsia, Oleksii Sahir Staszak, David Taylor, Matthew E. Bawel, Brent |
| contents | Optimizing warehouse layouts is crucial due to its significant impact on efficiency and productivity. We present an AI-driven framework for automated warehouse layout generation. This framework employs constrained beam search to derive optimal layouts within given spatial parameters, adhering to all functional requirements. The feasibility of the generated layouts is verified based on criteria such as item accessibility, required minimum clearances, and aisle connectivity. A scoring function is then used to evaluate the feasible layouts considering the number of storage locations, access points, and accessibility costs. We demonstrate our method's ability to produce feasible, optimal layouts for a variety of warehouse dimensions and shapes, diverse door placements, and interconnections. This approach, currently being prepared for deployment, will enable human designers to rapidly explore and confirm options, facilitating the selection of the most appropriate layout for their use-case. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_08633 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Novel Framework for Automated Warehouse Layout Generation Shahroudnejad, Atefeh Mousavi, Payam Perepelytsia, Oleksii Sahir Staszak, David Taylor, Matthew E. Bawel, Brent Artificial Intelligence Optimizing warehouse layouts is crucial due to its significant impact on efficiency and productivity. We present an AI-driven framework for automated warehouse layout generation. This framework employs constrained beam search to derive optimal layouts within given spatial parameters, adhering to all functional requirements. The feasibility of the generated layouts is verified based on criteria such as item accessibility, required minimum clearances, and aisle connectivity. A scoring function is then used to evaluate the feasible layouts considering the number of storage locations, access points, and accessibility costs. We demonstrate our method's ability to produce feasible, optimal layouts for a variety of warehouse dimensions and shapes, diverse door placements, and interconnections. This approach, currently being prepared for deployment, will enable human designers to rapidly explore and confirm options, facilitating the selection of the most appropriate layout for their use-case. |
| title | A Novel Framework for Automated Warehouse Layout Generation |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2407.08633 |