Certified Inventory Control of Critical Resources
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913361056235520 |
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| author | Hult, Ludvig Zachariah, Dave Stoica, Petre |
| author_facet | Hult, Ludvig Zachariah, Dave Stoica, Petre |
| contents | Inventory control is subject to service-level requirements, in which sufficient stock levels must be maintained despite an unknown demand. We propose a data-driven order policy that certifies any prescribed service level under minimal assumptions on the unknown demand process. The policy achieves this using any online learning method along with integral action. We further propose an inference method that is valid in finite samples. The properties and theoretical guarantees of the method are illustrated using both synthetic and real-world data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_15105 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Certified Inventory Control of Critical Resources Hult, Ludvig Zachariah, Dave Stoica, Petre Machine Learning Inventory control is subject to service-level requirements, in which sufficient stock levels must be maintained despite an unknown demand. We propose a data-driven order policy that certifies any prescribed service level under minimal assumptions on the unknown demand process. The policy achieves this using any online learning method along with integral action. We further propose an inference method that is valid in finite samples. The properties and theoretical guarantees of the method are illustrated using both synthetic and real-world data. |
| title | Certified Inventory Control of Critical Resources |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2405.15105 |