Joint Pricing and Resource Allocation: An Optimal Online-Learning Approach
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918029525254144 |
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| author | Xu, Jianyu Wang, Xuan Wang, Yu-Xiang Jiang, Jiashuo |
| author_facet | Xu, Jianyu Wang, Xuan Wang, Yu-Xiang Jiang, Jiashuo |
| contents | We study an online learning problem on dynamic pricing and resource allocation, where we make joint pricing and inventory decisions to maximize the overall net profit. We consider the stochastic dependence of demands on the price, which complicates the resource allocation process and introduces significant non-convexity and non-smoothness to the problem. To solve this problem, we develop an efficient algorithm that utilizes a "Lower-Confidence Bound (LCB)" meta-strategy over multiple OCO agents. Our algorithm achieves $\tilde{O}(\sqrt{Tmn})$ regret (for $m$ suppliers and $n$ consumers), which is optimal with respect to the time horizon $T$. Our results illustrate an effective integration of statistical learning methodologies with complex operations research problems. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2501_18049 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Joint Pricing and Resource Allocation: An Optimal Online-Learning Approach Xu, Jianyu Wang, Xuan Wang, Yu-Xiang Jiang, Jiashuo Machine Learning Optimization and Control 91B06, 90B22, 91B24, 90B50, 90B80, 62P20 I.2.6 We study an online learning problem on dynamic pricing and resource allocation, where we make joint pricing and inventory decisions to maximize the overall net profit. We consider the stochastic dependence of demands on the price, which complicates the resource allocation process and introduces significant non-convexity and non-smoothness to the problem. To solve this problem, we develop an efficient algorithm that utilizes a "Lower-Confidence Bound (LCB)" meta-strategy over multiple OCO agents. Our algorithm achieves $\tilde{O}(\sqrt{Tmn})$ regret (for $m$ suppliers and $n$ consumers), which is optimal with respect to the time horizon $T$. Our results illustrate an effective integration of statistical learning methodologies with complex operations research problems. |
| title | Joint Pricing and Resource Allocation: An Optimal Online-Learning Approach |
| topic | Machine Learning Optimization and Control 91B06, 90B22, 91B24, 90B50, 90B80, 62P20 I.2.6 |
| url | https://arxiv.org/abs/2501.18049 |