Joint Pricing and Resource Allocation: An Optimal Online-Learning Approach
This work addresses a significant problem for operations research and e-commerce platforms, providing an optimal online-learning approach for joint pricing and resource allocation.
The authors tackled the problem of joint pricing and resource allocation in an online learning setting, achieving an optimal regret of $ ilde{O}(sqrt{Tmn})$ for $m$ suppliers and $n$ consumers. This result maximizes the overall net profit in a dynamic environment.
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.