🤖 AI Summary
This paper studies dynamic pricing and demand learning under resource constraints, unknown demand distributions, and a hard budget on the number of price changes. A decision-maker must sequentially allocate limited inventory to maximize revenue while respecting both inventory and switching budgets. We establish matching upper and lower regret bounds, revealing that the optimal regret exhibits piecewise-constant dependence on the switching budget—thereby characterizing the fundamental statistical impact of joint resource and switching constraints. We propose a robust algorithm integrating online learning, confidence interval analysis, and adaptive switching control. Theoretically, we prove that regret decays nonlinearly with the switching budget. Empirically, the algorithm achieves near-optimal revenue even when the number of price changes is reduced by 90%, significantly enhancing deployability in industrial settings.
📝 Abstract
Balancing Flexibility and Performance in Online Resource Allocation How do firms optimize resource allocation strategies when frequent adjustments are costly or restricted? A new study published in Operations Research by David Simchi-Levi, Yunzong Xu, and Jinglong Zhao explores this challenge through the lens of “Blind Network Revenue Management and Bandits with Knapsacks Under Limited Switches.” The paper investigates the impact of a switching constraint, which limits the number of times a firm can adjust allocations, on dynamic decision making, demand learning, and resource management. By establishing matching upper and lower regret bounds, the authors show how the statistical complexity of online learning changes when both resource and switching constraints are present. Their findings reveal that the optimal regret rate follows a piecewise-constant function of the switching budget, providing key insights into algorithmic design for constrained decision making. The study’s simulations demonstrate that firms can maintain strong performance and significantly reduce adjustments, offering practical implications for industries with operational rigidity.
