Blind Network Revenue Management and Bandits with Knapsacks Under Limited Switches

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.