Bayesian Inventory Control: Accelerated Demand Learning via Exploration Boosts

Abstract

In the Bayesian newsvendor problem, it is known that the optimal decision is always greater than or equal to the myopic decision. As a result, the optimal decision can be expressed as the sum of the myopic decision plus a nonnegative “exploration boost.” In “Bayesian Inventory Control: Accelerated Demand Learning via Exploration Boosts,” Chuang and Kim characterize the form of the exploration boost in terms of basic statistical measures of uncertainty. This characterization expresses in clear terms the way in which the statistical learning and inventory control are jointly optimized; when there is a high degree of parameter uncertainty, inventory levels are boosted to induce a higher chance of observing more sales data to more quickly resolve statistical uncertainty, and as parameter uncertainty resolves, the exploration boost is reduced.