← Search

Bayesian Inventory Control: Accelerated Demand Learning via Exploration Boosts

Ya-Tang Chuang¹; Michael Jong Kim²

¹ Department of Industrial and Information Management, National Cheng Kung University, Tainan City 701, Taiwan; · ² Sauder School of Business, University of British Columbia, Vancouver, British Columbia M5S 3G8, Canada

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.

DOI

10.1287/opre.2023.2467

Volume

71 (5)

Pages

1515-1529

Language

en

Export

BibTeX

Sources

crossref