Skip to main content

Please enter a keyword and click the arrow to search the site

Learning and Pricing with Inventory Constraints

Subject

Management Science and Operations

Authors / Editors

Chen Q;Wang H;Wang Z

Biographies

Publication Year

2022

Abstract

The presence of inventory constraints is prevalent in revenue management applications and affects how pricing should be managed. This chapter reviews recent developments for the joint learning and pricing problem with inventory constraints using both frequentist and Bayesian approaches. As the total demand and supply in the system scales proportionally, information-theoretical lower bounds indicate that any algorithm must have a regret (i.e., the cumulative expected revenue loss comparing to the full-information optimal solution) that is at least in the square root order of the scaling factor. We introduce effective heuristics that match the square root regret up to multiplicative logarithmic terms. For the frequentist approach, if there is a single product, a shrinking price interval heuristic achieves square root regret. When there are multiple products, a self-adjusting heuristic achieves square root regret when the demand comes from a known class of parametric functions; if the class of functions is unknown but the demand function is sufficiently smooth, then such heuristic can attain a regret which is arbitrarily close to square root. For the Bayesian approach, a Thompson sampling-based heuristic can achieve square root regret.

Available on ECCH

No


Select up to 4 programmes to compare

Select one more to compare
×
subscribe_image_desktop 5949B9BFE33243D782D1C7A17E3345D0

Sign up to receive our latest news and business thinking direct to your inbox

×

Sign up to receive our latest course information and business thinking

Leave your details above if you would like to receive emails containing the latest thought leadership, invitations to events and news about courses that could enhance your career. If you would prefer not to receive our emails, you can still access the case study by clicking the button below. You can opt-out of receiving our emails at any time by visiting: https://london.edu/my-profile-preferences or by unsubscribing through the link provided in our emails. View our Privacy Policy for more information on your rights.