Data-Driven Inventory Control with Shifting Demand

34 Pages Posted: 19 Dec 2019 Last revised: 31 Dec 2019

See all articles by Boxiao Chen

Boxiao Chen

University of Illinois at Chicago - College of Business Administration

Date Written: December 11, 2019

Abstract

We consider an inventory control problem with lost-sales in a shifting demand environment. Over a planning horizon of T periods, demand distributions can change up to O(log T) times, but the firm does not know the demand distributions before or after each change, the time periods when changes occur, or the number of changes. Therefore, the firm needs to detect changes and learn the demand distributions only from historical sales data. We show that with censored demand, active exploration in the inventory space is needed for a reasonable detecting and learning algorithm. We provide a theoretical lower bound by partitioning all admissible policies into either exploration-heavy or exploitation-heavy, and for both categories we prove that the convergence rate cannot be better than Ω(√T). We then develop a nonparametric learning algorithm for this problem and prove that it achieves a convergence rate that (almost) matches the theoretical lower bound.

Keywords: inventory control, shifting demand, nonparametric learning, censored demand, asymptotic optimality

Suggested Citation

Chen, Boxiao, Data-Driven Inventory Control with Shifting Demand (December 11, 2019). Available at SSRN: https://ssrn.com/abstract=3503139 or http://dx.doi.org/10.2139/ssrn.3503139

Boxiao Chen (Contact Author)

University of Illinois at Chicago - College of Business Administration ( email )

601 S Morgan St
Chicago, IL 60607
United States

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