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TITLE: Nonparametric Algorithms for Joint Pricing and Inventory Control with Lost-Sales and Censored Demand
ABSTRACT:
We consider the classical joint pricing and inventory control problem with lost-sales and censored demand in which the customer's response to selling price and the demand distribution are not known a priori, and the only available information for decision-making is the past sales data. Conventional approaches, such as stochastic approximation, online convex optimization, and continuum-armed bandit algorithms, cannot be employed since neither the realized values of the profit function nor its derivatives are known. A major difficulty of this problem lies in the fact that the estimated profit function from observed sales data is multimodal even when the expected pro
fit function is concave. We develop a nonparametric data-driven algorithm that actively integrates exploration and exploitation through carefully designed cycles. The algorithm searches the decision space through a sparse discretization scheme to jointly learn and optimize a multimodal (sampled) profit function, and corrects the estimation biases caused by demand censoring. We show that the algorithm converges to the optimal policy as the planning horizon increases, and obtain the convergence rate of regret.
Bio
Boxiao (Beryl) Chen is a Ph.D. candidate from the department of Industrial and Operations Engineering, University of Michigan, Ann Arbor. Her research focuses on data-driven optimization with applications in supply chain management and revenue management. She is a finalist of the 2015 POMS College of Supply Chain Management Student Paper Competition and receives the Wilson Prize for best student paper from the IOE department.
