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Network revenue management with nonparametric demand learning: \sqrt{T}-regret and polynomial dimension dependency
Sentao Miao1, Yining Wang2
1McGill University, Canada; 2University of Florida
This paper studies the classic price-based network revenue management (NRM) problem with demand learning. The retailer dynamically decides prices of n products over a finite selling season (of length T) subject to m resource constraints, with the purpose of maximizing the cumulative revenue. In this paper, we focus on nonparametric demand model with some mild technical assumptions which are satisfied by most of the commonly used demand functions.
Optimal algorithm for solving composition of convex function with random functions and its applications in network revenue management
Xin Chen1, Niao He2, Yifan Hu1,2, Zikun Ye1
1University of Illinois at Urbana-Champaign, US; 2ETH Zurich, Switzerland
Various operations management problems can be formulated as stochastic optimization under random truncation. Leveraging a convex reformulation, we propose a mirror gradient method that achieves global convergence for the nonconvex objective with optimal complexity. The proposed method only operates in the original space using estimators of the nonconvex objective and consistently outperforms several state-of-the-art control policies in passenger and air-cargo network revenue management.
Joint assortment optimization and personalization
Omar El Housni, Huseyin Topaloglu
Cornell University, United States of America
We consider a joint customization and assortment optimization problem. A firm faces customers of different types, each making a choice according to a different MNL model. The firm picks an assortment of products to carry subject to a constraint. Then, a customer of a certain type arrives into the system and the firm customizes the assortment that it carries by, possibly, dropping products from the assortment. We study the value of customization, the complexity of the problem and design novel algorithms.