Conference Agenda

Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

 
 
Session Overview
Session
SC01 - SIG SCM3: Revenue Management
Time:
Sunday, 26/June/2022:
SC 13:00-14:30

Session Chair: Rachel Chen
Session Chair: Luyi Gui
Location: Forum 12


Show help for 'Increase or decrease the abstract text size'
Presentations

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.



 
Contact and Legal Notice · Contact Address:
Privacy Statement · Conference: MSOM 2022
Conference Software: ConfTool Pro 2.8.101+TC
© 2001–2024 by Dr. H. Weinreich, Hamburg, Germany