1152. Online Learning for Dynamic Pricing in Consumer Electronics Trade-in Program
Invited abstract in session MA-29: Machine learning in pricing and revenue management, stream Pricing and Revenue Management Innovations.
Monday, 8:30-10:00Room: Maurice Keyworth 1.04
Authors (first author is the speaker)
| 1. | Sean Xiang Zhou
|
| Department of Decisions, Operations and Technology, The Chinese University of Hong Kong | |
| 2. | Zhuoluo ZHANG
|
| Management Science, Xiamen University | |
| 3. | Wenhao Li
|
| Department of Operations Management, Shanghai University of Finance and Economics |
Abstract
The electronics trade-in market is large and has experienced rapid growth in recent years, generating significant value for both companies offering trade-in programs and individual consumers seeking to upgrade their used devices at favorable prices or purchase high-quality pre-owned devices. A key operational decision for the firm is setting the “right” upgrade prices for used products and the resale prices for refurbished products. Due to the lack of prior knowledge of how supply and demand depend on prices, companies need to learn the supply and demand functions while making pricing decisions. In this paper, we consider joint learning and pricing for an electronics trade-in platform that buys and sells multiple types of used products. Both supply and demand functions belong to a general class of parametric family. We propose a Parametric Batched-Adjustment Control (PBC) Policy and analyze its theoretical performance.
Keywords
- Revenue Management and Pricing
- Machine Learning
- Reverse Logistics / Remanufacturing
Status: accepted
Back to the list of papers