Robust optimization strategies for seller based on uncertainty sets in context of sequential auction
作者：
Highlights：
• Based on the traditional revenue model of seller, a robust revenue optimization method is proposed to solve the uncertainty of deal prices.
• This research establishes four robust maximum revenue models under different uncertainty sets (Box, Ellipsoid, Polyhedron and Box-polyhedron).
• Through employing the RO method, the optimal revenue can be found, and the optimal solution that satisfies all situations in the worst case can be obtained.
• Comparing different revenues among models, the value strategies are not optimal for auctioneer in despite of whether to consider uncertainty.
摘要
•Based on the traditional revenue model of seller, a robust revenue optimization method is proposed to solve the uncertainty of deal prices.•This research establishes four robust maximum revenue models under different uncertainty sets (Box, Ellipsoid, Polyhedron and Box-polyhedron).•Through employing the RO method, the optimal revenue can be found, and the optimal solution that satisfies all situations in the worst case can be obtained.•Comparing different revenues among models, the value strategies are not optimal for auctioneer in despite of whether to consider uncertainty.
论文关键词：Robust optimization,Strategy analysis,Used cars,Uncertainty sets
论文评审过程：Received 16 February 2020, Revised 29 June 2020, Accepted 22 August 2020, Available online 11 September 2020, Version of Record 11 September 2020.
论文官网地址：https://doi.org/10.1016/j.amc.2020.125650
