喻园管理论坛2023年第18期(总第841期)

演讲主题: A Robust Data-Driven Approach for the Newsvendor Problem with Nonparametric Information

主 讲 人: 徐  亮，西南财经大学大数据研究院教授

主 持 人: 秦  虎，管理学院管理科学与信息管理系教授

活动时间: 2023年3月20日（周一）15:00-17:00

活动地点: 管理大楼116教室

主讲人简介：

徐亮，西南财经大学大数据研究院教授、博导。主持西南财经大学大数据研究院日常工作。北京师范大学本科，中山大学硕士、香港理工大博士。现任交易软件公司乾隆科技首席科学家。主要从事物流、供应链、组合投资等运营管理相关研究。主要研究兴趣包括车辆路径和鲁棒优化，在车辆路径领域主要提出了多个车辆路径公开问题的近似算法，在鲁棒优化领域主要提出了“非参鲁棒优化”。主持国家自然科学基金项目3项。在Manufacturing &Service Operations Management, INFORMS Journal on Computing和Transportation Research: Part B等期刊发表第一作者或通讯作者文章。获银保监会科技进步二等奖、中国期货业协会联合研究项目二类优秀成果奖。受邀担任Financial Innovation期刊专刊首席客座主编，并担任编委。任系统工程学会理事、天府对冲基金学会金融产品创新委员会主任委员。

活动简介：

For the standard newsvendor problem with an unknown demand distribution, we develop an approach that uses data input to construct a distribution ambiguity set with the nonparametric characteristics of the true distribution, and we use it to make robust decisions. Empirical approach relies on historical data to estimate the true distribution. Although the estimated distribution converges to the true distribution, its performance with limited data is not guaranteed. Our approach generates robust decisions from a distribution ambiguity set that is constructed by data-driven estimators for nonparametric characteristics and includes the true distribution with the desired probability. It fits situations where data size is small. We apply a robust optimization approach with nonparametric information. Under a fixed method to partition the support of the demand, we construct a distribution ambiguity set, build a protection curve as a proxy for the worst-case distribution in the set, and use it to obtain a robust stocking quantity in closed form. Implementation-wise, we develop an adaptive method to continuously feed data to update partitions with a prespecified confidence level in their unbiasedness and adjust the protection curve to obtain robust decisions. We theoretically and experimentally compare the proposed approach with existing approaches. Our nonparametric approach under adaptive partitioning guarantees that the realized average profit exceeds the worst-case expected profit with a high probability. Using real data sets from Kaggle.com, it can outperform existing approaches in yielding profit rate and stabilizing the generated profits, and the advantages are more prominent as the service ratio decreases. Nonparametric information is more valuable than parametric information in profit generation provided that the service requirement is not too high. Moreover, our proposed approach provides a means of combining nonparametric and parametric information in a robust optimization framework. This research has been published in MSOM in 2022.

信息变更提醒：该讲座原定教室107，已更改为116教室，请老师、同学们注意地点变更。