報告人：美國杜克大學 Jing-Sheng (Jeannette) Song教授
時間：2021年10月13日(周三)上午9:00-10:30
騰訊會議號： 515 288 836
報告內容簡介：
We consider a grocery retailer selling a perishable product in a dynamic environment where consumers’ price sensitivity changes at unknown times (due to pandemics, weather events, etc.), and the product perishes at an unknown rate. We design online price experiments for learning about these unknown features over time. We then prescribe how to use the newly gained knowledge and the most up-to-date data to make informed joint pricing and inventory ordering decisions. Depending on whether the demand shock distribution is parametric or nonparametric, we design two versions of the data-driven pricing and ordering (DDPO) algorithm with the best achievable performance guarantee. Implementing our algorithm on a real-life data set from a supermarket chain, we show that our data-driven, learning-and-earning approach significantly outperforms the historical decisions of the supermarket chain by reducing the profit loss due to uncertainty by over 80%. In particular, avoiding active learning for price-sensitivity changes leads to an annual profit loss of over 62 million U.S. dollars; avoiding active learning for perishability results in a yearly profit loss of over 11 million U.S. dollars. (Joint work with Bora Keskin and Yuexing Li of Duke University.)
報告人簡介：
Jing-Sheng Song博士為美國杜克大學富卡商學院 R. David Thomas講席教授。長期致力于供應鏈管理與運營戰略領域的研究，研究方向包括庫存和物流系統規劃與設計、3D打印、動態定價、全球供應鏈風險管理和社會責任; 在國際主流期刊上發表學術論文70余篇，包括運作管理領域頂級期刊Management Science、Operations Research、Production and Operations Management 、Manufacturing & Service Operations Management。Jing-Sheng Song教授為教育部長江講座教授、中國自然科學基金委海外杰出青年、INFORMS Fellow、MSOM Fellow; 目前擔任Management Science和Service Science部門主編、曾擔任Operations Research 區域主編和IIE Transactions部門主編。
（承辦：管理科學與物流系、科研與學術交流中心）
