主讲人

（单位）

龙卓瑜

(香港中文大学)

主持人

（单位）

李四杰、金子亮

（电影网站 ）

讲座时间

2026年9月22日10点00分

讲座地点

综合楼310

主讲人简介

Daniel Zhuoyu Long is a Professor in the Department of Systems Engineering and Engineering Management at The Chinese University of Hong Kong. He received his Bachelor's degree from Tsinghua University in 2005, Master's degree from the Chinese Academy of Sciences in 2008, and Ph.D. from the National University of Singapore Business School in 2013, and joined CUHK in the same year. His research primarily focuses on distributionally robust optimization theory and its applications to various operations management problems, including logistics and supply chain management, project management, healthcare operations management, and revenue management. His work was selected as a finalist for the 2021 Best OM Paper Award in OR and the 2026 POMS CHOM Best Paper Competition. He currently serves as an AE for MSOM and on the editorial board of Engineering.

讲座内容摘要

Motivated by the need for more efficient and reliable methods of group testing during widespread infectious outbreaks, this paper introduces a novel operational improvement to the widely used Dorfman’s group testing procedure, where a single test is conducted on the pooled sample, followed by individual testing of positive pools. Our method minimizes a weighted sum of testing volume and misclassifications, taking prevalence rates and interindividual Pearson correlation coefficients as inputs, and employs a distributionally robust optimization (DRO) framework to address the ambiguity in the joint infection distribution induced by these correlations. We study two correlation structures within a population. In single-cluster cases, where all subject pairs share equal correlation, we connect our analysis to Nash equilibrium principles and show that higher correlation favors larger testing groups, whereas higher prevalence often calls for individual testing. In multicluster cases, where the population consists of several intracorrelated but interindependent clusters, we highlight the effectiveness of mixed-cluster testing strategies, particularly under low prevalence and correlation. This is a notable addition to the prevailing view that advocates pooling correlated individuals. We provide polynomial-time solutions for both correlation structures and demonstrate the trade-offs and benefits of our DRO approach through a thorough comparison with stochastic alternatives. Using a case study based on a real-world COVID-19 data set, we show that our proposed pooling strategy can save up to 0.1 tests per individual compared with an independence-based pooling scheme and up to 0.5 tests per individual compared with a heuristic pooling strategy implemented in the studied region.