时 间：2023年12月11日（星期一） 14:30

地 点：电子科技大学清水河校区，经管楼C101      主持人：舒嘉  教授

讲座一：Optimizing Initial Screening for Colorectal Cancer Detection with Adherence Behavior

主 讲 人：郑智超 副教授

主讲人简介：

Zhichao Zheng is an Associate Professor of Operations Management at the Singapore Management University. His main research interests lie in data analytics and optimization for healthcare operations management and medical decision-making. He also applies his research in sharing economics, supply chain risk management, etc. His research has appeared in Operations Research, Management Science, and Manufacturing & Service Operations Management, among others. He received his BS (First Class Honors) in Applied Mathematics from the National University of Singapore in 2009 and Ph.D. in Management from the Department of Decision Sciences (renamed to Department of Analytics & Operations) at the National University of Singapore in 2013.

讲座简介：

Two-stage screening programs are widely adopted for early colorectal cancer (CRC) detection, where individuals receiving positive outcomes in the first-stage (initial) test are recommended to undergo a second-stage test (colonoscopy) for further diagnosis. We study the initial test design—the selection of cutoffs for reporting test outcomes—to balance the trade-off between screening effectiveness (i.e., CRC and polyp detection) and efficiency (i.e., colonoscopy costs), incorporating the fact that not all individuals follow up with a colonoscopy after receiving positive outcomes. We integrate the Bayesian persuasion framework with information avoidance to model this problem and apply it to Singapore's CRC screening program design. We calibrate the model using various sources of data, including a nationwide survey with 3,920 responses in Singapore. We show that under certain conditions, using a single cutoff is optimal for maximizing follow-up, while showing exact biomarker readings is optimal for maximizing effectiveness. Our results suggest that, compared to the current practice, raising the cutoff to our recommended level of 39 µg/g can detect 20.83% more CRC and polyp incidences, reduce 26.98% colonoscopies, and lower the lifetime risk of CRC by 11.03%. This could reduce public healthcare expenditure by S$19.93 million and individual spending by S$11.96 million on average in screening costs. The current practice of using lower cutoffs to achieve high sensitivity can result in an excessive number of unnecessary colonoscopies and low adherence rates.

讲座二：The Value of Flexible Response in Resource Allocation Competition

主 讲 人：高旖旎 助理教授

主讲人简介：

Sarah Yini Gao is the Assistant Professor of Operations Management at Lee Kong Chian School of Business, Singapore Management University. Her current research interests lie in applying optimization theory and data analytics in various domains, including supply chain risk management, healthcare and humanitarian operations, and topics on innovative business models. Her research has been published in leading journals, including Management Science, Operations Research, Manufacturing & Service Operations Management, and Marketing Science. She graduated with a Bachelor's double degree in Business Administration and Chemical Engineering from the National University of Singapore in 2012. She received her Ph.D. in Management from the Department of Decision Sciences (renamed to the Department of Analytics & Operations) at the National University of Singapore in 2017.

讲座简介：

This paper explores the role of limited flexibility in resource allocation competition. Specifically, we consider a two-player constant-sum game where both players allocate capacitated resources over multiple locations and one of the two players has the capability to respond by reallocating resources according to a redeployment network. The payoff depends on the final allocation profiles of both players. The study investigates how different levels of flexibility in the redeployment network affect equilibrium strategies and payoffs.

Solving the game is computationally challenging due to the large-dimensional action space with the exponential number of pure strategies and inherent correlations among allocations at multiple locations. We show that for any redeployment network, the game can be equivalently characterized by conic programs when the payoff function satisfies a concavity property.

To explore the value of flexible redeployment, we consider two special cases of the game under a k-chain redeployment network with flow-maximizing payoff and hinge-type payoff. Closed-form solutions are derived for these two special cases. By examining different values of k, we find that limited flexibility has no advantages with a flow-maximizing payoff in our adversarial setting, i.e., the payoff scales linearly in k, in contrast to the results in the classic process flexibility literature that considers an exogenous stochastic environment. When the payoff is hinge-type, the payoff grows sublinearly in k, implying a more pronounced benefit of limited flexibility. However, these benefits are less marked compared to process flexibility literature results. This underscores the limiting effect of an adversarial context on the value of flexibility, suggesting that the strategic intelligence of the opponent can significantly diminish the advantages of a sparse structure. We further show that a sparse network structure, called an “expander,” exhibits superior performance that can approach the performance of fully flexible networks, particularly with classic auction-type payoffs.

讲座三：Reliable Supply Network Design under Disruptions with Correlated Uncertainties in Supply, Demand, and Links

主 讲 人：李泳臻 副研究员

主讲人简介：

李泳臻，东南大学经济管理学院副研究员、硕士生导师。同济大学本科，香港大学博士。主要从事供应链设计与管理、算法设计与优化、不确定性优化等方向的研究，研究工作发表在INFORMS Journal on Computing等期刊上。

讲座简介：

Disruptive events in supply chains usually lead to uncertainties to the supply side, the demand side, and the links between them. These uncertainties are inherently correlated particularly when the disruptions are caused by natural disasters or systemic threats. This paper studies the supply network design problem under uncertain disruptive events, which can affect the demand side, the supply side (the availability of prepositioned inventory), and the links (the shipment capacities) between supply and demand nodes at the same time. We characterize the disruptive events with an unknown joint distribution, which belongs to an ambiguity set based on the marginal and cross disruption probabilities. The uncertainties across the demand and supply sides and the links between them are characterized by linear functions of disruptive events. A two-stage distributionally robust model is formulated to simultaneously minimize the fixed location-allocation cost, the inventory pre-positioning cost, and the expected transportation cost under the worst-case disruption distribution. To solve this challenging model, we deploy a cutting plane algorithm based on the Benders decomposition, where the separation problem to calculate the worst-case disruption distribution is solved by a column generation approach. We explore two interesting special cases focusing on bottleneck links and bottleneck inventory, respectively. For the first one focusing on bottleneck links with an application in disaster-relief network design, the robust model admits a tractable mixed integer linear programming reformulation. For the second one focusing on bottleneck inventory with an application in sourcing and capacity planning, the robust model is equivalent to a two-stage stochastic model after proving the closed-form worst-case distribution for the second-stage problem. Extensive numerical experiments, including a case study on the Jiuzhaigou earthquake, are conducted to validate the effectiveness and efficiency of the proposed models, reformulations, and algorithms.

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