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X-WR-CALDESC:Events for IORA - Institute of Operations Research and Analytics
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DTSTART;TZID=Asia/Singapore:20220401T100000
DTEND;TZID=Asia/Singapore:20220401T113000
DTSTAMP:20260407T132821
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SUMMARY:IORA Seminar Series – Ruoxuan Xiong
DESCRIPTION:Ruoxuan Xiong is an assistant professor in the Department of Quantitative Theory and Methods at Emory University. She completed her Ph.D. in Management Science and Engineering from Stanford University in 2020\, and was a postdoctoral fellow at the Stanford Graduate School of Business from 2020 to 2021. Her research is at the intersection of econometrics and operations research\, focusing on factor modeling\, causal inference\, and experimental design\, and with applications in finance and healthcare. Her work was awarded the Honorable Mention in the 2019 INFORMS George Nicholson Student Paper Competition\, and was in the finalists of the 2020 MSOM Student Paper Competition. \n\n\n\nName of speaker\nRuoxuan Xiong\n\n\nSchedule \n1 April 2022\, 10am – 11.30am\n\n\nLink to register \nhttps://nus-sg.zoom.us/meeting/register/tZUpc-uqqzkvHNTjASt7gjozPEloElm7Uuzg\n\n\nTitle of talk\nOptimal experimental design for staggered rollouts\n\n\nAbstract\nIn this paper\, we study the problem of designing experiments that are conducted on a set of units\, such as users or groups of users in an online marketplace\, for multiple time periods such as weeks or months. These experiments are particularly useful to study the treatments that have causal effects on both current and future outcomes (instantaneous and lagged effects). The design problem involves selecting a treatment time for each unit\, before or during the experiment\, in order to most precisely estimate the instantaneous and lagged effects\, post experimentation. This optimization of the treatment decisions can directly minimize the opportunity cost of the experiment by reducing its sample size requirement. The optimization is an NP-hard integer program for which we provide a near-optimal solution\, when the design decisions are performed all at the beginning (fixed-sample-size designs). Next\, we study sequential experiments that allow adaptive decisions during the experiments\, and also potentially early stop the experiments\, further reducing their cost. However\, the sequential nature of these experiments complicates both the design phase and the estimation phase. We propose a new algorithm\, PGAE\, that addresses these challenges by adaptively making treatment decisions\, estimating the treatment effects\, and drawing valid post-experimentation inference. PGAE combines ideas from Bayesian statistics\, dynamic programming\, and sample splitting. Using synthetic experiments on real data sets from multiple domains\, we demonstrate that our proposed solutios for fixed-sample-size and sequential experiments reduce the opportunity cost of the experiments by over 50% and 70%\, respectively\, compared to benchmarks.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-ruoxuan-xiong/
CATEGORIES:IORA Seminar Series
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BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20220408T100000
DTEND;TZID=Asia/Singapore:20220408T113000
DTSTAMP:20260407T132821
CREATED:20211216T020639Z
LAST-MODIFIED:20220401T084157Z
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SUMMARY:IORA Seminar Series – Stefanus Jasin
DESCRIPTION:Stefanus Jasin is an Associate Professor of Technology and Operations at Stephen M. Ross Business School\, University of Michigan. His research focuses on developing tools/algorithms for predictive and prescriptive analytics\, with recent applications in pricing and revenue management\, assortment optimization\, supply chain management\, e-commerce/omni-channel logistics\, and online learning. He is currently serving as the Department Editor for Revenue Management and Market Analytics at POMS. He also serves as an Associate Editor at Management Science\, Operations Research\, Manufacturing and Service Operations Management\, and Naval Research Logistics. \n\n\n\nName of speaker\nStefanus Jasin\n\n\nSchedule \n8 April 2022\, 10am – 11.30am\n\n\nLink to register  \n \nhttps://nus-sg.zoom.us/meeting/register/tZ0rcOmsrzkvEtbis9jZu_QqWm6iTqZVAnR5\n\n\nTitle of talk\nRevenue Management Meets Inventory Management\n\n\nAbstract\nHistorically\, Revenue Management (RM) and Inventory Management have developed as two separate fields. Although they sometimes share similar research questions (e.g.\, pricing)\, they do not always share either the same focus or methodology. In this talk\, I will give an overview of several recent works that focus on applying RM techniques to inventory management problems\, all of which are motivated by applications in retail. Given enough time\, the plan is to divide the talk into two parts. The first part will focus on a joint inventory and pricing problem with one warehouse and multiple stores\, in which the retailer needs to make a one-time decision on the amount of inventory to be placed at the warehouse at the beginning of the selling season\, followed by periodic joint replenishment and pricing decisions for each store throughout the season. We study the performance of heuristic controls based on a deterministic/fluid relaxation of the original stochastic problem. Our contributions are two-fold. We first show that simple re-optimization of deterministic/fluid problems may yield a very poor performance by causing a “spiraling down” movement in price trajectory\, which in turn yields a “spiraling up” movement in expected lost sales quantity (i.e.\, lost sales quantity keeps going up as we continue re-optimizing the model). This cautions against a naive use of simple re-optimizations in the joint inventory and pricing setting with lost sales. Second\, we propose a better heuristic by combining four ideas: (1) order-up-to control\, (2) linear rate adjustment\, (3) replenishment batching\, and (4) random errors averaging. We show for a particular choice of control parameters that the heuristic is close to optimal when demand is Poisson and the annual market size for each store is large. In the second part of the talk\, I will briefly discuss other works along the same theme\, including some recent papers that use Lagrangian-based methods and another paper that uses a fluid approximation for a joint pricing and inventory problem with stochastic purchase returns and lost sales. I will discuss some insights on lessons learned when applying RM techniques in inventory-related problems. Overall\, these works highlight the potential in adopting RM techniques in solving complex inventory problems.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-stefanus-jasin/
CATEGORIES:IORA Seminar Series
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BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20220422T100000
DTEND;TZID=Asia/Singapore:20220422T113000
DTSTAMP:20260407T132821
CREATED:20220406T060607Z
LAST-MODIFIED:20220406T061314Z
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SUMMARY:IORA Seminar Series - Chen Ying
DESCRIPTION:Dr. Ying Chen is a financial statistician and data scientist. She develops statistical modelling and machine learning methods customized for nonstationary\, high frequency and large dimensional complex data such as cryptocurrency\, limit order book\, and renewable energy. She also works on business intelligence\, forecasting\, text mining and sentiment analysis\, and network analysis. Dr. Chen is Associate Professor in Department of Mathematics and Joint Appointee in Risk Management Institute\, National University of Singapore. She also holds Courtesy Appointment in Econ and DSDS. \nWebpage: https://blog.nus.edu.sg/matcheny/ \n\n\n\nName of speaker\nChen Ying\n\n\nSchedule \n22 April 2022\, 10am – 11.30am\n\n\nLink to register  \n \nhttps://nus-sg.zoom.us/meeting/register/tZEucO2pqjosGNPn3FVvmPCZOZrX9T1faGx0\n\n\nTitle of talk\nPolicy Effectiveness on the Global Covid-19 Pandemic and Unemployment Outcomes: A Large Mixed Frequency Spatial Approach\n\n\nAbstract\nWe propose a mixed frequency spatial VAR (MF-SVAR) modeling framework to measure the effectiveness of policies conditional on the spillover and diffusion effects of the global pandemic and unemployment. We study the effects of two aspects of policy effectiveness\, namely policy start date and policy timeliness\, from a spatio-temporal perspective. The spatial panel data contain weekly new case growth rates and monthly unemployment rate changes for 68 countries across six continents at mixed frequencies from January 2020 to August 2021. We find that government policies have a significant impact on the growth of new cases\, but only a marginal effect on the change in unemployment rates. A policy’s start date is critical for its effectiveness. In terms of both immediate impact on the near term and total impact over the following four weeks\, starting a policy in the 4th week of a month is most effective at reducing the growth of new cases. At the same time\, starting in the 2nd or 3rd week is counterproductive for a one-time policy start date. In addition\, our estimates suggest that the spillover and diffusion effects are much stronger than a country’s temporal effect during a global pandemic\, both for new case growth and changes in unemployment. We also find that new case growth influences changes in unemployment\, but not vice versa. Counterfactual experiments provide further evidence of policy effectiveness in various scenarios and also reveal the main risk-vulnerable and risk-spillover countries. This is a joint work with Xiaoyi Han\, Yanli Zhu and Yijiong Zhang. The paper is available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4049509
URL:https://iora.nus.edu.sg/events/iora-seminar-series-chen-ying/
CATEGORIES:IORA Seminar Series
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