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BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20221104T100000
DTEND;TZID=Asia/Singapore:20221104T113000
DTSTAMP:20260417T204924
CREATED:20220812T034742Z
LAST-MODIFIED:20221102T064417Z
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SUMMARY:IORA Seminar Series - Ying-Ju Chen
DESCRIPTION:Ying-Ju Chen is a Chair Professor at HKUST. Prior to the current position\, he was a faculty in the Department of IEOR at UC Berkeley. He obtained a PhD degree in Operations Management from Stern School of Business at New York University in 2007\, and he also holds master’s and bachelor’s degrees of Electrical Engineering from National Taiwan University. \nHe is a recipient of Franklin Prize for Teaching Excellence (MBA non-required/MSc\, highest honor at HKUST Business School\, 2 winners per year)\, NYU teaching excellence award\, Most Influential Service Operations Paper Award in Production and Operations Management\, Harold W. Kuhn Award of Naval Research Logistics\, Second place of INFORMS Junior Faculty Interest Group (JFIG) paper competition\, Higher Education Outstanding Scientific Research Output Award (Social Science\, third prize)\, Best paper award of CSAMSE (third prize)\, the Harold MacDowell Award from Stern School\, Meritorious Service Awards from Management Science and Manufacturing & Service Operations Management\, and other awards and fellowships during his academic journey. He is ranked No. 2 among researchers world-wide by weighted corrected publication rate in Operations Management according to an article in Decision Sciences (2021). \nHe serves as a department editor for NRL and a senior/associate editor for POM and M&SOM journals. His current research interests lie in network economics\, socially responsible operations\, operations-marketing interface\, and supply chain management. His work has appeared in several leading journals in the fields of economics\, electrical engineering\, information systems\, marketing\, and operations research. \n  \n\n\n\nVenue \nTalk will be held via Zoom\n\n\nLink to Register \n(Zoom Session)\nhttps://nus-sg.zoom.us/meeting/register/tZArceuhqj4jG9K-Xgwq9YDyfLWIDuRkt2_i\n\n\nTitle\nPostgraduate program applications: simultaneous search\, sequential outcomes\, and reservation fees\n\n\nAbstract\nThis paper studies a simultaneous-search problem in which a player observes the outcomes sequentially\, and must pay reservation fees to maintain eligibility for recalling the earlier offers. We use postgraduate program applications to illustrate the key ingredients of this family of problems. We develop a parsimonious model with two categories of schools: reach schools\, which the player feels very happy upon joining\, but the chance of getting into one is low; and safety schools\, which are a safer choice but not as exciting. The player first decides on the application portfolio\, and then the outcomes from the schools applied to arrive randomly over time. We start with the extreme case wherein the safety schools always admit the player. We show that it suffices to focus on the last safety school\, which allows us to conveniently represent the player’s value function by a product form of the probability of entering the last safety period and the expected payoff from then on. \nWe show that the player’s payoff after applications is increasing and discrete concave in the number of safety schools. We also develop a recursive dynamic programming algorithm when  admissions to safety schools are no longer guaranteed. We demonstrate instances in which the player applies to more safety schools when either the reservation fee gets higher or the admission probability drops lower\, and articulate how these arise from the portfolio optimization consideration. This has strong managerial implications for service providers in devising their reservation fees and admission rates\, especially for institutions that are not universally favored by prospective applicants.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-ying-ju-chen/
CATEGORIES:IORA Seminar Series
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BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20221111T100000
DTEND;TZID=Asia/Singapore:20221111T113000
DTSTAMP:20260417T204924
CREATED:20221101T081611Z
LAST-MODIFIED:20221107T010932Z
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SUMMARY:IORA Seminar Series - Long Zhao
DESCRIPTION:Long Zhao is an assistant professor in the Department of Analytics & Operations (DAO) at NUS Business School\, National University of Singapore. He received his Ph.D. in Decision Sciences from the McCombs School of Business at the University of Texas at Austin. Dr. Zhao’s research interests lie in data-driven decision-making. His research has been the finalist of INFORMS Data mining best paper competition and the finance section best student paper competition. \n  \n\n\n\nVenue \nHon Sui Sen Memorial Library – HSS 4-5\n\n\nLink to Register \n(Hybrid session)\nhttps://nus-sg.zoom.us/meeting/register/tZcsd–vqT4pGtaBggmJWaMKIuN6uTstODTJ\n\n\nTitle\nConstructing Quantiles via Forecast Errors: Theory and Empirical Evidence\n\n\nAbstract\nProbabilistic forecasts (such as quantiles) are essential inputs to decision-making in the face of uncertainty. However\, the most common type often comes in the form of point forecasts. As such\, it is necessary for the decision maker to construct uncertainty measures around the obtained point forecasts. One simple approach proposed in the literature suggests leveraging historical forecast errors to create quantile estimators around the given point forecast (referred to as the E2Q method). The sample quantile and normal approximation are two popular E2Q estimators. The former relies on the empirical distribution of the forecast errors while the latter treats the underlying distribution as if it were normal. Despite their prevalence\, the relative performances of the two estimators remain unknown. In this paper\, we find that the performance of a quantile estimator depends on its bias and variance. In particular\, higher variance always leads to worse performance. Furthermore\, unbiasedness is never optimal for a fixed variance and becomes less and less appealing as variance increases. Thus\, as an asymptotically unbiased estimator\, the sample quantile is appealing only when its variance is small. We confirm our theoretical findings using the M5 forecast competition data. Since this competition consists of both the “accuracy” (point) and “uncertainty” (quantile) tracks\, we also compare the E2Q method with other methods that directly forecast quantiles. We found that the E2Q method using the top point forecasts can outperform the top direct quantile forecasts. This empirical finding suggests that the E2Q method can be a promising alternative to forecasting quantiles directly.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-long-zhao/
CATEGORIES:IORA Seminar Series
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BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20221125T100000
DTEND;TZID=Asia/Singapore:20221125T113000
DTSTAMP:20260417T204924
CREATED:20221101T081722Z
LAST-MODIFIED:20221123T011119Z
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SUMMARY:IORA Seminar Series - He Wang
DESCRIPTION:He Wang is an Assistant Professor and Colonel John B. Day Early Career Professor in the School of Industrial and Systems Engineering at Georgia Tech. His research interests include pricing and revenue management\, supply chain\, transportation\, and machine learning. His works have received 1st place in INFORMS Junior Faculty Interest Group paper competition\, Best Paper in Operation Research Award by the MSOM Society\, NSF CAREER Award\, and faculty research awards from Amazon and Didi. \n  \n\n\n\nVenue \nSeminar Room at Innovation 4.0 building (Level 1)\n\n\nLink to Register \n(Hybrid session)\nhttps://nus-sg.zoom.us/meeting/register/tZwudemurj4sHdy-N4Bl9b1CoBEnCO0vZ7mw\n\n\nTitle\nConstant Regret Re-solving Heuristics for Revenue Management Problems\n\n\nAbstract\nWe will discuss a classic network revenue management model of Gallego and van Ryzin (1997)\, which considers a retailer who sells a product (or multiple products) subject to initial inventory constraints over T consecutive periods. Because the optimal policy via dynamic programming is computationally intractable\, researchers have proposed various approximate policies for this problem. We are interested in the so-called “re-solving heuristic\,” which periodically solves the fluid approximation model. In the quantity-based revenue management setting with discrete types (joint work with P. Bumpensanti)\, we find that the re-solving heuristic has a worst-case regret of O(T^{1/2}) compared to the optimal policy\, whereas a modified re-solving heuristic can achieve uniformly bounded O(1) regret. In the price-based revenue management setting with continuous price sets (joint work with Yining Wang)\, we show that the re-solving heuristic attains O(1) regret compared to the value of the optimal policy. This improves the O(lnT) regret upper bound established by Jasin (2014). In addition\, we prove that there is an Ω(lnT) gap between the value of the optimal policy and that of the fluid model\, implying that the fluid model is not an adequate benchmark for constant regret.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-he-wang/
CATEGORIES:IORA Seminar Series
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