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X-WR-CALDESC:Events for IORA - Institute of Operations Research and Analytics
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BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20211210T140000
DTEND;TZID=Asia/Singapore:20211210T163000
DTSTAMP:20260417T112059
CREATED:20211203T032006Z
LAST-MODIFIED:20211203T032803Z
UID:14663-1639144800-1639153800@iora.nus.edu.sg
SUMMARY:Seminar on IORA Industry Engagement
DESCRIPTION:In conjunction with the launch of our second collaboration project with Kulicke and Soffa\, we would like to invite you to attend a seminar on IORA Industry Engagement.  This will be an in-person seminar at Innovation 4.0 Seminar Room. \n  \n\n\n\nTime\nSchedule\n\n\n2.00 to 2.05pm\nOpening Address\nby IORA Research Director\, Prof Jussi Keppo\n\n\n2.05 to 2.25pm\n\nOverview of Industry Engagements Activities for IORA\nby Executive Director\, Prof Teo Chung Piaw \n\nWith increasing demand from industry and government agencies for research collaboration and technical assistance\, how can IORA align its activities to respond to these challenges\, while maintaining its core focus on pushing the frontier in research?\nOverview of recent projects completed by staff  in the institute\, and key lessons learned\, say from the development of the SIA-NUS Corp Lab\n\n\n\n\n2.45 to 3.45pm\n\nProjects with K&S \n\nApplication of Machine Learning in Smart Manufacturing\nSmart Manufacturing through Predictive Process Monitoring using Machine Learning\n\n\n\n\n3.45 to 4.00pm\n\nLaunch of Second Collaborative Project with K&S\n\n\n4.00 to 4.30pm\n\nTour of SIA-NUS Digital Aviation Corporate Laboratory\n\n\n\nRegister here https://forms.office.com/r/PLvWfVR8sy \n \n 
URL:https://iora.nus.edu.sg/events/seminar-on-iora-industry-engagement/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20220218T100000
DTEND;TZID=Asia/Singapore:20220218T113000
DTSTAMP:20260417T112059
CREATED:20211216T014818Z
LAST-MODIFIED:20220211T061320Z
UID:14703-1645178400-1645183800@iora.nus.edu.sg
SUMMARY:IORA Seminar Series  - Spyros Zoumpoulis
DESCRIPTION:Spyros Zoumpoulis is an assistant professor of Decision Sciences at INSEAD. His research is on using analytics to optimize decision making\, with applications in marketing\, healthcare\, and revenue management. His current focus is on investigating how to design\, and use data from\, experiments in order to make optimal personalized decisions\, as well as how to evaluate policies that make personalized decisions\, such as targeting decisions in marketing and personalized treatment decisions in healthcare. \nMore generally\, he is broadly interested in problems at the interface of learning with data and decision making. His research has appeared in leading management science academic journals such as Management Science and Operations Research. \nSpyros has worked with companies including Microsoft\, LinkedIn\, IBM\, Oracle\, and Accenture and serves on the advisory board of start-ups in the areas of his expertise. At INSEAD\, he teaches the MBA core course on uncertainty\, data and judgment\, the MBA electives on data science for business and decision models\, the MBA business foundations course on quantitative methods\, the PhD courses on probability and statistics\, and the INSEAD-Sorbonne business foundations course on uncertainty\, data and judgment. He has won the Dean’s Commendation for Excellence in MBA Teaching award numerous times and has been nominated for the best MBA elective professor award. \nSpyros received the B.S.\, M.Eng.\, and Ph.D. degrees in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology. \n  \n\n\n\nName of Speaker\n Dr Spyros Zoumpoulis\n\n\nSchedule\n18 February 2022\, 10am – 11.30am \n(60 min talk + 30 min Q&A) \n\n\nLink to Register\nhttps://nus-sg.zoom.us/meeting/register/tZIocu2rpzwtGNDoe-8hOR8eA_-nL7H_Fp6r\n\n\nTitle \nQuantifying the Benefits of Targeting for Pandemic Response\n\n\nAbstract\nProblem definition: To respond to pandemics such as COVID-19\, policy makers have relied on interventions that target specific population groups or activities. Since targeting is potentially contentious\, rigorously quantifying its benefits is critical for designing effective and equitable pandemic control policies. \nMethodology/results: We propose a flexible modeling framework and a set of associated algorithms that compute optimally targeted\, time-dependent interventions that coordinate across two dimensions of heterogeneity: age of different groups and the specific activities that individuals engage in during the normal course of a day. We showcase a complete implementation focused on the Île-de-France region of France\, based on commonly available public data. We find that targeted policies generate substantial complementarities that lead to Pareto improvements\, reducing the number of deaths and the economic losses\, as well as the time in confinement for each age group. Optimized dual-targeted policies are interpretable: by fitting decision trees to our raw policy’s decisions across many problem instances\, we find that a feature corresponding to the ratio of marginal economic value prorated by social contacts is highly salient in explaining the confinements that any group – activity pair experiences. We also quantify the impact of fairness requirements that explicitly limit the differential treatment of distinct groups\, and find that satisfactory trade-offs are achievable through limited targeting. \nImplications: Given that some amount of targeting of activities and age groups is already in place in real-world pandemic responses\, our framework highlights the significant benefits in explicitly and transparently modelling targeting and identifying the interventions that rigorously optimize overall societal welfare.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-spyros-zoumpoulis/
CATEGORIES:IORA Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://iora.nus.edu.sg/wp-content/uploads/2021/12/Spyros-Zoumpoulis-pic.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20220225T100000
DTEND;TZID=Asia/Singapore:20220225T113000
DTSTAMP:20260417T112059
CREATED:20211216T014656Z
LAST-MODIFIED:20220221T025401Z
UID:14701-1645783200-1645788600@iora.nus.edu.sg
SUMMARY:IORA Seminar Series - Yehua Wei
DESCRIPTION:Yehua Wei is an Associate Professor in the Decision Sciences area at Fuqua School of Business. He received his Ph.D. in Operations Research from MIT in 2013. His research interest can be broadly defined as decisions under uncertainty\, including optimization problems on operational and strategical levels. More recently\, he has been working on topics in dynamic resource allocation\, vehicle routing\, strategic routing\, and e-commerce fulfillment. \n\n\n\nName of Speaker\n  Wei Yehua\n\n\nSchedule\n 25 February 2022\, 10am – 11.30am \n(60 min talk + 30 min Q&A) \n\n\nLink to register\nhttps://nus-sg.zoom.us/meeting/register/tZctdOmorjIjGdUto_SJO3EzN_kdorcJyJVr\n\n\nTitle\n Approximate Submodularity in Network Design\n\n\nAbstract\nNetwork design problems are ubiquitous in long term planning for modern marketplaces\, where firms constantly innovate new ways to match supply and demand. They are often challenging to solve due to the problem scale and the uncertainties that affect the decisions. In this talk\, we establish a novel structural property for a large class of network design problems. The property can be interpreted as an approximate form of submodularity\, where local changes in the objective function can be used to bound global changes. We use this structure to analyze simple heuristics and establish theoretical guarantees for network design problems in e-retailing\, online market platforms\, and manufacturing. Further\, using our analysis\, we identify new heuristics for solving network design problems that lead to an order of magnitude gains in computational efficiency\, without loss of optimization performance. This is a joint work with Levi DeValve (Chicago) and Sasa Pekec (Duke).
URL:https://iora.nus.edu.sg/events/iora-seminar-series-yehua-wei/
CATEGORIES:IORA Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://iora.nus.edu.sg/wp-content/uploads/2021/12/Pic-Wei-YH.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20220311T100000
DTEND;TZID=Asia/Singapore:20220311T113000
DTSTAMP:20260417T112059
CREATED:20211216T015014Z
LAST-MODIFIED:20220307T054220Z
UID:14705-1646992800-1646998200@iora.nus.edu.sg
SUMMARY:IORA Seminar Series – Assaf Zeevi
DESCRIPTION:Assaf Zeevi is Professor and holder of the Kravis chair at the Graduate School of Business\, Columbia University. His research and teaching interests lie at the intersection of Operations Research\, Statistics\, and Machine Learning. In particular\, he has been developing theory and algorithms for reinforcement learning\, Bandit problems\, stochastic optimization\, statistical learning and stochastic networks. Assaf’s work has found applications in online retail\, healthcare analytics\, dynamic pricing\, recommender systems\, and social learning in online marketplaces. \nAssaf received his B.Sc. and M.Sc. (Cum Laude) from the Technion\, in Israel\, and subsequently his Ph.D. from Stanford University. He spent time as a visitor at Stanford University\, the Technion and Tel Aviv University. He is the recipient of several teaching and research awards including a CAREER Award from the National Science Foundation\, an IBM Faculty Award\, Google Research Award\, as well as several best paper awards including the 2019 Lanchester Prize.   Assaf has recently served a term as Vice Dean at Columbia Business School and Editor-in-Chief of Stochastic Systems (the flagship journal of INFORMS’ Applied Probability Society). He also serves on various other editorial boards and program committees in the Operations Research and Machine Learning communities\, as well as scientific advisory boards for startup companies in the high technology sector. \n\n\n\nName of Speaker \nAssaf Zeevi\n\n\nSchedule\n11 March 2022\, 10am – 11.30am \n(60 min talk + 30 min Q&A)\n\n\nLink to register  \n(Zoom)\nhttps://nus-sg.zoom.us/meeting/register/tZYtdOusrj4qGtYQnSrSqKvhRN7RjGgj8vM3\n\n\nTitle\nOnline Learning in Sequential Selection Problems \n(A Two Part Talk…)\n\n\nAbstract\nIn this sequence of two (self-contained) talks\, I will describe some recent work on learning theoretic formulations in sequential selection problems\, focusing on two vignettes. \nThe first (to be covered in part 1) will focus on an optimal stopping problem:  given a random sequence of independent observations revealed one at a time over some finite horizon of play\, the objective is to design an algorithm that “stops’’ this sequence to maximize the expected value of the “stopped” observation.  (Once the sequence is stopped there is no recourse and the game terminates.) When the (common) distribution governing the random sequence is known\, the optimal rule is a (distribution-dependent) threshold policy that is obtained by backward induction; work on this problem has a long and storied history.  Surprisingly\, if one does *not* assume the distribution to be known a priori\, there is fairly little work in extant literature\, and the talk will develop this formulation\, expound some of the challenges involved in its learning theoretic formulations\, and an indication of what can (and cannot) be achieved in this setting. \nThe second vignette (to be covered in part 2) will focus on a sequential stochastic assignment problem\, which dates back roughly 50 years.  In this problem a known number of sequentially arriving items\, say\, “jobs\,” need to be assigned to a pool of\, say\, “workers\,” and once each job is assigned to a worker\, both job and worker are no longer admissible for further assignment. Each job is characterized by a quality / complexity indicator drawn independently from an underlying distribution\, and each worker is characterized by a known “productivity coefficient”  (for example\, the effectiveness by which that person can process said job).  The objective is to assign jobs to workers so that the expected overall work time required for performing all the jobs will be minimal.  This formulation has been used extensively in the OR literature in a variety of application domains\, and is increasingly relevant in the study of online marketplaces and matching markets.  As in the case of the optimal stopping problem\, when the ambient distribution is known a priori the optimal assignment policy is obtained using backward induction arguments. Naturally\, in most realistic applications knowledge of this key problem primitive is not available\, giving rise\, again\, to learning theoretic formulations which will be the main focus of this part of the talk.
URL:https://iora.nus.edu.sg/events/assafzeevi2022p1/
CATEGORIES:IORA Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://iora.nus.edu.sg/wp-content/uploads/2021/12/assaf-pic_w.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20220316T100000
DTEND;TZID=Asia/Singapore:20220316T113000
DTSTAMP:20260417T112059
CREATED:20211216T015214Z
LAST-MODIFIED:20220311T082205Z
UID:14709-1647424800-1647430200@iora.nus.edu.sg
SUMMARY:IORA Seminar Series – Assaf Zeevi (Talk Part 2)
DESCRIPTION:Assaf Zeevi is Professor and holder of the Kravis chair at the Graduate School of Business\, Columbia University. His research and teaching interests lie at the intersection of Operations Research\, Statistics\, and Machine Learning. In particular\, he has been developing theory and algorithms for reinforcement learning\, Bandit problems\, stochastic optimization\, statistical learning and stochastic networks. Assaf’s work has found applications in online retail\, healthcare analytics\, dynamic pricing\, recommender systems\, and social learning in online marketplaces. \nAssaf received his B.Sc. and M.Sc. (Cum Laude) from the Technion\, in Israel\, and subsequently his Ph.D. from Stanford University. He spent time as a visitor at Stanford University\, the Technion and Tel Aviv University. He is the recipient of several teaching and research awards including a CAREER Award from the National Science Foundation\, an IBM Faculty Award\, Google Research Award\, as well as several best paper awards including the 2019 Lanchester Prize.   Assaf has recently served a term as Vice Dean at Columbia Business School and Editor-in-Chief of Stochastic Systems (the flagship journal of INFORMS’ Applied Probability Society). He also serves on various other editorial boards and program committees in the Operations Research and Machine Learning communities\, as well as scientific advisory boards for startup companies in the high technology sector. \n\n\n\nName of Speaker  \nAssaf Zeevi\n\n\nSchedule \n16 March 2022\, 10am – 11.30am\n\n\nVenue: \nI4-01-03 Seminar Room  (For NUS Staff & Students)\n\n\nLink to register  (Via Zoom)\nhttps://nus-sg.zoom.us/meeting/register/tZYpceigpzIiEtMuQAiHk7PrMuiZcWUqgvfx\n\n\nTitle \nOnline Learning in Sequential Selection Problems (Part 2)\n\n\nAbstract \nIn this sequence of two (self-contained) talks\, I will describe some recent work on learning theoretic formulations in sequential selection problems\, focusing on two vignettes. \nThe first (to be covered in part 1) will focus on an optimal stopping problem:  given a random sequence of independent observations revealed one at a time over some finite horizon of play\, the objective is to design an algorithm that “stops’’ this sequence to maximize the expected value of the “stopped” observation.  (Once the sequence is stopped there is no recourse and the game terminates.) When the (common) distribution governing the random sequence is known\, the optimal rule is a (distribution-dependent) threshold policy that is obtained by backward induction; work on this problem has a long and storied history.  Surprisingly\, if one does *not* assume the distribution to be known a priori\, there is fairly little work in extant literature\, and the talk will develop this formulation\, expound some of the challenges involved in its learning theoretic formulations\, and an indication of what can (and cannot) be achieved in this setting. \nThe second vignette (to be covered in part 2) will focus on a sequential stochastic assignment problem\, which dates back roughly 50 years.  In this problem a known number of sequentially arriving items\, say\, “jobs\,” need to be assigned to a pool of\, say\, “workers\,” and once each job is assigned to a worker\, both job and worker are no longer admissible for further assignment. Each job is characterized by a quality / complexity indicator drawn independently from an underlying distribution\, and each worker is characterized by a known “productivity coefficient”  (for example\, the effectiveness by which that person can process said job).  The objective is to assign jobs to workers so that the expected overall work time required for performing all the jobs will be minimal.  This formulation has been used extensively in the OR literature in a variety of application domains\, and is increasingly relevant in the study of online marketplaces and matching markets.  As in the case of the optimal stopping problem\, when the ambient distribution is known a priori the optimal assignment policy is obtained using backward induction arguments. Naturally\, in most realistic applications knowledge of this key problem primitive is not available\, giving rise\, again\, to learning theoretic formulations which will be the main focus of this part of the talk.\n\n\nAbout the speaker \nAssaf Zeevi is Professor and holder of the Kravis chair at the Graduate School of Business\, Columbia University. His research and teaching interests lie at the intersection of Operations Research\, Statistics\, and Machine Learning. In particular\, he has been developing theory and algorithms for reinforcement learning\, Bandit problems\, stochastic optimization\, statistical learning and stochastic networks. Assaf’s work has found applications in online retail\, healthcare analytics\, dynamic pricing\, recommender systems\, and social learning in online marketplaces. \nAssaf received his B.Sc. and M.Sc. (Cum Laude) from the Technion\, in Israel\, and subsequently his Ph.D. from Stanford University. He spent time as a visitor at Stanford University\, the Technion and Tel Aviv University. He is the recipient of several teaching and research awards including a CAREER Award from the National Science Foundation\, an IBM Faculty Award\, Google Research Award\, as well as several best paper awards including the 2019 Lanchester Prize.   Assaf has recently served a term as Vice Dean at Columbia Business School and Editor-in-Chief of Stochastic Systems (the flagship journal of INFORMS’ Applied Probability Society). He also serves on various other editorial boards and program committees in the Operations Research and Machine Learning communities\, as well as scientific advisory boards for startup companies in the high technology sector.
URL:https://iora.nus.edu.sg/events/assafzeevi2022p2/
CATEGORIES:IORA Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://iora.nus.edu.sg/wp-content/uploads/2021/12/assaf-pic_w.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20220318T100000
DTEND;TZID=Asia/Singapore:20220318T113000
DTSTAMP:20260417T112059
CREATED:20211216T015847Z
LAST-MODIFIED:20220311T081817Z
UID:14711-1647597600-1647603000@iora.nus.edu.sg
SUMMARY:IORA Seminar Series – Jiding Zhang
DESCRIPTION:Jiding Zhang is an Assistant Professor of Operations Management at NYU Shanghai. Jiding’s primary research interests lie in the field of marketplace analytics. In her recent work\, she analyzes the operations and economics of various digital platforms using both data analytic and mathematical modeling tools. She is also interested in developing data-driven methods for analysis of online markets. Jiding obtained her PhD from the Operations\, Information and Decisions Department of The Wharton School\, under supervision of Professors Senthil Veeraraghavan\, Ken Moon and Sergei Savin. \n\n\n\nName of Speaker\nZhang Jiding\n\n\nSchedule \n18 March\, 10am – 11.30am\n\n\nLink to Register\nhttps://nus-sg.zoom.us/meeting/register/tZApfuqppj4oHNZ-X8Btkjv-RqJAHz1zdBva\n\n\nTitle of Talk\nDoes Fake News Content Create Echo Chambers?\n\n\nAbstract\nPlatforms have recently come under criticism from regulatory agencies\, policymakers\, and media scholars for the burgeoning influence of unfettered fake news online. There has been debate regarding whether such online false news content creates echo chambers—segments of the market in which false news is exclusively or predominantly consumed.  We use a large-scale dataset reporting individual households’ online activity to understand the trends in online news consumption and examine the claim that online news creates echo chambers. We find that the consumption of false news online is widespread\, yet despite that\, such echo chambers are minimal. Through a structural model we analyze the joint consumption of online news from mainstream sources and from sources producing false content. Using a natural experiment created by a policy change on the largest social media platform\, we find that not only are echo chamber effects not pronounced on the aggregate level\, the causal effect of consuming more from false news sources is greater countervailing consumption of mainstream news. Naive\, operational interventions such as reducing the supply of false news sources may unnecessarily reduce the overall consumption of news from mainstream sources without adequately protecting the small minority most vulnerable to consuming only false news.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-jiding-zhang/
CATEGORIES:IORA Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://iora.nus.edu.sg/wp-content/uploads/2021/12/jiding_zhang1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20220325T100000
DTEND;TZID=Asia/Singapore:20220325T113000
DTSTAMP:20260417T112059
CREATED:20211216T020507Z
LAST-MODIFIED:20220316T062542Z
UID:14713-1648202400-1648207800@iora.nus.edu.sg
SUMMARY:IORA Seminar Series – Luyi Yang
DESCRIPTION:Luyi YANG is an assistant professor in the Operations and Information Technology Management Group at the University of California\, Berkeley’s Haas School of Business. His research interests include service operations\, business model innovation\, digital marketplaces\, smart mobility\, sustainability\, and operations-marketing interface. His research is published or forthcoming in leading journals such as Management Science\, Operations Research\, and Manufacturing & Service Operations Management and recognized by various research awards such as M&SOM Service Management SIG Best Paper Award\, INFORMS Service Science Best Cluster Paper Award\, INFORMS Minority Issues Forum Paper Competition\, and INFORMS Junior Faculty Interest Group (JFIG) Forum Paper Competition (Honorable Mention). He has taught courses in business analytics\, data mining\, and operations management. Prior to joining Berkeley Haas\, he was an assistant professor of operations management and business analytics at Johns Hopkins University’s Carey Business School. He received his Ph.D. and MBA from the University of Chicago\, Booth School of Business\, and his BS in Industrial Engineering and BA in English\, both from Tsinghua University. \n\n\n\nName of speaker\nLuyi Yang\n\n\nSchedule \n25 March 2022\, 10am – 11.30am\n\n\nLink to register (via Zoom)\nhttps://nus-sg.zoom.us/meeting/register/tZMuf-yppzwoGtRzBFsorYVikmPRtPTr8Ac6\n\n\nTitle of talk\nRight to Repair: Pricing\, Welfare\, and Environmental Implications\n\n\nAbstract\nThe “right to repair” (RTR) movement calls for government legislation that requires manufacturers to provide repair information\, tools\, and parts so that consumers can independently repair their own products with more ease. The initiative has gained global traction in recent years. Repair advocates argue that such legislation would break manufacturers’ monopoly on the repair market and benefit consumers. They further contend that it would reduce the environmental impact by reducing e-waste and new production. Yet\, the RTR legislation may also trigger a price response in the product market as manufacturers try to mitigate the profit loss. This paper employs an analytical model to study the pricing\, welfare\, and environmental implications of RTR. We find that as the RTR legislation continually lowers the independent repair cost\, manufacturers may initially cut the new product price and then raise it. This non-monotone price adjustment may further induce a non-monotone change in consumer surplus\, social welfare\, and environmental impact. Strikingly\, the RTR legislation can potentially lead to a “lose-lose-lose” outcome that compromises manufacturer profit\, reduces consumer surplus\, and increases the environmental impact\, despite repair being made easier and more affordable.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-luyi-yang/
CATEGORIES:IORA Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://iora.nus.edu.sg/wp-content/uploads/2021/12/Yang-Luyi-photo-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20220401T100000
DTEND;TZID=Asia/Singapore:20220401T113000
DTSTAMP:20260417T112059
CREATED:20211216T020545Z
LAST-MODIFIED:20220323T080818Z
UID:14715-1648807200-1648812600@iora.nus.edu.sg
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
ATTACH;FMTTYPE=image/jpeg:https://iora.nus.edu.sg/wp-content/uploads/2021/12/xiong-ruoxuan-pic.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20220408T100000
DTEND;TZID=Asia/Singapore:20220408T113000
DTSTAMP:20260417T112059
CREATED:20211216T020639Z
LAST-MODIFIED:20220401T084157Z
UID:14717-1649412000-1649417400@iora.nus.edu.sg
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:20260417T112059
CREATED:20220406T060607Z
LAST-MODIFIED:20220406T061314Z
UID:15262-1650621600-1650627000@iora.nus.edu.sg
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
ATTACH;FMTTYPE=image/jpeg:https://iora.nus.edu.sg/wp-content/uploads/2022/04/photo-chen-ying.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20220506T100000
DTEND;TZID=Asia/Singapore:20220506T113000
DTSTAMP:20260417T112059
CREATED:20211216T020732Z
LAST-MODIFIED:20220430T132246Z
UID:14719-1651831200-1651836600@iora.nus.edu.sg
SUMMARY:IORA Seminar Series – Basak Kalkanci
DESCRIPTION:Basak Kalkanci is an associate professor of operations management at the Scheller College of Business at Georgia Tech. Her research focuses on socially and environmentally responsible supply chain management\, and contracting and the role of information in decentralized supply chains. Her research aims to lay the necessary groundwork to enable real-time measurement and management of environmental and social impacts in global supply chains. She earned her Ph.D in Management Science and Engineering from Stanford University and was a postdoctoral associate at the Massachusetts Institute of Technology prior to joining Georgia Tech. Her work appeared in premier journals including Management Science\, Operations Research\, Manufacturing & Service Operations Management\, and Production and Operations Management\, and has been funded by the National Science Foundation. She is the recipient of the Paul Kleindorfer Award in Sustainability (2020)\, Alliance for Research on Corporate Sustainability Emerging Sustainability Scholar Award (2019)\, Georgia Power Professor of Excellence (2015)\, Management Science Meritorious Service Award (2015\, 2017\, 2019\, 2020)\, and M&SOM Meritorious Service Award (2014). She serves as a Senior Editor for the Production and Operations Management Journal and as an Associate Editor for M&SOM. \n\n\n\nName of speaker\nBasak Kalkanci\n\n\nSchedule \n6 May 2022\, 10am – 11.30am\n\n\nLink to register  \n \nhttps://nus-sg.zoom.us/meeting/register/tZcqfuGqqzsrG927xqVoLigsdXKlE82X8kKi\n\n\nTitle of talk\nHow Transparency into Internal and External Responsibility Initiatives Influences Consumer Choice\n\n\nAbstract\nAmid growing calls for transparency and social and environmental responsibility\, companies are employing different strategies to improve consumer perceptions of their brands. Some pursue internal initiatives that reduce their negative social or environmental impacts through responsible operations practices (such as paying a living wage to workers  or engaging in environmentally sustainable manufacturing). Others pursue external responsibility initiatives (such as philanthropy or cause-related marketing). Through two experiments conducted in the field and complementary online experiments\, we compare how transparency into these internal and external initiatives affects customer perceptions and sales. We find that transparency into both internal and external responsibility initiatives tends to dominate generic brand marketing in motivating consumer purchases\, supporting the view that consumers take companies’ responsibility efforts into account in their decision making. Furthermore\, the results provide converging evidence that transparency into a company’s internal responsibility practices can be at least as motivating of consumer sales as transparency into its external responsibility initiatives\, incrementally increasing a consumer’s probability of purchase by 6.40% and 45.85% across our two field experiments\, conducted in social and environmental domains\, respectively. Our results suggest that it may be in the interest of both business and society for managers to prioritize internal responsible operations initiatives to achieve both top- and bottom-line benefits while mitigating social and environmental harms.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-andrew-lim/
CATEGORIES:IORA Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://iora.nus.edu.sg/wp-content/uploads/2021/12/kalkanci_basak_pic.jpg
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BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20220530T100000
DTEND;TZID=Asia/Singapore:20220530T113000
DTSTAMP:20260417T112059
CREATED:20220430T132641Z
LAST-MODIFIED:20220812T033515Z
UID:15350-1653904800-1653910200@iora.nus.edu.sg
SUMMARY:IORA Seminar Series - Hoi-To Wai
DESCRIPTION:Hoi-To Wai received his PhD degree from Arizona State University (ASU) in Electrical Engineering in Fall 2017\, B. Eng. (with First Class Honor) and M. Phil. degrees in Electronic Engineering from The Chinese University of Hong Kong (CUHK) in 2010 and 2012\, respectively. He is an Assistant Professor in the Department of Systems Engineering & Engineering Management at CUHK. He has held research positions at ASU\, UC Davis\, Telecom ParisTech\, Ecole Polytechnique\, LIDS\, MIT. Hoi-To’s research interests are in the broad area of signal processing\, machine learning and distributed optimization with applications to network science. His dissertation has received the 2017’s Dean’s Dissertation Award from the Ira A. Fulton Schools of Engineering of ASU\, and he is a recipient of a Best Student Paper Award at ICASSP 2018. \n\n\n\nName of Speaker\nHoi-To Wai\n\n\nSchedule \n30 May 2022\, 10am – 11.30am\n\n\nVenue (face-to-face)\nI4-01-03 Seminar Room (next to the level 1 café)\n\n\nLink to Register (Online)\nhttps://nus-sg.zoom.us/meeting/register/tZMsf-mpqj0tH9e76YMDTvL9xA1B20JT9uAD\n\n\nTitle of Talk\nStochastic Approximation Schemes with Decision Dependent Data\n\n\nAbstract\nStochastic approximation (SA) is a key method which forms the backbone of many online algorithms relying on streaming data with applications to reinforcement and statistical learning. This talk considers a setting in which the streaming data is not i.i.d.\, but is correlated and decision dependent. First\, we analyze a general SA scheme that indirectly minimizes a smooth but possibly non-convex objective function. We consider an update procedure whose drift term depends on a decision dependent Markov chain and the mean field is not necessarily a gradient map\, leading to asymptotic bias for the one-step updates. We analyze the expected non-asymptotic convergence rate for such general scheme and llustrate this setting with the policy-gradient method for average reward maximization. Second\, we consider extensions of the SA scheme and its analysis. For bi-level optimization via two timescale SA\, we present the non-asymptotic complexity analysis and demonstrate an application to natural actor-critic. For performative prediction with stateful users\, we illustrate that the SGD algorithm in strategical classification can be interpreted as an SA scheme with decision dependent data\, and we present recent results on its expected convergence rate towards a performative stable solution.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-hoi-to-wai/
CATEGORIES:IORA Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://iora.nus.edu.sg/wp-content/uploads/2022/04/Pic-HTW.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20220707T090000
DTEND;TZID=Asia/Singapore:20220707T170000
DTSTAMP:20260417T112059
CREATED:20220627T063627Z
LAST-MODIFIED:20220627T063627Z
UID:15747-1657184400-1657213200@iora.nus.edu.sg
SUMMARY:S3 Optimization Day 2022
DESCRIPTION:On behalf of the organising committee\, it is our pleasure to announce the S3 Optimization Day 2022 workshop jointly organised by National University of Singapore (NUS)\, The Chinese University of Hong Kong\, Shenzhen (CUHK-Shenzhen)\, Shanghai University of Finance and Economics (SHUFE) and Singapore University of Technology and Design (SUTD). \nProf Ye Yinyu will provide a Keynote address for this Hybrid workshop which will include exciting talks from academics across Shanghai\, Shenzhen and Singapore! Everyone is welcome to participate. Please see the event website for further details https://iora.nus.edu.sg/s3-optimization-day-2022/
URL:https://iora.nus.edu.sg/events/s3-optimization-day-2022/
ATTACH;FMTTYPE=image/jpeg:https://iora.nus.edu.sg/wp-content/uploads/2022/06/ye_yinyu.jpeg
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BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20220902T100000
DTEND;TZID=Asia/Singapore:20220902T113000
DTSTAMP:20260417T112059
CREATED:20220812T033552Z
LAST-MODIFIED:20220829T070504Z
UID:15964-1662112800-1662118200@iora.nus.edu.sg
SUMMARY:IORA Seminar Series - Andrew Lim
DESCRIPTION:Andrew Lim is a Professor in the Department of Analytics and Operations and the Department of Finance at NUS Business School. He is also affiliated with the Institute for Operations Research and Analytics. His research is in the area of stochastic control\, optimization under uncertainty\, financial engineering\, and robust and data driven decision making. From 2002 — 2014\, he was on the faculty of the Department of Industrial Engineering and Operations Research at the University of California (Berkeley). He is a past recipient of the National Science Foundation CAREER Award. He serves as an Associate Editor for Operations Research and Management Science\, and was previously on the editorial board of the IEEE Transactions on Automatic Control. \n  \n\n\n\nName of Speaker\nProfessor Andrew Lim\n\n\nSchedule\n2 September 2022\, Friday at 10:00am \n(60 minutes talk + 30 minutes Q&A)\n\n\nVenue \nInnovation 4.0 Building\, level 1\, Seminar Room (next to the level 1 café)\n\n\nLink to Register \n(Via Zoom)\nhttps://nus-sg.zoom.us/meeting/register/tZYld-mhpj4tGNWdd6GD8BGY-whthwWbduyq\n\n\nTitle\nMechanisms for Coordinating Systems of Decentralized Agents \n \n\n\nAbstract\nA typical service/operations system is populated by multiple interacting decentralized agents who make decisions that collectively determine system performance. Decentralized agents are hired because they are domain experts\, but the aggregate system is usually not efficient if agents optimize in isolation. Coordination is difficult\, however\, as it requires a decision maker/mechanism designer/principal who can optimize the aggregate system\, which is unrealistic when the system is complex and domain experts are needed to control its many parts. We consider these issues in the setting of a service system modeled by a single server queue where the arrival rate is controlled by multiple agents who dynamically sets prices and earn revenue on each arrival\, and the service rate by a different agent who is concerned about minimizing service costs. We show that transfer payments between agents can induce decisions that optimize system efficiency even if every agent misspecifies the impact of the other agents in their models. The optimal transfers\, however\, depend on the “private” models of each agent and can only be directly computed by a “smart” mechanism designer/principal who can optimize the aggregate system. We propose a mechanism for computing the optimal transfers where decentralized agents iteratively share their valuations of shared resources. We show that this algorithm converges to the optimal transfer function at a geometric rate\, and provide natural conditions under which it is optimal for agents to report resource valuations truthfully. This algorithm can be implemented without a “smart” mechanism designer/principal\, and decentralized agents are not required to share information about their domain of expertise\, or even correctly specify the models\, actions or number of other agents when optimizing their decisions. \n 
URL:https://iora.nus.edu.sg/events/iora-seminar-series-andrew-lim-2/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20220909T100000
DTEND;TZID=Asia/Singapore:20220909T113000
DTSTAMP:20260417T112059
CREATED:20220812T033640Z
LAST-MODIFIED:20220902T070837Z
UID:15966-1662717600-1662723000@iora.nus.edu.sg
SUMMARY:IORA Seminar Series - Roland Yap
DESCRIPTION:Roland Yap is an associate professor in the School of Computing\, National University of Singapore. He obtained his PhD from Monash University\, Australia. His research interests are in Artificial Intelligence\, Big Data\, Constraints\, Programming Languages\, Security. He is well known for his work on Constraint Logic Programming and the CLP(R) programming language which is one of the first programming languages where constraints are first class. CLP(R) has led to many developments in Constraint Programming (CP) and Logic Programming. \n  \n\n\n\nVenue \nInnovation 4.0 Building\, level 1\, Seminar Room (next to the level 1 café) \n \n\n\nLink to Register \n(Via Zoom)\nhttps://nus-sg.zoom.us/meeting/register/tZYqd-mhrTopHtSTBPnJnbgrD4SqeGsCmMJz\n\n\nTitle\nA Bus Routing Problem with Multiple Stop Preferences and Tradeoffs \n \n\n\nAbstract\nWe present a variant of the School Bus Routing Problem. In this variant\, there multiple stop locations per passenger with preferences on the stops. There is a multimodal transportation aspect. There is also a tradeoff between route cost and passenger preferences. For the realistic scenario\, a real-time requirement that the route be obtained quickly. This necessitates efficient solution which can give good solutions. We discuss one instance of this problem with real datasets in the Singapore context.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-roland-yap/
CATEGORIES:IORA Seminar Series
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BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20220916T100000
DTEND;TZID=Asia/Singapore:20220916T113000
DTSTAMP:20260417T112059
CREATED:20220812T033747Z
LAST-MODIFIED:20220912T013308Z
UID:15968-1663322400-1663327800@iora.nus.edu.sg
SUMMARY:IORA Seminar Series - Phebe Vayanos
DESCRIPTION:Phebe Vayanos is an Assistant Professor of Industrial & Systems Engineering and Computer Science at the University of Southern California. She is also an Associate Director of CAIS\, the Center for Artificial Intelligence in Society at USC. Her research is focused on Operations Research and Artificial Intelligence and in particular on optimization and machine learning. Her work is motivated by problems that are important for social good\, such as those arising in public housing allocation\, public health\, and biodiversity conservation. Prior to joining USC\, she was lecturer in the Operations Research and Statistics Group at the MIT Sloan School of Management\, and a postdoctoral research associate in the Operations Research Center at MIT. She holds a PhD degree in Operations Research and an MEng degree in Electrical & Electronic Engineering\, both from Imperial College London. She serves as a member of the ad hoc INFORMS AI Strategy Advisory Committee\, she is an elected member of the Committee on Stochastic Programming (COSP)\, and the VP of Communications for the INFORMS Section on Public Sector Operations Research. She is an Associate Editor for Operations Research Letters and Computational Management Science. She is a recipient of the NSF CAREER award and the INFORMS Diversity\, Equity\, and Inclusion Ambassador Program Award. \n\n\n\nVenue \nTalk will be held via Zoom \n \n\n\nLink to Register \n(Via Zoom)\nhttps://nus-sg.zoom.us/meeting/register/tZIlduGgqz0sEtFr4NIgpQnX2vhMelYAA5b6\n\n\nTitle\nInterpretability\, Robustness\, and Fairness in Predictive and Prescriptive Analytics for Social Impact \n \n\n\nAbstract\nMotivated by problems in homeless services delivery\, suicide prevention\, and substance use prevention\, we consider the problem of learning optimal interpretable\, robust\, and fair models in the form of decision-trees to assist with decision-making in socially sensitive\, high-stakes settings. We propose new models and algorithms\, showcase their flexibility\, and theoretical and practical benefits\, and demonstrate substantial improvements over the state of the art. This presentation is based on the following papers: \nStrong optimal classification trees\, S. Aghaei\, A. Gómez\, P. Vayanos. Under second round of review at Operations Research\, January 2021. \nLearning optimal fair classification trees\, N. Jo\, S. Aghaei\, J. Benson\, A. Gómez\, P. Vayanos. Under review for ACM Conference on Fairness\, Accountability\, and Transparency (FAccT)\, 2022. \nLearning optimal prescriptive trees from observational data\, N. Jo\, S. Aghaei\, A. Gómez\, P. Vayanos\, Under Review at Management Science\, August 2021. \nOptimal robust classification trees\, N. Justin\, S. Aghaei\, A. Gómez\, P. Vayanos\, AAAI  Workshop on Adversarial Machine Learning and Beyond\, 2022.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-phebe-vayanos/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20220930T100000
DTEND;TZID=Asia/Singapore:20220930T113000
DTSTAMP:20260417T112059
CREATED:20220812T033845Z
LAST-MODIFIED:20221004T092447Z
UID:15970-1664532000-1664537400@iora.nus.edu.sg
SUMMARY:IORA Seminar Series - Ozlem Ergun
DESCRIPTION:Dr. Özlem Ergun is a COE Distinguished Professor and Associate Chair for Graduate Studies in Mechanical and Industrial Engineering at Northeastern University. Dr. Ergun’s research focuses on design and management of large-scale and decentralized networks. She has applied her work on network design\, management\, and resilience to problems arising in many critical systems including transportation\, pharmaceuticals\, and healthcare.  She has worked with organizations that respond to emergencies and humanitarian crises around the world\, including USAID\, UNWFP\, UNHCR\, IFRC\, OXFAM America\, CARE USA\, FEMA\, USACE\, CDC\, AFCEMA\, and MedShare International.  Recently\, Dr. Ergun partnered with the Massachusetts’ Executive Office of Elder Affairs (EOEA) to help match qualified medical professionals to Long Term Care facilities with open positions around the state as part of the state’s response efforts to COVID19. Dr. Ergun also served as a member of the National Academies Committee on Building Adaptable and Resilient Supply Chains after Hurricanes Harvey\, Irma\, and Maria and the National Academies Committee on Security of America’s Medical Supply Chain. She was the President of INFORMS Section on Public Programs\, Service and Needs in 2013. She currently serves as the Area Editor at the Operations Research journal for Policy Modeling and the Public Sector Area and a Department co-Editor at MSOM journal for Environment\, Health and Society Department. \n  \nPrior to joining Northeastern Dr. Ergun was the Coca-Cola Associate Professor in the School of Industrial and Systems Engineering at Georgia Institute of Technology\, where she also co-founded and co-directed the Health and Humanitarian Systems Research Center at the Supply Chain and Logistics Institute.  She received a B.S. in Operations Research and Industrial Engineering from Cornell University in 1996 and a Ph.D. in Operations Research from the Massachusetts Institute of Technology in 2001. \n  \n\n\n\nVenue \nTalk will be held via Zoom \n \n\n\nLink to register\nhttps://nus-sg.zoom.us/meeting/register/tZwtceyurzkoHd2Hh7IItvgFzCRgXgwVQzxD \n \n\n\nTitle\nOptimizing Post-Disruption Response Operations to Improve Resilience of Critical Infrastructure Systems \n \n\n\nAbstract\nCritical infrastructure systems (CIS) underpin almost every aspect of the modern society by enabling the essential functions through overlaying service networks. After a disruption impacting the CIS\, the functionality of the overlaying service networks degrades. Thus\, after an extreme event\, in order to minimize the negative impact to society\, it is crucial to restore the disrupted CIS as soon as possible. In this talk\, we focus on disruptions created by natural hazards on transportation CIS and develop methods to efficiently plan the post-disaster response operations. \n  \nIn the aftermath of a natural disaster\, the transportation network is disrupted due to the debris blocking the roads and obstructing the flow of relief aid and search-and-rescue teams between critical facilities and disaster sites. In the first few days following a disaster\, in order to deliver aid to those in need\, blocked roads must be cleared by pushing the debris to the sides. In this context\, we define the road network recovery problem (RNRP) as finding a schedule to clear the roads with limited resources such that all the service demanding locations are served in the shortest possible time. First\, we address the deterministic RNRP and propose a novel network science inspired measure to quantify the criticality of the components within a disrupted service network and develop a restoration heuristic. Next\, we consider RNRP with stochastic demand and propose an approximate dynamic programming approach for identifying an effective policy under uncertainty. \n 
URL:https://iora.nus.edu.sg/events/iora-seminar-series-ozlem-ergun/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20221007T100000
DTEND;TZID=Asia/Singapore:20221007T113000
DTSTAMP:20260417T112059
CREATED:20220812T033922Z
LAST-MODIFIED:20221004T092529Z
UID:15972-1665136800-1665142200@iora.nus.edu.sg
SUMMARY:IORA Seminar Series - Kaidi Yang
DESCRIPTION:Kaidi Yang is an Assistant Professor in the Department of Civil and Environmental Engineering at the National University of Singapore. He aims to develop efficient and trustworthy algorithms for the design and operation of future mobility systems\, with a particular focus on advances in vehicular technology (e.g.\, connected and automated vehicles\, electric vehicles\, etc.) and shared mobility. Before joining NUS\, he was a postdoctoral scholar with the Autonomous Systems Lab at Stanford University. He received his Ph.D. from ETH Zurich in 2019\, M.Sc. in Control Science and Engineering from Tsinghua University in 2014\, and dual bachelor’s degrees in Automation and Mathematics from Tsinghua University in 2011. \n  \n\n\n\nVenue \nInnovation 4.0 Building\, level 1\, Seminar Room (next to the level 1 café) \n \n\n\nLink to Register \n(Hybrid Session)\nhttps://nus-sg.zoom.us/meeting/register/tZYtfuisrjovHNE8qXjzeBwFwSDutMZlaLbu\n\n\nTitle\nOperation of Traditional and Autonomous Mobility-on-Demand\n\n\nAbstract\nThe past decade has witnessed the widespread deployment of Mobility-on-Demand (MoD) services\, such as the ride-hailing services provided by Uber and Grab. One key operational challenge associated with MoD services is the vehicle imbalances due to asymmetric transportation demand: vehicles tend to accumulate in some regions while becoming depleted in others\, giving rise to inefficient operations of the MoD system. We aim to employ emerging automated vehicles (AVs) to improve the operation of MoD systems\, leveraging their capability of being globally coordinated. In the first part of the talk\, we consider the transition period of AV deployment\, whereby an MoD system operates a mixed fleet of AVs and human-driven vehicles (HVs). In such systems\, AVs are centrally coordinated by the operator\, and the HVs might strategically respond to the coordination of AVs. We model such a system using a Stackelberg framework where the MoD operator serves as the leader and human-driven vehicles serve as the followers. We further develop a real-time coordination algorithm for AVs. In the second part of the talk\, we propose a set of reinforcement learning (RL)-based algorithms to improve the efficiency of MoD systems operating a fleet of AVs. We demonstrate that graph neural networks enable RL agents to recover behaviour policies significantly more transferable\, generalisable\, and scalable than policies learned through other approaches. We further improve the generalisability by integrating meta-learning to transfer to unseen scenarios (e.g.\, different cities). \n 
URL:https://iora.nus.edu.sg/events/iora-seminar-series-kaidi-yang/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20221028T100000
DTEND;TZID=Asia/Singapore:20221028T113000
DTSTAMP:20260417T112059
CREATED:20220812T034645Z
LAST-MODIFIED:20221025T014948Z
UID:15974-1666951200-1666956600@iora.nus.edu.sg
SUMMARY:IORA Seminar Series - Asa Palley
DESCRIPTION:Asa Palley is an Assistant Professor of Operations and Decision Technologies at the Kelley School of Business at Indiana University. He develops and studies methods to gather and aggregate expert opinions for use in managerial making. Secondary interests include learning in sequential decision problems\, carbon pricing and investment in renewable generation and storage capacity\, and the application of decision analysis to public policy questions. His work has been published in the journals Management Science\, Experimental Economics\, and Risk Analysis. \n\n\n\nVenue \nInnovation 4.0 Building\, level 1\, Seminar Room (next to the level 1 café) \n \n\n\nLink to Register \n(Hybrid Session)\nhttps://nus-sg.zoom.us/meeting/register/tZwpde-tqTkjHtS27eyS1k611RkaNyQDO4DT\n\n\nTitle\nBoosting the Wisdom of Crowds Within a Single Judgment Problem: Weighted Averaging Based on Peer Predictions\n\n\nAbstract\nA combination of point estimates from multiple judges often provides a more accurate aggregate estimate than a point estimate from a single judge\, a phenomenon called “the wisdom of crowds”. However\, if the judges use shared information when forming their estimates\, the simple average will end up over-emphasizing this common component at the expense of the judges’ private information. A decision maker could in theory obtain a more accurate estimate by appropriately combining all information behind the judges’ opinions. Although this information underlies the judges’ individual estimates\, it is typically unobservable and thus cannot be directly aggregated by a decision maker. In this article\, we propose a weighting of judges’ individual estimates that appropriately combines their collective information within a single estimation problem. Judges are asked to provide both a point estimate of the quantity of interest and a prediction of the average estimate that will be given by all other judges. Predictions of others are then used as part of a criterion to determine weights that are applied to each judge’s estimate to form an aggregate estimate. Our weighting procedure is robust to noise in the judges’ responses and can be expressed in closed form. We use both simulation and data from a collection of experimental studies to illustrate that the weighting procedure outperforms existing methods. An R package called metaggR implements our method and is available on CRAN. \n 
URL:https://iora.nus.edu.sg/events/iora-seminar-series-asa-palley/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20221104T100000
DTEND;TZID=Asia/Singapore:20221104T113000
DTSTAMP:20260417T112059
CREATED:20220812T034742Z
LAST-MODIFIED:20221102T064417Z
UID:15976-1667556000-1667561400@iora.nus.edu.sg
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20221110T100000
DTEND;TZID=Asia/Singapore:20221110T113000
DTSTAMP:20260417T112059
CREATED:20221107T010749Z
LAST-MODIFIED:20221107T011038Z
UID:16455-1668074400-1668079800@iora.nus.edu.sg
SUMMARY:DAO-IORA joint seminar: Zhang Jiheng
DESCRIPTION:Jiheng Zhang is the head and a professor in the Department of Industrial Engineering and Decision Analytics of HKUST. He also hold a joint appointment at the Department of Mathematics of HKUST. His research interests are in the areas of Stochastic Modeling and Optimization\, Statistical Learning\, Numerical Methods and Algorithms; with applications in Operations Management\, Large Communication Networks\, and Financial Technology. He serves as an associate editor for Operations Research\, Stochastic Systems\, Probability in the Engineering and Informational Sciences. He has been the co-director of Elliptic lab since 2018\, focusing on various practical projects with industry partners including Huawei and Webank. He has invented several patents on large-scale production planning and blockchain consensus mechanism design with industry partners. He received his Ph.D. degree in operations research from the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology in 2009. He also holds an M.S. in mathematics from Ohio State University and a B.S. in mathematics from Nanjing University. \n  \n\n\n\nVenue \nSeminar room at i4.0 building (Level 1)\n\n\nLink to Register \n(Hybrid session)\nhttps://nus-sg.zoom.us/meeting/register/tZAlc-2uqz4vGddG2dIGCNr8NaOpCl8Wp_Xw\n\n\nTitle\nOn-Demand Ride-Matching in a Spatial Model with Abandonment and Cancellation\n\n\nAbstract\nRide-hailing platforms such as Uber\, Lyft\, and DiDi coordinate supply and demand by matching passen- gers and drivers. The platform has to promptly dispatch drivers when receiving requests\, since otherwise passengers may lose patience and abandon the service by switching to alternative transportation methods. However\, having less idle drivers results in a possible lengthy pick-up time\, which is a waste of system capacity and may cause passengers to cancel the service after they are matched. Due to complex spatial and queueing dynamics\, the analysis of the matching decision is challenging. In this paper\, we propose a spatial model to approximate the pick-up time based on the number of waiting passengers and idle drivers. We analyze the dynamics of passengers and drivers in a queueing model where the platform can control the matching process by setting a threshold on the expected pick-up time. Applying fluid approximations\, we obtain accurate performance evaluations and an elegant optimality condition\, based on which we propose a policy that adapts to time-varying demand.
URL:https://iora.nus.edu.sg/events/dao-iora-joint-seminar-zhang-jiheng/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20221111T100000
DTEND;TZID=Asia/Singapore:20221111T113000
DTSTAMP:20260417T112059
CREATED:20221101T081611Z
LAST-MODIFIED:20221107T010932Z
UID:16407-1668160800-1668166200@iora.nus.edu.sg
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20221125T100000
DTEND;TZID=Asia/Singapore:20221125T113000
DTSTAMP:20260417T112059
CREATED:20221101T081722Z
LAST-MODIFIED:20221123T011119Z
UID:16409-1669370400-1669375800@iora.nus.edu.sg
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20221206T100000
DTEND;TZID=Asia/Singapore:20221206T113000
DTSTAMP:20260417T112059
CREATED:20220812T034841Z
LAST-MODIFIED:20221205T061819Z
UID:15978-1670320800-1670326200@iora.nus.edu.sg
SUMMARY:IORA Seminar Series - Mika Sumida
DESCRIPTION:Mika Sumida is an Assistant Professor of Data Sciences and Operations in the Marshall School of Business at the University of Southern California. Her research focuses on developing efficient\, provably good algorithms for revenue management and resource allocation problems\, with applications in the sharing economy\, online marketplaces\, and delivery systems. She holds a Ph.D. in Operations Research and Information Engineering from Cornell University\, and a B.A. from Yale University. Prior to her Ph.D.\, she spent two years working in operations consulting at Analytics Operations Eng.\, Inc. \n\n\n\nLink to Register \n(Hybrid session)\nhttps://nus-sg.zoom.us/meeting/register/tZctcuGvqDksE9HugB0-sYRJ-g5nksO-uEaG\n\n\nTitle\nRevenue Management with Heterogeneous Resources\n\n\nVenue\nBIZ 1 – 0204\n\n\nAbstract\nWe study revenue management problems with heterogeneous resources\, each with unit capacity. An arriving customer makes a booking request for a particular interval of days in the future. We offer an assortment of resources in response to each booking request. The customer makes a choice within the assortment to use the chosen resource for her desired interval of days. The goal is to find a policy that determines an assortment of resources to offer to each customer to maximize the total expected revenue over a finite selling horizon. The problem has two useful features. First\, each resource is unique with unit capacity. Second\, each customer uses the chosen resource for a number of consecutive days. We consider static policies that offer each assortment of resources with a fixed probability. We show that we can efficiently perform rollout on any static policy\, allowing us to build on any static policy and construct an even better policy. Next\, we develop two static policies\, each of which is derived from linear and polynomial approximations of the value functions. We give performance guarantees for both policies\, so the rollout policies based on these static policies inherit the same guarantee. Lastly\, we develop an approach for computing an upper bound on the optimal total expected revenue. Our results for efficient rollout\, static policies\, and upper bounds all exploit the aforementioned two useful features of our problem. We use our model to manage hotel bookings based on a dataset from a real-world boutique hotel\, demonstrating that our rollout approach can provide remarkably good policies and our upper bounds can significantly improve those provided by existing techniques.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-mika-sumida/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20221209T140000
DTEND;TZID=Asia/Singapore:20221209T150000
DTSTAMP:20260417T112059
CREATED:20221201T075852Z
LAST-MODIFIED:20221201T080015Z
UID:16468-1670594400-1670598000@iora.nus.edu.sg
SUMMARY:DAO-IORA joint seminar: Ethan X. Fang
DESCRIPTION:Ethan X. Fang is an Assistant Professor of Biostatistics & Bioinformatics at Duke Medical School and affiliated with Decision Sciences of Fuqua Business School and Rhodes Information Initiative at Duke University. He works on different data science problems from computational and statistical perspectives. Before joining Duke\, he was an assistant professor of Statistics at Penn State. He got his PhD from Princeton University under the direction of Han Liu and Robert Vanderbei\, and got his Bachelor’s degree from National University of Singapore under the direction of Kim-Chuan Toh. His works have appeared at top venues in different areas such as statistics\, optimization\, machine learning\, and operations research. He received 2016 Best Paper Prize in Continuous Optimization for Young Researchers (1 paper selected in 3 years). \n  \n\n\n\nVenue \nBIZ1 – 0204\n\n\nLink to Register\nhttps://forms.office.com/r/WgcFdUQQYW\n\n\nTitle\nInference for Ranking Problems\n\n\nAbstract\nWe propose a novel combinatorial inference framework to conduct general uncertainty quantification in ranking problems. We consider the widely adopted Bradley-Terry-Luce (BTL) model\, where each item is assigned a positive preference score that determines the Bernoulli distributions of pairwise comparisons’ outcomes. Our proposed method aims to infer the general ranking properties of the BTL model. The general ranking properties include the “local” properties such as if an item is preferred over another and the “global” properties such as if an item is among the top K-ranked items. We further generalize our inferential framework to multiple testing problems where we control the false discovery rate (FDR) and apply the method to infer the top-K ranked items. We also derive the information-theoretic lower bound to justify the minimax optimality of the proposed method. We conduct extensive numerical studies using both synthetic and real data sets to back up our theory.\n\n\n\nYou may contact WONG Cecilia/TAN Dorothy at 6516 6225/6516 3067 for enquiries.
URL:https://iora.nus.edu.sg/events/dao-iora-joint-seminar-ethanfang/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20221213T100000
DTEND;TZID=Asia/Singapore:20221213T113000
DTSTAMP:20260417T112059
CREATED:20220812T035011Z
LAST-MODIFIED:20221211T084251Z
UID:15982-1670925600-1670931000@iora.nus.edu.sg
SUMMARY:IORA Seminar Series - Saif Benjaafar
DESCRIPTION:Saif Benjaafar is McKnight Presidential Endowed Professor and Distinguished McKnight University Professor at the University of Minnesota. He is Head of the Department of Industrial & Systems Engineering at the University of Minnesota\, where he also directs the Initiative on the Sharing Economy. He is a founding member of the Singapore University of Technology and Design where he served as Head of Engineering Systems and Design. He is the Editor in Chief of the INFORMS journal Service Science. He serves on the board of directors of Hourcar\, a social car sharing organization. His research is in the area of operations broadly defined\, with a current focus on sustainable operations and innovation in business models\, including sharing economy\, on-demand services\, and digital marketplaces. He is a Fellow of INFORMS and IISE. \n\n\n\nVenue \nBIZ 1 – 0304\n\n\nLink to Register \n(Hybrid session)\nhttps://nus-sg.zoom.us/meeting/register/tZEqdumsqTIqHdynlcuEWYlO-g6cmUD5yg1t\n\n\nTitle\nDimensioning and Pricing of Shared Vehicle Networks\n\n\nAbstract\nIn the first part of the talk\, we consider the problem of optimal fleet sizing (dimensioning) in an on-demand and one way vehicle sharing system. We leverage a property of closed queueing networks that relates the dynamics of a network with K items to one with K-1 items to obtain explicit and closed form lower and upper bounds on the optimal number of vehicles that are asymptotically exact. We use the bounds to show that buffer capacity (capacity in excess of the nominal load) can be expressed in terms of three explicit terms that can be interpreted as follows: (1) standard buffer capacity that is protection against randomness in demand and rental times\, (2) buffer capacity that is protection against vehicle roaming\, and (3) a correction term. We show that the capacity needed to buffer against vehicle roaming can be substantial even in systems with vanishingly small demand. In the second part of the talk\, give a fixed fleet size\, we consider the dynamic pricing in such a network and show that a static pricing policy that arises from solving a maximum flow relaxation of the problem guarantees a performance ratio of order 1- O(1/) where K is the number of vehicles. The approach used\, which leverages the same property of closed queueing networks used in the dimensioning problem\, is startingly simple and yields performance guarantees that are tighter than those previously obtained in the literature. Time permitting\, we will also discuss ongoing work that considers dimensioning and pricing in other settings with spatial queueing network features. \n  \n(The talk will draw on material from the following two papers: \nhttps://pubsonline.informs.org/doi/epdf/10.1287/mnsc.2021.3957\, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3998297 and \nhttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=4130757. \n 
URL:https://iora.nus.edu.sg/events/iora-seminar-series-saif-benjaafar/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20230127T100000
DTEND;TZID=Asia/Singapore:20230127T113000
DTSTAMP:20260417T112059
CREATED:20221101T081834Z
LAST-MODIFIED:20230110T031345Z
UID:16411-1674813600-1674819000@iora.nus.edu.sg
SUMMARY:IORA Seminar Series - Ville Satopaa
DESCRIPTION:TBA
URL:https://iora.nus.edu.sg/events/iora-seminar-series-ville-satopaa/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20230207T100000
DTEND;TZID=Asia/Singapore:20230207T113000
DTSTAMP:20260417T112059
CREATED:20230110T031633Z
LAST-MODIFIED:20230130T034859Z
UID:16647-1675764000-1675769400@iora.nus.edu.sg
SUMMARY:IORA Seminar Series - Timothy Chan
DESCRIPTION:Timothy Chan is the Associate Vice-President and Vice-Provost\, Strategic Initiatives at the University of Toronto\, the Canada Research Chair in Novel Optimization and Analytics in Health\, a Professor in the department of Mechanical and Industrial Engineering\, and a Senior Fellow of Massey College. His primary research interests are in operations research\, optimization\, and applied machine learning\, with applications in healthcare\, medicine\, sustainability\, and sports. He received his B.Sc. in Applied Mathematics from the University of British Columbia (2002)\, and his Ph.D. in Operations Research from the Massachusetts Institute of Technology (2007). Before coming to Toronto\, he was an Associate in the Chicago office of McKinsey and Company (2007-2009)\, a global management consulting firm. During that time\, he advised leading companies in the fields of medical device technology\, travel and hospitality\, telecommunications\, and energy on issues of strategy\, organization\, technology and operations. \n  \n\n\n\nName of Speaker\nTimothy Chan\n\n\nSchedule\nTuesday 7 February 2023\, 10am – 1130am\n\n\nVenue \nBIZ2 4-13A (BIZ 2\, level 4 Seminar Room)\n\n\nLink to Register \n(Via Zoom)\nhttps://nus-sg.zoom.us/meeting/register/tZcpdO2tpz8jEt3qhGZYuJ2GhZEuzV8r3skw\n\n\nTitle\nGot (optimal) milk?\n\n\nAbstract\nHuman donor milk is considered the ideal nutrition for millions of infants that are born preterm each year. Donor milk is collected\, processed\, and distributed by milk banks. The macronutrient content of donor milk is directly linked to infant brain development and can vary substantially across donations\, which is why multiple donations are typically pooled together to create a final product. Approximately half of all milk banks in North America do not have the resources to measure the macronutrient content of donor milk\, which means pooling is done heuristically. We propose a data-driven framework combining machine learning and optimization to predict macronutrient content of deposits and then optimally combine them in pools\, respectively. In collaboration with our partner milk bank\, we collect a data set of milk to train our predictive models. We rigorously simulate milk bank practices to fine-tune our optimization models and evaluate operational scenarios such as changes in donation habits during the COVID-19 pandemic. Finally\, we conduct a year-long trial implementation\, where we observe the current nurse-led pooling practices followed by our intervention. Pools created by our approach meet clinical macronutrient targets between 31% to 76% more often than the baseline\, while taking 67% less recipe creation time. This is the first paper in the broader blending literature that combines machine learning and optimization. We demonstrate that such pipelines are feasible to implement and can yield significant improvements over current practices. Our insights can guide practitioners in any application area seeking to implement machine learning and optimization-based decision-support.\n\n\n\n 
URL:https://iora.nus.edu.sg/events/iora-seminar-series-timothy-chan/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20230210T100000
DTEND;TZID=Asia/Singapore:20230210T113000
DTSTAMP:20260417T112059
CREATED:20230110T031729Z
LAST-MODIFIED:20230213T021652Z
UID:16650-1676023200-1676028600@iora.nus.edu.sg
SUMMARY:IORA Seminar Series – Antoine Désir
DESCRIPTION:Antoine is an Assistant professor of Technology and Operations Management at INSEAD. His research applies mathematical modeling and analytics to operations management problems with an aim to: (1) quantify fundamental tradeoffs\, and (2) design efficient data-driven algorithms to support operational decisions. More precisely\, he focuses on revenue management and choice modeling with applications such as online advertising. He was an MSOM student paper finalist in 2014 and 2017 and a Nicholson student paper finalist in 2014 and 2015. He spent a year as a post-doctoral researcher at Google NYC. \n\n\n\nName of Speaker\nAntoine Desir\n\n\nSchedule\nFriday 10 February 2023\, 10.00am – 11.30am\n\n\nVenue \nI4-01-03 (Innovation 4.0\, level 1 Seminar Room)\n\n\nLink to Register \n(Via Zoom)\nhttps://nus-sg.zoom.us/meeting/register/tZwtdOyoqjIuGtIP444shDdv-KQrbeOQcFl3\n\n\nTitle\nRepresenting Random Utility Choice Models with Neural Networks\n\n\nAbstract\nMotivated by the successes of deep learning\, we propose a class of neural network-based discrete choice models\, called RUMnets\, which is inspired by the random utility maximization (RUM) framework. This model formulates the agents’ random utility function using the sample average approximation (SAA) method. We show that RUMnets sharply approximate the class of RUM discrete choice models: any model derived from random utility maximization has choice probabilities that can be approximated arbitrarily closely by a RUMnet. Reciprocally\, any RUMnet is consistent with the RUM principle. We derive an upper bound on the generalization error of RUMnets fitted on choice data\, and gain theoretical insights on their ability to predict choices on new\, unseen data depending on critical parameters of the dataset and architecture.  By leveraging open-source libraries for neural networks\, we find that RUMnets outperform other state-of-the-art choice modeling and machine learning methods by a significant margin on two real-world datasets. This is joint work with Ali Aouad (LBS). \n 
URL:https://iora.nus.edu.sg/events/iora-seminar-series-antoine-desir/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20230303T100000
DTEND;TZID=Asia/Singapore:20230303T113000
DTSTAMP:20260417T112059
CREATED:20230209T084917Z
LAST-MODIFIED:20230227T024024Z
UID:16703-1677837600-1677843000@iora.nus.edu.sg
SUMMARY:DAO-IORA Seminar Series – Jing Wu
DESCRIPTION:Prof. Jing Wu is an Associate Professor in the Department of Decision Sciences and Managerial Economics at the Chinese University of Hong Kong (CUHK) Business School. He receives his Ph.D. (major in operations management\, minor in economics & finance) and MBA from the University of Chicago Booth School of Business and his bachelor’s degree in Electronic Engineering from Tsinghua University. Prof. Wu’s primary research fields are the operations-finance interface\, global supply chains\, FinTech\, and business intelligence. His papers are published in leading journals such as Management Science\, M&SOM\, and POMS. His articles appear in business magazines such as MIT Sloan Management Review\, the Economist\, and Forbes. In particular\, his quantitative research findings on the supply chain impact of COVID-19 and the Trade War have been reported by over 400 media outlets in over 20 countries worldwide. He is a Senior Editor for Production and Operations Management and serves on the Editorial Board for Journal of Operations Management. He has been a committee/track chair for leading international academic conferences such as INFORMS and POMS meetings. Before academia\, he worked as a quantitative strategist at Deutsche Bank New York. \n\n\n\nName of Speaker\nJing Wu\n\n\nSchedule\nFriday 3 March 2023\, 10.00am – 11.30am\n\n\nVenue \nI4-01-03 (Innovation 4.0\, level 1 Seminar Room)\n\n\nLink to Register\nhttps://nus-sg.zoom.us/meeting/register/tZYkc-yurjsuGtSdKCtZD62yxhugAKzE-6Nh\n\n\nTitle\nThe Golden Revolving Door: Hedging through Hiring Government Officials\n\n\nAbstract\nUsing both the onset of the US-China trade war in 2018 and the most recent Russia-Ukraine conflict and associated trade tensions\, we show that government-linked firms increase their importing activity by roughly 33% (t=4.01) following the shock\, while non-government linked firms trading to the same countries do the opposite\, decreasing activity. These increases appear targeted\, in that we see no increase for government-linked supplier firms generally to other countries (even countries in the same regions) at the same time\, nor of these same firms in these regions at other times of no tension. In terms of mechanism\, government supplier-linked firms are nearly twice as likely to receive tariff exemptions as equivalent firms doing trade in the region who are not also government suppliers. More broadly\, these effects are increasing in level of government connection. For example\, firms that are geographically closer to the agencies to which they supply increase their imports more acutely. Using micro-level data\, we find that government supplying firms that recruit more employees with past government work experience also increase their importing activity more – particularly when the past employee worked in a contracting role. Lastly\, we find evidence that this results in sizable accrued benefits in terms of firm-level profitability\, market share gains\, and outsized stock returns.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-jing-wu/
CATEGORIES:IORA Seminar Series
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