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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:20250207T100000
DTEND;TZID=Asia/Singapore:20250207T113000
DTSTAMP:20260419T200844
CREATED:20250207T092300Z
LAST-MODIFIED:20250207T092300Z
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SUMMARY:DAO-ISEM-IORA Seminar Series: Chamsi Hssaine
DESCRIPTION:Name of Speaker\nChamsi Hssaine\n\n\nSchedule\n7 February 2025\, 10am\n\n\nVenue \nBIZ1-0302\n\n\nLink to Register\nhttps://nus-sg.zoom.us/j/84342221512?pwd=HlK2CjDj99BuaI5be4rJEIUxg9nh2F.1\n\n\nTitle\nTarget-Following Online Resource Allocation Using Proxy Assignments\n\n\nAbstract\nWe study a target-following variation of online resource allocation. As in classical resource allocation\, the decision-maker must assign sequentially arriving jobs to one of multiple available resources. However\, in addition to the assignment costs incurred from these decisions\, the decision-maker is also penalized for deviating from exogenously given\, nonstationary target allocations throughout the horizon. The goal is to minimize the total expected assignment and deviation penalty costs incurred throughout the horizon when the distribution of assignment costs is unknown. In contrast to traditional online resource allocation\, in our setting the timing of allocation decisions is critical due to the nonstationarity of allocation targets. Examples of practical problems that fit this framework include many physical resource settings where capacity is time-varying\, such as manual warehouse processes where staffing levels change over time\, and assignment of packages to outbound trucks whose departure times are scheduled throughout the day. We first show that naive extensions of state-of-the-art algorithms for classical resource allocation problems can fail dramatically when applied to target-following resource allocation. We then propose a novel “proxy assignment” primal-dual algorithm for the target-following online resource allocation problem that uses current arrivals to simulate the effect of future arrivals. We prove that our algorithm incurs the optimal sublinear regret bound when the assignment costs of the arriving jobs are drawn i.i.d. from a fixed distribution. We demonstrate the practical performance of our approach by conducting numerical experiments on synthetic datasets\, as well as real-world datasets from retail fulfillment operations. Joint with Huseyin Topaloglu and Garrett van Ryzin (link: https://arxiv.org/pdf/2412.12321)\n\n\nAbout the Speaker\nChamsi Hssaine is an assistant professor of Data Sciences and Operations at the University of Southern California’s Marshall School of Business. Prior to joining Marshall\, Chamsi was a postdoctoral scientist in the Supply Chain Optimization Technologies Group at Amazon\, working under the supervision of Garrett van Ryzin. She received her Ph.D. in Operations Research at Cornell University\, and before that graduated magna cum laude from Princeton University with a B.S.E. in Operations Research and Financial Engineering. Chamsi’s research interests lie broadly in data-driven decision-making\, with a special focus on pricing and inventory management. Much of her work focuses on the intersection of algorithmic decision-making and societal considerations such as fairness and social welfare. Chamsi’s work has received a number of recognitions\, including being named a Rising Star in EECS\, as well being a runner-up for the Minority Issues Forum and INFORMS Service Science Best DEIJ Paper Awards.
URL:https://iora.nus.edu.sg/events/dao-isem-iora-seminar-series-chamsi-hssaine/
CATEGORIES:IORA Seminar Series
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BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20250214T100000
DTEND;TZID=Asia/Singapore:20250214T113000
DTSTAMP:20260419T200844
CREATED:20250207T093131Z
LAST-MODIFIED:20250207T093323Z
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SUMMARY:DAO-ISEM-IORA Seminar Series: Chao Xiuli
DESCRIPTION:Name of Speaker\nChao Xiuli\n\n\nSchedule\n14 February 2025\, 10am\n\n\nVenue\nHon Sui Sen Auditorium\, Hon Sui Sen Memorial Library\n\n\nZoom Link \nhttps://nus-sg.zoom.us/meeting/register/1694uQvqQeGDmm_T4VQ10g\n\n\nTitle\nJoint pricing and delayed empty relocation policies for ride-sourcing systems\n\n\nAbstract\nWith the development of shared mobility (e.g.\, ride-sourcing systems such as Uber\, Lyft and Didi)\, there has been a growing interest in pricing and empty vehicle relocation to maximize system performance. Although customers exhibit patience during their waiting for available driver\, it has been neglected in most studies due to the complexities it introduces. In this work\, we develop a provably near-optimal dynamic pricing and empty vehicle relocation mechanism for a ride-sourcing system with limited customer patience. We model the ride-sourcing system as a network of double-ended queues. To derive a near-optimal control policy\, we first establish a fluid limit for the network in a large market regime and show that the fluid-based optimal solution provides an upper bound of the performance of the original ride-sourcing system for all dynamic policies. Then\, we develop a simple dynamic policy for the original problem based on the fluid solution and show that its performance almost matches the upper bound. Among our results\, we answer two open questions raised in the literature: (i) the performance of our policy converges to that of the true optimal value exponentially fast in time when the market size is large; (ii) the customer loss of our proposed policy decreases to zero exponentially fast when market size increases. This talk is based on joint work with M. Abdolmaleki\, T. Radvand\, and Y. Yin at the University of Michigan.\n\n\nAbout the Speaker\nXiuli Chao is the Ralph L. Disney Professor of Industrial and Operations Engineering at the University of Michigan\, Ann Arbor. His research interests include queueing\, scheduling\, inventory control\, game theory\, supply chain management\, and online learning and optimization. He is the co-author of two books\, “Operations Scheduling with Applications in Manufacturing and Services” (Irwin/McGraw-Hill\, 1998)\, and “Queueing Networks: Customers\, Signals\, and Product Form Solutions” (John Wiley & Sons\, 1999). Chao received the Erlang Prize from the Applied Probability Society of INFORMS in 1998\, and the David F. Baker Distinguished Research Award from Institute of Industrial and Systems Engineers (IISE) in 2005. He also received the Jon and Beverly Holt Award for Excellence in Teaching from the University of Michigan. Chao is a co-developer of scheduling system Lekin. He is a fellow of INFORMS and IISE.
URL:https://iora.nus.edu.sg/events/dao-isem-iora-seminar-series-chao-xiuli/
CATEGORIES:IORA Seminar Series
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BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20250220T100000
DTEND;TZID=Asia/Singapore:20250220T110000
DTSTAMP:20260419T200844
CREATED:20250214T070448Z
LAST-MODIFIED:20250214T070448Z
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SUMMARY:DAO-ISEM-IORA Seminar Series: Jong-Shi Pang
DESCRIPTION:Name of Speaker\nJong-Shi Pang\n\n\nSchedule\n20 February 2025\, 10am – 11am\n\n\nVenue \nE1-07-21/22 – ISEM Executive Classroom\n\n\nLink to Register \n \nhttps://nus-sg.zoom.us/j/89954075233?pwd=HWYCdpXSJy2T6aCirNUZgUJhO9RYHd.1\n\n\nTitle\nHeaviside Composite Optimization: A New Paradigm in Optimization\n\n\nAbstract\nThis talk introduces the topic of Heaviside composite optimization and briefly covers its many facets: breadth in modeling\, roles in old and new applications\, theory of optimizers and stationary solutions\, bridge with discrete optimization\, and the progressive integer programming method.   By definition\, a univariate Heaviside function is the (discontinuous) indicator of an interval.  By its name\, a Heaviside composite function is the composition of a Heaviside function with a continuous multivariate function that may be nonconvex and nondifferentiable.  While very natural in modeling many physical phenomena\, a Heaviside composite optimization problem\, possibly with Heaviside composite functional constraints\, has never been formally studied.  Our work aims to fill this void with a comprehensive research program covering the applications\, theory\, and algorithms for this novel class of very challenging optimization problems.\n\n\nAbout the Speaker\nElected a member of the National Academy of Engineering in February 2021 and appointed a Distinguished Professor in April 2023\, Jong-Shi Pang joined the University of Southern California as the Epstein Family Chair and Professor of Industrial and Systems Engineering in August 2013. Prior to this position\, he was the Caterpillar Professor and Head of the Department of Industrial and Enterprise Systems Engineering at the University of Illinois at Urbana-Champagne for six years between 2007 and 2013. He held the position of the Margaret A. Darrin Distinguished Professor in Applied Mathematics in the Department of Mathematical Sciences and was a Professor of Decision Sciences and Engineering Systems at Rensselaer Polytechnic Institute from 2003 to 2007. He was a Professor in the Department of Mathematical Sciences at the Johns Hopkins University from 1987 to 2003\, an Associate Professor and then Professor in the School of Management from 1982 to 1987 at the University of Texas at Dallas\, and an Assistant and then an Associate Professor in the Graduate School of Industrial Administration at Carnegie-Mellon University from 1977 to 1982. During 1999 and 2001 (full time) and 2002 (part-time)\, he was a Program Director in the Division of Mathematical Sciences at the National Science Foundation. Professor Pang has served as the Department Academic Advisor of the Department of Mathematics at the Hong Kong Polytechnic University. He has given many distinguished lectures at universities worldwide and plenary lectures at international conferences.
URL:https://iora.nus.edu.sg/events/dao-isem-iora-seminar-series-jong-shi-pang/
CATEGORIES:IORA Seminar Series
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BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20250228T100000
DTEND;TZID=Asia/Singapore:20250228T113000
DTSTAMP:20260419T200844
CREATED:20250214T071455Z
LAST-MODIFIED:20250214T071455Z
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SUMMARY:DAO-ISEM-IORA Seminar Series: Dan Iancu
DESCRIPTION:Name of Speaker\nDan Iancu\n\n\nSchedule\n28 February 2025\, 10am – 11.30am\n\n\nVenue \nBIZ1-0302\n\n\nLink to Register\nhttps://nus-sg.zoom.us/meeting/register/R4hPhsB9S6y5EikHYbD48A\n\n\nTitle\nManaging Sustainable Food Systems\n\n\nAbstract\nWe discuss a set of problems arising in the management of global food systems where technology and data analytics combined with operational innovations and incentives can lead to substantial improvements. The first part of the talk focuses on the issue of tropical deforestation associated with the supply of agricultural commodities such as palm oil\, cocoa\, or coffee\, which causes alarming CO2 emissions and loss of biodiversity and ecosystem services. To prevent this\, governments and multinational commodity buyers are offering positive incentives for local communities conditional on preventing deforestation in a specified area. We propose and analyze alternative weaker conditions related to preventing the use of timber or cleared land for economic purposes. To compare various incentive schemes\, we propose a new robust solution concept for cooperative games with externalities and use it to characterize the best condition and the feasible incentives that prevent deforestation and compensate local community members for missed economic opportunities. We then leverage this framework to determine whether conditional price premiums for palm fruit would successfully prevent deforestation in the Indonesian palm oil supply chain. Using survey data from 58 villages in East Kalimantan and robust data envelopment analysis\, we find that the Roundtable on Sustainable Palm Oil (RSPO) price premium is too low\, but moderate price premiums combined with our novel conditions would prevent deforestation in most villages and would be remarkably robust to entry by outsiders. The second part of the talk considers problems related to food retail\, and more specifically the management of perishable foods prepared in stores. We show how retailers that optimize their operational policies — optimally choosing the shelf life of products\, the issuance rule\, the price\, and whether to add timestamps that inform customers about the age of products — can successfully boost profit as well as increase customer welfare and reduce food waste.\n\n\nAbout the Speaker\nDan Iancu is an Associate Professor of Operations\, Information and Technology at the Stanford Graduate School of Business. His research and teaching interests are in responsible analytics and AI and data-driven optimization\, with applications in global supply chain management\, FinTech\, and healthcare. Broadly speaking\, his work has two main threads: (i) improving existing methodological tools for data-driven decision-making (e.g.\, making them more scalable\, fair\, robust\, or transparent) and (ii) applying these to address complex operational problems where choices of material\, financial or information flows carry substantial impact on the lives of millions of people and on the natural environment. His research has received several best paper awards and has been published in the top journals in operations research and operations management\, and he serves on the editorial board of several of these journals. His teaching has received commendations at Harvard\, MIT\, Stanford\, and INSEAD.
URL:https://iora.nus.edu.sg/events/dao-isem-iora-seminar-series-dan-iancu/
CATEGORIES:IORA Seminar Series
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