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BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20210409T100000
DTEND;TZID=Asia/Singapore:20210409T110000
DTSTAMP:20260504T144618
CREATED:20210409T061603Z
LAST-MODIFIED:20210409T061603Z
UID:13064-1617962400-1617966000@iora.nus.edu.sg
SUMMARY:What is your data worth? Quantifying the value of data in AI.
DESCRIPTION:James Zou is an assistant professor of biomedical data science\, CS and EE at Stanford University. He is also a Chan-Zuckerberg investigator. James work on making ML more reliable\, accountable and human compatible. Several of his methods are used by tech\, biotech and pharma companies. He has received several best paper awards at top CS venues\, the 2019 RECOMB best paper award\, NSF CAREER Award\, Google Faculty Award\, Tencent AI award\, Amazon Research Award and the Sloan Fellowship. \n\n\n\nSchedule \n  \nFriday 9 April 2021 \, 10am \n \n\n\nLink \n  \nhttps://nus-sg.zoom.us/j/86231051396?pwd=MHA5K3REWmx4enNWK1FjN0VycHpsUT09\n\n\nID\n862 3105 1396\n\n\nPassword\n492241\n\n\nTitle \n  \nWhat is your data worth? Quantifying the value of data in AI. \n \n\n\nAbstract \nAs data becomes the fuel driving technological and economic growth\, a fundamental challenge is how to quantify the value of data in algorithmic predictions and decisions. For example\, CA Gov. Newsom recently proposed “data dividend” whereby consumers are compensated by companies for the data that they generate. In this talk we will present a principled framework to quantify the value of different data in AI. Beyond regulatory implications\, we will discuss applications to denoising\, active learning\, and domain adaption on large biomedical datasets. I will conclude by discussing how data valuation contributes to a broader framework of accountable AI.
URL:https://iora.nus.edu.sg/events/what-is-your-data-worth-quantifying-the-value-of-data-in-ai/
CATEGORIES:IORA Seminar Series
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BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20210326T100000
DTEND;TZID=Asia/Singapore:20210326T110000
DTSTAMP:20260504T144618
CREATED:20210329T060811Z
LAST-MODIFIED:20210329T061339Z
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SUMMARY:
DESCRIPTION:Dr. Swati Gupta is an Assistant Professor and Fouts Family Early Career Professor in the H. Milton Stewart School of Industrial & Systems Engineering\, and School of Computer Science (by courtesy) at Georgia Institute of Technology. She received a Ph.D. in Operations Research from MIT in 2017 and a joint Bachelors and Masters in CS from IIT\, Delhi in 2011. Dr. Gupta’s research interests are in optimization\, machine learning and algorithmic fairness. Her work spans various application domains such as revenue management\, energy and quantum computation. She received the NSF CISE Research Initiation Initiative (CRII) Award in 2019. She was also awarded the prestigious Simons-Berkeley Research Fellowship in 2017-2018\, where she was selected as the Microsoft Research Fellow in 2018. Dr. Gupta received the Google Women in Engineering Award in India in 2011. Dr. Gupta’s research is partially funded by the NSF and DARPA. \n\n\n\nName of Speaker\nDr Swati Gupta\n\n\nSchedule \nFriday 26 March 2021 \, 10am\n\n\nLink \nhttps://nus-sg.zoom.us/j/84784213927?pwd=eGs5U0Mwcm9XY1htcGlNQ3J5aEhCdz09\n\n\nID\n847 8421 3927\n\n\nPassword\n329485\n\n\nTitle \nMitigating the Impact of Bias in Selection Algorithms\n\n\nAbstract \nThe introduction of automation into the hiring process has put a spotlight on a persistent problem: discrimination in hiring on the basis of protected-class status. Left unchecked\, algorithmic applicant-screening can exacerbate pre-existing societal inequalities and even introduce new sources of bias; if designed with bias-mitigation in mind\, however\, automated methods have the potential to produce fairer decisions than non-automated methods. In this work\, we focus on selection algorithms used in the hiring process (e.g.\, resume-filtering algorithms) given access to a “biased evaluation metric”. That is\, we assume that the method for numerically scoring applications is inaccurate in a way that adversely impacts certain demographic groups. \nWe analyze the classical online secretary algorithms under two models of bias or inaccuracy in evaluations: (i) first\, we assume that the candidates belong to disjoint groups (e.g.\, race\, gender\, nationality\, age)\, with unknown true utility Z\, and “observed” utility Z/\beta for some unknown \beta that is group-dependent\, (ii) second\, we propose a “poset” model of bias\, wherein certain pairs of candidates can be declared incomparable.  We show that in the biased setting\, group-agnostic algorithms for online secretary problem are suboptimal\, often causing starvation of jobs for groups with \beta>1. We bring in techniques from matroid secretary literature and order theory to develop group-aware algorithms that are able to achieve certain “fair” properties\, while obtaining near-optimal competitive ratios for maximizing true utility of hired candidates in a variety of adversarial and stochastic settings. Keeping in mind the requirements of U.S. anti-discrimination law\, however\, certain group-aware interventions can be construed as illegal\, and we will conclude the talk by partially addressing tensions with the law and ways to argue legal feasibility of our proposed interventions. This talk is based on work with Jad Salem and Deven R. Desai.
URL:https://iora.nus.edu.sg/events/12506/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20210312T100000
DTEND;TZID=Asia/Singapore:20210312T113000
DTSTAMP:20260504T144618
CREATED:20210305T090935Z
LAST-MODIFIED:20210305T091145Z
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SUMMARY:Probabilistic Forecasting for Daily Electricity Loads and Quantiles for Curve-to-Curve Regression
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 (1 July 2019 to 30 June 2021)\, National University of Singapore. Dr. Chen is Associate Editor of 5 journals including Statistica Sinica (August 1\, 2017 to July 31\, 2023)\, Statistics and Its Interface\, Computational Statistics\, Digital Finance\, and Journal of Operations Research and Decisions. She is ISI Elected Member since March 2016. She is regular member of the Advisory Board of Institute of Statistical Mathematics\, Japan from 1 April 2018 to 31 March 2020. \n\n\n\nName of Speaker\nA/P Chen Ying\n\n\nSchedule \nFriday 12 March 2021 \, 10am\n\n\nLink \nhttps://nus-sg.zoom.us/j/87277632379?pwd=MElkRGxnd2x2QUg4VnFwMUp6WE9iZz09\n\n\nID\n872 7763 2379\n\n\nPassword\n247866\n\n\nTitle \nProbabilistic Forecasting for Daily Electricity Loads and Quantiles for Curve-to-Curve Regression\n\n\nAbstract \nProbabilistic forecasting of electricity load curves is of fundamental importance for effective scheduling and decision making in the increasingly volatile and competitive energy markets. We propose a novel approach to construct probabilistic predictors for curves (PPC)\, which leads to a natural and new definition of quantiles in the context of curve-to-curve linear regression. There are three types of PPC: a predict set\, a predictive band and a predictive quantile\, and all of them are defined at a pre-specified nominal probability level. In the simulation study\, the PPC achieve promising coverage probabilities under a variety of data generating mechanisms. When applying to one day ahead forecasting for the French daily electricity load curves\, PPC outperform several state-of-the-art predictive methods in terms of forecasting accuracy\, coverage rate and average length of the predictive bands. For example\, PPC achieve up to 2.8-fold of the coverage rate with much smaller average length of the predictive bands. The predictive quantile curves provide insightful information which is highly relevant to hedging risks in electricity supply management. (Joint work with Xiuqin Xu\, Yannig Goude and Qiwei Yao. Available at https://arxiv.org/abs/2009.01595)
URL:https://iora.nus.edu.sg/events/probabilistic-forecasting-for-daily-electricity-loads-and-quantiles-for-curve-to-curve-regression/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20210305T100000
DTEND;TZID=Asia/Singapore:20210305T120000
DTSTAMP:20260504T144618
CREATED:20210304T080714Z
LAST-MODIFIED:20210304T084313Z
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SUMMARY:The Dao of Robustness
DESCRIPTION:We present a general framework for data-driven optimization called robustness optimization that favors solutions for which a risk-aware objective function would best attain an acceptable target even when the actual probability distribution would deviate from the empirical distribution.  Unlike data-driven robust optimization approaches\, the decision maker does not have to size the  ambiguity set\, but specifies an acceptable target\, or loss of optimality compared to the baseline optimization model\, as a trade off for the model’s ability to withstand greater uncertainty.  We axiomatize the decision criterion associated with robustness optimization\, termed as the  fragility measure  and present its representation theorem. We present practicable robustness optimization models including models with safegurarding constraints\, adaptive and dynamic optimization models. Similar to robust optimization\, we show that robustness optimization can also be done in a tractable way. We also provide numerical studies on adaptive problems and show that the solutions to the robustness optimization models are effective in alleviating the Optimizer’s Curse (Smith and Winkler 2006) and yielding superior out-of-sample performance compared the empirical optimization model and current data-driven robust optimization models. This is a joint work with Mingling Zhou and Zhuoyu Long. \n  \n\n\n\nName of Speaker\nProf Melvyn Sim\n\n\nSchedule \nFriday 5 March 2021 \, 10am\n\n\nLink \nhttps://nus-sg.zoom.us/j/84738208342?pwd=UTlkN3FqRTBRbFZ6dEJvcVpVYUlkdz09\n\n\nID\n847 3820 8342\n\n\nPassword\n008617\n\n\nTitle \nThe Dao of Robustness\n\n\nAbstract \nWe present a general framework for data-driven optimization called robustness optimization that favors solutions for which a risk-aware objective function would best attain an acceptable target even when the actual probability distribution would deviate from the empirical distribution.  Unlike data-driven robust optimization approaches\, the decision maker does not have to size the  ambiguity set\, but specifies an acceptable target\, or loss of optimality compared to the baseline optimization model\, as a trade off for the model’s ability to withstand greater uncertainty.  We axiomatize the decision criterion associated with robustness optimization\, termed as the  fragility measure  and present its representation theorem. We present practicable robustness optimization models including models with safegurarding constraints\, adaptive and dynamic optimization models. Similar to robust optimization\, we show that robustness optimization can also be done in a tractable way. We also provide numerical studies on adaptive problems and show that the solutions to the robustness optimization models are effective in alleviating the Optimizer’s Curse (Smith and Winkler 2006) and yielding superior out-of-sample performance compared the empirical optimization model and current data-driven robust optimization models. This is a joint work with Mingling Zhou and Zhuoyu Long.\n\n\nAbout the Speaker\nDr Melvyn Sim is a professor at the Department of Analytics and Operations\, National University of Singapore. For the past twenty years\, he has been working the the area of optimization and decision making under uncertainty. \nFind out more about Prof Sim and Robust Optimization: https://youtu.be/eGXBd7KxjEY
URL:https://iora.nus.edu.sg/events/the-dao-of-robustness/
ATTACH;FMTTYPE=image/jpeg:https://iora.nus.edu.sg/wp-content/uploads/2021/01/melvynsim.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20210226T100000
DTEND;TZID=Asia/Singapore:20210226T110000
DTSTAMP:20260504T144618
CREATED:20210224T074018Z
LAST-MODIFIED:20210301T083122Z
UID:10098-1614333600-1614337200@iora.nus.edu.sg
SUMMARY:IORA Seminar Series | 26 Feb | 10am
DESCRIPTION:Extensive research has shown that human decision makers in operations and supply chains are often influenced by cognitive biases and social preferences\, and as a consequence fail to achieve the optimal performance prescribed by normative operations theories.  In this talk\, we will discuss how behavioral factors affect decisions and economic outcomes in two operations management settings.  In the first setting\, we analyze how cognitive biases affect inventory decisions when biases are endogenously given in competition.  In the second setting\, we study how fairness and competition interact in a supply chain of one supplier and two retailers.  In both settings\, we find that the presence of competition can reverse the impact that the behavioral factors have on decision and performance in individual settings. \n  \n\n\n\nName of Speaker\n  \nA/P Wu Yaozhong \n \n\n\nSchedule \n  \nFriday 26 Feb 2021 \, 10am \n \n\n\nLink \nhttps://nus-sg.zoom.us/j/85139484987?pwd=TE1OMDNnSkwwNEwxQ2xjbTJ4MVQvUT09 \n\n\n\nJoin our Cloud HD Video Meeting \nZoom is the leader in modern enterprise video communications\, with an easy\, reliable cloud platform for video and audio conferencing\, chat\, and webinars across mobile\, desktop\, and room systems. Zoom Rooms is the original software-based conference room solution used around the world in board\, conference\, huddle\, and training rooms\, as well as executive offices and classrooms. Founded in 2011\, Zoom helps businesses and organizations bring their teams together in a frictionless environment to get more done. Zoom is a publicly traded company headquartered in San Jose\, CA. \nnus-sg.zoom.us\n\n\n\n\n\n\nID\n851 3948 4987\n\n\nPassword\n000323\n\n\nTitle \n  \nUnderstanding the role of behavioral factors in competitive operations and supply chains \n \n\n\nAbstract \n  \nExtensive research has shown that human decision makers in operations and supply chains are often influenced by cognitive biases and social preferences\, and as a consequence fail to achieve the optimal performance prescribed by normative operations theories.  In this talk\, we will discuss how behavioral factors affect decisions and economic outcomes in two operations management settings.  In the first setting\, we analyze how cognitive biases affect inventory decisions when biases are endogenously given in competition.  In the second setting\, we study how fairness and competition interact in a supply chain of one supplier and two retailers.  In both settings\, we find that the presence of competition can reverse the impact that the behavioral factors have on decision and performance in individual settings. \n \n\n\nAbout the Speaker\n  \nYaozhong Wu is an Associate Professor of Analytics and Operations at the NUS Business School. He received his Ph.D. in Technology and Operations Management from INSEAD. His main research interests are in the field of behavioral operations management. His papers have appeared in academic journals\, such as Management Science\, Operations Research\, Manufacturing & Service Operations Management\, Production and Operations Management\, and Journal of Operations Management.  A co-authored paper won the second place in the Wickham Skinner Awards for Best Paper Published in Production and Operations Management in 2014.  Another co-authored paper won the Best Working Paper Award of the INFORMS Behavioral Operations Management Section in 2016. Currently\, he serves as a senior editor for Production and Operations Management.\n\n\n\n 
URL:https://iora.nus.edu.sg/events/iora-seminar-series-26-feb-10am/
ATTACH;FMTTYPE=image/jpeg:https://iora.nus.edu.sg/wp-content/uploads/2021/01/Wu-Yaozhong.jpg
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BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20210115T100000
DTEND;TZID=Asia/Singapore:20210116T110000
DTSTAMP:20260504T144618
CREATED:20210112T090755Z
LAST-MODIFIED:20210303T030021Z
UID:5521-1610704800-1610794800@iora.nus.edu.sg
SUMMARY:IORA Seminar Series | 15 Jan | 10am
DESCRIPTION:Shape-constrained convex regression problem deals with fitting a convex function to the observed data\, where additional constraints are imposed\, such as component-wise monotonicity and uniform Lipschitz continuity. This talk presents a unified framework for computing the least squares estimator of a multivariate shape-constrained convex regression function in $mathbb{R}^d$. We prove that the least squares estimator is computable via solving a constrained convex quadratic programming (QP) problem with $(n+1)d$ variables\, $n(n-1)$ linear inequality constraints and $n$ possibly non-polyhedral inequality constraints\, where $n$ is the number of data points. To efficiently solve the generally very large-scale convex QP\, we design a proximal augmented Lagrangian method ({tt pALM}) whose subproblems are solved by the semismooth Newton method ({tt SSN}). To further accelerate the computation when $n$ is huge\, we design a practical implementation of the constraint generation method such that each reduced problem is efficiently solved by our proposed {tt pALM}. Comprehensive numerical experiments\, including those in the pricing of basket options and estimation of production functions in economics\, demonstrate that our proposed {tt pALM} outperforms the state-of-the-art algorithms\, and the proposed acceleration technique further shortens the computation time by a large margin.   [This talk is based on joint work with Meixia Lin and Defeng Sun]   \n  \n\n\n\nName of Speaker\n  Prof Toh Kim Chuan  \n\n\nSchedule \n  Friday 15 January 2021 \, 10am  \n\n\nLink \nhttps://nus-sg.zoom.us/j/83515146165?pwd=eUpLZm5NWSs0RUpxTU5jV3JTeFQ5UT09\n\n\nID\n835 1514 6165\n\n\nPassword\n700968\n\n\nTitle \n  An augmented Lagrangian method with constraint generations for shape-constrained convex regression problems  \n\n\nAbstract \n Shape-constrained convex regression problem deals with fitting a convex function to the observed data\, where additional constraints are imposed\, such as component-wise monotonicity and uniform Lipschitz continuity. This talk presents a unified framework for computing the least squares estimator of a multivariate shape-constrained convex regression function in $mathbb{R}^d$. We prove that the least squares estimator is computable via solving a constrained convex quadratic programming (QP) problem with $(n+1)d$ variables\, $n(n-1)$ linear inequality constraints and $n$ possibly non-polyhedral inequality constraints\, where $n$ is the number of data points. To efficiently solve the generally very large-scale convex QP\, we design a proximal augmented Lagrangian method ({tt pALM}) whose subproblems are solved by the semismooth Newton method ({tt SSN}). To further accelerate the computation when $n$ is huge\, we design a practical implementation of the constraint generation method such that each reduced problem is efficiently solved by our proposed {tt pALM}. Comprehensive numerical experiments\, including those in the pricing of basket options and estimation of production functions in economics\, demonstrate that our proposed {tt pALM} outperforms the state-of-the-art algorithms\, and the proposed acceleration technique further shortens the computation time by a large margin.   [This talk is based on joint work with Meixia Lin and Defeng Sun]  \n\n\nAbout the Speaker\nKim–Chuan Toh is a Professor at the Department of Mathematics\, National University of Singapore (NUS). He obtained his BSc degree in Mathematics from NUS and PhD degree in Applied Mathematics from Cornell University.   His current research focuses on designing efficient algorithms and software for convex programming and its applications\, particularly large–scale optimization problems arising from data science/machine learning\, and large–scale matrix optimization problems such as linear semidefinite programming (SDP) and convex quadratic semidefinite programming (QSDP).   He is currently an Area Editor for Mathematical Programming Computation\, an Associate Editor for the SIAM Journal on Optimization\, Mathematical Programming Series B\, and ACM Transactions on Mathematical Software. He received the Farkas Prize awarded by the INFORMS Optimization Society in 2017 and the triennial Beale–Orchard Hays Prize awarded by the Mathematical Optimization Society in 2018. He was elected as a Fellow of the Society for Industrial and Applied Mathematics in 2018.  \n\n\n\n 
URL:https://iora.nus.edu.sg/events/iora-seminar-series-15-jan-10am/
CATEGORIES:IORA Seminar Series
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END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20191020
DTEND;VALUE=DATE:20191024
DTSTAMP:20260504T144618
CREATED:20210303T070839Z
LAST-MODIFIED:20210303T071614Z
UID:10645-1571529600-1571875199@iora.nus.edu.sg
SUMMARY:INFORMS 2019
DESCRIPTION:The 2019 INFORMS Annual Meeting is a unique opportunity to connect and network with the more than 6\,000 INFORMS members\, students\, prospective employers and employees\, and academic and industry experts who compose the INFORMS community. \nIt is with great pleasure to share the presentations and awards of IORA faculty\, students at the INFORMS 2019 conference that took place in Seattle. Professor Teo Chung Piaw\, Provost’s Chair and Director of the Institute of Operations Research and Analytics at NUS Business School\, has been honoured by The Institute for Operations Research and Management Sciences (INFORMS) as a fellow\, one of a mere dozen selected worldwide annually. \n  \n\n\n\nAWARDS\n\n\nINFORMS FELLOWS: CLASS OF 2019\nPROFESSOR TEO CHUNG PIAW \nProfessor Teo Chung Piaw\, Provost’s Chair and Director of the Institute of Operations Research and Analytics at NUS Business School\, has been honoured by The Institute for Operations Research and Management Sciences (INFORMS) as a fellow\, one of a mere dozen selected worldwide annually.\n\n\nGEORGE B.DANTZIG DISSERTATION AWARD COMPETITION\nDR. GUODONG LYU \nDr. Guodong Lyu is the finalist with his PhD thesis “Online Resource Allocation: Theory and Applications”\, under the supervision by Professor Chung-Piaw Teo (IORA\, NUS).\n\n\n2019 INFORMS-SECTION ON FINANCE BEST STUDENT PAPER COMPETITION\nMR. SHUAIJIE QIAN \nMr. Shuaijie Qian was awarded the first place in “2019 Informs-Section on Finance Best Student Paper Competition”. This year the competition has attracted many talented students worldwide\, including Columbia University and University of Pennsylvania.\n\n\nPAPER PRESENTATION\n\n\nPROFESSOR JOEL GOH\n  \nIORA Faculty Member\, Prof Joel Goh presented a paper titled “Design of Incentive Programs for Optimal Medication Adherence“\n\n\nPROFESSOR WENJIE TANG\nIORA Faculty Member\, Prof Wenjie Tang presented a paper titled “Invention Integrality And Gender Composition In Innovation Teams.“\n\n\nDR AARON\, JINJIA HUANG\nIORA Research Fellow\, Dr Aaron\, Jinjia Huang presented a paper titled “Sparse and Efficient Rebalancing Operations: Concentrating the Flows in Dynamic Network“.”\n\n\nHONG MING TAN\nIORA PHD Student\, Hong Ming Tan presented a paper titled “Information\, Investment and Risk management.”\n\n\nDR QINSHEN TANG\nIORA Research Fellow\, Dr Qinshen Tang presented a paper titled ” \n\nJoint pricing and production: an analytics perspective\nRepositioning for vehicle sharing: a risk mitigation perspective\n\n\n\n\nDR SATYANATH BHAT\nIORA Research Fellow\, Dr Satyanath Bhat presented a paper titled “Doubled Dipping of Two-sided Platform Economy“\n\n\nDR SHEN YICHI\nIORA Research Fellow\, Dr Shen Yichi presented a paper titled “Using Radial Basis Functions to Optimize Expensive Functions with Heterogenous Noise“
URL:https://iora.nus.edu.sg/events/informs-2019/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20190809T093000
DTEND;TZID=Asia/Singapore:20190809T133000
DTSTAMP:20260504T144618
CREATED:20210303T025406Z
LAST-MODIFIED:20210303T025639Z
UID:10621-1565343000-1565357400@iora.nus.edu.sg
SUMMARY:NUS National Observance Ceremony 2019
DESCRIPTION:IORA took part in NUS National Day Observance Ceremony held on Thursday\, 8 August 2019 at University Hall\, National University of Singapore. Dr Huang Jinjia\, IORA Research Fellow\, presented the  SDPNAL+ software. \nThe software SDPNAL+ is designed for solving semidefinite programming (SDP)\, an important subfield of mathematical optimization and its applications are growing rapidly. Many practical problems in operations research and machine learning can be modeled or approximated as SDP problems. Traditional optimization methods can only solve small and medium scale (say\, matrix dimension is less than 2000 and the number of constraints is less than 5000) SDP. Fortunately\, large-scale SDP can be solved efficiently by SDPNAL+ now. Numerical experiments in the paper and other benchmark tests show that SDPNAL+ is a state-of-the-art solver for large-scale SDP and it is the only viable software to solve many large-scale SDPs at present. The largest SDP problem that is solved has matrix dimension 9261 and the number of constraints more than 12 million\, which boosts the solvable scale to thousands of times. This software\, developed by IORA faculty Prof Toh Kim Chuan\,  has won the 2018 Beale-Orchard-Hays Prize\, the highest honor in the field of Computational Mathematical Optimization. In particular\, the prize jury chair Dr. Michael Grant presented a concrete example shared by the nominator. It takes 122 hours for the traditional solver to solve a problem in a cluster with 56 cores CPU and 128 GPUs while SDPNAL+ solves it within 1.5 hours in a normal desktop PC. This new solver has many applications in practice. For instance\, in a recent study\, another IORA team of researchers have used this software to develop the backbone network structure to support bike rebalancing operations by volunteers \nin a system like the Bike Angel program in New York\, and demonstrated big reduction in the number of redundant moves by volunteers in such a system (i.e. with much less incentives)\, but with essentially the same level of performance (number of rides supported).
URL:https://iora.nus.edu.sg/events/nus-national-observance-ceremony-2019/
LOCATION:University Hall\, National University of Singapore\, 21 Lower Kent Ridge Rd\, 119077\, Singapore
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