IORA - Institute of Operations Research and Analytics

PhD Oral Defense: Tan Hong Ming
2021-10-20 10:00 - 11:00 (Asia/Singapore)
Hong Ming is a final year PhD candidate at the Institute of Operations Research and Analytics, National University of Singapore. His research interests include stochastic modelling, information economics, and decision making under uncertainty. He has won teaching awards and was previously a lecturer at Temasek Polytechnic where he was subject leader and also co-authored three textbooks.

Name of Speaker
Tan Hong Ming

Schedule
20 October 2021, 10am

Link to Register
https://nus-sg.zoom.us/meeting/register/tZAldeGqqjIqHN3Ww-3WJ8rbHo9m5QGIMuP-

Title
Risky Investments under Static and Dynamic Information Acquisition

Abstract
This thesis studies risky investments under different information acquisition processes. Under a dynamic information acquisition process, the investor’s unconditional expected optimal quantity of information and investment amount are higher than those under the corresponding static information acquisition process. However, when the initial belief of the investment payoff is either high or low, static and dynamic information acquisitions provide similar expected results. To incentivize firms to acquire information and invest when the initial belief of a risky investment payoff is mediocre, governments should allow firms to obtain information dynamically. This has implications on, e.g., clinical trials. Furthermore, the falling marginal cost of information raises investment amounts and leverage, which leads to higher losses during crises. Hence, companies need to understand their tail risks better.

IORA Seminar Series – Yilun Chen
2021-10-15 10:00 - 11:30
Yilun Chen is an assistant professor in the School of Data Science at CUHK Shenzhen (currently on leave), and a postdoctoral researcher at Columbia Business School. He obtained a PhD in Operations Research at Cornell University in 2021. Yilun’s research interest lies broadly in applied probability, with a specific focus on decision-making under uncertainty and its wide applications in Operations Research/Operations Management. His work was awarded first place in the 2019 INFORMS Nicholson Best Student Paper Competition.

Name of Speaker
Dr Chen Yilun

Schedule
Friday 15 October, 10am – 11.30am
(60 min talk + 30 min Q&A)

Link to Register
https://nus-sg.zoom.us/meeting/register/tZcrde-rrDMsHNRtAlSfk8uzRR1XMiPuCd-f

Title
Tractability of high-dimensional online decision-making with limited action changes

Abstract
Data-driven online decision-making tasks arise frequently in various practical settings in OR/ OM, finance, healthcare, etc. Such tasks often boil down to solving certain stochastic dynamic programs (DPs) with prohibitively large state space and complicated dynamics, suffering from the computational challenge known as the curse of dimensionality. Existing approaches (e.g. ADP, deep learning) typically focus on achieving practical success and have limited performance guarantees. We propose an algorithm that overcomes this computational obstacle for a rich class of problems, subject only to a “limited-action-change’’ constraint, which incorporate important special cases including optimal stopping and limited-price-change dynamic pricing. Assuming a black-box simulator of the problem’s dynamics, our algorithm can return epsilon-optimal policies with sample and computational complexity scaling polynomially in T (the time horizon), and effectively independent of the underlying state space, analogous to a “PTAS’’. We further demonstrate that the limited-action-change constraint is crucial for such efficient algorithms to exist through the construction of a hard instance. The recent algorithmic progress in high-dimensional optimal stopping of Chen and Goldberg 21 is a key building block of our algorithm.

IORA Seminar Series – Van-Anh Truong
2021-10-08 10:00 - 11:00
Van-Anh Truong joined the Industrial Engineering and Operations Research Department at the Columbia University in 2010. She received a Bachelor’s degree from University of Waterloo in Mathematics in 2002, and a Ph.D. from Cornell University in Operations Research in 2007. Before coming to Columbia, she was a quantitative associate at Credit Suisse, and a quantitative researcher at Google.
She is interested in a broad class of problems that arise in Supply Chain Management, Healthcare, and Business Analytics. These problems address decision making under uncertainty in information-rich and highly dynamic environments. Her recent work focuses on real-time optimization approaches for large e-commerce, healthcare, and service applications.
Her research is supported by an NSF Faculty Early Career Development (CAREER) Award. She is currently an Associate Editor for Operations Research, MSOM, and Naval Research Logistics.

Name of Speaker
A/P Van-Anh Truong

Schedule
Friday 8 October 2021, 10am (Singapore time)

Link to Register
https://nus-sg.zoom.us/meeting/register/tZEpde2trzguGdY7aOovoJ5HUtpniis4z_MM

Title
Prophet Inequality with Correlated Arrival Probabilities, with Application to Two Sided Matchings

Abstract
The classical Prophet Inequality arises from a fundamental problem in optimal-stopping theory. In this problem, a gambler sees a finite sequence of independent, non-negative random variables. If he stops the sequence at any time, he collects a reward equal to the most recent observation. The Prophet Inequality states that, knowing the distribution of each random variable, the gambler can achieve at least half as much reward in expectation, as a prophet who knows the entire sample path of random variables (Krengel and Sucheston 1978). In this paper, we prove a corresponding bound for correlated non-negative random variables. We analyze two methods for proving the bound, a constructive approach, which produces a worst-case instance, and a reductive approach, which characterizes a certain submartingale arising from the reward process of our online algorithm. We apply this new prophet inequality to the design of algorithms for a class of two-sided bipartite matching problems that underlie online task assignment problems. In these problems, demand units of various types arrive randomly and sequentially over time according to some stochastic process. Tasks, or supply units, arrive according to another stochastic process. Each demand unit must be irrevocably matched to a supply unit or rejected. The match earns a reward that depends on the pair. The objective is to maximize the total expected reward over the planning horizon. The problem arises in mobile crowd-sensing and crowd sourcing contexts, where workers and tasks must be matched by a platform according to various criteria. We derive the first online algorithms with worst-case performance guarantees for our class of two-sided bipartite matching problems.

IORA Seminar Series – Hamsa Bastani
2021-10-01 10:00 - 11:00
Hamsa Bastani is an Assistant Professor of Operations, Information, and Decisions at the Wharton School, University of Pennsylvania. Her research focuses on developing novel machine learning algorithms for data-driven decision-making, with applications to healthcare operations, social good, and revenue management. She designs methods for sequential decision-making, transfer learning and human-in-the-loop analytics. Her applied work uses large-scale, novel data sources to inform policy around impactful societal problems. Her work has received several recognitions, including the Pierskalla Award for the best paper in healthcare, as well as first place in the George Nicholson and MSOM student paper competitions.

Name of Speaker
Dr Hamsa Bastani

Schedule
Friday 1 October 2021, 10am

Link to Register
https://nus-sg.zoom.us/meeting/register/tZAtcO2rqT8jG9BLnqZzrhXlMX1cRbn316Hv

Title
Efficient and targeted COVID-19 border testing via reinforcement learning

Abstract
Throughout the COVID-19 pandemic, countries relied on a variety of ad-hoc border control protocols to allow for non-essential travel while safeguarding public health: from quarantining all travellers to restricting entry from select nations based on population-level epidemiological metrics such as cases, deaths or testing positivity rates. Here we report the design and performance of a reinforcement learning system, nicknamed ‘Eva’. In the summer of 2020, Eva was deployed across all Greek borders to limit the influx of asymptomatic travellers infected with SARS-CoV-2, and to inform border policies through real-time estimates of COVID-19 prevalence. In contrast to country-wide protocols, Eva allocated Greece’s limited testing resources based upon incoming travellers’ demographic information and testing results from previous travellers. By comparing Eva’s performance against modelled counterfactual scenarios, we show that Eva identified 1.85 times as many asymptomatic, infected travellers as random surveillance testing, with up to 2-4 times as many during peak travel, and 1.25-1.45 times as many asymptomatic, infected travellers as testing policies that only utilize epidemiological metrics. We demonstrate that this latter benefit arises, at least partially, because population-level epidemiological metrics had limited predictive value for the actual prevalence of SARS-CoV-2 among asymptomatic travellers and exhibited strong country-specific idiosyncrasies in the summer of 2020. Our results raise serious concerns on the effectiveness of country-agnostic internationally proposed border control policies that are based on population-level epidemiological metrics. Instead, our work represents a successful example of the potential of reinforcement learning and real-time data for safeguarding public health.
Paper Link: https://www.nature.com/articles/s41586-021-04014-z

IORA Seminar Series - Michael Choi
2021-09-24 10:00 - 11:00
Michael Choi is currently an Assistant Professor at the Yale-NUS College, and by courtesy, at the Department of Statistics and Data Science at National University of Singapore (NUS). He is also affiliated with the Institute of Operations Research and Analytics (iORA). Prior to joining Yale-NUS, he was an Assistant Professor for three years in the School of Data Science at The Chinese University of Hong Kong, Shenzhen. He received his PhD in Operations Research from Cornell University in 2017, and his undergraduate degree in Actuarial Science (First Class Honours) from The University of Hong Kong in 2013.
His research interests centre around stochastic processes and their broad applications and intersections with other fields such as data science, with a particular focus on Markov chains theory and stochastic algorithms driven by Markov chains. He has published extensively in leading journals of his area, including Transactions of the American Mathematical Society, Stochastic Processes and their Applications, Combinatorics, Probability and Computing, and Electronic Communications in Probability.

Name of Speaker
Dr Michael Choi

Schedule
Friday 24 September, 10am

Link to Register
https://nus-sg.zoom.us/meeting/register/tZ0ofu6hqzkiGtFQQExJuuRXSZnKC2bA9CO_

Title
On the convergence of an improved and adaptive kinetic simulated annealing

Abstract
Inspired by the work of [Fang et al.. An improved annealing method and its large-time behaviour. Stochastic Process. Appl. (1997), Volume 71 Issue 1 Page 55-74.], who propose an improved simulated annealing algorithm based on a variant of overdamped Langevin diffusion with state-dependent diffusion coefficient, we cast this idea in the kinetic setting and develop an improved kinetic simulated annealing (IKSA) method for minimizing a target function U. To analyze its convergence, we utilize the framework recently introduced by [Monmarché. Hypocoercivity in metastable settings and kinetic simulated annealing. Probab. Theory Related Fields (2018), Volume 172 Page 1215-1248.] for the case of kinetic simulated annealing (KSA). The core idea of IKSA rests on introducing a parameter c > inf U, which de facto modifies the optimization landscape and clips the critical height in IKSA at a maximum of c – inf U. Consequently IKSA enjoys improved convergence with faster logarithmic cooling than KSA. To tune the parameter c, we propose an adaptive method that we call IAKSA which utilizes the running minimum generated by the algorithm on the fly, thus avoiding the need to manually adjust c for better performance. We present positive numerical results on some standard global optimization benchmark functions that verify the improved convergence of IAKSA over other Langevin-based annealing methods.

IORA Seminar Series – Chen Ningyuan
2021-09-17 10:00 - 11:00
Dr. Ningyuan Chen is currently an assistant professor at the Department of Management at the University of Toronto Mississauga and cross-appointed at the Rotman School of Management, University of Toronto. Before joining the University of Toronto, he was an assistant professor at the Hong Kong University of Science and Technology. He received his Ph.D. from the Industrial Engineering and Operations Research (IEOR) department at Columbia University in 2015. He is interested in various approaches to making data-driven decisions in applications including revenue management. His research is supported by the UGC of Hong Kong and the Discovery Grants Program of Canada.

Name of Speaker
Dr Chen Ningyuan

Schedule
Friday 17 September, 10am

Link to Register
https://nus-sg.zoom.us/meeting/register/tZ0td-ysrz8uGtLavCw5BEWFm37wQmWdiVsx

Title
Model-Free Assortment Pricing with Transaction Data

Abstract
We study the problem when a firm sets prices for products based on the transaction data, i.e., which product past customers chose from an assortment and what were the historical prices that they observed. Our approach does not impose a model on the distribution of the customers’ valuations and only assumes, instead, that purchase choices satisfy incentive-compatible constraints. The individual valuation of each past customer can then be encoded as a polyhedral set, and our approach maximizes the worst-case revenue assuming that new customers’ valuations are drawn from the empirical distribution implied by the collection of such polyhedra. We show that the optimal prices in this setting can be approximated at any arbitrary precision by solving a compact mixed-integer linear program. Moreover, we study the single-product case and relate it to the traditional model-based approach. We also design three approximation strategies that are of low computational complexity and interpretable. Comprehensive numerical studies based on synthetic and real data suggest that our pricing approach is uniquely beneficial when the historical data has a limited size or is susceptible to model misspecification.

IORA Seminar Series – Dennis Zhang
2021-09-10 10:00 - 11:00
Dennis Zhang is a tenured associate professor of Operations and Manufacturing Management at the Olin Business School. A/P Zhang’s research focuses on data-driven operations in digital economy and platforms. He implements field experiments and use observational data to improve operations.

Name of Speaker
A/P Dennis Zhang

Schedule
Friday 10 September, 10am

Registration Link
https://nus-sg.zoom.us/meeting/register/tZUkdOCgrDkoHtKyWVqFtDPx_-nGbnESs2tL

Title
Choice Overload with Search Cost and Anticipated Regret: Theoretical Framework and Field Evidence

Abstract
As consumers are offered an ever-increasing number of options for almost every purchase decision in online retail, understanding the impact of assortment size on consumer choice decisions––especially on both search and purchase behavior––is critical. Our research speaks to this question by combining empirical analyses with theoretical modeling. First, via a large-scale field experiment involving $1.6$ million consumers on Alibaba’s online retail platforms, we causally examine how consumers’ click and purchase behavior changes as the number of products in a choice set increases. We document that consumers’ likelihood of clicking or purchasing at least one product increases at first but then decreases as the number of offered products rises. To explain this inverted-U-shaped relationship, we develop a “consider-then-choose-with-regret” (CTCR) choice model that incorporates consumers’ search cost and anticipated regret. Numerical experiments suggest that our CTCR model leads to smaller optimal assortments containing products of higher expected utilities and lower prices on average than the classical multinomial logit choice model. Altogether, this work presents real-world experimental evidence for choice overload on both search and purchase behaviour, advances the field’s understanding of how assortment sizes alter consumer choices, and provides a theoretical foundation for incorporating the choice overload effect in operational decisions.
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3890056

IORA Seminar Series – Ilya O. Ryzhov
2021-09-03 10:00 - 11:00
Ilya O. Ryzhov is an Associate Professor of Operations Management and Management Science at the Robert H. Smith School of Business, University of Maryland. His research focuses on decision-making under uncertainty with applications in business analytics. He is an Associate Editor at Operations Research, and received the 2017 INFORMS Simulation Society Outstanding Publication Award, as well as the 2020 INFORMS Urban Transportation SIG Outstanding Paper Award.

Name of Speaker
A/P Ilya O. Ryzhov

Schedule
Friday 3 September 2021, 10am

Link to Register
https://nus-sg.zoom.us/meeting/register/tZ0lce-grTMpHNKr4ldsTPL-uZEjwfAgtNAx

Title
Data-driven robust resource allocation with monotonic cost functions

Abstract
This work illustrates the potential of statistical methods in operations research problems. We consider a two-stage planning problem (arising, e.g., in city logistics) where a resource is first divided among a set of independent regions, and then costs are incurred based on the allocation to each region. Costs are decreasing in the quantity of the resource, but their precise values are unknown. We develop a new data-driven uncertainty model for monotonic cost functions, which can be used in conjunction with robust optimization to obtain tractable allocation decisions that significantly improve worst-case performance outcomes.

IORA Seminar Series - Designing Video Games: A Research Overview
2021-08-27 10:00 - 11:30
A/P Christopher Thomas Ryan teaches at the University of British Columbia in the Sauder School of Business. His research interests include optimization (broadly defined), theoretical economics, operations management, organizational learning, pedagogy, and business history.

Name of Speaker
A/P Christopher Thomas Ryan

Schedule
Friday 27 August 2021, 10am

Link to Register
https://nus-sg.zoom.us/meeting/register/tZUtduutqT0uH92GC20RTeo9_WTQvoDW4DCg

Title
Designing video games: A research overview

Abstract
Video games are the fastest-growing and largest sector of the entertainment industry. Video games have also been at the heart of technological innovations for decades. However, there is, I believe, a largely untapped opportunity for operations research methods to study questions of game design. In the words of Paul Tozour, a well-known game designer, in 2013:
“If used properly, decision modeling can significantly enhance many aspects of the design process … we should be able to search through a much larger number of possible solutions than we could do by hand or with our imaginations … we can often get better results, get results faster, and in some cases, we can even solve problems that simply can’t be solved any other way.”
In this talk I will review some of the literature on using operations research tools to study video game design. I will also talk about four of my recent papers looking at four important problems in video game design: level design, world design, designing for diversity of play, and designing “boosters” in puzzle games. I will also present a framework for future research in video games and an invitation for more researchers to join us.
I will present work based on projects with the following co-authors: Oussama Hanguir (Columbia), Yifu Li (Xian Jiao Tong Liverpool), Will Ma (Columbia), Lifei Sheng (University of Houston Clear Lake), Benny Wong (UBC), and Xuying Zhao (Notre Dame).

What is your data worth? Quantifying the value of data in AI.
2021-04-09 10:00 - 11:00
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.

Schedule

Friday 9 April 2021 , 10am

Link

https://nus-sg.zoom.us/j/86231051396?pwd=[REDACTED]

ID
862 3105 1396

Password
492241

Title

What is your data worth? Quantifying the value of data in AI.

Abstract
As 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.

2021-03-26 10:00 - 11:00
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.

Name of Speaker
Dr Swati Gupta

Schedule
Friday 26 March 2021 , 10am

Link
https://nus-sg.zoom.us/j/84784213927?pwd=[REDACTED]

ID
847 8421 3927

Password
329485

Title
Mitigating the Impact of Bias in Selection Algorithms

Abstract
The 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.
We 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.

Probabilistic Forecasting for Daily Electricity Loads and Quantiles for Curve-to-Curve Regression
2021-03-12 10:00 - 11:30
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.

Name of Speaker
A/P Chen Ying

Schedule
Friday 12 March 2021 , 10am

Link
https://nus-sg.zoom.us/j/87277632379?pwd=[REDACTED]

ID
872 7763 2379

Password
247866

Title
Probabilistic Forecasting for Daily Electricity Loads and Quantiles for Curve-to-Curve Regression

Abstract
Probabilistic 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)

The Dao of Robustness
2021-03-05 10:00 - 12:00
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.

Name of Speaker
Prof Melvyn Sim

Schedule
Friday 5 March 2021 , 10am

Link
https://nus-sg.zoom.us/j/84738208342?pwd=[REDACTED]

ID
847 3820 8342

Password
008617

Title
The Dao of Robustness

Abstract
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.

About the Speaker
Dr 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.
Find out more about Prof Sim and Robust Optimization: https://youtu.be/eGXBd7KxjEY

IORA Seminar Series | 26 Feb | 10am
2021-02-26 10:00 - 11:00
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.

Name of Speaker

A/P Wu Yaozhong

Schedule

Friday 26 Feb 2021 , 10am

Link
https://nus-sg.zoom.us/j/85139484987?pwd=[REDACTED]

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ID
851 3948 4987

Password
000323

Title

Understanding the role of behavioral factors in competitive operations and supply chains

Abstract

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.

About the Speaker

Yaozhong 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.

IORA Seminar Series | 15 Jan | 10am
2021-01-15 10:00 - 2021-01-16 11:00
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]

Name of Speaker
Prof Toh Kim Chuan

Schedule
Friday 15 January 2021 , 10am

Link
https://nus-sg.zoom.us/j/83515146165?pwd=[REDACTED]

ID
835 1514 6165

Password
700968

Title
An augmented Lagrangian method with constraint generations for shape-constrained convex regression problems

Abstract
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]

About the Speaker
Kim–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.

INFORMS 2019
2019-10-20 - 2019-10-23
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.
It 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.

AWARDS

INFORMS FELLOWS: CLASS OF 2019
PROFESSOR TEO CHUNG PIAW
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.

GEORGE B.DANTZIG DISSERTATION AWARD COMPETITION
DR. GUODONG LYU
Dr. 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).

2019 INFORMS-SECTION ON FINANCE BEST STUDENT PAPER COMPETITION
MR. SHUAIJIE QIAN
Mr. 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.

PAPER PRESENTATION

PROFESSOR JOEL GOH

IORA Faculty Member, Prof Joel Goh presented a paper titled “Design of Incentive Programs for Optimal Medication Adherence“

PROFESSOR WENJIE TANG
IORA Faculty Member, Prof Wenjie Tang presented a paper titled “Invention Integrality And Gender Composition In Innovation Teams.“

DR AARON, JINJIA HUANG
IORA Research Fellow, Dr Aaron, Jinjia Huang presented a paper titled “Sparse and Efficient Rebalancing Operations: Concentrating the Flows in Dynamic Network“.”

HONG MING TAN
IORA PHD Student, Hong Ming Tan presented a paper titled “Information, Investment and Risk management.”

DR QINSHEN TANG
IORA Research Fellow, Dr Qinshen Tang presented a paper titled ”

Joint pricing and production: an analytics perspective
Repositioning for vehicle sharing: a risk mitigation perspective

DR SATYANATH BHAT
IORA Research Fellow, Dr Satyanath Bhat presented a paper titled “Doubled Dipping of Two-sided Platform Economy“

DR SHEN YICHI
IORA Research Fellow, Dr Shen Yichi presented a paper titled “Using Radial Basis Functions to Optimize Expensive Functions with Heterogenous Noise“

NUS National Observance Ceremony 2019
2019-08-09 09:30 - 13:30 | University Hall, National University of Singapore, 21 Lower Kent Ridge Rd, 119077, Singapore
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.
The 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
in 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).
