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 – Kimon Drakopoulos
2021-10-22 10:00 - 11:30
Kimon Drakopoulos is an Assistant Professor of Data Sciences and Operations at USC Marshall School of Business, where he researches complex networked systems, control of contagion, information design and information economics. During the Summer of 2020 he served as the Chief Data Scientist of the Greek National COVID-19 Taskforce and Data Science and Operations Advisor to the Greek Prime Minister. He completed his Ph.D. in the Laboratory for Information and Decision Systems at MIT, focusing on the analysis and control of epidemics within networks. His current research revolves around controlling contagion, epidemic or informational as well as the use of information as a lever to improve operational outcomes in the context of testing allocation, fake news propagation and belief polarization.

Name of Speaker
Kimon Drakopoulos

Schedule
22 October 2021, 10am – 11.30am
(60 min talk + 30 min Q&A)

Link to Register
https://nus-sg.zoom.us/meeting/register/tZEqdeqqqzwiEtTecjPommMjKlLsJKr4UArz

Title
Why Perfect Tests May Not be Worth Waiting For: Information as a Commodity

Abstract
Information products provide agents with additional information that is used to update their actions. In many situations, access to such products can be quite limited. For instance, in epidemics, there tends to be a limited supply of medical testing kits or tests. These tests are information products because their output of a positive or a negative answer informs individuals and authorities on the underlying state and the appropriate course of action. In this paper, using an analytical model, we show how the accuracy of a test in detecting the underlying state affects the demand for the information product differentially across heterogeneous agents. Correspondingly, the test accuracy can serve as a rationing device to ensure that the limited supply of information products is appropriately allocated to the heterogeneous agents. When test availability is low and the social planner is unable to allocate tests in a targeted manner to the agents, we find that moderately good tests can outperform perfect tests in terms of social outcome. On the policy side, we use a numerical study of an evolving epidemic to confirm our theoretically derived insight that in the early stages of an epidemic with low test availability, releasing a moderately good test can be an optimal strategy. The work is forthcoming in Management Science (FastTrack).

IORA Seminar Series – Junyu Zhang
2021-10-29 10:00 - 11:30
Junyu Zhang is an assistant professor at the Department of Industrial Systems Engineering and Management (ISEM), National University of Singapore. Before joining NUS, he was a postdoctoral research fellow at Princeton University, Department of Electrical and Computer Engineering. He received his Ph.D. degree from University of Minnesota, Twin Cities, in 2020. And he received his B.Sc. degree from Peking University in 2015. He is currently focusing on the convergence and complexity of various stochastic optimization and saddle point problems, as well as their closed related problems in reinforcement learning.

Name of Speaker
Dr Zhang Junyu

Schedule
29 October 2021, 10am – 11.30am
(60 min talk + 30 min Q&A)

Link to Register
https://nus-sg.zoom.us/meeting/register/tZYuc-yqqT4tGN3bunp6tZ9-6SjY3JFy4lYq

Title
Primal-Dual First-Order Methods for Affinely Constrained Multi-Block Saddle Point Problems

Abstract
We consider the convex-concave saddle point problems where the decision variables are subject to certain multi-block structure and affine coupling constraints, and the objective function possesses a certain separable structure. Although the minimization counterpart of such a problem has been widely studied under the topics of ADMM, this minimax problem is rarely investigated. In this paper, a convenient notion of $epsilon$-saddle point is proposed, under which the convergence rate of several proposed algorithms are analyzed. When only one of the decision variables has multiple blocks and affine constraints, several natural extensions of ADMM are proposed to solve the problem. Depending on the number of blocks and the level of smoothness, O(1/T) or O(1/sqrt{T}) convergence rates are derived for our algorithms. When both decision variables have multiple blocks and affine constraints, a new algorithm called ExtraGradient Method of Multipliers (EGMM) is proposed. Under desirable smoothness conditions, an O(1/T) rate of convergence can be guaranteed regardless of the number of blocks in the decision variables. An in-depth comparison between EGMM (fully primal-dual method) and ADMM (approximate dual method) is made over the multi-block optimization problems to illustrate the benefits of the EGMM.

IORA Seminar Series – Yuqian Xu
2021-11-05 10:00 - 11:30
Yuqian Xu is an assistant professor of Operations Management at Kenan-Flagler Business School, University of North Carolina, Chapel Hill. Her research focuses on understanding worker and consumer behaviors in the banking industry and digital platforms, in which she investigates both theoretical and empirical problems. Her focus of methodology includes applied probability, stochastic modeling, econometrics, and machine learning. In her research, she has been collaborating with different companies, including JD.com, Tencent, Bank of China, etc. She has given talks in different academic, industry, and government conferences and organizations, such as Federal Reserve Bank and China Banking Regulatory Committee.
Her research has been published in journals including Management Science, Operations Research, Production and Operations Management, etc. She has a B.S. in Mathematics from the Kuang Yaming Honors School of Intensive Instruction in Science and Arts at Nanjing University, China. She received her Ph.D. degree (Beta Gamma Sigma) in 2017 from NYU Stern School of Business with the Herman E. Krooss Dissertation Award.

Name of Speaker
Dr Xu Yuqian

Schedule
5 November 2021, 10am – 11.30am
(60 min talk + 30 min Q&A)

Link to Register
https://nus-sg.zoom.us/meeting/register/tZ0sdu-rqDMiHNTXwyxJUDNqD7WOMTf-u8dp

Title
Operational Risk Management: Optimal Inspection Policy

Abstract
Operational risk is one of the major risks in the financial industry; major banks around the world lost nearly $210 billion from operational risk events between 2011 and 2016 (Huber and Funaro 2018) and inspection on operational risk is required by the Basel Committee on Banking Supervision. Motivated by the importance of operational risk and its current industry regulation, we study how a financial firm can optimally design inspection policies to manage operational risk losses. Specifically, we propose a continuous-time principal-agent model framework to examine a financial firm’s (principal) optimal inspection policy and their employees’ (agent) effort towards lowering the risk event occurrences. We first consider two commonly used inspection policies, namely, random and periodic policies, and characterize the optimal inspection strategy under each policy. We identify conditions for two different modes of inspection (effort inducement and error correction) as well as nuanced interactions among inspection frequency, penalty charged on errors, and wage paid to employees. We then compare the performances of random and periodic policies. We find that contrary to conventional wisdom, the random policy is not always optimal; it is dominated by the periodic policy if the inspection cost is sufficiently low. Furthermore, we construct a hybrid policy that strictly dominates random policy and weakly dominates periodic policy, which suggests that a proper reduction of the random element in the inspection policy can always improve its performance. Finally, calibrating model parameters using operational risk data from a major bank in China, we numerically show that our key insights about random, periodic, and hybrid policies are robust to various model extensions.

IORA Seminar Series – Vijay Kamble
2021-11-12 10:00 - 11:30
Vijay Kamble is an Assistant Professor of Information and Decision Sciences in the College of Business Administration, and of Computer Science (by courtesy), at the University of Illinois at Chicago. He previously obtained his Ph.D. in Electrical Engineering and Computer Sciences from UC Berkeley (2015) and was a postdoc in the Social Algorithms lab at the Management Science and Engineering Dept. of Stanford University (2015-17).
His current research interests are in the areas of machine learning, statistical learning theory, market design, and optimization with applications to revenue management, pricing, and the design and optimization of online platforms and marketplaces.

Name of Speaker
Dr Vijay Kamble

Schedule
12 November 2021, 10am – 11.30am
(60 min talk + 30 min Q&A)

Link to Register
https://nus-sg.zoom.us/meeting/register/tZcsf-CqqT8rHt2NWfT9LgIr1u-ghDpdIlPc

Title
Pseudo-competitive games and algorithmic pricing

Abstract
With recent advances in artificial intelligence methodologies, algorithmic pricing in the face of unknown or uncertain demand has become ubiquitous in the practice of revenue management. While such algorithmic approaches are known to satisfy attractive revenue guarantees in well-behaved, non-strategic environments, the outcomes arising from such approaches in competitive settings remain poorly understood. Motivated by the goal of studying outcomes of algortihmic price competition in practical environments, we study a game of price competition amongst firms selling homogeneous goods, defined by the property that a firm’s revenue is independent of any competing prices that are strictly lower. We call this the pseudo-competitive property and the games of price competition induced by such revenue functions pseudo-competitive games.
We show that this property is induced by any customer choice model involving utility-maximizing choice from an adaptively determined consideration set, encompassing a variety of empirically validated choice models studied in the literature. For these games, we show a one-to-one correspondence between pure-strategy local Nash equilibria with distinct prices and the prices generated by the firms sequentially setting local best-response prices in different orders. In other words, despite being simultaneous-move games, they have a sequential-move equilibrium structure. Although this structure is attractive from a computational standpoint, we find that it makes these games particularly vulnerable to the existence of strictly-local Nash equilibria, in which the price of a firm is only a local best-response to competitors’ prices when a globally optimal response with a potentially unboundedly higher payoff is available. We moreover show, both theoretically and empirically, that price dynamics resulting from the firms utilizing gradient-based dynamic pricing algorithms to respond to competition may often converge to such an undesirable outcome. To address this concern, we finally propose an algorithmic approach that incorporates global experimentation under certain regularity assumptions on the revenue curves.
This is joint work with Chamsi Hssaine and Sid Banerjee, both from Cornell ORIE.

Seminar on IORA Industry Engagement
2021-12-10 14:00 - 16:30
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.

Time
Schedule

2.00 to 2.05pm
Opening Address
by IORA Research Director, Prof Jussi Keppo

2.05 to 2.25pm

Overview of Industry Engagements Activities for IORA
by Executive Director, Prof Teo Chung Piaw

With 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?
Overview of recent projects completed by staff in the institute, and key lessons learned, say from the development of the SIA-NUS Corp Lab

2.45 to 3.45pm

Projects with K&S

Application of Machine Learning in Smart Manufacturing
Smart Manufacturing through Predictive Process Monitoring using Machine Learning

3.45 to 4.00pm

Launch of Second Collaborative Project with K&S

4.00 to 4.30pm

Tour of SIA-NUS Digital Aviation Corporate Laboratory

Register here https://forms.office.com/r/PLvWfVR8sy

IORA Seminar Series - Spyros Zoumpoulis
2022-02-18 10:00 - 11:30
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.
More 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.
Spyros 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.
Spyros received the B.S., M.Eng., and Ph.D. degrees in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology.

Name of Speaker
Dr Spyros Zoumpoulis

Schedule
18 February 2022, 10am – 11.30am
(60 min talk + 30 min Q&A)

Link to Register
https://nus-sg.zoom.us/meeting/register/tZIocu2rpzwtGNDoe-8hOR8eA_-nL7H_Fp6r

Title
Quantifying the Benefits of Targeting for Pandemic Response

Abstract
Problem 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.
Methodology/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.
Implications: 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.

IORA Seminar Series - Yehua Wei
2022-02-25 10:00 - 11:30
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.

Name of Speaker
Wei Yehua

Schedule
25 February 2022, 10am – 11.30am
(60 min talk + 30 min Q&A)

Link to register
https://nus-sg.zoom.us/meeting/register/tZctdOmorjIjGdUto_SJO3EzN_kdorcJyJVr

Title
Approximate Submodularity in Network Design

Abstract
Network 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).

IORA Seminar Series – Assaf Zeevi
2022-03-11 10:00 - 11:30
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.
Assaf 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.

Name of Speaker
Assaf Zeevi

Schedule
11 March 2022, 10am – 11.30am
(60 min talk + 30 min Q&A)

Link to register
(Zoom)
https://nus-sg.zoom.us/meeting/register/tZYtdOusrj4qGtYQnSrSqKvhRN7RjGgj8vM3

Title
Online Learning in Sequential Selection Problems
(A Two Part Talk…)

Abstract
In 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.
The 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.
The 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.

IORA Seminar Series – Assaf Zeevi (Talk Part 2)
2022-03-16 10:00 - 11:30
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.
Assaf 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.

Name of Speaker
Assaf Zeevi

Schedule
16 March 2022, 10am – 11.30am

Venue:
I4-01-03 Seminar Room (For NUS Staff & Students)

Link to register (Via Zoom)
https://nus-sg.zoom.us/meeting/register/tZYpceigpzIiEtMuQAiHk7PrMuiZcWUqgvfx

Title
Online Learning in Sequential Selection Problems (Part 2)

Abstract
In 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.
The 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.
The 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.

About the speaker
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.
Assaf 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.

IORA Seminar Series – Jiding Zhang
2022-03-18 10:00 - 11:30
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.

Name of Speaker
Zhang Jiding

Schedule
18 March, 10am – 11.30am

Link to Register
https://nus-sg.zoom.us/meeting/register/tZApfuqppj4oHNZ-X8Btkjv-RqJAHz1zdBva

Title of Talk
Does Fake News Content Create Echo Chambers?

Abstract
Platforms 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.

IORA Seminar Series – Luyi Yang
2022-03-25 10:00 - 11:30
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.

Name of speaker
Luyi Yang

Schedule
25 March 2022, 10am – 11.30am

Link to register (via Zoom)
https://nus-sg.zoom.us/meeting/register/tZMuf-yppzwoGtRzBFsorYVikmPRtPTr8Ac6

Title of talk
Right to Repair: Pricing, Welfare, and Environmental Implications

Abstract
The “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.

IORA Seminar Series – Ruoxuan Xiong
2022-04-01 10:00 - 11:30
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.

Name of speaker
Ruoxuan Xiong

Schedule
1 April 2022, 10am – 11.30am

Link to register
https://nus-sg.zoom.us/meeting/register/tZUpc-uqqzkvHNTjASt7gjozPEloElm7Uuzg

Title of talk
Optimal experimental design for staggered rollouts

Abstract
In 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.

IORA Seminar Series – Stefanus Jasin
2022-04-08 10:00 - 11:30
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.

Name of speaker
Stefanus Jasin

Schedule
8 April 2022, 10am – 11.30am

Link to register

https://nus-sg.zoom.us/meeting/register/tZ0rcOmsrzkvEtbis9jZu_QqWm6iTqZVAnR5

Title of talk
Revenue Management Meets Inventory Management

Abstract
Historically, 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.

IORA Seminar Series - Chen Ying
2022-04-22 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, National University of Singapore. She also holds Courtesy Appointment in Econ and DSDS.
Webpage: https://blog.nus.edu.sg/matcheny/

Name of speaker
Chen Ying

Schedule
22 April 2022, 10am – 11.30am

Link to register

https://nus-sg.zoom.us/meeting/register/tZEucO2pqjosGNPn3FVvmPCZOZrX9T1faGx0

Title of talk
Policy Effectiveness on the Global Covid-19 Pandemic and Unemployment Outcomes: A Large Mixed Frequency Spatial Approach

Abstract
We 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

IORA Seminar Series – Basak Kalkanci
2022-05-06 10:00 - 11:30
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.

Name of speaker
Basak Kalkanci

Schedule
6 May 2022, 10am – 11.30am

Link to register

https://nus-sg.zoom.us/meeting/register/tZcqfuGqqzsrG927xqVoLigsdXKlE82X8kKi

Title of talk
How Transparency into Internal and External Responsibility Initiatives Influences Consumer Choice

Abstract
Amid 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.

IORA Seminar Series - Hoi-To Wai
2022-05-30 10:00 - 11:30
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.

Name of Speaker
Hoi-To Wai

Schedule
30 May 2022, 10am – 11.30am

Venue (face-to-face)
I4-01-03 Seminar Room (next to the level 1 café)

Link to Register (Online)
https://nus-sg.zoom.us/meeting/register/tZMsf-mpqj0tH9e76YMDTvL9xA1B20JT9uAD

Title of Talk
Stochastic Approximation Schemes with Decision Dependent Data

Abstract
Stochastic 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.

S3 Optimization Day 2022
2022-07-07 09:00 - 17:00
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).
Prof 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/

IORA Seminar Series - Andrew Lim
2022-09-02 10:00 - 11:30
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.

Name of Speaker
Professor Andrew Lim

Schedule
2 September 2022, Friday at 10:00am
(60 minutes talk + 30 minutes Q&A)

Venue
Innovation 4.0 Building, level 1, Seminar Room (next to the level 1 café)

Link to Register
(Via Zoom)
https://nus-sg.zoom.us/meeting/register/tZYld-mhpj4tGNWdd6GD8BGY-whthwWbduyq

Title
Mechanisms for Coordinating Systems of Decentralized Agents

Abstract
A 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.

IORA Seminar Series - Roland Yap
2022-09-09 10:00 - 11:30
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.

Venue
Innovation 4.0 Building, level 1, Seminar Room (next to the level 1 café)

Link to Register
(Via Zoom)
https://nus-sg.zoom.us/meeting/register/tZYqd-mhrTopHtSTBPnJnbgrD4SqeGsCmMJz

Title
A Bus Routing Problem with Multiple Stop Preferences and Tradeoffs

Abstract
We 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.

IORA Seminar Series - Phebe Vayanos
2022-09-16 10:00 - 11:30
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.

Venue
Talk will be held via Zoom

Link to Register
(Via Zoom)
https://nus-sg.zoom.us/meeting/register/tZIlduGgqz0sEtFr4NIgpQnX2vhMelYAA5b6

Title
Interpretability, Robustness, and Fairness in Predictive and Prescriptive Analytics for Social Impact

Abstract
Motivated 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:
Strong optimal classification trees, S. Aghaei, A. Gómez, P. Vayanos. Under second round of review at Operations Research, January 2021.
Learning 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.
Learning optimal prescriptive trees from observational data, N. Jo, S. Aghaei, A. Gómez, P. Vayanos, Under Review at Management Science, August 2021.
Optimal robust classification trees, N. Justin, S. Aghaei, A. Gómez, P. Vayanos, AAAI Workshop on Adversarial Machine Learning and Beyond, 2022.

IORA Seminar Series - Ozlem Ergun
2022-09-30 10:00 - 11:30
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.

Prior 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.

Venue
Talk will be held via Zoom

Link to register
https://nus-sg.zoom.us/meeting/register/tZwtceyurzkoHd2Hh7IItvgFzCRgXgwVQzxD

Title
Optimizing Post-Disruption Response Operations to Improve Resilience of Critical Infrastructure Systems

Abstract
Critical 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.

In 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.

IORA Seminar Series - Kaidi Yang
2022-10-07 10:00 - 11:30
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.

Venue
Innovation 4.0 Building, level 1, Seminar Room (next to the level 1 café)

Link to Register
(Hybrid Session)
https://nus-sg.zoom.us/meeting/register/tZYtfuisrjovHNE8qXjzeBwFwSDutMZlaLbu

Title
Operation of Traditional and Autonomous Mobility-on-Demand

Abstract
The 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).

IORA Seminar Series - Asa Palley
2022-10-28 10:00 - 11:30
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.

Venue
Innovation 4.0 Building, level 1, Seminar Room (next to the level 1 café)

Link to Register
(Hybrid Session)
https://nus-sg.zoom.us/meeting/register/tZwpde-tqTkjHtS27eyS1k611RkaNyQDO4DT

Title
Boosting the Wisdom of Crowds Within a Single Judgment Problem: Weighted Averaging Based on Peer Predictions

Abstract
A 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.

IORA Seminar Series - Ying-Ju Chen
2022-11-04 10:00 - 11:30
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.
He 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).
He 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.

Venue
Talk will be held via Zoom

Link to Register
(Zoom Session)
https://nus-sg.zoom.us/meeting/register/tZArceuhqj4jG9K-Xgwq9YDyfLWIDuRkt2_i

Title
Postgraduate program applications: simultaneous search, sequential outcomes, and reservation fees

Abstract
This 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.
We 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.

DAO-IORA joint seminar: Zhang Jiheng
2022-11-10 10:00 - 11:30
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.

Venue
Seminar room at i4.0 building (Level 1)

Link to Register
(Hybrid session)
https://nus-sg.zoom.us/meeting/register/tZAlc-2uqz4vGddG2dIGCNr8NaOpCl8Wp_Xw

Title
On-Demand Ride-Matching in a Spatial Model with Abandonment and Cancellation

Abstract
Ride-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.

IORA Seminar Series - Long Zhao
2022-11-11 10:00 - 11:30
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.

Venue
Hon Sui Sen Memorial Library – HSS 4-5

Link to Register
(Hybrid session)
https://nus-sg.zoom.us/meeting/register/tZcsd–vqT4pGtaBggmJWaMKIuN6uTstODTJ

Title
Constructing Quantiles via Forecast Errors: Theory and Empirical Evidence

Abstract
Probabilistic 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.

IORA Seminar Series - He Wang
2022-11-25 10:00 - 11:30
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.

Venue
Seminar Room at Innovation 4.0 building (Level 1)

Link to Register
(Hybrid session)
https://nus-sg.zoom.us/meeting/register/tZwudemurj4sHdy-N4Bl9b1CoBEnCO0vZ7mw

Title
Constant Regret Re-solving Heuristics for Revenue Management Problems

Abstract
We 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.

IORA Seminar Series - Mika Sumida
2022-12-06 10:00 - 11:30
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.

Link to Register
(Hybrid session)
https://nus-sg.zoom.us/meeting/register/tZctcuGvqDksE9HugB0-sYRJ-g5nksO-uEaG

Title
Revenue Management with Heterogeneous Resources

Venue
BIZ 1 – 0204

Abstract
We 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.

DAO-IORA joint seminar: Ethan X. Fang
2022-12-09 14:00 - 15:00
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).

Venue
BIZ1 – 0204

Link to Register
https://forms.office.com/r/WgcFdUQQYW

Title
Inference for Ranking Problems

Abstract
We 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.

You may contact WONG Cecilia/TAN Dorothy at 6516 6225/6516 3067 for enquiries.
