IORA - Institute of Operations Research and Analytics

DAO-ISEM-IORA Seminar Series: Ling Chun Kai
2025-03-21 10:00 - 11:30 (Asia/Singapore)
Name of Speaker
Ling Chun Kai

Schedule
21 March 2025, 10am – 11.30am

Venue
BIZ1 – 0302

Link to Register
https://nus-sg.zoom.us/meeting/register/WpZ4beTPQUqSyE-w-DZcPA

Title
Game Theory: The Art and Science of Strategy

Abstract
Game Theory has emerged as one of the primary tools for multi-agent decision making, with applications ranging from expert or even superhuman performance in recreational games (e.g., Poker, Starcraft, Stratego, Diplomacy) to optimal scheduling of security patrols. Compared to their single agent counterparts, the multiagent setting is much richer, with agents exhibiting strategic behavior such as lying, coercion, and collusion. In this talk, I will first introduce basic game theoretic concepts such as the Nash equilibrium. To illustrate the effectiveness of game theoretic approaches, a series of real-world applications are examined, including some recent work on optimal patrolling and contested logistics. Finally, I will briefly cover some problems I am currently working on, including potential applications in cybersecurity.

About the Speaker
Chun Kai Ling is an Assistant Professor in the Department of Computer Science in NUS. His research is on multiagent systems and computational game theory. He develops methods in machine learning and optimization to problems in security and logistics. He currently works on modeling and solving large games, featuring multiple players or general-sum utilities. His work on inverse game theory and multi-defender security games were best papers in IJCAI 2018 and GameSec 2023, 2024. He was awarded the Sung Kah Kay Assistant Professorship in 2024 and NUS development grant from 2022-2024. Prior to joining NUS, he was a Postdoc at Columbia University working with Christian Kroer and Garud Iyengar. He completed his PhD at Carnegie Mellon University under the supervision of Zico Kolter and Fei Fang.

DAO-ISEM-IORA Seminar Series : Dong Lingxiu
2025-03-14 10:00 - 11:30
Name of Speaker
Dong Lingxiu

Schedule
14 March 2025, 10am – 11.30am

Venue
BIZ1-0302

Link to Register
https://nus-sg.zoom.us/meeting/register/bA-KLhL7Tzibr9SRmbZpGg

Title
Role of Wellness Valuation Uncertainty and Operational Cost Structure in Product Line Design

Abstract
The rise of wellness-conscious consumers has led to record demand for products with wellness attributes, such as low-sugar foods and ultraviolet-protective clothing. This market trend presents a profit-growth opportunity for established companies, which have dominated the market based on traditional attributes, such as the taste of food and the appearance of clothing. Yet, taking advantage of this opportunity is challenging due to the increased operational costs associated with delivering products with wellness attributes and the lack of information on consumers’ valuation of wellness. We present a model of a monopolist developing and producing conventional and wellness products to serve a two-segment market consisting of wellness-conscious and wellness-neutral consumers. We propose a two-dimensional differentiation-contingency framework to depict the rich set of possible optimal strategies the firm could use to segment the market and explore how the market environment and the firm’s operational environment affect the firm’s choice of the optimal product strategy. We find that while the high expected wellness valuation drives the optimal strategy to be differentiated, variability in the wellness valuation drives contingency. The contingency dimension is a novel feature in product line design research and enables the firm to hedge its risk of low customer wellness valuation. The firm’s operational cost structure further leads to different prioritization within the wellness product’s quality dimensions: high development cost (resp. high coupling cost between the two quality dimensions) induces prioritization of the traditional (resp. wellness) quality of a wellness product. Examining consumer wellness consumption under the firm’s optimal product strategy offers insights for policy interventions such as consumer educational initiatives and financial incentives to foster innovation.

About the Speaker
Lingxiu Dong is the Frahm Family Chair Professor of Supply Chain, Operations, and Technology at the Olin Business School, Washington University in St. Louis. Her research focuses on supply chain management, with particular interests in supply chain control and design, operational flexibility, and integrated risk management. Her recent work explores technology-enabled innovation, sustainability, food and agriculture supply chains, and the interface of operations and finance.

DAO-ISEM-IORA Seminar Series : Lim Yun Fong
2025-03-12 10:00 - 11:30
Name of Speaker
Lim Yun Fong

Schedule
12 March 2025, 10am – 11.30am

Venue
BIZ2-0413B

Link to Register
https://nus-sg.zoom.us/meeting/register/WYuc2F2xQmuBpvxASTlXcg

Title
Integrating EV Charging and Discharging into Power Grid Through Bilateral Negotiation

Abstract
To deal with demand uncertainty on a power grid, a power plant with limited ramping capability can collaborate with an electric vehicle (EV) company. With proper charging and discharging prices, the EV company voluntarily withdraws electricity from or returns electricity to the power grid in suitable phases. We model the two parties’ interactions as a bargaining game on the prices, followed by the EV company’s charging and discharging problem and the power plant’s electricity generation problem. To solve this bargaining game, we propose a novel “Guess and Verify” approach. Specifically, we first find an optimal solution within a restricted price set in which the two parties’ total cost is minimized, and then verify its global optimality. Under an equilibrium contract, we find that the power plant can reduce its expected cost from the collaboration. This is because the EV company fully charges in a low electricity demand phase, reducing the power plant’s curtailment cost, and fully discharges to the power grid in a high electricity demand phase, lowering the power plant’s electricity generation cost. Based on real data, our numerical experiments suggest that the EV company’s charging and discharging can substantially harmonize the power flow within the grid and save significant cost, especially when the electricity demand gap across different phases increases or the power plant’s ramping capability decreases. Surprisingly, the EV company’s percentage cost saving can exceed 100%, implying that it can make a profit from the collaboration. For the power plant, the percentage cost saving is 2-7%.

About the Speaker
Yun Fong LIM is Professor of Operations Management at the Lee Kong Chian School of Business, Singapore Management University (SMU). He has been a Chang Jiang Chair Professor, Lee Kong Chian Fellow, MPA Research Fellow, and NOL Fellow. Yun Fong’s research has appeared in Operations Research, Management Science, Manufacturing and Service Operations Management, and Production and Operations Management. He has delivered keynote and plenary speeches in several international conferences. In addition, his work has received funding by MOE, A*STAR, RGC-HK, and NNSF, and media coverage by Financial Times, The Business Times, CNA938, and Channel 8. His current research interests include online retailing (supply chains and fulfillment), online platforms (business model innovations), sustainable urban systems, and flexible workforce and resource management. Yun Fong serves as Senior Editor for Production and Operations Management and Associate Editor for Naval Research Logistics. He has placed his PhD students and postdoc to CUHK (Shenzhen), USTC, SUSS, and ShanghaiTech as well as supervised some DBA students who lead influential firms in China.
At SMU, Yun Fong founded the OM PhD Program. He also served as Academic Director of the Master of Science in Management (MiM) Program in 2020-2023 and played an instrumental role in elevating the program from 83rd to 41st worldwide in the Financial Times Rankings. Yun Fong is a recipient of the SMU Teaching Excellence Innovative Teacher Award. He teaches both undergraduate and postgraduate courses in Operations Management. He has provided consulting service and executive development to corporations such as Alibaba, Maersk, McMaster-Carr Company, Resorts World Sentosa, Schneider Electrics, Temasek Holdings, and Zalora. Yun Fong obtained both his PhD and MSc degrees in Industrial and Systems Engineering from the Georgia Institute of Technology.

DAO-ISEM-IORA Seminar Series : Nam Ho-Nguyen
2025-03-07 10:00 - 11:30
Name of Speaker
Nam Ho-Nguyen

Schedule
7 March 2025, 10am – 11.30am

Venue
BIZ1-0302

Link to Register

https://nus-sg.zoom.us/meeting/register/LmEAt_ZaTS6N4MlO8pFnbg

Title
Mistake, Manipulation and Margin Guarantees in Online Strategic Classification

Abstract
We consider an online strategic classification problem where each arriving agent can manipulate their true feature vector to obtain a positive predicted label, while incurring a cost that depends on the amount of manipulation. The learner seeks to predict the agent’s true label given access to only the manipulated features. After the learner releases their prediction, the agent’s true label is revealed. Previous algorithms such as the strategic perceptron guarantee finitely many mistakes under a margin assumption on agents’ true feature vectors. However, these are not guaranteed to encourage agents to be truthful. Promoting truthfulness is intimately linked to obtaining adequate margin on the predictions, thus we provide two new algorithms aimed at recovering the maximum margin classifier in the presence of strategic agent behavior. We prove convergence, finite mistake and finite manipulation guarantees for a variety of agent cost structures. We also provide generalized versions of the strategic perceptron with mistake guarantees for different costs. Our numerical study on real and synthetic data demonstrates that the new algorithms outperform previous ones in terms of margin, number of manipulation and number of mistakes.
(This is joint work with Lingqing Shen, Khanh-Hung Giang-Tran and Fatma Kılınç-Karzan.)

About the Speaker
Nam Ho-Nguyen is a Senior Lecturer in the Discipline of Business Analytics at The University of Sydney Business School. His research focuses on data-driven optimization models and scalable algorithms for decision-making problems under uncertainty. Prior to joining The University of Sydney, he received his PhD in Operations Research from Carnegie Mellon University, and was a postdoctoral researcher at the University of Wisconsin-Madison. He was a past recipient of the INFORMS Optimization Society Young Researchers’ Prize 2022, and received a Discovery Early Career Researchers Award Fellowship from the Australian Research Council in 2024.

DAO-ISEM-IORA Seminar Series: Dan Iancu
2025-02-28 10:00 - 11:30
Name of Speaker
Dan Iancu

Schedule
28 February 2025, 10am – 11.30am

Venue
BIZ1-0302

Link to Register
https://nus-sg.zoom.us/meeting/register/R4hPhsB9S6y5EikHYbD48A

Title
Managing Sustainable Food Systems

Abstract
We discuss a set of problems arising in the management of global food systems where technology and data analytics combined with operational innovations and incentives can lead to substantial improvements. The first part of the talk focuses on the issue of tropical deforestation associated with the supply of agricultural commodities such as palm oil, cocoa, or coffee, which causes alarming CO2 emissions and loss of biodiversity and ecosystem services. To prevent this, governments and multinational commodity buyers are offering positive incentives for local communities conditional on preventing deforestation in a specified area. We propose and analyze alternative weaker conditions related to preventing the use of timber or cleared land for economic purposes. To compare various incentive schemes, we propose a new robust solution concept for cooperative games with externalities and use it to characterize the best condition and the feasible incentives that prevent deforestation and compensate local community members for missed economic opportunities. We then leverage this framework to determine whether conditional price premiums for palm fruit would successfully prevent deforestation in the Indonesian palm oil supply chain. Using survey data from 58 villages in East Kalimantan and robust data envelopment analysis, we find that the Roundtable on Sustainable Palm Oil (RSPO) price premium is too low, but moderate price premiums combined with our novel conditions would prevent deforestation in most villages and would be remarkably robust to entry by outsiders. The second part of the talk considers problems related to food retail, and more specifically the management of perishable foods prepared in stores. We show how retailers that optimize their operational policies — optimally choosing the shelf life of products, the issuance rule, the price, and whether to add timestamps that inform customers about the age of products — can successfully boost profit as well as increase customer welfare and reduce food waste.

About the Speaker
Dan Iancu is an Associate Professor of Operations, Information and Technology at the Stanford Graduate School of Business. His research and teaching interests are in responsible analytics and AI and data-driven optimization, with applications in global supply chain management, FinTech, and healthcare. Broadly speaking, his work has two main threads: (i) improving existing methodological tools for data-driven decision-making (e.g., making them more scalable, fair, robust, or transparent) and (ii) applying these to address complex operational problems where choices of material, financial or information flows carry substantial impact on the lives of millions of people and on the natural environment. His research has received several best paper awards and has been published in the top journals in operations research and operations management, and he serves on the editorial board of several of these journals. His teaching has received commendations at Harvard, MIT, Stanford, and INSEAD.

DAO-ISEM-IORA Seminar Series: Jong-Shi Pang
2025-02-20 10:00 - 11:00
Name of Speaker
Jong-Shi Pang

Schedule
20 February 2025, 10am – 11am

Venue
E1-07-21/22 – ISEM Executive Classroom

Link to Register

https://nus-sg.zoom.us/j/89954075233?pwd=[REDACTED]

Title
Heaviside Composite Optimization: A New Paradigm in Optimization

Abstract
This talk introduces the topic of Heaviside composite optimization and briefly covers its many facets: breadth in modeling, roles in old and new applications, theory of optimizers and stationary solutions, bridge with discrete optimization, and the progressive integer programming method. By definition, a univariate Heaviside function is the (discontinuous) indicator of an interval. By its name, a Heaviside composite function is the composition of a Heaviside function with a continuous multivariate function that may be nonconvex and nondifferentiable. While very natural in modeling many physical phenomena, a Heaviside composite optimization problem, possibly with Heaviside composite functional constraints, has never been formally studied. Our work aims to fill this void with a comprehensive research program covering the applications, theory, and algorithms for this novel class of very challenging optimization problems.

About the Speaker
Elected a member of the National Academy of Engineering in February 2021 and appointed a Distinguished Professor in April 2023, Jong-Shi Pang joined the University of Southern California as the Epstein Family Chair and Professor of Industrial and Systems Engineering in August 2013. Prior to this position, he was the Caterpillar Professor and Head of the Department of Industrial and Enterprise Systems Engineering at the University of Illinois at Urbana-Champagne for six years between 2007 and 2013. He held the position of the Margaret A. Darrin Distinguished Professor in Applied Mathematics in the Department of Mathematical Sciences and was a Professor of Decision Sciences and Engineering Systems at Rensselaer Polytechnic Institute from 2003 to 2007. He was a Professor in the Department of Mathematical Sciences at the Johns Hopkins University from 1987 to 2003, an Associate Professor and then Professor in the School of Management from 1982 to 1987 at the University of Texas at Dallas, and an Assistant and then an Associate Professor in the Graduate School of Industrial Administration at Carnegie-Mellon University from 1977 to 1982. During 1999 and 2001 (full time) and 2002 (part-time), he was a Program Director in the Division of Mathematical Sciences at the National Science Foundation. Professor Pang has served as the Department Academic Advisor of the Department of Mathematics at the Hong Kong Polytechnic University. He has given many distinguished lectures at universities worldwide and plenary lectures at international conferences.

DAO-ISEM-IORA Seminar Series: Chao Xiuli
2025-02-14 10:00 - 11:30
Name of Speaker
Chao Xiuli

Schedule
14 February 2025, 10am

Venue
Hon Sui Sen Auditorium, Hon Sui Sen Memorial Library

Zoom Link
https://nus-sg.zoom.us/meeting/register/1694uQvqQeGDmm_T4VQ10g

Title
Joint pricing and delayed empty relocation policies for ride-sourcing systems

Abstract
With the development of shared mobility (e.g., ride-sourcing systems such as Uber, Lyft and Didi), there has been a growing interest in pricing and empty vehicle relocation to maximize system performance. Although customers exhibit patience during their waiting for available driver, it has been neglected in most studies due to the complexities it introduces. In this work, we develop a provably near-optimal dynamic pricing and empty vehicle relocation mechanism for a ride-sourcing system with limited customer patience. We model the ride-sourcing system as a network of double-ended queues. To derive a near-optimal control policy, we first establish a fluid limit for the network in a large market regime and show that the fluid-based optimal solution provides an upper bound of the performance of the original ride-sourcing system for all dynamic policies. Then, we develop a simple dynamic policy for the original problem based on the fluid solution and show that its performance almost matches the upper bound. Among our results, we answer two open questions raised in the literature: (i) the performance of our policy converges to that of the true optimal value exponentially fast in time when the market size is large; (ii) the customer loss of our proposed policy decreases to zero exponentially fast when market size increases. This talk is based on joint work with M. Abdolmaleki, T. Radvand, and Y. Yin at the University of Michigan.

About the Speaker
Xiuli Chao is the Ralph L. Disney Professor of Industrial and Operations Engineering at the University of Michigan, Ann Arbor. His research interests include queueing, scheduling, inventory control, game theory, supply chain management, and online learning and optimization. He is the co-author of two books, “Operations Scheduling with Applications in Manufacturing and Services” (Irwin/McGraw-Hill, 1998), and “Queueing Networks: Customers, Signals, and Product Form Solutions” (John Wiley & Sons, 1999). Chao received the Erlang Prize from the Applied Probability Society of INFORMS in 1998, and the David F. Baker Distinguished Research Award from Institute of Industrial and Systems Engineers (IISE) in 2005. He also received the Jon and Beverly Holt Award for Excellence in Teaching from the University of Michigan. Chao is a co-developer of scheduling system Lekin. He is a fellow of INFORMS and IISE.

DAO-ISEM-IORA Seminar Series: Chamsi Hssaine
2025-02-07 10:00 - 11:30
Name of Speaker
Chamsi Hssaine

Schedule
7 February 2025, 10am

Venue
BIZ1-0302

Link to Register
https://nus-sg.zoom.us/j/84342221512?pwd=[REDACTED]

Title
Target-Following Online Resource Allocation Using Proxy Assignments

Abstract
We study a target-following variation of online resource allocation. As in classical resource allocation, the decision-maker must assign sequentially arriving jobs to one of multiple available resources. However, in addition to the assignment costs incurred from these decisions, the decision-maker is also penalized for deviating from exogenously given, nonstationary target allocations throughout the horizon. The goal is to minimize the total expected assignment and deviation penalty costs incurred throughout the horizon when the distribution of assignment costs is unknown. In contrast to traditional online resource allocation, in our setting the timing of allocation decisions is critical due to the nonstationarity of allocation targets. Examples of practical problems that fit this framework include many physical resource settings where capacity is time-varying, such as manual warehouse processes where staffing levels change over time, and assignment of packages to outbound trucks whose departure times are scheduled throughout the day. We first show that naive extensions of state-of-the-art algorithms for classical resource allocation problems can fail dramatically when applied to target-following resource allocation. We then propose a novel “proxy assignment” primal-dual algorithm for the target-following online resource allocation problem that uses current arrivals to simulate the effect of future arrivals. We prove that our algorithm incurs the optimal sublinear regret bound when the assignment costs of the arriving jobs are drawn i.i.d. from a fixed distribution. We demonstrate the practical performance of our approach by conducting numerical experiments on synthetic datasets, as well as real-world datasets from retail fulfillment operations. Joint with Huseyin Topaloglu and Garrett van Ryzin (link: https://arxiv.org/pdf/2412.12321)

About the Speaker
Chamsi Hssaine is an assistant professor of Data Sciences and Operations at the University of Southern California’s Marshall School of Business. Prior to joining Marshall, Chamsi was a postdoctoral scientist in the Supply Chain Optimization Technologies Group at Amazon, working under the supervision of Garrett van Ryzin. She received her Ph.D. in Operations Research at Cornell University, and before that graduated magna cum laude from Princeton University with a B.S.E. in Operations Research and Financial Engineering. Chamsi’s research interests lie broadly in data-driven decision-making, with a special focus on pricing and inventory management. Much of her work focuses on the intersection of algorithmic decision-making and societal considerations such as fairness and social welfare. Chamsi’s work has received a number of recognitions, including being named a Rising Star in EECS, as well being a runner-up for the Minority Issues Forum and INFORMS Service Science Best DEIJ Paper Awards.

DAO-ISEM-IORA Seminar Series: Chen Mingliu
2025-01-17 10:00 - 11:30
Name of Speaker

Chen Mingliu

Schedule

17 January 2025, 10am – 11.30am

Venue

BIZ1 03-02

Link to Register

https://nus-sg.zoom.us/meeting/register/H3CGlNujTwaHoiOLaaIkpw

Title

Optimal Capacity and Price Designs under Ex Ante and Ex Post Theft

Abstract

Internal theft poses a significant challenge in retail firms’ operations. Owing to a lack of effective monitoring tools, a firm cannot observe every action in daily operations from its employees, providing room for wrongdoings, such as capacity and cash stealing. As a result, a common practice is to increase the price of goods to offset the loss in revenue due to the increasing threat of theft. However, we show that such practices are not optimal. In this paper, we model the internal theft problem in retailing as a principal-agent model, where the principal (firm) contracts an agent (retail manager) for capacity planning and daily sales. The agent is subject to moral hazard and may steal the capacity (procurement budget or company asset) before demand realization (ex ante stealing) or steal the sales revenue after demand realization (ex post stealing). We solve for the optimal capacity, price, and agent’s commission decisions to maximize the principal’s utility. We find that capacity and price decisions are not monotone in terms of the severity of moral hazards. In particular, the principal should first decrease and then increase (increase and then decrease) the price (the capacity) when ex post stealing becomes more prevalent. As a result, simply increasing retail prices and shifting the margin to consumers to combat loss in revenue caused by internal theft can amplify the agency problem in some scenarios since it leads to a significant loss in demand and insufficient commission to the agent. Retail firms should instead focus on jointly optimizing capacity and price and providing their employees with appropriate commissions. Finally, we investigate the sensitivities of price and capacity decisions to demand uncertainties in the presence of moral hazard.

About the Speaker

Mingliu Chen is an Assistant Professor in Operations Management at Jindal School of Management, the University of Texas at Dallas. He is interested in applying analytical and modeling techniques in market and mechanism design issues. He has contributed to the literature on contract designs and developed novel models describing problems in the sharing economy and online matching platforms. Recently, his research focused on design problems in some emerging markets and the interface between contracting theory and operations.

DAO-ISEM-IORA Seminar Series | 8 Nov | 10am
2024-11-08 10:00 - 11:00
Name of Speaker
Wang Shixin

Schedule
8 November 2024, 10am

Venue
E1-07-21/22 – ISEM Executive Classroom

Title
Simple Menus in Single- and Multi-item Robust Screening

Abstract
This talk explores simple menus in robust screening. In the first part, we address a robust selling problem where a seller, uncertain about the buyer’s valuation distribution, aims to sell one item. While robust screening offers stronger guarantees than deterministic pricing, it often requires complex menus with infinite options. Our research introduces simple mechanisms with finite menus, balancing performance and implementation simplicity. We propose a tractable framework for various ambiguity sets, including support, mean, and quantile, and derive optimal mechanisms and performance ratios for different menu sizes. We show a modest menu size can deliver similar benefits to those achieved by the optimal infinite-menu mechanism. Notably, even a two-option menu significantly improves the performance ratio over deterministic pricing.
The second part discusses multi-item mechanism design, where characterizing the optimal mechanism is challenging, even when the seller knows the buyer’s valuation distributions. A key result in the literature shows that separable mechanisms (where items are sold independently) are robustly optimal when only marginal distributions are known. However, separate selling may fail to capture a substantial portion of the optimal revenue. To improve performance while retaining simplicity, we introduce “semi-separable mechanisms,” where allocation and payment rules for each item depend solely on that item’s valuation, but leverage joint distributional information. We prove that semi-separable mechanisms achieve the optimal performance ratio among all incentive-compatible and individually rational mechanisms when only marginal support information is available. Our framework also extends to scenarios where sellers have information on the aggregate valuations of product bundles.

About the Speaker
Shixin Wang is an Assistant Professor in the Department of Decisions, Operations and Technology at The Chinese University of Hong Kong (CUHK) Business School. Before joining CUHK, she obtained her doctoral degree in Operations Management from NYU Stern School of Business. Her research interests lie in developing simple and robust pricing policies in revenue management, and designing sparse and reliable networks in supply chain and service systems. Her works have been published in Management Science, Operations Research, and Manufacturing & Service Operations Management.

DAO-ISEM-IORA Seminar Series : Zhang Xiaoge
2024-10-14 17:00 - 18:00
Name of Speaker
Zhang Xiaoge

Schedule
14 October 2024, 5pm

Venue
E1-07-21/22 – ISEM Executive Classroom

Link to Register
https://nus-sg.zoom.us/j/88657161589?pwd=[REDACTED]

Title
Reliability Engineering in the era of AI: An Uncertainty Quantification Framework

Abstract
Establishing trustworthiness is fundamental for the responsible utilization of medical artificial intelligence (AI), particularly in cancer diagnostics, where misdiagnosis can lead to devastating consequences. However, there is currently a lack of systematic approaches to resolve the reliability challenges stemming from the model limitations and the unpredictable variability in the application domain. In this work, we address trustworthiness from two complementary aspects—data trustworthiness and model trustworthiness—in the task of subtyping non-small cell lung cancers using whole side images. We introduce TRUECAM, a framework that provides trustworthiness-focused, uncertainty-aware, end-to-end cancer diagnosis with model-agnostic capabilities by leveraging spectral-normalized neural Gaussian Process (SNGP) and conformal prediction (CP) to simultaneously ensure data and model trustworthiness. Specifically, SNGP enables the identification of inputs beyond the scope of trained models, while CP offers a statistical validity guarantee for models to contain correct classification. Systematic experiments performed on both internal and external cancer cohorts, utilizing a widely adopted specialized model and two foundation models, indicate that TRUECAM achieves significant improvements in classification accuracy, robustness, fairness, and data efficiency (i.e., selectively identifying and utilizing only informative tiles for classification). These highlight TRUECAM as a general wrapper framework around medical AI of different sizes, architectures, purposes, and complexities to enable their responsible use.

About the Speaker
Dr. Xiaoge Zhang is an Assistant Professor in the Department of Industrial and Systems Engineering (ISE) at The Hong Kong Polytechnic University. His research interests center on risk management, reliability engineering, and safety assurance of AI/ML systems using uncertainty quantification, knowledge-enabled AI, and fail-safe measures. He received his Ph.D. in Systems Engineering and Operations Research at Vanderbilt University, Nashville, Tennessee, United States in 2019. He has won multiple awards, including Peter G. Hoadley Best Paper Award, Chinese Government Award for Outstanding Self-Financed Students Studying Abroad, Bravo Zulu Award, Pao Chung Chen Fellowship, among others. He has published more than 70 papers in leading academic journals, such as Nature Communications, IEEE Transactions on Information Forensics and Security, IEEE Transactions on Reliability, IEEE Transactions on Cybernetics, IEEE Transactions on Industrial Informatics, Reliability Engineering & Systems Safety, Risk Analysis, Decision Support Systems, and Annals of Operations Research, among others. He is on the editorial board of Journal of Organizational Computing and Electronic Commerce, Journal of Reliability Science and Engineering. He is a member of INFORMS, IEEE and IISE.

DAO-ISEM-IORA Seminar Series : Negin Golrezaei
2024-10-11 10:00 - 11:30
Name of Speaker
Negin Golrezaei

Schedule
11 October 2024, 10am – 11.30am

Venue
BIZ1 – 0201 (Mochtar Riady Building, level 2 Seminar Room)

Link to Register

https://nus-sg.zoom.us/meeting/register/tZYvd-iuqjksG937fGGVd2M-Dn16gT0CImgP

Title
Online Learning via Offline Greedy Algorithms: Applications in Market Design and Optimization

Abstract
In today’s digital landscape, the ability to make timely and informed decisions is paramount. Our research addresses the challenge of adapting offline algorithms to dynamic online scenarios, presenting a versatile framework with wide-reaching implications. We focus on combinatorial problems that lend themselves to efficient approximations through robust greedy algorithms. Our framework leverages the concept of “Blackwell approachability” to seamlessly transform these algorithms from offline to online settings.
Under full information conditions, our online algorithms exhibit approximate regrets of 𝑂(√𝑇). Taking our approach further, we introduce “Bandit Blackwell approachability,” extending its applicability to dynamic online decision-making. In the bandit setting, our framework achieves approximate regrets of 𝑂(𝑇^{2/3}).
Our research extends to various domains, including revenue management, market design, and online optimization. We tackle challenges such as product ranking optimization, auction reserve price optimization, and submodular maximization. Moreover, we apply our techniques to first-order methods in continuous optimization, facilitating efficient solutions in various contexts. Numerical simulations demonstrate that our approach outperforms theoretical expectations in real-world scenarios.

About the Speaker
Negin Golrezaei is the W. Maurice Young (1961) Career Development Associate Professor of Management and an Associate Professor of Operations Management at the MIT Sloan School of Management. Her research focuses on advancing online marketplaces—such as e-commerce, online advertising, and emissions trading systems—by designing and implementing data-driven strategies and algorithmic innovations. She aims to create more resilient, equitable, and sustainable digital ecosystems. Before joining MIT, Negin was a postdoctoral fellow at Google Research in New York, where she collaborated with the Market Algorithm team to develop and test new mechanisms for online marketplaces. She holds a BSc (2007) and MSc (2009) in electrical engineering from Sharif University of Technology, Iran, and a PhD (2017) in operations research from the University of Southern California. Negin serves as an associate editor for Operations Research, Production and Operations Management, Operations Research Letters, and Naval Research Logistics. Her recognitions include the 2021 ONR Young Investigator Award, the 2018 Google Faculty Research Award, the 2017 George B. Dantzig Dissertation Award, the INFORMS Revenue Management and Pricing Section Dissertation Prize, and the USC Outstanding Teaching Award (2017).

DAO-ISEM-IORA Seminar Series: Song-Hee Kim
2024-10-03 10:00 - 11:30
Name of Speaker
Song-Hee Kim

Schedule
3 October 2024, 10am – 11.30am

Venue
HSS 3-2 (Hon Sui Sen Memorial Library, level 3, Seminar Room 2)

Link to Register
https://nus-sg.zoom.us/meeting/register/tZMkfu2vqzovHdT3wuo-DasFqx2x7PnDzw1V

Title
To Each Their Own (Shifts): Incorporating Heterogeneous Worker Preferences into Shift Work Schedules

Abstract
Shifts are the dominant way to organize work in many contexts requiring 24/7 coverage. While the detriments of shift work are well-documented both at the individual and organizational levels, its deployment is often unavoidable given round-the-clock staffing needs. We explore a potential operational lever-incorporating heterogeneous preferences over shift characteristics, which we refer to as the shift choice system-to mitigate ramifications of shift work on worker well-being and turnover. Leveraging rich and novel survey, shift, and administrative data, we document that inpatient nurses exhibit heterogeneous preferences over shift schedules, driven by both pecuniary and non-pecuniary considerations. We also show that nursing managers largely reflect preferences into scheduled shifts, albeit imperfectly. We find that the shift choice system improves worker well-being, as measured by self-reported fatigue and work-life balance. Using a difference-in-differences approach, we also estimate a 0.58 p.p. decrease in probability of quitting, but only among more experienced nurses. We find these effects are not driven by differences in the degree to which preferences are reflected in scheduled shifts, but rather by corresponding improvements in fatigue and work-life balance that are concentrated among more experienced nurses. We do not find evidence to suggest that the shift choice system affects care quality. Our results indicate that allowing for shift choice is an effective responsible scheduling strategy that can improve worker well-being and reduce turnover for highly experienced nurses. Paper available at https://ssrn.com/abstract=4750664.

About the Speaker
Song-Hee Kim is the CS Wind Associate Professor of Operations Management at SNU Business School of Seoul National University in South Korea. Her research focuses on making data-driven and evidence-based decisions within service systems, with an emphasis on problems related to healthcare delivery. She has received several academic awards, including the Best OM Paper in Management Science Award (winner), MSOM Best Paper Award (finalist), and INFORMS Pierskalla Award (finalist). She currently serves as an associate/senior editor for the journals Management Science, Operations Research, Manufacturing & Services Operations Management, Production and Operations Management, Service Science, and Health Care Management Science. She received her BS from Cornell University and her PhD from Columbia University. Prior to joining SNU Business School, she was a postdoctoral associate at the Yale School of Management and an assistant professor at the Marshall School of Business, University of Southern California.

IORA Industry Day 2024
2024-08-30 09:00 - 13:00 | Innovation 4.0, Level 1, 3 Research Link, 117602, Singapore
The Institute of Operations Research and Analytics (IORA) is proud to host the inaugural IORA Industry Day 2024. This half-day event will highlight innovative applications of Operations Research and Data Analytics in addressing real-world industry challenges.
This year’s program will showcase six pioneering projects, developed in partnership with research collaborators from the airline, manufacturing, charity/food, and data center sectors. These projects address critical issues including the food distribution supply chain, pricing engines, human-AI collaboration, pilot training, and energy consumption optimization.
We are pleased to welcome guest speakers from Optimisation Analytics Technology, who will present their innovative work with McDonald’s China.
Additionally, the event will feature an engaging panel discussion with esteemed experts: Marc Dragon (Managing Director, Reefknot Investments), Dr. Simon See (Senior Director, NVIDIA Global Technology Centre), and Dr. Yang Nan (Senior Economist and Head of Economics and Decision Sciences at Amazon Japan). They will discuss future opportunities for Operations Research in solving industry problems. The session will be moderated by Prof. Jussi Keppo, Research Director (Industry) at IORA.
We invite you to join us for this exciting event. Please register by 26th August 2024 (Mon) to reserve your seats.

Analytics for X 2024
2024-08-28 - 2024-08-29 | Innovation 4.0, Level 1, 3 Research Link, 117602, Singapore
The Institute of Operations Research and Analytics (IORA), Department of Industrial Systems Engineering and Management (College of Design and Engineering) and Department of Analytics and Operations (NUS Business School) of the National University of Singapore are proud to jointly host the Analytics for X 2024 conference in conjunction with the Next-Gen Scholars Symposium on 28-29 August 2024.
Analytics for X is an Annual Conference hosted by IORA that brings together researchers, educators, consultants, practitioners, and students to contribute to the Fourth Industrial Revolution conversation. After a successful international event in 2023 in Chongqing, China, this year, the conference is back in Singapore. As an annual event, the conference aims to promote the regular enhancement and dissemination of knowledge related to operations research and analytics, as well as efficient industrial practices.
During this year’s event, we are excited to announce the inaugural Next-Gen Scholars Symposium. This symposium, exclusive to PhD students, aims to recognize outstanding scholars in Analytics, Operations Research (OR) and Operations Management (OM) who are nearing the completion of their PhD, typically within a year or two. It is dedicated to honoring and accelerating the paths of outstanding young scholars who find themselves at a pivotal stage in their academic journey. The symposium will feature the groundbreaking research of these emerging scholars, and offer them a platform to engage with and learn from the Analytics & OR/OM community, as well as efficient industrial practices through networking opportunities.

DAO-ISEM-IORA Seminar Series - Daewon Sun
2024-08-23 10:00 - 11:30
Name of Speaker
Daewon Sun

Schedule
23 August 2024, 10am – 11.30am

Venue
BIZ1- 0305

Link to Register

https://nus-sg.zoom.us/meeting/register/tZckdu-tqzsiGdVFbEmxw3iRRCw-MKTKz2pe

Title
Co-Developing Technology Products by Asymmetric Competitors

Abstract
Technology products often have vital components owned or patented by different firms, each of whom also seeks to compete in the end-user market. Consequently, firms engage in a hybrid of collaboration and competition, where one firm might provide a crucial component or capability to another that sells a competing end-market product. For instance, Samsung sells smartphones and supplies organic light-emitting diode displays for Apple’s smartphones. The component buyer firm (i.e., firm A) may consider co-investing to improve the quality of a shared component (made by firm B). But how do firms gain strategic benefits in such arrangements? What factors govern their investment level? Who benefits, and who loses? This paper addresses these questions. We find that $B$’s own investment is motivated more by increased partnership profit than by higher profit in the end-user market because competition limits the gains from a better product. Indeed, when A has sufficient end-product superiority, B exits the end-user market and becomes more aggressive in improving the quality of the shared capability. A strategic reason for A to co-invest in the shared capability is the desire for a higher performance level than that chosen by B (on its own) which self-throttles its own investment because it cannot fully monetize the gains in a competitive market. In doing so, both A and B make higher-quality end-user products and earn higher profits. Still, B gains more because it can extract part of A’s gains through an higher component unit price. Crucially, firm A co-invests even though the investment prolongs firm B’s ability to maintain an end-user market presence in direct competition with A. Finally, we demonstrate that B could limit its Stackelberg leadership power to further improve the joint investment in an asymmetric partner relationship, which can yield a win-win outcome for both firms.

About the Speaker
Daewon Sun is a professor in Department of IT, Analytics, and Operations at Mendoza College of Business, University of Notre Dame. He holds a Ph.D. in Management Science and Information Systems from the Pennsylvania State University. Professor Sun’s primary research interests are in pricing strategies and resource management, including IT Product Pricing and Launching Strategies, eBusiness Strategies, Operations Management and Marketing Interfaces, and Supply Chain Conflict and Coordination. Professor Sun’s published papers appear in top-tier journals and his research was recognized by several professional awards including 2013 AIS Best Information Systems Publications Award of the Year. He is a senior editor for Production and Operations Management and an associate editor for Decision Sciences journals. Professor Sun teaches information systems, operations management, and business analytics core courses, including Systems Analysis and Design, IS Capstone Project, Introduction to Process Analytics, Business Intelligence, and predictive analytics.

DAO-IORA Seminar Series - Tauhid Zaman
2024-05-31 10:00 - 11:30
Name of Speaker
Tauhid Zaman

Schedule
31 May 2024, 10am – 11.30am

Venue
BIZ1- 0307

Link to Register
https://nus-sg.zoom.us/meeting/register/tZMlduChqDoiGNbvqS_7q_XH-E66BMzMMYIl

Title
Social Media Suspensions and Shadow Banning: Political Bias or Genuine Disinformation Control?

Abstract
Social media platforms like Twitter and TikTok wield significant influence over the visibility and reach of content, raising critical questions: Are certain voices marginalized based on political leanings, or are platform interventions primarily aimed at mitigating the spread of disinformation? Recent discussions highlight allegations of political bias in account suspensions and “shadow banning,” a subtle mechanism where certain content is less visible to users, often without their knowledge.
Drawing from a field study on Twitter, we investigate these claims. Our findings suggest that while some account suspensions may appear biased, there is equally strong evidence indicating that these actions are strategic efforts to combat the spread of disinformation.
We then delve into the issue of shadow banning, demonstrating how this mechanism can be used to shape opinions into arbitrary distributions by solving a linear program. Through simulations on real social network topologies, we show that if shadow banning were driven by partisanship, its implementation could be so subtle that overt political biases remain undetected. This underscores the imperative for rigorous transparency and oversight in the actions of social media platforms.
This talk is based on the following papers:

Analysis: Trade-offs between reducing misinformation and politically-balanced enforcement on social media: https://osf.io/preprints/psyarxiv/ay9q5
Shaping Opinions in Social Networks with Shadow Banning: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0299977

About the Speaker
Tauhid is an Associate Professor of Operations Management at the Yale School of Management. He received his BS, MEng, and PhD degrees in electrical engineering and computer science from MIT. His research focuses on information operations problems in the context of social media. Some of the topics he has studied include combating online extremists, identifying bots, and designing influence campaigns. His broader interests cover data driven approaches to investing in startup companies, algorithmic sports betting, and biometric data. His work has been featured in the Wall Street Journal, Wired, Mashable, the LA Times, Bloomberg, and Time Magazine.
Website: https://www.zlisto.com/

DAO-IORA Seminar Series - Michael Freeman
2024-04-12 10:00 - 11:30
Name of Speaker
Michael Freeman

Schedule
12 April 2024, 10am – 11.30am

Venue
Hon Sui Sen Memorial Library HSS 3-1

Link to Register
https://nus-sg.zoom.us/meeting/register/tZIofu-hqTosGdL3UD4H6gyub6ROFYIUrSpf

Title
From full-time to flexi: Investigating the impact of workforce shifts in primary care on service provision and quality

Abstract
Primary care practices worldwide are experiencing a shift away from traditional full-time roles toward increased part-time and temporary staffing models. This transformation necessitates understanding the implications for primary healthcare delivery. Our paper provides an empirical analysis examining the evolving work patterns and schedules of general practitioners (GPs) in the UK over the past decade. Using primary care records, we first examine how GP work volume impacts healthcare service utilization and patient outcomes. We also explore factors that may mediate this relationship. Going further, given the seeming inevitability of more flexible staffing models, our investigation also aims to inform effective workforce strategies that optimise healthcare delivery. Specifically, we examine scheduling approaches that may allow primary care practices to adapt to part-time and locum GPs. Our findings aim to inform healthcare leaders seeking to adapt to emerging flexible staffing models while maintaining high-quality and accessible primary care.

About the Speaker
Michael Freeman is an Assistant Professor of Technology and Operations Management at INSEAD. His research focuses on healthcare operations, leveraging large empirical datasets and rigorous analysis to uncover actionable insights that inform practices to enhance healthcare delivery. This work centers on two key themes – improving patient routing and care continuity and assessing the impacts of organisational changes and predictive technologies on healthcare productivity and quality. He has published several papers in Management Science and M&SOM, and his research has also been recognized with various awards, including winning the 2016 MSOM Student Paper Competition. His research has also earned attention from media outlets (BBC, The Guardian, and others) and healthcare leaders, including the UK’s Royal College of Emergency Medicine and College of General Practitioners. Alongside research, Michael is an award-winning teacher at INSEAD, where he directs and teaches in executive education, the EMBA, MBA, and PhD programmes.

DAO-IORA Seminar Series - Wang Guihua
2024-04-05 10:00 - 11:00
Name of Speaker
Wang Guihua

Schedule
5 April 2024, 10am – 11am

Link to Register
https://nus-sg.zoom.us/meeting/register/tZ0ucOmorj8sEtZEDn8rf411mXUZJEvpgeaO

Title
The Spillover Effect of Suspending Non-essential Surgery: Evidence from Kidney Transplantation

Abstract
Organ transplantation is a life-saving procedure for patients with end-stage organ disease; delaying transplantation can have life-or-death consequences. Between March and April 2020, in the midst of the COVID-19 pandemic, multiple state governors issued orders to temporarily suspend non-essential surgery. Although such suspensions were not intended for essential surgery (e.g., deceased-donor kidney transplants), the literature on service operations management suggests that these suspensions may have either a positive or a negative spillover effect on essential surgery, depending on whether hospitals maintain or reduce resources in response to such suspensions. Motivated by this dichotomy, we estimate the potential spillover effect of suspending nonessential surgery on deceased-donor kidney transplantation. Through analyzing a dataset of all U.S. kidney transplant procedures, we observe a steep decline in the transplant volume in almost all states during the early months of the pandemic. However, states that suspended nonessential surgery experienced steeper declines than those that did not. Using a difference-in-differences approach, we estimate a state-level suspension of non-essential surgery led to a 23.6% reduction in transplant volume. This negative spillover effect is particularly pronounced for low-efficiency transplant centers with long cold ischemia times (CITs), but less so for high-efficiency centers. Our mediation analysis shows 38.7% of the spillover effect can be attributed to the change in healthcare employment. Our study suggests that in the event of a future public health crisis, policymakers should consider more nuanced approaches to securing the healthcare workforce critical to supporting essential services, especially for transplant centers with long CITs.

About the Speaker
Guihua is an Assistant Professor of Operations Management and a Sydney Smith Hicks Faculty Fellow at the Naveen Jindal School of Management, The University of Texas at Dallas. He obtained his PhD from the University of Michigan, MSc from the Georgia Institute of Technology, MSc and BEng from the National University of Singapore. Prior to his PhD study, Guihua worked as a supervisor of the industrial engineering department at UPS Asia headquartered in Singapore. Guihua’s research focuses on the intersection of empirical econometrics and machine learning with application to personalized healthcare. More specifically, Guihua has developed new empirical machine learning techniques such as instrumental variable forest and first-difference causal tree for heterogeneous treatment effect analysis using observational healthcare data. Guihua’s research has been published at Management Science, Manufacturing & Service Operations Management, Production and Operations Management, Advances in Applied Probability, Surgery, and Annals of Thoracic Surgery, and received media coverage by Associate Press, Crain’s Detroit, Houston Chronicle, Medical Xpress, National Interest, NPR, PRI, Science Daily, Simply Flying, The Conversation, and Yahoo! News. His research was named a finalist of both the Pierskalla Best Paper Award and the MSOM Service Management SIG Best Paper Award, a runner-up of both the Financial Times Responsible Business Education Award and the INFORMS Service Science Best Cluster Paper Award, the winner of the INFORMS Health Applications Society Student Paper Competition, two-time finalists of the MSOM Student Paper Competition, and a finalist of the INFORMS Service Section Best Student Paper Competition.

DAO-IORA Seminar Series - Sasa Zorc
2024-03-22 10:00 - 11:30
Name of Speaker
Sasa Zorc

Schedule
22 March 2024, 10am – 11.30am

Venue
BIZ1-0206

Link to Register

https://nus-sg.zoom.us/meeting/register/tZIpce2srDwrHdyCUf-w1Rtdh_3c-YlU9nTz

Title
Search with Recall and Gaussian Learning

Abstract
The classic sequential search problem rewards the decision maker with the highest sampled value, minus a cost per sample. If the sampling distribution is unknown, then a Bayesian decision maker faces a complex balance between exploration and exploitation. We solve the stopping problem of sampling from a Normal distribution with unknown mean and unknown variance and a conjugate prior, a longstanding open problem. The optimal stopping region may be empty (it may be optimal to continue the search regardless of the offer one receives, especially at the early stages), or it may consist of one or two bounded intervals. While a single reservation price cannot describe the optimal rule, we do find a standardized reservation rule: stop if and only if the standardized value of the current offer is sufficiently high relative to the standardized search cost. We also introduce the index function, which provides a computationally practical way to implement the standardized stopping rule for any given prior, sampling history, and sampling horizon.

About the Speaker
Sasa Zorc is an assistant professor at the Darden School of Business, University of Virginia. Sasa obtained his PhD in Management from INSEAD. He studies incentives in multi-agent systems such as health care and matching markets (both centralized and decentralized). Methodologically, his research relies on stochastic dynamic games, search theory, dynamic mechanism design, contract theory and data-driven simulations.

DAO-IORA Seminar Series - Zhu Weiming
2024-03-19 10:00 - 11:30
Name of Speaker
Zhu Weiming

Schedule
19 March 2024, 10am – 11.30am

Venue
BIZ1-0206

Link to Register
https://nus-sg.zoom.us/meeting/register/tZcvdu-rpzkuHNTHWtUz_oEaagwlXP1_FVHp

Title
Extracting Efficiency from Chaos: Rider Behavior, Performance and Negative Externality in Dockless Bike Sharing Systems

Abstract
In recent years, urban bike-sharing systems developed in two main forms: Dock-based systems that rely on fixed location docks to park at and pick up bicycles from, and Dockless systems which allow users to pick up and drop off bikes anywhere. By eliminating reliance on fixed docks, dockless systems provide significantly improved mobility and convenience to riders, yet they have downsides such as high operational costs and occupying valuable sidewalk space. Collaborating with a major dockless bike-sharing platform, we empirically analyze riders’ economic incentives, explore the efficiency and measure the negative externality of this business model. Specifically, we address the following questions: (i) What is the impact of the number of bicycles in the system on efficiency? (ii) How does the efficiency of dockless and dock-based systems compare? (iii) How can bike-share company minimize the negative externalities generated during service provision through smart relocation?

About the Speaker
Weiming Zhu is an Associate Professor in Innovation and Information Management at HKU Business School. Weiming obtained his bachelor’s degree in Physics from HKUST and Ph.D. in Operations Management from the Robert H. Smith School of Business at University of Maryland. Prior to joining the University of Hong Kong, he was an Associate Professor in IESE’s Department of Production, Technology and Operations Management. Weiming has also been a visiting professor in the Institute for Data, Systems, and Society (IDSS) at Massachusetts Institute of Technology and Kellogg School of Management at Northwestern University. Weiming’s research interests include operations in the platform economy, urban mobility, and supply chain finance. His work has been published at Management Science, M&SOM and Journal of International Economics, and has been recognized in M&SOM, POMS, Service Science and CSAMSE best paper award competitions.

DAO-IORA Seminar Series - Zhang Fuqiang
2024-03-13 10:00 - 11:30
Name of Speaker
Zhang Fuqiang

Schedule
13 March 2024, 10am – 11.30am

Venue
BIZ1-0307

Link to Register

https://nus-sg.zoom.us/meeting/register/tZUvdu2grDItHdZl1LhIH4lgx0-lY42S7eks

Title
The Bright Side of Price Volatility in Global Commodity Procurement

Abstract
This paper studies two competing firms’ choices between the contingent-price contract (CPC) and fixed-price contract (FPC) in global commodity procurement. The FPC price is determined when signing the contract, whereas the CPC price is pegged to an underlying index and remains open until the delivery date. Under both contracts, each firm determines its order quantity based on the updated belief about the market demand. The unrealized CPC price correlates with the market demand, allowing a firm to update its belief about the CPC price using demand information, thereby generating a price-learning effect. We find that, contrary to conventional wisdom, a larger price volatility could benefit the firms, and, under differentiated contracts, a firm might benefit from the improvement of forecast accuracy at its rival. We further show that the price-learning effect plays a critical role in the firms’ contract choices. First, significant price volatility forces the firms to pursue the responsiveness of the CPC. Second, the firms may adopt differentiated contracts to enhance their responses to market changes and dampen competition, and a higher competition intensity more likely leads to contract differentiation. Third, the firms in a small market seek responsiveness and contract differentiation rather than cost efficiency. This study reveals the bright side of price volatility and takes a step toward understanding the effect of two-dimensional information updating.

About the Speaker
Fuqiang Zhang is the Dan Broida professor of Supply Chain, Operations, and Technology (SCOT) at Olin Business School, Washington University in St. Louis. He also serves as the SCOT area chair and academic director of MBA programs at Olin. Professor Zhang obtained his Ph.D. in Managerial Science and Applied Economics from the Wharton School, University of Pennsylvania. His research interests focus on supply chain and technology innovation, consumer analytics in operations management, and sustainable operations. In recent years, he has been working on research topics that are driven by empirical data. Professor Zhang’s research has appeared in top-tier academic journals such as Management Science, Manufacturing & Service Operations Management, Operations Research, Marketing Science, and Production and Operations Management.

DAO-IORA Seminar Series - Arnoud den Boer
2024-03-11 10:00 - 11:30
Name of Speaker
Arnoud den Boer

Schedule
11 March 2024, 10am – 11.30am

Venue
BIZ1- 0304

Link to Register

https://nus-sg.zoom.us/meeting/register/tZcrdeytpjgsGtEen4nnptCLRFl85dvfKFvR

Title
Can price algorithms learn to form a cartel?

Abstract
Can price algorithms learn to form a cartel instead of compete against each other, potentially leading to higher consumer prices and lower social welfare? The question is controversial among economists and competition policy regulators. One the one hand, concerns have been expressed that self-learning price algorithms do not only make it easier to form price cartels, but also that this can be achieved within the boundaries of current antitrust legislation – raising the question whether the existing competition law needs to be adjusted to mitigate undesired algorithmic collusion. On the other hand, a number of economists believe that algorithms learning to collude is science fiction, except by using forms of signaling or communication that are already illegal, and argue that there is no need to change antitrust laws. Motivated by this discussion, I will present work that shows that under some market conditions, price algorithms can learn to collude. Based on joint work with Janusz Meylahn, Thomas Loots, Maarten Pieter Schinkel, Ali Aouad.

About the Speaker
Arnoud is Associate Professor at the Korteweg-de Vries Institute for Mathematics of the University of Amsterdam. He studied Mathematics at Utrecht University (2006), Mathematics for Industry at Eindhoven University of Technology (2008) and wrote his PhD thesis `Dynamic Pricing and Learning’ (2013) about data-driven price algorithms at the CWI Centrum for Wiskunde and Computer Science in Amsterdam. Arnoud’s research focuses on the interface of learning and optimization, with applications in dynamic pricing and revenue management. He is the recipient of several awards and grants, including the 2015 Gijs de Leve prize for best PhD Thesis in operations research defended in the Netherlands in the period 2012-2014, personal grants from the Dutch Science Foundation, and the INFORMS Revenue Management & Pricing Section Prize. Arnoud serves as editor for Management Science, M&SOM, and POMS, is board member of the Euro Working Group on Pricing and Revenue Management and board member of the INFORMS Revenue Management and Pricing Section.

DAO-IORA Seminar Series - Kimon Drakopoulos
2024-03-08 10:00 - 11:30
Name of Speaker
Kimon Drakopoulos

Schedule
8 March 2024, 10am – 11.30am

Venue
BIZ1- 0206

Link to Register
https://nus-sg.zoom.us/meeting/register/tZ0scuyqrT4vGtDnHl1AToXlPUExri0y7Suq

Title
Blockchain Mediated Persuasion

Abstract
An ex-post informed Sender wishes to persuade a rational Bayesian Receiver to take a desired action, as in the classic Bayesian Persuasion model studied by Kamenica and Gentzkow (2011). However, we consider settings in which Sender cannot reliably commit to a signal mechanism. An alternative approach is to consider a trustworthy mediator that receives a reported state of the world from Sender and then, based on this report, generates a signal realization for Receiver. Such mediation can be implemented via costly blockchain technology. Surprisingly, we show that this cost differentiated mediation succeeds where free mediation fails. By requiring Sender to pay the mediator for different signal realizations, we can effectively incentivize them to truthfully report, which in turn allows for beneficial persuasion to take place. Joint with Justin Mulvany, Irene Lo

About the Speaker
Kimon Drakopoulos is an Associate Professor in Business Administration at the Data Sciences and Operations department at the USC Marshall School of Business. His research focuses on the operations of complex networked systems, social networks, stochastic modeling, game theory and information economics. Kimon is currently serving in the high level advisory committee to the Greek government on AI regulation and implementation. In 2020 he served as the Chief Data Scientist of the Greek National COVID-19 Scientific taskforce and a Data Science and Operations Advisor to the Greek Prime Minister. He has been awarded the Wagner Prize for Excellence in Applied Analytics and the Pierskalla Award for contributions to Healthcare Analytics.

DAO-IORA Seminar Series - Mohamed Mostagir
2024-02-26 10:00 - 11:30
Name of Speaker
Mohamed Mostagir

Schedule
26 February 2024, 10am – 11.30am

Venue
BIZ1- 0203

Link to Register
https://nus-sg.zoom.us/meeting/register/tZUrc–gqj4uE9WGbEbpRoBVA4DGCZes88wN

Title
A Theory of Ghosting

Abstract
Ghosting is a phenomenon where communication between two parties abruptly stops after one side becomes deliberately unresponsive. This occurs in a variety of settings, but the term entered the mainstream after its usage to describe an important aspect of the dating experience that has been sparsely studied. Both online dating platforms and their users report that ghosting is one of the primary drivers hurting user experience and preventing good outcomes. We develop a model of ghosting and study the efficacy of different policies that platforms have implemented to deal with this problem.

About the Speaker
Mohamed Mostagir is an associate professor of Technology and Operations at the University of Michigan Ross School of Business. He is interested in social learning and belief formation and their applications in a wide variety of settings.

DAO-IORA Seminar Series - Kai Hoberg
2024-02-16 10:00 - 11:30
Name of Speaker
Kai Hoberg

Schedule
16 February 2024, 10am – 11.30am

Venue
I4-01-03 Seminar Room

Link to Register
https://nus-sg.zoom.us/meeting/register/tZYuceusrjouE9P7Pu7BAYbWh7quOmNJ-xcw

Title
Using (inaccurate) data to drive better supply chain decision making

Abstract
More and more data is available to improve supply chain decision making but it needs to be carefully applied considering human limitations. Against this background, I will present two studies that focus on the role of human judgment in supply chain management decision making, first exploring the influence of planners’ adjustments to AI-generated demand forecasts and second examining the effectiveness of human decision-making in inventory management subject to inaccurate data. Study 1 investigates the role of human judgment in demand forecasting. We analyze planners’ adjustments to AI-generated forecasts using a dataset containing 30 million SKU-store-day level forecasts and associated variables. We employ random forest and decision tree approaches to understand the drivers and quality of adjustments. Our findings suggest product characteristics such as price, freshness, and discounts are important factors in adjustments. Large positive adjustments are frequent but often inaccurate, while large negative adjustments are accurate but less common which indicates behavioral biases. In Study 2, we focus on decisions made under the inaccurate inventory data due to shrinkage and loss. We explore the trade-off between cleaning inventory data centrally and allowing decision makers to adjust ordering decisions based on their judgment. In light of human biases in decision making, we present a set of hypotheses on the cleaning-adjustment trade-off and test them in a laboratory setting. The study raises questions about the effectiveness of normative models in determining whether to clean data centrally or rely on decision makers’ judgments, providing insights into optimizing human knowledge utilization in supply chain management.

About the Speaker
Kai Hoberg is Professor of Supply Chain and Operations Strategy at the Kühne Logistics University in Hamburg. His research focuses on supply chain analytics, the role of technology in supply chains, and supply chain strategy. His research findings have been published in academic journals like Journal of Operations Management, Production and Operations Management or Journal of Supply Chain Management. Kai was a visiting researcher at international universities such as the National University of Singapore, Cornell University, the Israel Institute of Technology and the University of Oxford. Prior to his return to academia, he was a project manager in the operations team at Booz & Company. For the past 10 years he has supported the McKinsey Supply Chain practice in teaching and research. His team at KLU is closely working industry partners such as Bayer, Procter & Gamble, Jungheinrich or Infineon.

DAO-IORA Seminar Series - Canberk Ucel
2024-02-02 10:00 - 11:30
Name of Speaker
Canberk Ucel

Schedule
2 February 2024, 10am – 11.30am

Venue
I4-01-03 Seminar Room

Link to Register
https://nus-sg.zoom.us/meeting/register/tZcvcuivqDMjGdS7y3CPbCx_Y701qvieVxVJ

Title
The Value of Advice: Evidence from Thousands of Smallholder Farms in the Philippines

Abstract
Increasing the productivity of Philippine coconut farms that are well below world standards could improve the livelihoods of 3.4 million farming families, most suffering poverty. Government and supporting organizations have long promoted Good Agricultural Practices (GAPs), which decades of public research suggests would double farm productivity with little capital investment, but have failed to achieve widespread adoption and productivity gains. We study the role of access to change agents in facilitating GAP adoption and effective implementation using proprietary data on the productivity, granular farming practices and characteristics of 1,998 smallholders. Our quantitative analysis leverages the pseudo-exogenous variation in agricultural extension office locations to find that being within 8 kilometers of an extension office is associated with greater awareness of central recommendations for 7 of 8 GAPs, increased adoption rates for three most effective GAPs, and 36% higher productivity, on average, on otherwise comparable farms. Our post-hoc analysis further suggests that physical interactions enable change agents to support complex practice adoption and local implementation decisions. Moreover, we find significant heterogeneity in the effects of agent access, and offer facility reallocation and farm visit schedules to improve service coverage and effectiveness using existing agent capacity. Our results suggest that supporting organizations should integrate change agent support or otherwise focus on developing better customized farming advice, integrate farmer feedback, and assist smallholders with the finer details of implementation leveraging emerging information technologies. Evidence-based provision of advisory services, extended beyond the Philippine context, could potentially benefit two billion people worldwide dependent on smallholder farms, and redound benefits to small heterogenous firms that dominate vital functions in other industries. We suggest new avenues for research on data-driven, evidence-based improvements in the provision and design of advisory services, e.g. related to optimal facility allocation and agent visit schedules and the development and communication of effective operational recommendations.

About the Speaker
Canberk Ucel is an Assistant Professor at Bilkent University in Turkey and a Visiting Scholar at INSEAD. He completed his PhD in Operations, Information and Decisions at the Wharton School at the University of Pennsylvania in 2022, and also holds an undergraduate degree in Industrial Engineering from Bilkent University. He is strongly interested in studying operational and organizational issues in understudied industries facing complex social, economic and environmental challenges, and his current research focuses on the agriculture industry, which contributes significantly to environmental conservation and economic development, and employs most of the world’s poorest workers. His research, which has been recognized by several academic awards, leverages proprietary, granular farm operations data he collected through several industry partnerships he built during his doctoral studies, as well as extensive field work, to generate practical recommendations for farmers, companies and policymakers to advance key economic, social and environmental goals. He strives to translate his research into positive change in the industry, including through large-scale randomized controlled field trials, and advocates agriculture as a fruitful context for managerial and operational research with potential to generate significant societal impact. His teaching experience spans various MBA, graduate and undergraduate courses at Wharton and Bilkent related to operations, supply chains, and data analytics and statistics, and includes writing teaching cases, migrating courses online, and designing and teaching new class sessions and courses.

DAO-IORA Seminar Series - Zhan Ruohan
2024-01-26 10:00 - 11:30
Name of Speaker
Zhan Ruohan

Schedule
26 January 2024, 10am – 11.30am

Venue
I4-01-03

Link to Register
https://nus-sg.zoom.us/meeting/register/tZApdeirrD8uGdK8Z3tiW6Can4XywK0kVD4C

Title
Estimation and Inference under Recommender Interference

Abstract
In digital platforms, recommender systems (RecSys) are essential for aligning content with viewer preferences. This work considers the evaluation of RecSys updates, referred to as “treatments”, by analyzing their “global treatment effect” (GTE) – the expected overall benefit of universally applying these treatments. Our focus is on treatments targeting content creators. We utilize A/B experiments on the creator side and identify that the conventional difference-in-mean estimator is biased for GTE estimation, due to interference among creators competing for visibility. To address this challenge, we introduce a semi-parametric model that combines a parametric choice model, designed to streamline the recommendation process, with a nonparametric component that employs machine learning to account for the heterogeneity among viewers and content. Using this model, we approximate GTE with a doubly robust estimator that satisfies Neyman orthogonality, ensuring consistency and asymptotic normality, and supporting hypothesis testing. We show the robustness and semiparametric efficiency of our estimator even under model mis-specification. We demonstrate the efficacy of our method through simulations and practical applications on a leading short video platform. This is joint work with Shichao Han, Yuchen Hu, and Zhenling Jiang.

About the Speaker
Ruohan Zhan is an assistant professor in the Department of Industrial Engineering and Decision Analytics at the Hong Kong University of Science and Technology. She earned her PhD from Stanford University and her BS from Peking University. Specializing in causal inference, statistics, and machine learning, Ruohan develops new methods to solve problems from online marketplaces, particularly on challenges related to causal effect identification, economic analysis, experimentation and operations. Her research has been published in top-tier journals including Management Science and Proceedings of National Academy of Sciences, as well as leading machine learning conferences including NeurIPS, ICLR, WWW, and KDD.

DAO-IORA Seminar Series - Qin Hanzhang
2024-01-19 10:00 - 11:30
Name of Speaker
Qin Hanzhang

Schedule
19 January 2024, 10am – 11.30am

Venue
I4-01-03 Seminar Room

Link to Register
https://nus-sg.zoom.us/meeting/register/tZEpce-orz8rEtZcddjRXnFf3EACPJSUMiAt

Title
Lower Sample Complexity of Reinforcement Learning for Structured MDPs: Evidence from Inventory Control

Abstract
I will discuss the important open problems of 1) What is the sample complexity (i.e., how may number of data samples is needed) of learning nearly optimal policy for multi-stage stochastic inventory control when the underlying demand distribution is initially unknown; and 2) How to compute such a policy when the required number of data samples are given. For the first half of the talk, without considering fixed ordering cost, I will start answering the questions from the backlog setting via SAIL, a novel SAmple based Inventory Learning algorithm. Then, results for the more practical lost-sales setting will be discussed, including the first sample complexity result for this more challenging setting with only mild assumptions (that ensures quality data), by leveraging both recent developments of variance reduction techniques for reinforcement learning and the structural properties of the dynamic programming formulation for inventory control settings. Numerical simulations show that SAIL significantly outperforms competing methods in terms of inventory cost minimization. Then in the second half, I will discuss several recent developments on sample complexity related to all three types of the MDP formulations (finite-horizon MDPs, infinite-horizon discounted/average-cost MDPs) for inventory control with fixed ordering cost. Somewhat surprisingly, in all three cases, it is found that sample complexity of the most naïve plug-in estimators is strictly lower than the “best possible” bounds derived for general MDPs. The first half will be based on joint work with David Simchi-Levi (MIT) and Ruihao Zhu (Cornell), and the second half will be based on joint work with Boxiao Chen (UIC), Xiaoyu Fan (NYU), Michael Pinedo (NYU) and Zhengyuan Zhou (NYU).

About the Speaker
Hanzhang Qin is an Assistant Professor at the Department of Industrial Systems Engineering and Management at NUS. He is also an affiliated faculty member at the NUS Institute for Operations Research and Analytics. His research was recognized by several awards, including INFORMS TSL Intelligent Transportation Systems Best Paper Award and MIT MathWorks Prize for Outstanding CSE Doctoral Research. Before joining NUS, Hanzhang spent one year as a postdoctoral scientist in the Supply Chain Optimization Technologies Group of Amazon NYC. He earned his PhD in Computational Science and Engineering under supervision of Professor David Simchi-Levi, and his research interests span stochastic control, applied probability and statistical learning, with applications in supply chain analytics and transportation systems. He holds two master’s, one in EECS and one in Transportation both from MIT. Prior to attending MIT, Hanzhang received two bachelor degrees in Industrial Engineering and Mathematics from Tsinghua University.

DAO-IORA Seminar Series - Zhao Jinglong
2024-01-12 10:00 - 11:30
Name of Speaker
Zhao Jinglong

Schedule
12 January 2024, 10am – 11.30am

Venue
BIZ1-0203

Link to Register

https://nus-sg.zoom.us/meeting/register/tZEpcOGopz4jE91kG6vFGQ78zjTCRdz9iGFZ

Title
Adaptive Neyman Allocation

Abstract
In experimental design, Neyman allocation refers to the practice of allocating subjects into treated and control groups, potentially in unequal numbers proportional to their respective standard deviations, with the objective of minimizing the variance of the treatment effect estimator. This widely recognized approach increases statistical power in scenarios where the treated and control groups have different standard deviations, as is often the case in social experiments, clinical trials, marketing research, and online A/B testing. However, Neyman allocation cannot be implemented unless the standard deviations are known in advance. Fortunately, the multi-stage nature of the aforementioned applications allows the use of earlier stage observations to estimate the standard deviations, which further guide allocation decisions in later stages. In this paper, we introduce a competitive analysis framework to study this multi-stage experimental design problem. We propose a simple adaptive Neyman allocation algorithm, which almost matches the information-theoretic limit of conducting experiments. Using online A/B testing data from a social media site, we demonstrate the effectiveness of our adaptive Neyman allocation algorithm, highlighting its practicality especially when applied with only a limited number of stages.

About the Speaker
Jinglong Zhao is an Assistant Professor of Operations and Supply Chain Management at Questrom School of Business at Boston University. He works at the interface between optimization and econometrics. His research leverages discrete optimization techniques to design field experiments with applications in online platforms. Jinglong completed his PhD in Social and Engineering Systems and Statistics at Massachusetts Institute of Technology.
