DAO-ISEM-IORA Seminar Series - Daewon Sun
2024-08-23 10:00 - 11:30 (Asia/Singapore)
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.

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.

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.

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.

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 : 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 | 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: 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: 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: 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: 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: 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 : 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 : 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 : 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: Ling Chun Kai
2025-03-21 10:00 - 11:30
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: Vinod Singhal
2025-04-01 10:00 - 11:30
Name of Speaker
Vinod Singhal

Schedule
1 April 2025, 10am – 11.30am

Venue
BIZ1 02-06

Link to Register
https://nus-sg.zoom.us/meeting/register/KIy_WyM9SIioyfk8l5-XzQ

Title
The Bullwhip Effect and Stock Returns

Abstract
The bullwhip effect (BWE) is an important phenomenon in the operations and supply chain management field. Although it is commonly accepted that the BWE is widespread and can have a significant adverse impact on financial performance, there is surprisingly limited objective evidence on the financial consequences of the BWE. This paper examines the impact of the BWE on financial performance by examining the relationship between the BWE and stock price performance. The empirical analysis is based on data from 1985 to 2018 from about 7,200 publicly traded firms and about 64,000 firm-years. We find that most results on the impact of the BWE on stock returns are statistically indistinguishable from zero. The few marginally significant results that we find suggest a positive relationship between the BWE and stock returns rather than the expected negative relationship. However, these marginally significant results do not hold when alternate methods are used to test the relationships. These conclusions are robust when we segment the sample by size, industry, and time periods. We also do not find a significant relationship between the BWE and stock returns for samples based on the propagation of the BWE from customers to suppliers. We do find some evidence to suggest that the BWE has a negative impact on inventory turnover. However, we do not find similar evidence for capacity utilization. The relationships between the BWE and return on assets measures are statistically insignificant. For margin measures, the relationships are positive and statistically significant but not economically significant.

About the Speaker
Vinod Singhal is the Charles W. Brady Chair Professor of Operations Management at the Scheller College of Business at Georgia Institute of Technology, Atlanta, USA. He has a Ph.D. from University of Rochester, Rochester, USA. Prior to joining Georgia Tech., he worked as a Senior Research Scientist at General Motors Research Labs.
Vinod’s research has focused on the impact of operating decisions on accounting and stock market-based performance measures. His research has been supported through grants from the US Department of Labor, National Science Foundation, the American Society of Quality, and the Sloan Foundation. He has published extensively in academic journals and has made more than 200 presentations at different universities. His research has been recognized in the practitioner community through his many articles in industry-practitioner journals and frequent invited presentations as keynote speaker at practitioner conferences. His research has been cited over 200 times in practitioner publications such as Business Week, The Economist, Fortune, Smart Money, CFO Europe, Financial Times, Investor’s Business Daily, and Daily Telegraph. His research has been extensively cited in academic publications, with nearly 15,000 citations. His paper “An empirical analysis of the effect of supply chain disruptions on long‐run stock price performance and equity risk of the firm” was voted by the POMS members in 2024 as one of the top-ten papers published in Production and Operations Management in the last 30 years.
Vinod is a Departmental Editor of Production and Operations Management, and Associate Editor of Management Science. He is a Fellow of the Production and Operations Management Society. He served on the Academic Advisory Board of the European School of Management and Technology, Germany.
Vinod’s teaching interests include operations strategy and supply chain management. He has contributed to teaching at an international level, as well, by offering research workshops in countries including Australia, China, France, Germany, Hong Kong, India, New Zealand, Singapore, South Korea, Spain, Sweden, and United Kingdom.

DAO-ISEM-IORA Seminar Series: Yuan Jun
2025-04-03 10:00 - 11:30
Name of Speaker
Yuan Jun

Schedule
3 April 2025, 10am – 11.30am

Venue
E1A-06-21/22 ISEM Conference Room

Link to Register
https://nus-sg.zoom.us/j/86085218517?pwd=[REDACTED]

Title
Optimal Planning of Electric Ship Charging Station Using Noisy-Expensive Constrained Bayesian Optimization

Abstract
Shipping plays a critical role in global transportation and is also a significant contributor to global carbon emissions. As environmental concerns continue to grow, the maritime industry faces increasing pressure to reduce its carbon footprint. In this context, electric ships, with their potential for low emissions and environmental sustainability, have garnered considerable attention. However, their application for long-distance transportation is currently limited by challenges such as battery capacity constraints and a lack of charging infrastructure. The strategic placement of charging stations is therefore essential for advancing the use of electric ships. This paper aims to design an on-shore electric ship charging station that integrates renewable energy sources—namely wind and photovoltaic power—with an energy storage system. Planning for these charging stations is complex due to the uncertainty of power generation from renewable sources and the fluctuating demand for charging services. Additionally, the developed model is intricate, time-consuming, and subject to noisy and expensive constraints. To address these challenges, this study proposes a Bayesian optimization method tailored to noisy and expensive constrained environments, enabling efficient optimization of the complex simulation model. The findings of this research offer valuable insights for decision-making regarding the development of on-shore electric ship charging stations. Furthermore, they provide a foundation for exploring business models that align with the growth of the electric shipping industry, fostering mutually beneficial outcomes for all stakeholders involved.

About the Speaker
Yuan Jun is an Associate Professor with China Institute of FTZ Supply chain, Shanghai Maritime University. He received his B.E. degree in Industrial Engineering and Management from Shanghai Jiao Tong University in 2008, and the Ph.D. degree in Industrial and Systems Engineering from National University of Singapore in 2013. From 2014-2017, he worked as a Research Fellow at National University of Singapore. He got the Young Oriental Scholar in 2017. In the past five years, he has published more than 20 SCI papers in Energy, Applied Energy, IISE Transactions, TOMACS etc., held or participated in more than 15 scientific research projects funded by the National Natural Science Foundation of China, the Shanghai Municipal Science and Technology Commission, the U.S. Energy Fund etc. His research interests include energy systems modeling, shipping energy systems, integrated port energy systems, computer simulation and optimization.

DAO-ISEM-IORA Seminar Series: Paat Rusmevichientong
2025-04-04 10:00 - 11:30
Name of Speaker
Paat Rusmevichientong

Schedule
4 April 2025, 10am – 11.30am

Venue
BIZ1 03-02

Link to Register
https://nus-sg.zoom.us/meeting/register/Lapg-26pTU620yd5j7DDAw

Title
An Endogenous Multinomial Logit Model with Population-Based Purchase Feedback

Abstract
We develop a choice model that incorporates feedback from a population of customers making a purchase within the assortment of offered products. Each customer posts feedback on the product that she purchases within an offered product assortment. The feedback is obtained by applying a feedback function to the utility that the customer derives from her purchased product. When another customer is to make a purchase within the assortment, she uses the expected feedback from the customers with purchases to form a reference point. Therefore, the utility of a product has three components: intrinsic deterministic utility, Gumbel distributed idiosyncratic term and reference effect. The utility of a product is determined through a fixed point, as the utility depends on the reference effect, which, in turn, depends on the utilities of the products in the assortment. We give a closed-form expression for the choice probabilities for a broad class of feedback functions that can even have discontinuities. We develop an efficient algorithm to compute the revenue-maximizing assortment when the customers choose under our choice model. We study the pricing problem under our choice model, giving structural properties of the optimal prices, which ultimately yield an efficient algorithm for computing the revenue-maximizing prices. We also provide necessary and sufficient conditions for the convexity of the negative log-likelihood function. Our computational experiments demonstrate that our choice model can help predict the choices of the customers better than a number of benchmarks.

About the Speaker
Paat Rusmevichientong is the Justin Dart Professor of Operations Management and Professor of Data Sciences and Operations in the Marshall School of Business at the University of Southern California. Prior to joining the Marshall School, he was a faculty in the School of Operations Research and Information Engineering at Cornell University. His research interests focus on revenue management, choice modeling, pricing, assortment optimization, and large-scale dynamic programming. From 2003 through 2004, he worked in the data mining and personalization group at Amazon.com. He received BA (1997) in Mathematics from University of California, Berkeley, and MS (1999) and PhD (2003) in Operations Research from Stanford University. He is a member of INFORMS.

DAO-ISEM-IORA Seminar Series: Bernhard von Stengel
2025-04-23 10:00 - 11:30
Name of Speaker
Bernhard von Stengel

Schedule
23 April 2025, 10am – 11.30am

Venue
BIZ1 03-02

Link to Register
https://nus-sg.zoom.us/meeting/register/AWc8AlsiQo67S7-kKnLwmQ

Title
Finding an Empirical Equilibrium by Machine Learning in a Pricing Game

Abstract
We apply machine learning to a classical multi-period pricing game. The game is a duopoly with demand inertia, introduced by Selten in 1965 and studied around 1990 with economic experiments. Its strategies are too complex to represent explicitly. Unlike standard multi-agent reinforcement learning of training agents against each other, our framework is that of a Policy-Space Response Oracle (PSRO) and the “double oracle” method. A few initial strategies that play against each other define a “meta-game”. A Nash equilibrium of this meta-game is computed and defines the training environment for a learning agent. Once the agent is sufficiently trained to get a higher payoff than in the current equilibrium, it is added as a new strategy to the meta-game, which expands in this way, and the process repeats. Various learning methods such as SAC (Soft Actor Critic) and PPO (Proximal Policy Optimisation) lead to different limit equilibria in this setup, namely either competitive pricing or cooperative price collusion in the duopoly.
(Joint work with Sahar Jahani and Rahul Savani)

About the Speaker
Bernhard von Stengel is Professor of Mathematics at the London School of Economics which he joined in 1998, after studies in Germany and the USA. He has been communications officer of the Game Theory Society, and was program chair of the GAMES 2016 congress (held jointly with EC’2016) in Maastricht. He is interested in mathematical questions of game theory. The geometry and computation of Nash equilibria is one of his research specialities.

DAO-ISEM-IORA Seminar Series: Erick Delage
2025-05-07 10:00 - 11:30
Name of Speaker
Erick Delage

Schedule
7 May 2025, 10am – 11.30am

Venue
BIZ1 03-02

Link to Register
https://nus-sg.zoom.us/meeting/register/GTx5hsDMTAep_DVlLOLRAA

Title
Data-driven Conditional Robust Optimization

Abstract
Conditional Robust Optimization (CRO) is a decision-making framework that blends the flexibility of robust optimization (RO) with the ability to incorporate additional information regarding the structure of uncertainty. This approach solves the RO problem where the uncertainty set structure adapts to account for the most recent information provided by a set of covariates. In this presentation, we will introduce two data-driven approaches to CRO: a sequential predict-then-optimize method and an integrated end-to-end method. We will also show how hypothesis testing can be integrated to the training in order to improve the quality of conditional coverage of the produced uncertainty sets.

About the Speaker
Erick Delage is a professor in the Department of Decision Sciences at HEC Montréal, a chairholder of the Canada Research Chair in decision making under uncertainty, and a member of the College of New Scholars, Artists and Scientists of the Royal Society of Canada. His research interests span the areas of robust and stochastic optimization, decision analysis, reinforcement learning, and risk management with applications to portfolio optimization, inventory management, energy, and transportation problems.

DAO-ISEM-IORA Seminar Series : Daniel Russo
2025-08-15 10:00 - 11:30
Name of speaker

Daniel Russo

Schedule

15 August 2025, 10am – 11.30am

Venue

BIZ2 – 0413C

Link to register

https://nus-sg.zoom.us/meeting/register/rT3lbgWGQB-LnXjhOw84MA

Title

Active Exploration via Autoregressive Generation of Missing Data

Abstract

We cast the challenges of uncertainty quantification and exploration in online decision-making as a problem of training and generation from an autoregressive sequence model, an area experiencing rapid innovation. Central to our approach is viewing uncertainty as arising from missing outcomes that would be revealed through appropriate action choices, rather than from unobservable latent parameters of the environment. This reformulation aligns naturally with modern machine learning capabilities: we can i) train generative models through next-token prediction rather than fit explicit priors, ii) assess uncertainty through autoregressive generation rather than parameter sampling, and iii) adapt to new information through in-context learning rather than explicit posterior updating. To showcase these ideas, we formulate a challenging informed bandit learning task where effective performance requires leveraging unstructured prior information (like text features) while exploring judiciously to resolve key remaining uncertainties. We validate our approach through both theory and experiments. Our theory establishes a reduction, showing success at offline next-outcome prediction translates to reliable online uncertainty quantification and decision-making, even with strategically collected data. Semi-synthetic experiments show our insights bear out in a news-article recommendation task where article text can be leveraged to minimize exploration.

About the Speaker

Daniel Russo is a Philip H. Geier Jr. Associate Professor in the Decision, Risk, and Operations division of the Columbia Business School. His research lies at the intersection of machine learning and online decision making, mostly falling under the broad umbrella of reinforcement learning. Outside academia, Dan works as an Amazon scholar applying reinforcement learning to supply chain optimization. He previously spent five years working with Spotify to apply reinforcement learning and large language models to audio recommendations. Dan completed his undergraduate studies in Math and Economics at the University of Michigan, doctoral studies at Stanford University under the supervision of Benjamin Van Roy, and worked as a postdoctoral researcher at Microsoft Research in New England. His research has been recognized by the Erlang Prize, the Frederick W. Lanchester Prize, a Junior Faculty Interest Group Best Paper Award, and first place in the George Nicholson Student Paper Competition.

DAO-ISEM-IORA Seminar Series : Alminas Žaldokas
2025-08-22 10:00 - 11:30
Name of Speaker

Alminas Žaldokas

Schedule

22 August 2025, 10am – 11.30am

Venue

HSS 4 – 7 (Hon Sui Sen Memorial Library, level 4 Seminar Room)

Link to Register

https://nus-sg.zoom.us/meeting/register/zUMnlP9MRy2HoqduBcxjmA

Title

ESG Shocks in Global Supply Chains

Abstract

We show that U.S. firms cut imports by 31.8% when their international suppliers experience environmental and social (E&S) incidents. These trade cuts are larger for publicly listed U.S. importers facing high E&S investor pressure and lead to crosscountry supplier reallocation, suggesting that E&S preferences in capital markets can be privately costly but have real effects for foreign suppliers. Larger trade cuts around the incident result in better supplier E&S performance in subsequent years, and in the eventual resumption of trade. Our results highlight the role of investors in ensuring suppliers’ E&S compliance along global supply chains.

About the Speaker

Alminas Žaldokas is currently an Associate Professor in Finance at the National University of Singapore (NUS). Prior to this appointment, Alminas Žaldokas has been teaching at the Hong Kong University of Science and Technology (HKUST) since 2012 with the primary focus on corporate finance and corporate valuation. Apart from the undergraduate and MSc courses, he was teaching in the HKUST-NYU MSc in Global Finance, HKUST bilingual EMBA, and Kellogg-HKUST EMBA programmes. He has also previously taught corporate valuation for the MBAs at the University of Texas in Austin McCombs School of Business in 2017/8 academic year.

Professor Žaldokas received his PhD in Finance at INSEAD in 2012. His previous academic degrees include MSc in Finance and Economics from London School of Economics and BSc in Business and Economics from Stockholm School of Economics in Riga.
Professor Žaldokas’s research focuses on the interaction between firm decisions in the financial and in the product markets. In particular, he studies corporate finance decisions that relate to the firm investment in innovation, the formation of collusive arrangements between firms, and the facilitation of ESG practices. This research has been published in top academic journals such as Journal of Financial Economics, Review of Financial Studies, Journal of Accounting Research, Management Science, RAND Journal of Economics, Journal of International Economics, and Journal of Financial Intermediation.

DAO-ISEM-IORA Seminar Series: Sean Zhou
2025-08-29 10:00 - 11:30
Name of Speaker
Sean Zhou

Schedule
29 August 2025, 10am – 11.30am

Venue
HSS 4 – 7 (Hon Sui Sen Memorial Library, level 4 Seminar Room)

Link to Register

https://nus-sg.zoom.us/meeting/register/HB2NQ5ZjRpuRwFiBsxDOxg

Title
Learning and Pricing for Consumer Electronics Trade-in Program

Abstract
We consider a dynamic pricing problem for a two-sided consumer electronics trade-in program, where a firm acquires and re-sells multiple types of pre-owned (used) products over a finite selling horizon. There are customers trading in their used products for new products at discounted prices and customers buying refurbished products. The firm sets trade-in prices and resale prices to maximize its total expected profit. We first discuss the scenario that the firm knows the choice models of customers. Due to the high-dimensional state space, deriving the optimal policy using dynamic programming is computationally intractable. To circumvent this, we develop simple and provably effective heuristic policies based on the solution to a deterministic upper-bound problem. We design a dynamic policy called the Batched-Adjustment Control (BAC) policy, under which the selling horizon is divided into different consecutive and disjoint batches for different products and the prices in one batch are updated based on the realized uncertainties in the previous batch. The profit loss of BAC relative to the optimal one is in the order of . When the firm does not know the choice model parameters of customers, it has to learn while making pricing decisions over time. We develop an algorithm called Parametric-Batched-Adjustment Control (PBAC), in which the firm first uses Maximum Likelihood Estimation to learn the trade-in and demand models’ parameters, and then adopt a similar pricing policy akin to BAC while using the estimated parameters. With carefully chosen algorithm parameters (e.g., length of exploration phase, batch size), we show that PBAC has a regret in the order of . This is based on joint work with Zhuoluo Zhang (Xiamen University), Murray Lei (Queen’s University), and Wenhao Li (SUFE).

About the Speaker
Sean Zhou is Professor and Chair of Department of Decisions, Operations and Technology, CUHK Business School, and Professor in Department of Systems Engineering and Engineering Management (by courtesy), at The Chinese University of Hong Kong (CUHK). He has held visiting positions at National University of Singapore and University of Toronto. He received his Ph.D. in Operations Research from North Carolina State University. His main research interests are inventory management, pricing, sustainable operations, data-driven supply chain optimization, and operations and marketing interface. He serves as Area Editor (Inventory and Supply Chain Optimization) of OR Letters and Associate Editor of various journals including Naval Research Logistics and Service Science.

DAO-ISEM-IORA Seminar Series: Nur Sunar
2025-09-04 10:00 - 11:30
Name of Speaker
Nur Sunar

Schedule
4 September 2025, 10am – 11.30am

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

Link to Register
https://nus-sg.zoom.us/meeting/register/lZ34DiDOTh6rI4zJehKPIQ

Title
Designing Renewable Power Purchase Agreements: Impact on Green Energy Investment

Abstract
This paper studies a long-term power purchase agreement (PPA) between a firm and a new renewable energy generator. The firm must dynamically satisfy uncertain electricity demand beyond its existing energy sources, while wholesale electricity prices evolve stochastically over time. Upon signing a PPA, a new renewable facility becomes operational, and the firm owns its output for the contract duration. The new facility’s capacity is determined based on PPA terms. The firm dynamically chooses when to initiate the PPA and how much to pay to maximize its expected total discounted benefit. We show that the firm’s optimal timing follows a (time-dependent) threshold policy. Our results offer key insights for policymakers and renewable energy developers. We find that, contrary to common wisdom, reducing investment costs for renewable technologies can lead to smaller renewable capacity, output, and emissions savings when projects are developed under PPAs. This finding calls for caution in applying investment tax credits in such contexts. We also show that total renewable energy generation and emissions savings may decrease with higher site productivity. Therefore, restricting renewable facility development to most productive sites might be counterproductive under PPAs. We establish the robustness of our findings across a broad range of practical scenarios.
(joint work with Zuguang Gao and John R. Birge)

About the Speaker
Nur Sunar is an Associate Professor of Operations and Sarah Graham Kenan Scholar at the Kenan-Flagler Business School of the UNC at Chapel Hill. She received her Ph.D. from Stanford Graduate School of Business with a thesis titled “Management Problems in Energy and Sustainability.” Her current research interest is to study innovative business models, technologies, and policies, with a focus on sustainability, energy, and digital platforms. A key theme of her recent research is doing good with management science.
Dr. Sunar is particularly interested in innovative business models and novel challenges related to renewable energy technologies (e.g., rooftop solar panels, large-scale renewable energy technologies, online solar marketplaces), sustainability practices of companies/organizations (e.g., voluntary carbon offsetting) and smart city technologies (e.g., the Internet of Things, smart meters, electric vehicles, and residential batteries). She is also passionate about innovative business solutions for inclusive health.
For more information, please see https://sites.google.com/view/nur-sunar/home

DAO-ISEM-IORA Seminar Series: Victor Martínez de Albéniz
2025-09-05 10:00 - 11:30
Name of Speaker
Victor Martínez de Albéniz

Schedule
5 September 2025, 10am – 11.30am

Venue
HSS 4 – 7 (Hon Sui Sen Memorial Library, level 4 Seminar Room)

Link to Register

https://nus-sg.zoom.us/meeting/register/kZSaUc7SR_GuEKdg2LC_SA

Title
Digital Nudges at the Van Gogh Museum Increase Engagement, Pace Visitors, and Reduce Congestion

Abstract
Digital nudges have the potential to enrich experiential services, but little is known about how they affect behaviors in the field. From 2022 to 2024, we run field experiments at the Van Gogh Museum, testing the effect of interventions on the multimedia tour on visitor content consumption and movements. We find that providing that providing a highlight selection with a simple information architecture can increase consumption, coverage of the collection, without requiring more visit duration, thereby containing museum fatigue. Furthermore, faster visitor flows reduce congestion, creating a positive externality on others. Thus, well-designed digital nudges can produce more effective visits, that improve both consumer and service provider outcomes.

About the Speaker
Victor Martínez de Albéniz is a Full Professor in the Operations, Information and Technology Department at IESE Business School. He joined IESE in 2004 after earning a PhD from the Operations Research Center at the Massachusetts Institute of Technology (MIT) and an engineering degree from École Polytechnique in France.
His research spans a broad spectrum of Operations Management. He began his career working on supply chain management, optimizing inventory and purchasing systems to combine low costs with flexibility and innovation. He then moved into the retail sector, developing fashion trend forecasts, leveraging big data to respond to demand shocks, and optimizing the in-store customer experience. More recently, he has applied his expertise to improving education systems.
For more information, please see https://blog.iese.edu/martinezdealbeniz/

DAO-ISEM-IORA Seminar Series: Chen Ningyuan
2025-09-17 10:00 - 11:30
Name of Speaker

Chen Ningyuan

Schedule

17 September 2025, 10am – 11.30am

Venue

BIZ1 – 0202

Link to Register
(Via Zoom)

https://nus-sg.zoom.us/meeting/register/n1RflnWjRwyd5RSu7W6rag

Title

Post-Estimation Adjustments in Data-Driven Decision-Making with Applications in Pricing

Abstract

The predict-then-optimize (PTO) framework is a standard approach in data-driven decision-making, where a decision-maker first estimates an unknown parameter from historical data and then uses this estimate to solve an optimization problem. While widely used for its simplicity and modularity, PTO can lead to suboptimal decisions because the estimation step does not account for the structure of the downstream optimization problem. We study a class of problems where the objective function, evaluated at the PTO decision, is asymmetric with respect to estimation errors. This asymmetry causes the expected outcome to be systematically degraded by noise in the parameter estimate, as the penalty for underestimation differs from that of overestimation. To address this, we develop a data-driven post-estimation adjustment that improves decision quality while preserving the practicality and modularity of PTO. We show that when the objective function satisfies a particular curvature condition, based on the ratio of its third and second derivatives, the adjustment simplifies to a closed-form expression. This condition holds for a broad range of pricing problems, including those with linear, log-linear, and power-law demand models. Under this condition, we establish theoretical guarantees that our adjustment uniformly and asymptotically outperforms standard PTO, and we precisely characterize the resulting improvement. Additionally, we extend our framework to multi-parameter optimization settings. Numerical pricing experiments demonstrate that our method consistently improves revenue, particularly in small-sample regimes where estimation uncertainty is most pronounced. This makes our approach especially well-suited for pricing new products or in settings with limited historical price variation.

About the Speaker

Dr. Ningyuan Chen is currently an associate professor at the Department of Management at the University of Toronto, Mississauga and at the Rotman School of Management, University of Toronto. Before joining the University of Toronto, he was an assistant professor at the Hong Kong University of Science and Technology. Prior to that, he was a postdoctoral fellow at the Yale School of Management. He received his Ph.D. from the Industrial Engineering and Operations Research (IEOR) department at Columbia University in 2015. His studies have been published in Management Science, Operations Research, Annals of Statistics, NeurIPS and other journals and proceedings. His research is supported by the UGC of Hong Kong and the Discovery Grants Program of Canada. He is the recipient of the Roger Martin Award for Excellence in Research and the IMI Research Award.

DAO-ISEM-IORA Seminar Series: Vinayak Deshpande
2025-11-06 10:00 - 11:30
Name of Speaker

Vinayak Deshpande

Schedule

6 Nov 2025, 10am – 11.30am
(60 min talk + 30 min Q&A)

Venue

BIZ1 0304

Link to register
(via Zoom)

https://nus-sg.zoom.us/meeting/register/MBGAcVExSZ2vrzd3MnrERg

Title

Data driven research for better Operations decisions

Abstract

The explosion in availability of data has enabled organizations to collect wealth of information for their business operations. In this talk, I will share my experience in using a data-driven approach for improving Operations decisions from two settings: Healthcare and Aviation. I will highlight the opportunities and challenges in a data driven research approach for operational decision making.
In the first half of the talk, I will discuss the challenge of improving the efficiency of surgical procedures which account for approximately 60% of the operating cost of a hospital the United States. Hospitals spend several million dollars annually on instrument sterilization, instrument tray assembly, and instrument repurchase costs. However, in a large majority of hospitals, less than 20%–30% of reusable instruments supplied to a surgery are used on average. We obtained actual surgical instrument usage at a large multispecialty hospital in partnership with OpFlow, a healthcare software company. We formulate a data-driven mathematical optimization model for surgical tray configuration and assignment with the goal of reducing costs of unused instruments, such as sterilization, instrument purchase, and tray assembly costs. Our solution was implemented at the UNC Rex Hospital, and we report on the results of our implementation. This analysis has quantified the value of collecting point-of-usage data to be at least $1.39 million per year from using the model-recommended solution at the hospital.
In the second half of the talk, I will discuss the challenge of flight delays in the aviation sector which impacts airlines’ operating cost including increased expenses for crew, fuel, and maintenance. Propagated delays due to late arriving aircraft contribute to 40% of all flight delays as reported by the Bureau of Transportation Statistics. The aircraft assignment problem is to assign tail numbers on scheduled arriving flights at an airport to scheduled departing flights at the same airport with the objective of minimizing propagated delays. In this paper, we propose a new data-driven approach for the aircraft assignment problem by formulating it as a balanced assignment problem between incoming and outgoing flights flown by the same aircraft type at the major hub airports. We propose a data-driven clustering method to account for factors such as the originating airport, time of day, and aircraft type that affect the primary delay distribution. These empirical cluster-based aircraft assignment costs serve as an input to our stochastic assignment model. These assignment costs are then used to derive the optimal stochastic aircraft assignment for an out-of-sample data set for Delta Airlines at its three largest hub airports. We show that the stochastic assignment derived from the data-driven approach performs 2.31% better than the benchmark FIFO assignment in total propagated delay at these hub airports.

About the Speaker

Professor Deshpande is the Mann Family Distinguished Professor of Operations at the Kenan-Flagler business school at University of North Carolina. He holds a Ph.D. in Operations Management from the Wharton School, University of Pennsylvania. He also holds a M.S. in Operations Research from Columbia University, New York, and a B.Tech. in Mechanical Engineering from I.I.T., Mumbai.

Prof. Deshpande was awarded with the Dantzig Dissertation award for his Ph.D. dissertation for his work with the US Navy and DLA in optimizing the weapon systems spare parts supply chain. He has worked with the US Coast Guard on a series of projects for optimizing the supply chain used for aircraft service parts. His work with the US Coast Guard was selected as a finalist for the Edelman award and he was honored as an Edelman Award Laureate for an outstanding example of management science and operations research practice. His work on airline operations has been honored with the AGIFORS best contribution award by the Airline Operations Research Society AGIFORS. His research using data from Alibaba’s Cainiao network and JD.com on e-commerce logistics received the MSOM data driven research challenge finalist award. His recent work on surgical tray optimization was selected as a finalist for the Innovative Applications of Analytics Award by the INFORMS society.

His research interests are in the area of Supply Chain Management, E-commerce logistics, Service/Spare Parts Management, Inventory Management, Sustainable Operations, and Healthcare Operations. His research has been motivated by contexts from various industry sectors such as defense, aviation, hi-tech, retail, e-commerce, airlines, and healthcare. His research has been published in premier academic journals such as Management Science, Operations Research, POMS, and M&SOM. He recently served as the president of the supply chain college of the Production and Operations Management society.

DAO-ISEM-IORA Seminar Series: Guillaume Roels
2025-11-14 10:00 - 11:30
Name of Speaker

Guillaume Roels

Schedule

14 Nov 2025, 10am – 11.30am
(60 min talk + 30 min Q&A)

Venue

HSS 4-1

Link to register
(via Zoom)

https://nus-sg.zoom.us/meeting/register/SWlclpbGT6eV8q9w_dkijA

Title

You, Me, or We? Co-Productive Principal-Agent Dynamics

Abstract

Problem Definition: Projects are often initiated by a single person – a principal – who then decides whether to execute it on their own (Single Execution) or to partner with someone else – an agent. If an agent is hired, the project execution may be joint (Joint Execution) or undertaken only by the agent (Delegated Execution). How do the resulting co-productive dynamics compare to what would be optimal to do?
Methodology/Results: We consider a co-productive principal-agent model with endogenous team formation. With financial transfers, as is typical in inter-organizational contexts, joint execution happens less frequently than optimally. Moreover, the optimal contract under joint execution turns out to be a 50%-50% equity split under mild conditions. When financial transfers are not allowed, as is typical in intra-organizational contexts, there might be too much joint execution and too little delegation if the agent is very efficient. Overall, the inefficiency created by moral hazard under delegated and joint execution is much less important than that created by the principal’s ability to engage in project hoarding and not form a team.
Managerial Implications: The problem of under-delegation should really be framed as a problem of project hoarding, i.e., principals do not partner enough. Moreover, it is only relevant within – and not between – organizations. When principals partner with an agent, they might delegate too much if agents are inefficient or contribute too much if agents are efficient. Although the principal-agent and team-production literature have essentially focused on setting incentives for effort exertion within a given operating mode, a more critical issue appears to induce principals to form a team (or not), leading to inefficient operating modes.

About the Speaker

Guillaume Roels is the Timken Chaired Professor of Global Technology and Innovation at INSEAD. His research lies on the interface of operational excellence, people-centric operations, and the management of services. Recent work has focused on collaborative dynamics in organizations, the design of service experiences, and customer ownership in service systems. Prior to joining INSEAD, Guillaume was an Associate Professor at the UCLA Anderson School of Management. He received an MS degree in Management Engineering and a DEA in Management from the Catholic University of Louvain, Belgium, and a PhD in Operations Research from MIT.
He is currently serving as the Editor-in-Chief of Service Science, an INFORMS journal was a Department Editor at M&SOM. He also served as the President of the M&SOM Technology, Innovation, and Entrepreneurship (TIE) Specific Interest Group (SIG) and the President of the M&SOM Service Management (SIG). Recent research awards include a finalist position on the 2023 POMS College of Service Operations Management Best Student Paper Competition, a finalist position in the 2023 INFORMS Social Media Analytics Best Student Paper Competition, and a second place in the 2023 INFORMS Service Science Cluster Best Paper Competition.

DAO-ISEM-IORA Seminar Series: Seungki Min
2026-01-30 10:00 - 11:30
Name of Speaker

Seungki Min

Schedule

30 Jan 2026, 10am – 11.30am
(60 min talk + 30 min Q&A)

Venue

BIZ1 0302

Link to register
(via Zoom)

https://nus-sg.zoom.us/meeting/register/psLI6qAmQPyKNZ3DOtqRvw

Title

An Information-Theoretic Analysis of Nonstationary Bandit Learning

Abstract

In many real-world bandit learning problems, the underlying environment evolves over time, requiring decision-makers to continually acquire information and adapt their action selection accordingly. In this talk, I study Bayesian formulations of nonstationary bandit problems, where environmental dynamics are modeled as stochastic processes, and develop an information-theoretic framework for analyzing attainable performance.
Our analysis yields generic regret upper bounds that extend classical results from stationary Bayesian bandits to nonstationary settings. A key insight is that the entropy rate of the optimal action process naturally quantifies the intrinsic difficulty introduced by nonstationarity. I further connect our results to existing frequentist analyses of nonstationary bandits, showing that several well-known regret bounds in the literature can be recovered as special cases within our unified framework.

About the Speaker

Seungki Min is an Assistant Professor of Operations Management at Seoul National University Business School. His research focuses on bandit optimization and reinforcement learning, with an emphasis on principled frameworks for dynamic decision problems arising in business and engineering applications, including online platforms, pricing, and finance. His research has appeared in Operations Research, Management Science, and leading AI/ML conferences such as ICML and NeurIPS. He earned his Ph.D. from Columbia Business School. Prior to academia, he worked in high-frequency trading domain.
