IORA - Institute of Operations Research and Analytics

DAO-IORA Seminar Series - Chun So Yeon
2023-05-12 10:00 - 11:30 (Asia/Singapore)
So Yeon Chun is an Associate Professor of Technology & Operations Management at INSEAD. So Yeon’s data-driven research focuses on the interface between operations and marketing in consumer loyalty reward programs (consumer choices, point currencies and monetization, customer lifetime values, and consumer behavior experiments), revenue management (pricing and forecasting), and risk management (risk measures, portfolio optimization) with applications in industries such as retail, transportation, finance, and hospitality. So Yeon holds a PhD in Operations Research and an MS in Applied Statistics from the School of Industrial and Systems Engineering at the Georgia Institute of Technology.

Name of Speaker
Chun So Yeon

Schedule
Friday 12 May 2023, 10.00am – 11.30am

Venue
BIZ1-0202

Link to Register
https://nus-sg.zoom.us/meeting/register/tZ0vdO-sqDwvHtaug9Pj_1n9e035FOi3xDX-

Title
Point vs. Money: Monetization of Loyalty Currency and Consumer Payment Choice Behavior

Abstract
In recent years, companies have made various changes to the design and operational management of their loyalty programs to further monetize their virtual currency, or points, by making them more like money. However, it remains unclear whether consumers treat points as they treat money when deciding whether to use them to pay for a purchase. In this talk, we aim to address this question and explore how and why consumers spend loyalty points differently than money. In the first part of the talk, we investigate the important factors that influence consumers’ decisions to pay with either points or money, and we examine how a co-branded credit card partnership affects consumers’ attitudes toward point currency. We develop a model of consumers’ payment decisions and estimate it using proprietary transaction data from a major US airline company through a hierarchical Bayesian framework. In the second part of the talk, we present a series of behavioral experiments that focus on the design and operation of the exchange rate between points and money. We examine the behavioral bias that consumers exhibit toward point currency and explore whether the effects of the exchange rate design are unique to loyalty currencies or more generally apply to other foreign currencies.

DAO-IORA Seminar Series – Javad Nasiry
2023-04-28 10:00 - 11:30
Javad Nasiry is a professor of Operations Management at McGill’s Desautels Faculty of Management where he joined in 2019. He is the director of Sustainable Growth Initiative (SGI) which is a cross-faculty initiative to mobilize the talent and expertise within McGill University to help businesses move towards more socially and environmentally sustainable business models.
His main research interests are in behavioural operations, supply chain management, sustainability, retail operations, and operations-marketing interface. His research in sustainable operations focuses on the environmental consequences of new business models in apparel, renewable energy, and agriculture industries.
Prior to joining McGill, he was an associate professor of operations management in the School of Business and Management at the Hong Kong University of Science and Technology (HKUST) where he joined in 2010.

Name of Speaker
Javad Nasiry

Schedule
Friday 28 April 2023, 10.00am – 11.30am

Venue
I4-01-03 (Innovation 4.0, level 1 Seminar Room)

Link to Register
https://nus-sg.zoom.us/meeting/register/tZEsfuihqDgqHNL63FOwX-SW-XU0E_mfDTdE

Title
Sustainability in the fast fashion industry

Abstract
We establish a much-needed link between the fast fashion business model and its environmental consequences. A fast fashion system allows firms to react quickly to changing consumer demand by replenishing inventory (via quick response) and introducing more fashion styles. We study the environmental impact of the fast fashion business model by analyzing its implications for product quality, variety, and inventory decisions. We find that a key driver of low product quality in the fast fashion industry is the firm’s incentive to offer variety to hedge against uncertain fashion trends. When variety is endogenous, quality decreases as consumers become more sensitive to fashion or as the cost of introducing new styles decreases.
We assess the effectiveness of three environmental initiatives (waste disposal regulations, consumer education, and production tax schemes) in countering the environmental impact of fast fashion.

DAO-IORA Seminar Series – Philip Zhang
2023-03-31 10:00 - 11:30
Renyu (Philip) Zhang has been an Associate Professor (with tenure) at the Department of Decision Sciences and Managerial Economics, The Chinese University of Hong Kong Business School since September 2022. He is also an economist and Tech Lead at Kwai, one of the world’s largest online video-sharing and live-streaming platforms. Philip’s recent research focuses on developing data science methodologies (e.g., data-driven optimization, causal inference, and machine learning) to evaluate and optimize the operations strategies in the contexts of online platforms and marketplaces, sharing economy, and social networks, especially their recommendation, advertising, pricing, and matching policies. His research works have appeared in top business journals such as Management Science, Operations Research, and Manufacturing & Service Operations Management, and have been recognized by various research awards of the INFORMS and POMS communities. His research projects have been funded by various funding agencies including HK RGC, NSFC, SMEC, and STCSM. Philip serves as a Senior Editor for Production and Operations Management, and an Associate Editor for Naval Research Logistics. He has also developed data science and economics frameworks to evaluate and optimize the user growth strategy and the platform ecosystem of Kwai. Prior to joining CUHK, Philip was an Assistant Professor of Operations Management at New York University Shanghai between 2016 and 2022. Please visit Philip’s personal website for more about him: https://rphilipzhang.github.io/rphilipzhang/

Name of Speaker
Zhang Renyu, Philip

Schedule
Friday 31 March 2023, 10.00am – 11.30am

Venue
I4-01-03 (Innovation 4.0, level 1 Seminar Room)

Link to Register
https://nus-sg.zoom.us/meeting/register/tZEtduirqT4iG9L-B7cNDO-sjXX8YFw9Csoz

Title
Deep Learning Based Causal Inference for Large-Scale Combinatorial Experiments: Theory and Empirical Evidence

Abstract
Large-scale online platforms launch hundreds of randomized experiments (a.k.a. A/B tests) every day to iterate their operations and marketing strategies, while the combinations of these treatments are typically not exhaustively tested. It triggers an important question of both academic and practical interests: Without observing the outcomes of all treatment combinations, how to estimate the causal effect of any treatment combination and identify the optimal treatment combination? We develop a novel framework combining deep learning and doubly robust estimation to estimate the causal effect of any treatment combination for each user on the platform when observing only a small subset of treatment combinations. Our proposed framework (called debiased deep learning, DeDL) exploits Neyman orthogonality and combines interpretable and flexible structural layers in deep learning. We prove theoretically that this framework yields efficient, consistent, and asymptotically normal estimators under mild assumptions, thus allowing for identifying the best treatment combination when only observing a few combinations. To empirically validate our method, we then collaborate with a large-scale video-sharing platform and implement our framework for three experiments involving three treatments where each combination of treatments is tested. When only observing a subset of treatment combinations, our DeDL approach significantly outperforms other benchmarks to accurately estimate and infer the average treatment effect (ATE) of any treatment combination, and to identify the optimal treatment combination.

DAO-IORA Seminar Series – Saed Alizamir
2023-03-23 10:00 - 11:30
Saed Alizamir is an Associate Professor of Operations Management at Yale School of Management. He joined Yale in 2013 after receiving a PhD in Decision Sciences from Duke University’s Fuqua School of Business. Professor Alizamir’s research interests lies in the area of social responsibility and public sector operations. In his research, he focuses on problems in public policy that involve private-public interactions and dynamic decision-making. The goal of his research is to provide normative recommendations that inform better policy decisions, especially in areas where not enough data exists to run full-fledged empirical studies. He has worked on government subsidy instruments in renewable energy industry and electric vehicle markets, agricultural supply chains, demand management in residential electricity sector, and optimal control of diagnostic systems such as nurse triage. In 2021, Professor Alizamir was named as one of the World’s Best 40 Under 40 Business School Professors by Poets & Quants. He serves as an Associate Editor for the Operations Research journal, and co-chaired the M&SOM cluster for the INFORMS conference in 2019. At Yale, he teaches MBA level courses in core Operations, Managing Sustainable Operations, and Quantitative Decision Models.

Name of Speaker
Saed Alizamir

Schedule
Thursday 23 March 2023, 10am – 11.30am

Venue
I4-01-03 Seminar Room

Registration Link
https://nus-sg.zoom.us/meeting/register/tZMkfuuurTwjGdfG2nYqcDCFI8vVGYD-qMR0

Title
Search Delegation Policies for Compliance Enforcement

Abstract

Enforcement of regulatory compliance over time often involves intermittent search in the form of inspection in order to reveal the compliance state of the regulated entity. To enable cost-effective enforcement of environmental compliance standards, regulatory agencies encourage production firms to voluntarily discover and correct compliance violations. Although such self-regulation activities often bring desired benefits, they create nontrivial challenges. To study this tradeoff, we develop a model that captures the interactions between a regulator and a firm that unfold over time. Because constant monitoring is prohibitive, the regulator and the firm perform costly inspections to discover the compliance state of production. If the regulator detects noncompliance, the firm is required to pay penalty and restore compliance. To avoid penalty, the firm performs self-inspections to preemptively detect noncompliance and restore compliance without reporting the action to the regulator. We show that inefficiency caused by the firm’s private action is amplified if the regulator adopts a policy of requiring permanent restoration. Under such a policy, the firm’s self-inspections may leave the regulator and the environment worse off. By contrast, self-inspections always bring a net benefit to the regulator if repeated temporary restorations are allowed. We also find that, due to self-inspections, a paradoxical situation arises where the regulator prefers mandating permanent restoration despite having a small chance of enforcing it.

DAO-IORA Seminar Series – Karthyek Murthy
2023-03-17 10:00 - 11:30
Karthyek Murthy serves as an Assistant Professor in Singapore University of Technology & Design. His research interests lie in data-driven operations research. Prior to joining SUTD, he was a postdoctoral researcher in Columbia University IEOR department. His research has been recognised with 2021 INFORMS Junior Faculty Forum (JFIG) Paper competition (Third place), 2019 WSC Best Paper Award, and IBM PhD fellowship. Karthyek serves as an Associate Editor for the INFORMS journal Stochastic Systems and as a judge for the INFORMS Nicholson student paper competition.

Name of Speaker
Karthyek Murthy

Schedule
Friday 17 March 2023, 10.00am – 11.30am

Venue
I4-01-03 (Innovation 4.0, level 1 Seminar Room)

Link to Register
https://nus-sg.zoom.us/meeting/register/tZ0pd-mrqT8sHdHKwZT0EH5wYD4D-WJxsVNx

Title
Locally robust models for optimization under tail-based data imbalance

Abstract
Many problems in operations and risk management require the familiar “estimate, then optimize” workflow involving a model estimation from data in the first step before plugging in the trained model to solve various optimization tasks. In this talk, we first give the ingredients for constructing locally robust optimization formulations in which the first step model estimation has no effect, locally, on the optimal solutions. Then delving specifically into optimization problems affected by tail-based data imbalance, we show that this local sensitivity translates to improved out-of-sample performance freed from the first-order impact of model errors caused by model selection and misspecification biases that are especially difficult to avoid when performing estimation with imbalanced data. The key ingredient in achieving this local robustness is a novel debiasing procedure that adds a non-parametric bias correction term to the objective. The debiased objective retains convexity, and the imputation of the correction term relies only on a non-restrictive large deviations behavior conducive for transferring knowledge from representative data-rich regions to the datascarce tail regions suffering from imbalance. The bias correction gets determined by the extent of model error in the estimation step and the specifics of the stochastic program in the optimization step, thereby serving as a scalable “smart-correction” step bridging the disparate goals in estimation and optimization. Besides showing the empirical effectiveness of the proposed formulation in real datasets, the numerical experiments bring out the utility of locally robust solutions in tackling model errors and shifts in distribution between training and deployment.

DAO-IORA Seminar Series – Jing Wu
2023-03-03 10:00 - 11:30
Prof. Jing Wu is an Associate Professor in the Department of Decision Sciences and Managerial Economics at the Chinese University of Hong Kong (CUHK) Business School. He receives his Ph.D. (major in operations management, minor in economics & finance) and MBA from the University of Chicago Booth School of Business and his bachelor’s degree in Electronic Engineering from Tsinghua University. Prof. Wu’s primary research fields are the operations-finance interface, global supply chains, FinTech, and business intelligence. His papers are published in leading journals such as Management Science, M&SOM, and POMS. His articles appear in business magazines such as MIT Sloan Management Review, the Economist, and Forbes. In particular, his quantitative research findings on the supply chain impact of COVID-19 and the Trade War have been reported by over 400 media outlets in over 20 countries worldwide. He is a Senior Editor for Production and Operations Management and serves on the Editorial Board for Journal of Operations Management. He has been a committee/track chair for leading international academic conferences such as INFORMS and POMS meetings. Before academia, he worked as a quantitative strategist at Deutsche Bank New York.

Name of Speaker
Jing Wu

Schedule
Friday 3 March 2023, 10.00am – 11.30am

Venue
I4-01-03 (Innovation 4.0, level 1 Seminar Room)

Link to Register
https://nus-sg.zoom.us/meeting/register/tZYkc-yurjsuGtSdKCtZD62yxhugAKzE-6Nh

Title
The Golden Revolving Door: Hedging through Hiring Government Officials

Abstract
Using both the onset of the US-China trade war in 2018 and the most recent Russia-Ukraine conflict and associated trade tensions, we show that government-linked firms increase their importing activity by roughly 33% (t=4.01) following the shock, while non-government linked firms trading to the same countries do the opposite, decreasing activity. These increases appear targeted, in that we see no increase for government-linked supplier firms generally to other countries (even countries in the same regions) at the same time, nor of these same firms in these regions at other times of no tension. In terms of mechanism, government supplier-linked firms are nearly twice as likely to receive tariff exemptions as equivalent firms doing trade in the region who are not also government suppliers. More broadly, these effects are increasing in level of government connection. For example, firms that are geographically closer to the agencies to which they supply increase their imports more acutely. Using micro-level data, we find that government supplying firms that recruit more employees with past government work experience also increase their importing activity more – particularly when the past employee worked in a contracting role. Lastly, we find evidence that this results in sizable accrued benefits in terms of firm-level profitability, market share gains, and outsized stock returns.

IORA Seminar Series – Antoine Désir
2023-02-10 10:00 - 11:30
Antoine is an Assistant professor of Technology and Operations Management at INSEAD. His research applies mathematical modeling and analytics to operations management problems with an aim to: (1) quantify fundamental tradeoffs, and (2) design efficient data-driven algorithms to support operational decisions. More precisely, he focuses on revenue management and choice modeling with applications such as online advertising. He was an MSOM student paper finalist in 2014 and 2017 and a Nicholson student paper finalist in 2014 and 2015. He spent a year as a post-doctoral researcher at Google NYC.

Name of Speaker
Antoine Desir

Schedule
Friday 10 February 2023, 10.00am – 11.30am

Venue
I4-01-03 (Innovation 4.0, level 1 Seminar Room)

Link to Register
(Via Zoom)
https://nus-sg.zoom.us/meeting/register/tZwtdOyoqjIuGtIP444shDdv-KQrbeOQcFl3

Title
Representing Random Utility Choice Models with Neural Networks

Abstract
Motivated by the successes of deep learning, we propose a class of neural network-based discrete choice models, called RUMnets, which is inspired by the random utility maximization (RUM) framework. This model formulates the agents’ random utility function using the sample average approximation (SAA) method. We show that RUMnets sharply approximate the class of RUM discrete choice models: any model derived from random utility maximization has choice probabilities that can be approximated arbitrarily closely by a RUMnet. Reciprocally, any RUMnet is consistent with the RUM principle. We derive an upper bound on the generalization error of RUMnets fitted on choice data, and gain theoretical insights on their ability to predict choices on new, unseen data depending on critical parameters of the dataset and architecture. By leveraging open-source libraries for neural networks, we find that RUMnets outperform other state-of-the-art choice modeling and machine learning methods by a significant margin on two real-world datasets. This is joint work with Ali Aouad (LBS).

IORA Seminar Series - Timothy Chan
2023-02-07 10:00 - 11:30
Timothy Chan is the Associate Vice-President and Vice-Provost, Strategic Initiatives at the University of Toronto, the Canada Research Chair in Novel Optimization and Analytics in Health, a Professor in the department of Mechanical and Industrial Engineering, and a Senior Fellow of Massey College. His primary research interests are in operations research, optimization, and applied machine learning, with applications in healthcare, medicine, sustainability, and sports. He received his B.Sc. in Applied Mathematics from the University of British Columbia (2002), and his Ph.D. in Operations Research from the Massachusetts Institute of Technology (2007). Before coming to Toronto, he was an Associate in the Chicago office of McKinsey and Company (2007-2009), a global management consulting firm. During that time, he advised leading companies in the fields of medical device technology, travel and hospitality, telecommunications, and energy on issues of strategy, organization, technology and operations.

Name of Speaker
Timothy Chan

Schedule
Tuesday 7 February 2023, 10am – 1130am

Venue
BIZ2 4-13A (BIZ 2, level 4 Seminar Room)

Link to Register
(Via Zoom)
https://nus-sg.zoom.us/meeting/register/tZcpdO2tpz8jEt3qhGZYuJ2GhZEuzV8r3skw

Title
Got (optimal) milk?

Abstract
Human donor milk is considered the ideal nutrition for millions of infants that are born preterm each year. Donor milk is collected, processed, and distributed by milk banks. The macronutrient content of donor milk is directly linked to infant brain development and can vary substantially across donations, which is why multiple donations are typically pooled together to create a final product. Approximately half of all milk banks in North America do not have the resources to measure the macronutrient content of donor milk, which means pooling is done heuristically. We propose a data-driven framework combining machine learning and optimization to predict macronutrient content of deposits and then optimally combine them in pools, respectively. In collaboration with our partner milk bank, we collect a data set of milk to train our predictive models. We rigorously simulate milk bank practices to fine-tune our optimization models and evaluate operational scenarios such as changes in donation habits during the COVID-19 pandemic. Finally, we conduct a year-long trial implementation, where we observe the current nurse-led pooling practices followed by our intervention. Pools created by our approach meet clinical macronutrient targets between 31% to 76% more often than the baseline, while taking 67% less recipe creation time. This is the first paper in the broader blending literature that combines machine learning and optimization. We demonstrate that such pipelines are feasible to implement and can yield significant improvements over current practices. Our insights can guide practitioners in any application area seeking to implement machine learning and optimization-based decision-support.

IORA Seminar Series - Ville Satopaa
2023-01-27 10:00 - 11:30
TBA

IORA Seminar Series - Saif Benjaafar
2022-12-13 10:00 - 11:30
Saif Benjaafar is McKnight Presidential Endowed Professor and Distinguished McKnight University Professor at the University of Minnesota. He is Head of the Department of Industrial & Systems Engineering at the University of Minnesota, where he also directs the Initiative on the Sharing Economy. He is a founding member of the Singapore University of Technology and Design where he served as Head of Engineering Systems and Design. He is the Editor in Chief of the INFORMS journal Service Science. He serves on the board of directors of Hourcar, a social car sharing organization. His research is in the area of operations broadly defined, with a current focus on sustainable operations and innovation in business models, including sharing economy, on-demand services, and digital marketplaces. He is a Fellow of INFORMS and IISE.

Venue
BIZ 1 – 0304

Link to Register
(Hybrid session)
https://nus-sg.zoom.us/meeting/register/tZEqdumsqTIqHdynlcuEWYlO-g6cmUD5yg1t

Title
Dimensioning and Pricing of Shared Vehicle Networks

Abstract
In the first part of the talk, we consider the problem of optimal fleet sizing (dimensioning) in an on-demand and one way vehicle sharing system. We leverage a property of closed queueing networks that relates the dynamics of a network with K items to one with K-1 items to obtain explicit and closed form lower and upper bounds on the optimal number of vehicles that are asymptotically exact. We use the bounds to show that buffer capacity (capacity in excess of the nominal load) can be expressed in terms of three explicit terms that can be interpreted as follows: (1) standard buffer capacity that is protection against randomness in demand and rental times, (2) buffer capacity that is protection against vehicle roaming, and (3) a correction term. We show that the capacity needed to buffer against vehicle roaming can be substantial even in systems with vanishingly small demand. In the second part of the talk, give a fixed fleet size, we consider the dynamic pricing in such a network and show that a static pricing policy that arises from solving a maximum flow relaxation of the problem guarantees a performance ratio of order 1- O(1/) where K is the number of vehicles. The approach used, which leverages the same property of closed queueing networks used in the dimensioning problem, is startingly simple and yields performance guarantees that are tighter than those previously obtained in the literature. Time permitting, we will also discuss ongoing work that considers dimensioning and pricing in other settings with spatial queueing network features.

(The talk will draw on material from the following two papers:
https://pubsonline.informs.org/doi/epdf/10.1287/mnsc.2021.3957, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3998297 and
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4130757.

DAO-IORA joint seminar: Ethan X. Fang
2022-12-09 14:00 - 15:00
Ethan X. Fang is an Assistant Professor of Biostatistics & Bioinformatics at Duke Medical School and affiliated with Decision Sciences of Fuqua Business School and Rhodes Information Initiative at Duke University. He works on different data science problems from computational and statistical perspectives. Before joining Duke, he was an assistant professor of Statistics at Penn State. He got his PhD from Princeton University under the direction of Han Liu and Robert Vanderbei, and got his Bachelor’s degree from National University of Singapore under the direction of Kim-Chuan Toh. His works have appeared at top venues in different areas such as statistics, optimization, machine learning, and operations research. He received 2016 Best Paper Prize in Continuous Optimization for Young Researchers (1 paper selected in 3 years).

Venue
BIZ1 – 0204

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

Title
Inference for Ranking Problems

Abstract
We propose a novel combinatorial inference framework to conduct general uncertainty quantification in ranking problems. We consider the widely adopted Bradley-Terry-Luce (BTL) model, where each item is assigned a positive preference score that determines the Bernoulli distributions of pairwise comparisons’ outcomes. Our proposed method aims to infer the general ranking properties of the BTL model. The general ranking properties include the “local” properties such as if an item is preferred over another and the “global” properties such as if an item is among the top K-ranked items. We further generalize our inferential framework to multiple testing problems where we control the false discovery rate (FDR) and apply the method to infer the top-K ranked items. We also derive the information-theoretic lower bound to justify the minimax optimality of the proposed method. We conduct extensive numerical studies using both synthetic and real data sets to back up our theory.

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

IORA Seminar Series - Mika Sumida
2022-12-06 10:00 - 11:30
Mika Sumida is an Assistant Professor of Data Sciences and Operations in the Marshall School of Business at the University of Southern California. Her research focuses on developing efficient, provably good algorithms for revenue management and resource allocation problems, with applications in the sharing economy, online marketplaces, and delivery systems. She holds a Ph.D. in Operations Research and Information Engineering from Cornell University, and a B.A. from Yale University. Prior to her Ph.D., she spent two years working in operations consulting at Analytics Operations Eng., Inc.

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

Title
Revenue Management with Heterogeneous Resources

Venue
BIZ 1 – 0204

Abstract
We study revenue management problems with heterogeneous resources, each with unit capacity. An arriving customer makes a booking request for a particular interval of days in the future. We offer an assortment of resources in response to each booking request. The customer makes a choice within the assortment to use the chosen resource for her desired interval of days. The goal is to find a policy that determines an assortment of resources to offer to each customer to maximize the total expected revenue over a finite selling horizon. The problem has two useful features. First, each resource is unique with unit capacity. Second, each customer uses the chosen resource for a number of consecutive days. We consider static policies that offer each assortment of resources with a fixed probability. We show that we can efficiently perform rollout on any static policy, allowing us to build on any static policy and construct an even better policy. Next, we develop two static policies, each of which is derived from linear and polynomial approximations of the value functions. We give performance guarantees for both policies, so the rollout policies based on these static policies inherit the same guarantee. Lastly, we develop an approach for computing an upper bound on the optimal total expected revenue. Our results for efficient rollout, static policies, and upper bounds all exploit the aforementioned two useful features of our problem. We use our model to manage hotel bookings based on a dataset from a real-world boutique hotel, demonstrating that our rollout approach can provide remarkably good policies and our upper bounds can significantly improve those provided by existing techniques.

IORA Seminar Series - He Wang
2022-11-25 10:00 - 11:30
He Wang is an Assistant Professor and Colonel John B. Day Early Career Professor in the School of Industrial and Systems Engineering at Georgia Tech. His research interests include pricing and revenue management, supply chain, transportation, and machine learning. His works have received 1st place in INFORMS Junior Faculty Interest Group paper competition, Best Paper in Operation Research Award by the MSOM Society, NSF CAREER Award, and faculty research awards from Amazon and Didi.

Venue
Seminar Room at Innovation 4.0 building (Level 1)

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

Title
Constant Regret Re-solving Heuristics for Revenue Management Problems

Abstract
We will discuss a classic network revenue management model of Gallego and van Ryzin (1997), which considers a retailer who sells a product (or multiple products) subject to initial inventory constraints over T consecutive periods. Because the optimal policy via dynamic programming is computationally intractable, researchers have proposed various approximate policies for this problem. We are interested in the so-called “re-solving heuristic,” which periodically solves the fluid approximation model. In the quantity-based revenue management setting with discrete types (joint work with P. Bumpensanti), we find that the re-solving heuristic has a worst-case regret of O(T^{1/2}) compared to the optimal policy, whereas a modified re-solving heuristic can achieve uniformly bounded O(1) regret. In the price-based revenue management setting with continuous price sets (joint work with Yining Wang), we show that the re-solving heuristic attains O(1) regret compared to the value of the optimal policy. This improves the O(lnT) regret upper bound established by Jasin (2014). In addition, we prove that there is an Ω(lnT) gap between the value of the optimal policy and that of the fluid model, implying that the fluid model is not an adequate benchmark for constant regret.

IORA Seminar Series - Long Zhao
2022-11-11 10:00 - 11:30
Long Zhao is an assistant professor in the Department of Analytics & Operations (DAO) at NUS Business School, National University of Singapore. He received his Ph.D. in Decision Sciences from the McCombs School of Business at the University of Texas at Austin. Dr. Zhao’s research interests lie in data-driven decision-making. His research has been the finalist of INFORMS Data mining best paper competition and the finance section best student paper competition.

Venue
Hon Sui Sen Memorial Library – HSS 4-5

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

Title
Constructing Quantiles via Forecast Errors: Theory and Empirical Evidence

Abstract
Probabilistic forecasts (such as quantiles) are essential inputs to decision-making in the face of uncertainty. However, the most common type often comes in the form of point forecasts. As such, it is necessary for the decision maker to construct uncertainty measures around the obtained point forecasts. One simple approach proposed in the literature suggests leveraging historical forecast errors to create quantile estimators around the given point forecast (referred to as the E2Q method). The sample quantile and normal approximation are two popular E2Q estimators. The former relies on the empirical distribution of the forecast errors while the latter treats the underlying distribution as if it were normal. Despite their prevalence, the relative performances of the two estimators remain unknown. In this paper, we find that the performance of a quantile estimator depends on its bias and variance. In particular, higher variance always leads to worse performance. Furthermore, unbiasedness is never optimal for a fixed variance and becomes less and less appealing as variance increases. Thus, as an asymptotically unbiased estimator, the sample quantile is appealing only when its variance is small. We confirm our theoretical findings using the M5 forecast competition data. Since this competition consists of both the “accuracy” (point) and “uncertainty” (quantile) tracks, we also compare the E2Q method with other methods that directly forecast quantiles. We found that the E2Q method using the top point forecasts can outperform the top direct quantile forecasts. This empirical finding suggests that the E2Q method can be a promising alternative to forecasting quantiles directly.

DAO-IORA joint seminar: Zhang Jiheng
2022-11-10 10:00 - 11:30
Jiheng Zhang is the head and a professor in the Department of Industrial Engineering and Decision Analytics of HKUST. He also hold a joint appointment at the Department of Mathematics of HKUST. His research interests are in the areas of Stochastic Modeling and Optimization, Statistical Learning, Numerical Methods and Algorithms; with applications in Operations Management, Large Communication Networks, and Financial Technology. He serves as an associate editor for Operations Research, Stochastic Systems, Probability in the Engineering and Informational Sciences. He has been the co-director of Elliptic lab since 2018, focusing on various practical projects with industry partners including Huawei and Webank. He has invented several patents on large-scale production planning and blockchain consensus mechanism design with industry partners. He received his Ph.D. degree in operations research from the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology in 2009. He also holds an M.S. in mathematics from Ohio State University and a B.S. in mathematics from Nanjing University.

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

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

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

Abstract
Ride-hailing platforms such as Uber, Lyft, and DiDi coordinate supply and demand by matching passen- gers and drivers. The platform has to promptly dispatch drivers when receiving requests, since otherwise passengers may lose patience and abandon the service by switching to alternative transportation methods. However, having less idle drivers results in a possible lengthy pick-up time, which is a waste of system capacity and may cause passengers to cancel the service after they are matched. Due to complex spatial and queueing dynamics, the analysis of the matching decision is challenging. In this paper, we propose a spatial model to approximate the pick-up time based on the number of waiting passengers and idle drivers. We analyze the dynamics of passengers and drivers in a queueing model where the platform can control the matching process by setting a threshold on the expected pick-up time. Applying fluid approximations, we obtain accurate performance evaluations and an elegant optimality condition, based on which we propose a policy that adapts to time-varying demand.

IORA Seminar Series - Ying-Ju Chen
2022-11-04 10:00 - 11:30
Ying-Ju Chen is a Chair Professor at HKUST. Prior to the current position, he was a faculty in the Department of IEOR at UC Berkeley. He obtained a PhD degree in Operations Management from Stern School of Business at New York University in 2007, and he also holds master’s and bachelor’s degrees of Electrical Engineering from National Taiwan University.
He is a recipient of Franklin Prize for Teaching Excellence (MBA non-required/MSc, highest honor at HKUST Business School, 2 winners per year), NYU teaching excellence award, Most Influential Service Operations Paper Award in Production and Operations Management, Harold W. Kuhn Award of Naval Research Logistics, Second place of INFORMS Junior Faculty Interest Group (JFIG) paper competition, Higher Education Outstanding Scientific Research Output Award (Social Science, third prize), Best paper award of CSAMSE (third prize), the Harold MacDowell Award from Stern School, Meritorious Service Awards from Management Science and Manufacturing & Service Operations Management, and other awards and fellowships during his academic journey. He is ranked No. 2 among researchers world-wide by weighted corrected publication rate in Operations Management according to an article in Decision Sciences (2021).
He serves as a department editor for NRL and a senior/associate editor for POM and M&SOM journals. His current research interests lie in network economics, socially responsible operations, operations-marketing interface, and supply chain management. His work has appeared in several leading journals in the fields of economics, electrical engineering, information systems, marketing, and operations research.

Venue
Talk will be held via Zoom

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

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

Abstract
This paper studies a simultaneous-search problem in which a player observes the outcomes sequentially, and must pay reservation fees to maintain eligibility for recalling the earlier offers. We use postgraduate program applications to illustrate the key ingredients of this family of problems. We develop a parsimonious model with two categories of schools: reach schools, which the player feels very happy upon joining, but the chance of getting into one is low; and safety schools, which are a safer choice but not as exciting. The player first decides on the application portfolio, and then the outcomes from the schools applied to arrive randomly over time. We start with the extreme case wherein the safety schools always admit the player. We show that it suffices to focus on the last safety school, which allows us to conveniently represent the player’s value function by a product form of the probability of entering the last safety period and the expected payoff from then on.
We show that the player’s payoff after applications is increasing and discrete concave in the number of safety schools. We also develop a recursive dynamic programming algorithm when admissions to safety schools are no longer guaranteed. We demonstrate instances in which the player applies to more safety schools when either the reservation fee gets higher or the admission probability drops lower, and articulate how these arise from the portfolio optimization consideration. This has strong managerial implications for service providers in devising their reservation fees and admission rates, especially for institutions that are not universally favored by prospective applicants.

IORA Seminar Series - Asa Palley
2022-10-28 10:00 - 11:30
Asa Palley is an Assistant Professor of Operations and Decision Technologies at the Kelley School of Business at Indiana University. He develops and studies methods to gather and aggregate expert opinions for use in managerial making. Secondary interests include learning in sequential decision problems, carbon pricing and investment in renewable generation and storage capacity, and the application of decision analysis to public policy questions. His work has been published in the journals Management Science, Experimental Economics, and Risk Analysis.

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

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

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

Abstract
A combination of point estimates from multiple judges often provides a more accurate aggregate estimate than a point estimate from a single judge, a phenomenon called “the wisdom of crowds”. However, if the judges use shared information when forming their estimates, the simple average will end up over-emphasizing this common component at the expense of the judges’ private information. A decision maker could in theory obtain a more accurate estimate by appropriately combining all information behind the judges’ opinions. Although this information underlies the judges’ individual estimates, it is typically unobservable and thus cannot be directly aggregated by a decision maker. In this article, we propose a weighting of judges’ individual estimates that appropriately combines their collective information within a single estimation problem. Judges are asked to provide both a point estimate of the quantity of interest and a prediction of the average estimate that will be given by all other judges. Predictions of others are then used as part of a criterion to determine weights that are applied to each judge’s estimate to form an aggregate estimate. Our weighting procedure is robust to noise in the judges’ responses and can be expressed in closed form. We use both simulation and data from a collection of experimental studies to illustrate that the weighting procedure outperforms existing methods. An R package called metaggR implements our method and is available on CRAN.

IORA Seminar Series - Kaidi Yang
2022-10-07 10:00 - 11:30
Kaidi Yang is an Assistant Professor in the Department of Civil and Environmental Engineering at the National University of Singapore. He aims to develop efficient and trustworthy algorithms for the design and operation of future mobility systems, with a particular focus on advances in vehicular technology (e.g., connected and automated vehicles, electric vehicles, etc.) and shared mobility. Before joining NUS, he was a postdoctoral scholar with the Autonomous Systems Lab at Stanford University. He received his Ph.D. from ETH Zurich in 2019, M.Sc. in Control Science and Engineering from Tsinghua University in 2014, and dual bachelor’s degrees in Automation and Mathematics from Tsinghua University in 2011.

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

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

Title
Operation of Traditional and Autonomous Mobility-on-Demand

Abstract
The past decade has witnessed the widespread deployment of Mobility-on-Demand (MoD) services, such as the ride-hailing services provided by Uber and Grab. One key operational challenge associated with MoD services is the vehicle imbalances due to asymmetric transportation demand: vehicles tend to accumulate in some regions while becoming depleted in others, giving rise to inefficient operations of the MoD system. We aim to employ emerging automated vehicles (AVs) to improve the operation of MoD systems, leveraging their capability of being globally coordinated. In the first part of the talk, we consider the transition period of AV deployment, whereby an MoD system operates a mixed fleet of AVs and human-driven vehicles (HVs). In such systems, AVs are centrally coordinated by the operator, and the HVs might strategically respond to the coordination of AVs. We model such a system using a Stackelberg framework where the MoD operator serves as the leader and human-driven vehicles serve as the followers. We further develop a real-time coordination algorithm for AVs. In the second part of the talk, we propose a set of reinforcement learning (RL)-based algorithms to improve the efficiency of MoD systems operating a fleet of AVs. We demonstrate that graph neural networks enable RL agents to recover behaviour policies significantly more transferable, generalisable, and scalable than policies learned through other approaches. We further improve the generalisability by integrating meta-learning to transfer to unseen scenarios (e.g., different cities).

IORA Seminar Series - Ozlem Ergun
2022-09-30 10:00 - 11:30
Dr. Özlem Ergun is a COE Distinguished Professor and Associate Chair for Graduate Studies in Mechanical and Industrial Engineering at Northeastern University. Dr. Ergun’s research focuses on design and management of large-scale and decentralized networks. She has applied her work on network design, management, and resilience to problems arising in many critical systems including transportation, pharmaceuticals, and healthcare. She has worked with organizations that respond to emergencies and humanitarian crises around the world, including USAID, UNWFP, UNHCR, IFRC, OXFAM America, CARE USA, FEMA, USACE, CDC, AFCEMA, and MedShare International. Recently, Dr. Ergun partnered with the Massachusetts’ Executive Office of Elder Affairs (EOEA) to help match qualified medical professionals to Long Term Care facilities with open positions around the state as part of the state’s response efforts to COVID19. Dr. Ergun also served as a member of the National Academies Committee on Building Adaptable and Resilient Supply Chains after Hurricanes Harvey, Irma, and Maria and the National Academies Committee on Security of America’s Medical Supply Chain. She was the President of INFORMS Section on Public Programs, Service and Needs in 2013. She currently serves as the Area Editor at the Operations Research journal for Policy Modeling and the Public Sector Area and a Department co-Editor at MSOM journal for Environment, Health and Society Department.

Prior to joining Northeastern Dr. Ergun was the Coca-Cola Associate Professor in the School of Industrial and Systems Engineering at Georgia Institute of Technology, where she also co-founded and co-directed the Health and Humanitarian Systems Research Center at the Supply Chain and Logistics Institute. She received a B.S. in Operations Research and Industrial Engineering from Cornell University in 1996 and a Ph.D. in Operations Research from the Massachusetts Institute of Technology in 2001.

Venue
Talk will be held via Zoom

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

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

Abstract
Critical infrastructure systems (CIS) underpin almost every aspect of the modern society by enabling the essential functions through overlaying service networks. After a disruption impacting the CIS, the functionality of the overlaying service networks degrades. Thus, after an extreme event, in order to minimize the negative impact to society, it is crucial to restore the disrupted CIS as soon as possible. In this talk, we focus on disruptions created by natural hazards on transportation CIS and develop methods to efficiently plan the post-disaster response operations.

In the aftermath of a natural disaster, the transportation network is disrupted due to the debris blocking the roads and obstructing the flow of relief aid and search-and-rescue teams between critical facilities and disaster sites. In the first few days following a disaster, in order to deliver aid to those in need, blocked roads must be cleared by pushing the debris to the sides. In this context, we define the road network recovery problem (RNRP) as finding a schedule to clear the roads with limited resources such that all the service demanding locations are served in the shortest possible time. First, we address the deterministic RNRP and propose a novel network science inspired measure to quantify the criticality of the components within a disrupted service network and develop a restoration heuristic. Next, we consider RNRP with stochastic demand and propose an approximate dynamic programming approach for identifying an effective policy under uncertainty.

IORA Seminar Series - Phebe Vayanos
2022-09-16 10:00 - 11:30
Phebe Vayanos is an Assistant Professor of Industrial & Systems Engineering and Computer Science at the University of Southern California. She is also an Associate Director of CAIS, the Center for Artificial Intelligence in Society at USC. Her research is focused on Operations Research and Artificial Intelligence and in particular on optimization and machine learning. Her work is motivated by problems that are important for social good, such as those arising in public housing allocation, public health, and biodiversity conservation. Prior to joining USC, she was lecturer in the Operations Research and Statistics Group at the MIT Sloan School of Management, and a postdoctoral research associate in the Operations Research Center at MIT. She holds a PhD degree in Operations Research and an MEng degree in Electrical & Electronic Engineering, both from Imperial College London. She serves as a member of the ad hoc INFORMS AI Strategy Advisory Committee, she is an elected member of the Committee on Stochastic Programming (COSP), and the VP of Communications for the INFORMS Section on Public Sector Operations Research. She is an Associate Editor for Operations Research Letters and Computational Management Science. She is a recipient of the NSF CAREER award and the INFORMS Diversity, Equity, and Inclusion Ambassador Program Award.

Venue
Talk will be held via Zoom

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

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

Abstract
Motivated by problems in homeless services delivery, suicide prevention, and substance use prevention, we consider the problem of learning optimal interpretable, robust, and fair models in the form of decision-trees to assist with decision-making in socially sensitive, high-stakes settings. We propose new models and algorithms, showcase their flexibility, and theoretical and practical benefits, and demonstrate substantial improvements over the state of the art. This presentation is based on the following papers:
Strong optimal classification trees, S. Aghaei, A. Gómez, P. Vayanos. Under second round of review at Operations Research, January 2021.
Learning optimal fair classification trees, N. Jo, S. Aghaei, J. Benson, A. Gómez, P. Vayanos. Under review for ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2022.
Learning optimal prescriptive trees from observational data, N. Jo, S. Aghaei, A. Gómez, P. Vayanos, Under Review at Management Science, August 2021.
Optimal robust classification trees, N. Justin, S. Aghaei, A. Gómez, P. Vayanos, AAAI Workshop on Adversarial Machine Learning and Beyond, 2022.

IORA Seminar Series - Roland Yap
2022-09-09 10:00 - 11:30
Roland Yap is an associate professor in the School of Computing, National University of Singapore. He obtained his PhD from Monash University, Australia. His research interests are in Artificial Intelligence, Big Data, Constraints, Programming Languages, Security. He is well known for his work on Constraint Logic Programming and the CLP(R) programming language which is one of the first programming languages where constraints are first class. CLP(R) has led to many developments in Constraint Programming (CP) and Logic Programming.

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

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

Title
A Bus Routing Problem with Multiple Stop Preferences and Tradeoffs

Abstract
We present a variant of the School Bus Routing Problem. In this variant, there multiple stop locations per passenger with preferences on the stops. There is a multimodal transportation aspect. There is also a tradeoff between route cost and passenger preferences. For the realistic scenario, a real-time requirement that the route be obtained quickly. This necessitates efficient solution which can give good solutions. We discuss one instance of this problem with real datasets in the Singapore context.

IORA Seminar Series - Andrew Lim
2022-09-02 10:00 - 11:30
Andrew Lim is a Professor in the Department of Analytics and Operations and the Department of Finance at NUS Business School. He is also affiliated with the Institute for Operations Research and Analytics. His research is in the area of stochastic control, optimization under uncertainty, financial engineering, and robust and data driven decision making. From 2002 — 2014, he was on the faculty of the Department of Industrial Engineering and Operations Research at the University of California (Berkeley). He is a past recipient of the National Science Foundation CAREER Award. He serves as an Associate Editor for Operations Research and Management Science, and was previously on the editorial board of the IEEE Transactions on Automatic Control.

Name of Speaker
Professor Andrew Lim

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

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

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

Title
Mechanisms for Coordinating Systems of Decentralized Agents

Abstract
A typical service/operations system is populated by multiple interacting decentralized agents who make decisions that collectively determine system performance. Decentralized agents are hired because they are domain experts, but the aggregate system is usually not efficient if agents optimize in isolation. Coordination is difficult, however, as it requires a decision maker/mechanism designer/principal who can optimize the aggregate system, which is unrealistic when the system is complex and domain experts are needed to control its many parts. We consider these issues in the setting of a service system modeled by a single server queue where the arrival rate is controlled by multiple agents who dynamically sets prices and earn revenue on each arrival, and the service rate by a different agent who is concerned about minimizing service costs. We show that transfer payments between agents can induce decisions that optimize system efficiency even if every agent misspecifies the impact of the other agents in their models. The optimal transfers, however, depend on the “private” models of each agent and can only be directly computed by a “smart” mechanism designer/principal who can optimize the aggregate system. We propose a mechanism for computing the optimal transfers where decentralized agents iteratively share their valuations of shared resources. We show that this algorithm converges to the optimal transfer function at a geometric rate, and provide natural conditions under which it is optimal for agents to report resource valuations truthfully. This algorithm can be implemented without a “smart” mechanism designer/principal, and decentralized agents are not required to share information about their domain of expertise, or even correctly specify the models, actions or number of other agents when optimizing their decisions.

S3 Optimization Day 2022
2022-07-07 09:00 - 17:00
On behalf of the organising committee, it is our pleasure to announce the S3 Optimization Day 2022 workshop jointly organised by National University of Singapore (NUS), The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), Shanghai University of Finance and Economics (SHUFE) and Singapore University of Technology and Design (SUTD).
Prof Ye Yinyu will provide a Keynote address for this Hybrid workshop which will include exciting talks from academics across Shanghai, Shenzhen and Singapore! Everyone is welcome to participate. Please see the event website for further details https://iora.nus.edu.sg/s3-optimization-day-2022/

IORA Seminar Series - Hoi-To Wai
2022-05-30 10:00 - 11:30
Hoi-To Wai received his PhD degree from Arizona State University (ASU) in Electrical Engineering in Fall 2017, B. Eng. (with First Class Honor) and M. Phil. degrees in Electronic Engineering from The Chinese University of Hong Kong (CUHK) in 2010 and 2012, respectively. He is an Assistant Professor in the Department of Systems Engineering & Engineering Management at CUHK. He has held research positions at ASU, UC Davis, Telecom ParisTech, Ecole Polytechnique, LIDS, MIT. Hoi-To’s research interests are in the broad area of signal processing, machine learning and distributed optimization with applications to network science. His dissertation has received the 2017’s Dean’s Dissertation Award from the Ira A. Fulton Schools of Engineering of ASU, and he is a recipient of a Best Student Paper Award at ICASSP 2018.

Name of Speaker
Hoi-To Wai

Schedule
30 May 2022, 10am – 11.30am

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

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

Title of Talk
Stochastic Approximation Schemes with Decision Dependent Data

Abstract
Stochastic approximation (SA) is a key method which forms the backbone of many online algorithms relying on streaming data with applications to reinforcement and statistical learning. This talk considers a setting in which the streaming data is not i.i.d., but is correlated and decision dependent. First, we analyze a general SA scheme that indirectly minimizes a smooth but possibly non-convex objective function. We consider an update procedure whose drift term depends on a decision dependent Markov chain and the mean field is not necessarily a gradient map, leading to asymptotic bias for the one-step updates. We analyze the expected non-asymptotic convergence rate for such general scheme and llustrate this setting with the policy-gradient method for average reward maximization. Second, we consider extensions of the SA scheme and its analysis. For bi-level optimization via two timescale SA, we present the non-asymptotic complexity analysis and demonstrate an application to natural actor-critic. For performative prediction with stateful users, we illustrate that the SGD algorithm in strategical classification can be interpreted as an SA scheme with decision dependent data, and we present recent results on its expected convergence rate towards a performative stable solution.

IORA Seminar Series – Basak Kalkanci
2022-05-06 10:00 - 11:30
Basak Kalkanci is an associate professor of operations management at the Scheller College of Business at Georgia Tech. Her research focuses on socially and environmentally responsible supply chain management, and contracting and the role of information in decentralized supply chains. Her research aims to lay the necessary groundwork to enable real-time measurement and management of environmental and social impacts in global supply chains. She earned her Ph.D in Management Science and Engineering from Stanford University and was a postdoctoral associate at the Massachusetts Institute of Technology prior to joining Georgia Tech. Her work appeared in premier journals including Management Science, Operations Research, Manufacturing & Service Operations Management, and Production and Operations Management, and has been funded by the National Science Foundation. She is the recipient of the Paul Kleindorfer Award in Sustainability (2020), Alliance for Research on Corporate Sustainability Emerging Sustainability Scholar Award (2019), Georgia Power Professor of Excellence (2015), Management Science Meritorious Service Award (2015, 2017, 2019, 2020), and M&SOM Meritorious Service Award (2014). She serves as a Senior Editor for the Production and Operations Management Journal and as an Associate Editor for M&SOM.

Name of speaker
Basak Kalkanci

Schedule
6 May 2022, 10am – 11.30am

Link to register

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

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

Abstract
Amid growing calls for transparency and social and environmental responsibility, companies are employing different strategies to improve consumer perceptions of their brands. Some pursue internal initiatives that reduce their negative social or environmental impacts through responsible operations practices (such as paying a living wage to workers or engaging in environmentally sustainable manufacturing). Others pursue external responsibility initiatives (such as philanthropy or cause-related marketing). Through two experiments conducted in the field and complementary online experiments, we compare how transparency into these internal and external initiatives affects customer perceptions and sales. We find that transparency into both internal and external responsibility initiatives tends to dominate generic brand marketing in motivating consumer purchases, supporting the view that consumers take companies’ responsibility efforts into account in their decision making. Furthermore, the results provide converging evidence that transparency into a company’s internal responsibility practices can be at least as motivating of consumer sales as transparency into its external responsibility initiatives, incrementally increasing a consumer’s probability of purchase by 6.40% and 45.85% across our two field experiments, conducted in social and environmental domains, respectively. Our results suggest that it may be in the interest of both business and society for managers to prioritize internal responsible operations initiatives to achieve both top- and bottom-line benefits while mitigating social and environmental harms.

IORA Seminar Series - Chen Ying
2022-04-22 10:00 - 11:30
Dr. Ying Chen is a financial statistician and data scientist. She develops statistical modelling and machine learning methods customized for nonstationary, high frequency and large dimensional complex data such as cryptocurrency, limit order book, and renewable energy. She also works on business intelligence, forecasting, text mining and sentiment analysis, and network analysis. Dr. Chen is Associate Professor in Department of Mathematics and Joint Appointee in Risk Management Institute, National University of Singapore. She also holds Courtesy Appointment in Econ and DSDS.
Webpage: https://blog.nus.edu.sg/matcheny/

Name of speaker
Chen Ying

Schedule
22 April 2022, 10am – 11.30am

Link to register

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

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

Abstract
We propose a mixed frequency spatial VAR (MF-SVAR) modeling framework to measure the effectiveness of policies conditional on the spillover and diffusion effects of the global pandemic and unemployment. We study the effects of two aspects of policy effectiveness, namely policy start date and policy timeliness, from a spatio-temporal perspective. The spatial panel data contain weekly new case growth rates and monthly unemployment rate changes for 68 countries across six continents at mixed frequencies from January 2020 to August 2021. We find that government policies have a significant impact on the growth of new cases, but only a marginal effect on the change in unemployment rates. A policy’s start date is critical for its effectiveness. In terms of both immediate impact on the near term and total impact over the following four weeks, starting a policy in the 4th week of a month is most effective at reducing the growth of new cases. At the same time, starting in the 2nd or 3rd week is counterproductive for a one-time policy start date. In addition, our estimates suggest that the spillover and diffusion effects are much stronger than a country’s temporal effect during a global pandemic, both for new case growth and changes in unemployment. We also find that new case growth influences changes in unemployment, but not vice versa. Counterfactual experiments provide further evidence of policy effectiveness in various scenarios and also reveal the main risk-vulnerable and risk-spillover countries. This is a joint work with Xiaoyi Han, Yanli Zhu and Yijiong Zhang. The paper is available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4049509

IORA Seminar Series – Stefanus Jasin
2022-04-08 10:00 - 11:30
Stefanus Jasin is an Associate Professor of Technology and Operations at Stephen M. Ross Business School, University of Michigan. His research focuses on developing tools/algorithms for predictive and prescriptive analytics, with recent applications in pricing and revenue management, assortment optimization, supply chain management, e-commerce/omni-channel logistics, and online learning. He is currently serving as the Department Editor for Revenue Management and Market Analytics at POMS. He also serves as an Associate Editor at Management Science, Operations Research, Manufacturing and Service Operations Management, and Naval Research Logistics.

Name of speaker
Stefanus Jasin

Schedule
8 April 2022, 10am – 11.30am

Link to register

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

Title of talk
Revenue Management Meets Inventory Management

Abstract
Historically, Revenue Management (RM) and Inventory Management have developed as two separate fields. Although they sometimes share similar research questions (e.g., pricing), they do not always share either the same focus or methodology. In this talk, I will give an overview of several recent works that focus on applying RM techniques to inventory management problems, all of which are motivated by applications in retail. Given enough time, the plan is to divide the talk into two parts. The first part will focus on a joint inventory and pricing problem with one warehouse and multiple stores, in which the retailer needs to make a one-time decision on the amount of inventory to be placed at the warehouse at the beginning of the selling season, followed by periodic joint replenishment and pricing decisions for each store throughout the season. We study the performance of heuristic controls based on a deterministic/fluid relaxation of the original stochastic problem. Our contributions are two-fold. We first show that simple re-optimization of deterministic/fluid problems may yield a very poor performance by causing a “spiraling down” movement in price trajectory, which in turn yields a “spiraling up” movement in expected lost sales quantity (i.e., lost sales quantity keeps going up as we continue re-optimizing the model). This cautions against a naive use of simple re-optimizations in the joint inventory and pricing setting with lost sales. Second, we propose a better heuristic by combining four ideas: (1) order-up-to control, (2) linear rate adjustment, (3) replenishment batching, and (4) random errors averaging. We show for a particular choice of control parameters that the heuristic is close to optimal when demand is Poisson and the annual market size for each store is large. In the second part of the talk, I will briefly discuss other works along the same theme, including some recent papers that use Lagrangian-based methods and another paper that uses a fluid approximation for a joint pricing and inventory problem with stochastic purchase returns and lost sales. I will discuss some insights on lessons learned when applying RM techniques in inventory-related problems. Overall, these works highlight the potential in adopting RM techniques in solving complex inventory problems.

IORA Seminar Series – Ruoxuan Xiong
2022-04-01 10:00 - 11:30
Ruoxuan Xiong is an assistant professor in the Department of Quantitative Theory and Methods at Emory University. She completed her Ph.D. in Management Science and Engineering from Stanford University in 2020, and was a postdoctoral fellow at the Stanford Graduate School of Business from 2020 to 2021. Her research is at the intersection of econometrics and operations research, focusing on factor modeling, causal inference, and experimental design, and with applications in finance and healthcare. Her work was awarded the Honorable Mention in the 2019 INFORMS George Nicholson Student Paper Competition, and was in the finalists of the 2020 MSOM Student Paper Competition.

Name of speaker
Ruoxuan Xiong

Schedule
1 April 2022, 10am – 11.30am

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

Title of talk
Optimal experimental design for staggered rollouts

Abstract
In this paper, we study the problem of designing experiments that are conducted on a set of units, such as users or groups of users in an online marketplace, for multiple time periods such as weeks or months. These experiments are particularly useful to study the treatments that have causal effects on both current and future outcomes (instantaneous and lagged effects). The design problem involves selecting a treatment time for each unit, before or during the experiment, in order to most precisely estimate the instantaneous and lagged effects, post experimentation. This optimization of the treatment decisions can directly minimize the opportunity cost of the experiment by reducing its sample size requirement. The optimization is an NP-hard integer program for which we provide a near-optimal solution, when the design decisions are performed all at the beginning (fixed-sample-size designs). Next, we study sequential experiments that allow adaptive decisions during the experiments, and also potentially early stop the experiments, further reducing their cost. However, the sequential nature of these experiments complicates both the design phase and the estimation phase. We propose a new algorithm, PGAE, that addresses these challenges by adaptively making treatment decisions, estimating the treatment effects, and drawing valid post-experimentation inference. PGAE combines ideas from Bayesian statistics, dynamic programming, and sample splitting. Using synthetic experiments on real data sets from multiple domains, we demonstrate that our proposed solutios for fixed-sample-size and sequential experiments reduce the opportunity cost of the experiments by over 50% and 70%, respectively, compared to benchmarks.

IORA Seminar Series – Luyi Yang
2022-03-25 10:00 - 11:30
Luyi YANG is an assistant professor in the Operations and Information Technology Management Group at the University of California, Berkeley’s Haas School of Business. His research interests include service operations, business model innovation, digital marketplaces, smart mobility, sustainability, and operations-marketing interface. His research is published or forthcoming in leading journals such as Management Science, Operations Research, and Manufacturing & Service Operations Management and recognized by various research awards such as M&SOM Service Management SIG Best Paper Award, INFORMS Service Science Best Cluster Paper Award, INFORMS Minority Issues Forum Paper Competition, and INFORMS Junior Faculty Interest Group (JFIG) Forum Paper Competition (Honorable Mention). He has taught courses in business analytics, data mining, and operations management. Prior to joining Berkeley Haas, he was an assistant professor of operations management and business analytics at Johns Hopkins University’s Carey Business School. He received his Ph.D. and MBA from the University of Chicago, Booth School of Business, and his BS in Industrial Engineering and BA in English, both from Tsinghua University.

Name of speaker
Luyi Yang

Schedule
25 March 2022, 10am – 11.30am

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

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

Abstract
The “right to repair” (RTR) movement calls for government legislation that requires manufacturers to provide repair information, tools, and parts so that consumers can independently repair their own products with more ease. The initiative has gained global traction in recent years. Repair advocates argue that such legislation would break manufacturers’ monopoly on the repair market and benefit consumers. They further contend that it would reduce the environmental impact by reducing e-waste and new production. Yet, the RTR legislation may also trigger a price response in the product market as manufacturers try to mitigate the profit loss. This paper employs an analytical model to study the pricing, welfare, and environmental implications of RTR. We find that as the RTR legislation continually lowers the independent repair cost, manufacturers may initially cut the new product price and then raise it. This non-monotone price adjustment may further induce a non-monotone change in consumer surplus, social welfare, and environmental impact. Strikingly, the RTR legislation can potentially lead to a “lose-lose-lose” outcome that compromises manufacturer profit, reduces consumer surplus, and increases the environmental impact, despite repair being made easier and more affordable.

IORA Seminar Series – Jiding Zhang
2022-03-18 10:00 - 11:30
Jiding Zhang is an Assistant Professor of Operations Management at NYU Shanghai. Jiding’s primary research interests lie in the field of marketplace analytics. In her recent work, she analyzes the operations and economics of various digital platforms using both data analytic and mathematical modeling tools. She is also interested in developing data-driven methods for analysis of online markets. Jiding obtained her PhD from the Operations, Information and Decisions Department of The Wharton School, under supervision of Professors Senthil Veeraraghavan, Ken Moon and Sergei Savin.

Name of Speaker
Zhang Jiding

Schedule
18 March, 10am – 11.30am

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

Title of Talk
Does Fake News Content Create Echo Chambers?

Abstract
Platforms have recently come under criticism from regulatory agencies, policymakers, and media scholars for the burgeoning influence of unfettered fake news online. There has been debate regarding whether such online false news content creates echo chambers—segments of the market in which false news is exclusively or predominantly consumed. We use a large-scale dataset reporting individual households’ online activity to understand the trends in online news consumption and examine the claim that online news creates echo chambers. We find that the consumption of false news online is widespread, yet despite that, such echo chambers are minimal. Through a structural model we analyze the joint consumption of online news from mainstream sources and from sources producing false content. Using a natural experiment created by a policy change on the largest social media platform, we find that not only are echo chamber effects not pronounced on the aggregate level, the causal effect of consuming more from false news sources is greater countervailing consumption of mainstream news. Naive, operational interventions such as reducing the supply of false news sources may unnecessarily reduce the overall consumption of news from mainstream sources without adequately protecting the small minority most vulnerable to consuming only false news.
