Latest Seminars

Optimal Policies and Heuristics To Match Supply With Demand For Online Retailing
Dr Yun-Fong Lim, Singapore Management University

We consider an online retailer selling multiple products to multiple zones over a single period. The retailer orders the products from a single supplier and stores them at multiple warehouses. At the start of the selling period, the retailer determines the order quantities of the products and their storage quantities at each warehouse subject to its capacity constraint. At the end of the period, after knowing the demands, the retailer determines the retrieval quantities from each warehouse to fulfill the demands. The retailer's objective is to maximize her expected profit. For the single- zone case, we solve the problem optimally. The optimal retrieval policy is a greedy policy. We design a polynomial-time algorithm to determine the optimal storage policy, which preserves a nested property: Among all non-empty warehouses, a smaller-index warehouse contains all the products stored in a larger-index warehouse. The optimal ordering policy is a newsvendor-type policy. The problem becomes intractable analytically if there are multiple zones and we propose an efficient heuristic to solve it. This heuristic involves a non-trivial hybrid approximation of the second- stage expected profit. Numerical experiments using both synthetic data and real data from a major fashion online retailer in Asia suggest that our heuristic outperforms state-of-the-art approaches with significantly less computational time. With flexible fulfillment, our heuristic improves the efficiency by 28% on average compared to a dedicated policy adopted by theretailer.

Date 07.05.2021
Time 10:30 - 11:45 am
Venue Zoom ID: 927 9987 9236 (passcode 632818)

Probabilistic Sequential Shrinking: A Best Arm Identification Algorithm for Stochastic Bandits with Corruptions
Dr Wang-Chi Cheung, National University of Singapore

Motivated by the issues of fraudulent clicks in online recommendation systems and contaminated samples in medical trials, we consider a best arm identification (BAI) problem for stochastic bandits with adversarial corruptions. The goal is to identify the best arm with a fixed number of pulls (which are also known as time steps), in the presence of an adversary who can corrupt the stochastic outcomes of the arms.

We design a novel randomized algorithm, PROBABILISTIC SEQUENTIAL SHRINKING (PSS), which is agnostic to the amount of corruptions. In the absence of corruptions, our proposed algorithm achieves the state-of- the-art performance guarantee. In the presence of corruptions, we construct settings where the state-of- the-art BAI algorithm (Karnin et al. 2013) fails to identify the best arm with probability at least 0.5, whereas PSS identifies the best arm with high probability. En route, we demonstrate the importance of randomized sampling for mitigating the impact of corruptions.

In addition, we identify the amount of corruptions per step (CPS) to be a crucial parameter that characterizes the possibility of BAI. When the CPS is below a certain threshold, PSS identifies the best arm with high probability. Otherwise, the optimality gap of the identified arm degrades gracefully with the CPS, while there is no guarantee on the probability of identifying the best arm. We demonstrate the necessity of such a bifurcation, by showing that BAI is impossible when the CPS is above a certain threshold.

Date 30.04.2021
Time 10:30 - 11:45 am
Venue Zoom ID: 961 8647 4544 (passcode 405397)

The Impact of GDPR on Content Providers
Prof. Alessandro ACQUISTI, Professor, Carnegie Mellon University

Date 28.04.2021
Time 9:00 am - 10:30 am (Hong Kong Time)
Venue Zoom

Cold Start on Online Advertising Platforms: Data-Driven Algorithms and Field Experiments
Dr Philip Renyu Zhang, New York University Shanghai

Cold start describes a commonly recognized challenge in online advertising platforms: With limited data, the machine learning system cannot accurately estimate the click-through rates (CTR) nor the conversion rates (CVR) of new ads and in turn cannot efficiently price these new ads or match them with platform users. Unsuccessful cold start of new ads will prompt advertisers to leave the platform and decrease the thickness of the ad marketplace. To address the cold start issue for online advertising platforms, we build a data-driven optimization model that captures the essential trade-off between short-term revenue and long-term market thickness of advertisement. Based on duality theory and bandit algorithms, we develop the Shadow Bidding with Learning (SBL) algorithm with a provable regret upper bound of O(T^{2/3}K^{1/3}(log T)^{1/3}d^{1/2}), where K is the number of ads and d is the effective dimension of the underlying machine learning oracle for predicting CTR and CVR. Furthermore, our proposed algorithm can be straightforwardly implemented in practice with minimal adjustments to a real online advertising system. To demonstrate the practicality of our cold start algorithm, we collaborate with a large-scale online video sharing platform to implement the algorithm online. In this context, the traditional single-sided experiment would result in substantially biased estimates. Therefore, we conduct a novel two-sided randomized field experiment and devise unbiased estimates to examine the effectiveness of the SBL algorithm. Our experimental results show that the proposed algorithm could substantially increase the cold start success rate by 61.62% while only compromising the short-term revenue by 0.717%. Our new algorithm has also boosted the overall market thickness by 3.13% and the long-term life-time advertising revenue by at least 11.16%. Our study  bridges the gap between the bandit algorithm theory and the ads cold start practice, and highlights the significant value of well-designed cold start algorithms for online advertising platforms.

Date 16.04.2021
Time 10:30 - 11:45 am
Venue Zoom ID: 946 1118 7621 (passcode 677119)

Regret in the Newsvendor Model with Demand and Yield Randomness
Dr Zhi Chen, City University of Hong Kong

We study the fundamental stochastic newsvendor model that considers both demand and yield randomness. Although partial statistical information and empirical data are often accessible, it is usually difficult in practice to describe precisely the joint demand and yield distribution. We combat the issue of distributional ambiguity by taking a data-driven distributionally robust optimization approach. We adopt the minimax regret decision criterion to assess the optimal order quantity that minimizes the worst-case regret across all hedged distributions. Then we present several properties about the minimax regret model, including optimality condition, regret bound, and worst-case distribution, and we show that the optimal order quantity can be determined via an efficient golden section search. Finally, we present numerical comparisons of our data-driven minimax regret model with data-driven models based on Hurwicz decision criteria and with a minimax regret model based on partial statistical information on moments.

Date 09.04.2021
Time 10:30 - 11:45 am
Venue Zoom ID: 925 3222 9116 (passcode 195299)

Teaching Demonstration: Hypothesis Testing for a Single Population Mean
Dr Jason Man-Wai Ho, The Chinese University of Hong Kong

This is a teaching demonstration of a class on the topic of hypothesis testing for a single population mean. The class will start with examples of real-life applications to stimulate the audience’s interest in the topic. Fundamentals of the hypothesis test for a single population mean will be addressed. The class will be concluded with examples of hypothesis testing with both continuous data and dichotomous data.

Date 01.04.2021
Time 1:45 - 2:30 pm
Venue Zoom ID: 966 8479 8837 (Passcode: 596376)

Managing Order-Holding Problems in Online Retailing Platforms
Dr Yan Zhenzhen, Nanyang Technological University, Singapore

The booming of third-party logistics (3PL) changes the cost structure of an online retailer in the order fulfillment process. The online retailer pays a fixed amount of order arrangement fee to the 3PL to outsource the order fulfillment service for each service request. We study the problem of when an online retailer should send the service request. The trade-off is between the order arrangement fee and the order holding cost. We model the problem as a Markov Decision Process (MDP). By reducing the MDP to a sequence of single-dimensional counterparts, we analytically characterize the optimal order-holding policy. To calculate the policy, we apply a consumer sequential choice model to characterize the transition probabilities, which captures the heterogeneity across different orders and admits a personalized order-holding policy. We further get the closed form of the personalized order-holding policy and provide a piecewise linear approximation of the policy. Extensive numerical tests based on the data set from the 2020 MSOM Data-Driven Research Challenge show that (1) The gap of piecewise linear approximation is as small as 1; (2) The proposed policy achieves a considerable cost reduction compared to two benchmarks in the literature, with an average 30.12% and 14.01% cost reduction for enterprise users in all instances compared with two other widely used policies in the literature, respectively.

Date 26.03.2021
Time 10:30 - 11:45 am
Venue Zoom ID: 988 5854 1364 (passcode 568868)

Food Delivery Service and Restaurant: Friend or Foe?
Dr Jianfu Wang, Jeff, The City University of Hong Kong

With food delivery services, customers can hire delivery workers to pick up food on their behalf. To investigate the long-term impact of food delivery services on the restaurant industry, we model a restaurant serving food to customers as a stylized single-server queue with two streams of customers. One stream consists of tech-savvy customers who have access to a food delivery service platform. The other stream consists of traditional customers who are not able to use a food delivery service and only walk in by themselves. We study a Stackelberg game, in which the restaurant first sets the food price; the food delivery platform then sets the delivery fee; and, last, rational customers decide whether to walk in, balk, or use a food delivery service if they have access to one. We show that the food delivery platform does not necessarily increase demand for the restaurant but may just change the composition of customers, as the segment of tech-savvy customers grows. Hence, paying the platform for bringing in customers may hurt the restaurant's profitability. We demonstrate that a one-way revenue-sharing contract with a price ceiling or a two-way revenue-sharing contract can coordinate the system and create a win-win. Furthermore, under conditions of no coordination between the restaurant and the platform, we show, somewhat surprisingly, that more customers having access to a food delivery service may hurt the platform itself and the society, when the food delivery service is sufficiently convenient and the pool of delivery workers is large enough. This is because the restaurant can become a delivery-only kitchen and raise its food price by focusing on food-delivery customers only, leaving little surplus to the platform. This implies that limiting the number of delivery workers can provide a simple yet effective means for the platform to improve its own profit while benefiting the social welfare.

Date 19.03.2021
Time 10:30 - 11:45 am
Venue Zoom ID: 936 7156 0391 (password 696939)

Healthcare across Boundaries: Urban-Rural Differences in the Financial and Healthcare Consequences of Telehealth Adoption
Prof. Gordon BURTCH, The University of Minnesota

Date 17.03.2021
Time 9:00 am - 10:30 am (Hong Kong Time)
Venue Zoom

Targeting Pre-Roll Ads using Video Analytics
Prof. Gene Moo LEE, UBC Sauder School of Business, University of British Columbia

Date 17.02.2021
Time 10:00 am - 11:30 am (Hong Kong Time)
Venue Zoom

Learning from Crowdsourced Multi-Labeling – A Variational Bayesian Approach
Prof. Junming YIN, University of Arizona

Date 02.02.2021
Time 11:00 am - 12:15 pm (Hong Kong Time)
Venue Zoom

Longitudinal Google Trends: Data Creation and Applications
Dr Taeyong Park

Google search indices can be useful for measuring time-varying cross-regional public interests for which survey data are extremely rare. However, there is a practical difficulty with generating longitudinal Google Trends. Google Trends provides normalized counts from zero to 100 instead of absolute counts, thereby placing its cross-sectional indices across different times on different scales. Thus, merely pooling cross- sectional data fails to create desirable longitudinal data. To resolve this problem, we develop a method for rescaling Google Trends indices to build longitudinal data. We illustrate this method with applications to the issues of employment and the coronavirus. This new tool opens the door to using Google searches merged with various kinds of time-series cross-sectional data, which has not been possible.

Date 28.01.2021
Time 2:00 - 3:00 pm
Venue Online via Zoom

Fool Me Twice, Shame on Me: Structural Balance Theory Based Deep Learning Model for Identifying False Information
Mr. Kyuhan LEE, University of Arizona

Date 28.12.2020
Time 9:00 am - 10:30 am (Hong Kong Time)
Venue Zoom

Gift Contagion in Online Groups: Evidence from WeChat Red Packets
Mr. Yuan YUAN, Massachusetts Institute of Technology

Date 09.12.2020
Time 9:00 am - 10:30 am (Hong Kong Time)
Venue Zoom

Supply Diversification under Random Yield: The Impact of Price Postponement
Dr Guang Xiao, The Hong Kong Polytechnic University

Supply diversification and price postponement are two common mechanisms for dealing with supply yield uncertainty. In this talk, we investigate the interaction between the two aforementioned strategies and provide insights on how to effectively integrate them in combating supply yield risk. Specifically, we study a firm's pricing and sourcing decisions under supply yield uncertainty, and compare them under two distinct pricing schemes to investigate the impact of price postponement: (1) ex ante pricing - the firm simultaneously makes the sales price and sourcing decisions before production takes place; (2) responsive pricing - the pricing decision is postponed until after the yield realization. We find that the effect of price postponement on the optimal sourcing decision varies. With one unreliable supplier, responsive pricing mitigates the overage and the underage risks imposed by yield uncertainty, and results in a lower [higher] optimal order quantity than that under ex ante pricing when the procurement cost is low [high]. With two unreliable suppliers, when the sole- sourced supplier's reliability is low [high], responsive pricing promotes [discourages] supply diversification; when the sole-sourced supplier's reliability is moderate, responsive pricing promotes [discourages] supply diversification when its unit procurement cost is low [high]. The composition of supply portfolio also has a fundamental impact on such strategic interaction: When the supply portfolio consists of one unreliable and one reliable supplier, diversified sourcing is never optimal under ex ante pricing, but may be optimal under responsive pricing. Finally, we conclude by comparing our results with those obtained under random capacity model and discussing several related extensions to provide additional insights in mitigating supply yield risk.

Date 04.12.2020
Time 10:30 am - 11:45 am
Venue Online via Zoom