Latest Seminars

Nudging Patient Choice by Messaging
Miss Jiayi Liu, Emory University Goizueta Business School

Patient no-shows for scheduled medical appointments are of great concern for many health care providers. In this paper, we tackle the no-show problem by applying insights from behavioral science. Specifically, we ''nudge'' patients into arriving for their scheduled appointment using text reminders of their upcoming visit. We conduct a field experiment at an outpatient specialty clinic where we add to the standard message, an additional line of text that indicates a potentially long wait for the next available appointment (we call this intervention ''waits framing''). Based on a difference-in-differences estimation strategy, we find that waits framing messaging significantly reduced no-shows by a factor of 28.6%. In addition, we find that patients with greater sensitivity to wait, such as those with urgent conditions and those willing to select unpopular slots, are more responsive to the nudge. Through a laboratory experiment, we uncover the mechanism that underlies the nudge---waits framing serves to trigger loss- aversion pertaining to the individual's position in the queue, thereby increasing the perceived cost of missing an appointment. Through the combination of field and designed lab studies, we provide both external and internal validity to the effects of waits framing, and identify the underlying mechanism and heterogeneity in response. Our results have significant implications for clinical operations. At the study site, the resulting improvement in capacity utilization and patient throughput led to a 5.2% increase in clinic revenue. Our findings contribute to the literature on behavioral queuing by showing that through appropriately framed messages, queue operators can tap into the behavioral biases of individuals in order to engender a desired queuing response such as a reduction in queue abandonment.

Date 16.12.2021
Time 10:00 - 11:15 AM
Venue Zoom ID: 940 1790 0792 (passcode 350615)

Avoiding Fields on Fire: Information Dissemination Policies for Environmentally Safe Crop-Residue Management
Dr Mehdi Farahani, Massachusetts Institute of Technology

Agricultural open burning, i.e., the practice of burning crop residue in harvested fields to prepare land for sowing a new crop, is well-recognized as a significant contributor to CO2 and black-carbon emissions, and long-term climate change. Low-soil-tillage practices using a specific agricultural machine called Happy Seeder, which can sow the new seed without removing the previous crop residue, have emerged as the most effective and profitable alternative to open burning. However, given the limited number of Happy Seeders that the government can supply, and the fact that farmers incur a significant yield loss if they delay sowing the new crop, farmers are often unwilling to wait to be processed by the Happy Seeder and, instead, decide to burn their crop residue. We study how the government can use effective information-disclosure policies in the operation of Happy Seeders to minimize agricultural open burning. A Happy Seeder is assigned to process a group of farms in an arbitrary order. The government knows, but does not necessarily disclose, the Happy Seeder’s schedule at the start of the sowing season. Farmers incur a disutility per unit of time while waiting for the Happy Seeder due to the yield loss as a result of late sowing of the new crop. If the Happy Seeder processes a farm, then the farmer gains a positive utility. At the beginning of each period, each farmer decides whether to burn her crop residue or to wait, given the information provided by the government about the Happy Seeder’s schedule. We propose a class of information-disclosure policies, which we refer to as dilatory policies that provide no information to the farmers about the schedule until a pre-specified period and then reveal the entire schedule. By obtaining the unique symmetric Markov perfect equilibrium under any dilatory policy, we show that the use of an optimal dilatory policy can significantly lower the number of farms burnt compared to that under the full- disclosure and the no-disclosure policies. Using data from the rice-wheat crop system in northwestern India – an area of the world with the highest prevalence of open burning – we conduct a comprehensive case study and demonstrate that the optimal dilatory policy can reduce CO2 and black-carbon emissions by at least 14%.

Date 13.12.2021
Time 10:00 - 11:15 AM
Venue Zoom ID: 915 8229 8109 (passcode 162225)

Deep Reinforcement Learning for Sequential Targeting
Ms. Wen Wang, Carnegie Mellon University

Deep reinforcement learning (DRL) has opened up many unprecedented opportunities in revolutionizing the digital marketing field. In this study, we designed a DRL-based personalized targeting strategy. We show that the strategy is able to address four important challenges in this area. 1) Sequential-decisions: accounting for the dynamic sequential behavior of consumers; 2) Forward-looking: balancing between a firm’s current revenue and future revenues; 3) Earningwhile-learning: maximizing profits while continuously learning through exploration-exploitation; 4) Scalability: coping with a high-dimensional state and policy space. We illustrate the above through a novel design of a DRL-based artificial intelligence (AI) agent. Further, in order to better understand the potential underlying mechanisms, we conducted multiple interpretability analyses to explain the patterns of learned optimal policy at both the individual and population levels. Our findings provide important managerial-relevant and theory-consistent insights. For instance, consecutive price promotions at the beginning can capture price-sensitive consumers’ immediate attention, while carefully spaced non-promotional “cool-down” periods between price promotions can allow consumers to adjust their reference points. Besides, consideration of future revenues is necessary from a long-term horizon, but weighing the future too much can also dampen revenues. In addition, analyses of heterogeneous treatment effects suggest that the optimal promotion sequence pattern highly varies across the consumer engagement stages. Overall, our study results demonstrate DRL’s potential to optimize these strategies’ combination to maximize long-term revenues.

Date 13.12.2021
Time 9:30am - 11:00am (Hong Kong Time)
Venue Zoom ID: 954 7599 5651 (Passcode:246467)

Supply Chain Visibility: Impact and Value of Real-time Resource Allocation
Dr Guodong Lyu, National University of Singapore

In recent years, we have seen a surge of interest in supply chain visibility. Under this paradigm, decision- makers are able to trace the real-time data (e.g., stock level, resource allocation flow) along the entire supply chain so that they can identify the decision-making bottlenecks and take actions more efficiently. Motivated by the Gaze Heuristic, we propose a target-based online planning framework to deal with real-time resource allocation problems in both stationary and nonstationary environments. Leveraging on the Blackwell's Approachability Theorem and Online Convex Optimization tools, we characterize the near-optimal performance guarantee of our online solution in comparison with the offline optimal solution, and explore the properties of different allocation policies.

We use synthetic and real data from various industries, from supply chain planning in manufacturing, to resource deployment in ride-sharing markets, to examine the impact and value of these real-time solutions in practice: (1) we present a new insight into the impact of supply chain visibility on the capacity configuration in the capacity pooling system. Our results show that the pooling system does not need to hold any safety stock to deliver the required demand fulfillment service if real-time allocation with full visibility is utilized, when the number of customers is sufficiently large in the system; (2) we study a real-time ride-matching problem in the ride-sourcing context, with multi-objectives (e.g., service quality, revenue) to be considered. We develop a new technique that can be used to choose the weight adaptively over time, based on real-time tracking of the gaps in attained performance and a set of performance targets. Our results show that the real-time matching policy could potentially contribute to the long-term sustainability and reputation of the ride-sourcing platform by dispatching more orders to drivers with higher service quality, without sacrificing the short-term platform revenue.

Date 10.12.2021
Time 10:00 - 11:15 AM
Venue Zoom ID: 948 3060 9615 (passcode 646201)

Video Game Analytics
Mr Xiao Lei, Columbia University

Video games represent the largest and fastest-growing segment of the entertainment industry, which involves 3 billion gamers and garners $180 billion annually.  Despite its popularity in practice, it has received limited attention from the operations community. Managing product monetization and engagement presents unique challenges due to the characteristics of gaming platforms, where players and the gaming platform have repeated (and endogenously controlled) interactions. In this talk, we describe a body of work that provides the first analytical results for this emerging market. In the first part, we discuss a prevailing selling mechanism in online gaming known as a loot box. A loot box can be viewed as a random bundle of virtual items, whose contents are not revealed until after purchase. We consider how to optimally price and design loot boxes from the perspective of a revenue-maximizing video game company, and provide insights on customer surplus and protection under such selling strategies. In the second part, we consider how to manage player engagement in a game where players are repeatedly matched to compete against one another. Players have different skill levels which affect the outcomes of matches, and the win-loss record influences their willingness to remain engaged. Leveraging optimization and real data, we provide insights on how engagement may increase with optimal matching policies, adding AI bots, and providing a pay-to-win feature.

Date 09.12.2021
Time 10:00 - 11:15 AM
Venue Zoom ID: 937 4443 9854 (passcode 923966)

Empirical and Analytical Approaches to Healthcare Operations
Professor Jayashankar Swaminathan, University of North Carolina, Chapel Hill

Operations management research could provide great insights into healthcare operations. In this seminar, I will describe two of my most recent projects related to healthcare operations. In the first paper (with Rath and Coleman), we study collaborative care model to treat patients who suffer from Diabetes and Depression. In particular, we create a model that facilitates decision support for such collaborative care. In the second paper (with Bhatia), using inpatient discharge data from hospitals in California between 2008-2016, we create a metric for standardization of healthcare services delivered to patients. Leveraging our standardization metric, we examine the impact of a hospital’s  healthcare service standardization on its cost, quality, and variation in both cost and quality of service.

Date 04.12.2021
Time 10:00 am - 12:00 noon
Venue Zoom ID: 986 6220 1525 (passcode 399649)

Agriculture 4.0 and Broader Research Perspectives
Professor Tava Olsen, University of Auckland Business School

Agriculture is changing in many ways. This talk gives an overview of these changes, with a particular focus on agricultural supply chains. Like many supply chains around the world, agricultural supply chains are subject to digital disruption in a variety of interesting ways. I will outline what these disruptions mean for agriculture today and make some projections for the future. New Zealand case studies will be presented. Further, I will also discuss new agricultural technologies and precision agriculture and what they mean for research in this important area. Ideas for future research will be discussed throughout the talk. At the end I will give some broader perspectives on research publishing.

Date 03.12.2021
Time 10:00 am - 12:00 noon
Venue Zoom ID: 977 5724 5354 (passcode 804409)

Supermodularity in Two-Stage Distributionally Robust Optimization
Dr Daniel Zhuoyu Long, The Chinese University of Hong Kong

Many Operations Management problems involve two-stage decision-making and hence are computationally difficult to be solved in general. In this work, we solve a class of two-stage distributionally robust optimization problems which have the property of supermodularity. We exploit the explicit worst-case expectation of supermodular functions and derive the worst-case distribution for the robust counterpart. This enables us to develop an efficient method to obtain an exact optimal solution of these two-stage problems. We also show that the optimal scenario-wise segregated affine decision rule returns the same optimal value in our setting. Further, we provide a necessary and sufficient condition for checking whether any given two-stage optimization problem has the supermodularity property. We apply this framework to several classic problems, including the multi-item newsvendor problem, the facility location design problem, the lot-sizing problem on a network, the appointment scheduling problem and the assemble-to-order problem. While these problems are typically computationally challenging, they can be solved efficiently using our approach.

Date 03.12.2021
Time 2:00 - 2:30 PM
Venue Room G012, LSK Business Building, HKUST

How Does Risk Hedging Impact Operations? Insights from a Price-setting Newsvendor Model
Dr Liao Wang, The Hong Kong University Business School

Financial asset price movement impacts product demand, and thus influences the pricing and production decisions of a firm. We develop and solve a general model that integrates pricing, production, and financial risk hedging decisions for firms of newsvendor type. We find that in general, the presence of hedging reduces the optimal price; it also reduces the optimal service level when the asset price positively impacts the product demand (“asset price benefits demand”), while it may increase the optimal service level by a small margin when the impact is negative (“asset price hurts demand”). We construct the mean-variance efficient frontier that characterizes the risk-return trade-off and quantify the risk reduction achieved by the hedging decision. Our numerical case study using real data of Ford Motor Company shows that the markdowns in pricing and service levels are small under our model, and the hedging decision can substantially reduce risk without materially decreasing operational profit.

Date 03.12.2021
Time 2:30 - 3:00 PM
Venue Room G012, LSK Business Building, HKUST

Dimensioning On-demand Vehicle Sharing Systems
Dr Shining Wu, The Hong Kong Polytechnic University

We consider the problem of optimal fleet sizing in a vehicle sharing system. Vehicles are available for short-term rental and are accessible from multiple locations. The size of the fleet must account not only for the nominal load and for the randomness in demand and rental duration but also for the randomness in the number of vehicles that are available at each location due to vehicle roaming (vehicles not returning to the same location from which they were picked up). We model the system as a closed queueing network and obtain a closed form approximation of the optimal fleet size (the minimum number of vehicles needed to meet a target service level). The approximation is remarkably accurate and highly interpretable with buffer capacity 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. Our analysis reveals important differences between the optimal sizing of standard queueing systems and that of systems where servers roam.

Date 03.12.2021
Time 3:00 - 3:30 PM
Venue Room G012, LSK Business Building, HKUST

Capacity Optimization and Resource Allocation under Service Level Constraints
Dr Shixin Wang, The Chinese University of Hong Kong

Service level requirement is an important measure of service quality in the real-life business. A  big challenge for the companies is to appropriately harness the resources to meet their target service levels for customers. Companies gain competitive advantages by optimizing (1) the capacity level of pooled resources in anticipation of random demand of multiple customers and (2) the capacity allocation to fulfill customer demands after demand realization.

We present a general framework to study this two-stage resource allocation problem when customers require individual and possibly different service levels. Our modeling framework generalizes and unifies  many existing models in the literature. We propose a simple randomized rationing policy for any fixed feasible capacity level. Our main result is the optimality of this Max-Weighted-Service policy for very general service-level constraints, including Type-I and Type-II constraints and beyond. The result follows from a semi- infinite linear programming formulation of the problem and its dual. We also prove the optimality of priority policies for a large class of problems when the set of feasible fulfilled demands is a polymatroid. Moreover, with a slight change in one step of the Max-Weighted-Service policy, it is also optimal when there is differentiated allocation cost from resources to demands. This is based on joint work with Jiashuo Jiang and Jiawei Zhang from NYU stern.

Date 03.12.2021
Time 4:00 - 4:30 PM
Venue Room G012, LSK Business Building, HKUST

Set a Goal for Yourself: Model and a Field Experiment on a Gig Platform
Dr Xing Hu, The Hong Kong University Business School

On-demand service platforms have its gig workers to use self-set nonbinding performance goals to regulate their effort and overcome potential self-control problems. To examine the effect of such self-goal setting mechanisms, we build a behavioral model, derive theoretic results and testable hypotheses, and conduct a field experiment on a large gig platform of food deliveries. The model incorporates the reference-dependent utility theory of goal setting into the two-self framework of self-control. Our model analysis finds that individual workers' optimal self-set goal may exhibit a spectrum of difficulty level, ranging from trivially to impossibly achievable, depending on their reference-dependent utility coefficients and their self-control cost; and that their effort is always higher with a properly set goal than the no-goal benchmark, although the difference is significant only when both the reference-dependent utility coefficients and the self-control cost are sufficiently large. Our experiment data confirms heterogeneous treatment effects: While the average treatment effect is insignificant, a causal tree algorithm identifies a sub-group of population whose effort significantly increases under the goal- setting treatments. Our study compares the two common types of performance metrics for goal setting, the number of completed orders versus the total revenue. Both our model and experiment data suggest that the two types of goals lead to equal effort improvement but different attainment probabilities. In particular, the goal attainment rate is lower in the revenue-goal treatment than the quantity-goal treatment because workers tent to set excessively high revenue goal. Our study demonstrates the efficacy and the limitations of self-goal setting mechanisms, and yields two important managerial implications. First, there exists a reasonably sized population for target marketing of the self-goal setting mechanisms; second, platforms would better encourage the use of order-quantity goals instead of revenue-goals for higher attainment rates.

Date 03.12.2021
Time 4:30 - 5:00 PM
Venue Room G012, LSK Business Building, HKUST

Joint Assortment Optimization and Customization under a Mixture of Multinomial Logit models: On the Value of Personalized Assortments
Professor Huseyin Topaloglu, Cornell University

We consider a joint assortment optimization and customization problem under a mixture of multinomial logit models. In this problem, a firm faces customers of different types, each making a choice within an offered assortment according to the multinomial logit model with different parameters. The problem takes place in two stages. In the first stage, the firm picks an assortment of products to carry subject to a cardinality constraint. In the second stage, a customer of a certain type arrives into the system. Observing the type of the customer, the firm customizes the assortment that it carries by, possibly, dropping products from the assortment. The goal of the firm is to find an assortment to carry and a customized assortment for each customer type that can arrive in the second stage to maximize the expected revenue from a customer visit. The problem arises, for example, in online platforms, where retailers commit to a selection of products before the start of the selling season, but they can potentially customize the displayed assortments for each customer. We give an approximation algorithm that obtains 1/log m fraction of the optimal expected revenue, where m is the number of customer types. Contrasting this problem with the variant where customization is not possible, it is NP-hard to approximate the latter variant within a factor better than 1/m. Thus, from computational complexity perspective, the variant with customization is fundamentally different.

Date 02.12.2021
Time 10:00 am - 12:00 noon
Venue Zoom ID: 974 8479 7912 (passcode 196610)

Patient Sensitivity to Emergency Department Waiting Time Announcements
Dr Eric Park, The Hong Kong University Business School

We study how Emergency Department (ED) patients incorporate announced ED waiting time in their decision of choosing which ED to attend. Using a discrete choice framework, we structurally estimate the patients’ sensitivity to announced ED waiting time and potential travel distance to the ED. We find that approximately 30% of ED patients in Hong Kong are sensitive to the announced waiting time while the remaining 70% are not and their decisions are mainly driven by the distance to the ED only. Patients that are sensitive to the announced waiting time would travel an additional 1 km to save approximately 4 hours of waiting at the ED. We also study patient characteristics that differentiate their sensitivity to ED waiting time.

Date 02.12.2021
Time 2:00 - 2:30 PM
Venue Room G012, LSK Business Building, HKUST

A Model of Credit Refund Policies
Dr Yan Liu, The Hong Kong Polytechnic University

Consumers often receive a full or partial refund for product returns or service cancellations.  Much of the existing literature studies cash refunds, where consumers get their money back minus a fee upon a product return or service cancellation. However, not all refunds are issued in cash. Sometimes consumers receive credits that can be used for future purchases, often times with an expiration date after which the credits are forfeited. A prominent example is the airline industry, where consumers who purchase non-refundable fares are often issued a credit that is valid within a fixed time window (typically a year) upon ticket cancellation. We study the optimal design of credit refund policies. Different from models that consider cash refunds, we explicitly model repeated interactions between the seller and consumers over time. We assume that consumers’ valuation for the product/service varies over time, and that there is an  exogenous probability for product returns. Several interesting results emerge. First, a credit refund policy facilitates intra-consumer price discrimination for a single type of consumers with stochastic valuation. Second, an optimal policy often involves an intermediate credit expiration term, under which a consumer with a high product valuation always makes a purchase, while a consumer with a low product valuation may be induced to make a purchase as the credit approaches expiration, leading to a demand induction effect. Finally, a credit refund policy can be more profitable than a cash refund policy, and can lead to a win-win outcome for both the firm and consumers under certain conditions. We also consider several extensions to check the robustness of our findings.

Date 02.12.2021
Time 2:30 - 3:00 PM
Venue Room G012, LSK Business Building, HKUST