Events & Seminars

Facility location models are ubiquitously involved in modern transportation and logistics problems. We present recent results of two types of facility-location models that involve (i) competition and probabilistic customer choice or (ii) location-dependent uncertain demand with ambiguously known distribution. For (i), we study a Stackelberg game that admits a bilevel mixed-integer nonlinear program (MINLP) formulation, and derive an equivalent, single-level MINLP reformulation and exploit the problem structures to derive valid inequalities, based on submodularity and concave overestimation, respectively. We also study various model extensions by considering general facility setup costs, multiple competitors, as well as other types of decisions for planning facilities. We conduct numerical studies to demonstrate that the exact algorithm significantly accelerates the computation of CFLP on large-sized instances that have not been solved optimally or even heuristically by existing methods. For (ii), we represent moment information of stochastic demand as piecewise linear functions of location decisions, and then develop an exact mixed-integer linear programming reformulation of a decision-dependent distributionally robust optimization model. Our results draw attention to the need of considering various impacts of competition and location choices on customer demand during strategic facility planning. (Papers related to the talk: https://arxiv.org/abs/2103.04259 ; https://www.sciencedirect.com/science/article/abs/pii/S0377221720309449)

Generalized Gamma process is a pure-jump subordinator with infinite activity, it can be used to construct a flexible two-parameter complete random measure, whose application has appeared in various areas. In this talk, we present an exact simulation algorithm to sample from the largest n jumps of a generalized Gamma process. The algorithm immediately implies a method to sample from the celebrated Poisson-Dirichlet distribution, we will illustrate this method with numerical examples. As an application of our algorithm, we review the construction of the Caron-Fox random graph and discuss a potential modification to its simulation algorithm.

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.

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%.

Newcomers are important for the survival of online communities, but their contributions often receive negative reactions and comments from established members. Online communities realize that such negativity can take a toll on newcomers and harm the creation of user­-generated content. We study a novel intervention aimed at reducing hostility toward newcomers: a “newcomer nudge” that informs community members when they are interacting with a newcomer’s post and asks them to be more lenient toward its creator. Taking advantage of granular data from a large deal­-sharing community and a natural experiment, we use a difference-­in­-differences approach and find robust evidence that the newcomer nudge induced members to write 46% more responses per day with 10% fewer negative words during the first two days after a deal was published. Our results show that the nudge­-induced change in behavior toward newcomers increased newcomer retention. However, we also observe that before the nudge, newcomers’ second posts received more net votes (upvotes minus downvotes) than their first posts. After the nudge, newcomers’ subsequent posts were less popular than their first posts, which indicates that the nudge interrupted newcomers’ learning curve by suppressing helpful feedback.

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.

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.

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.

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.

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.