Events & Seminars

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.

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.

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.

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.

Motivated by an intriguing observation on the time-varying physician productivity (measured by the number of new patients seen per hour, or PPH), we study a continuous-time optimal control problem to understand the transient behavior of individual physicians within their shifts in emergency departments (EDs). By applying Pontryagin’s maximum principle, we characterize the optimal policy and provide insights into physician capacity, productivity, and throughput. We conclude that the transient behavior is intrinsic, mainly induced by shift-based scheduling. We leverage the insights from the time-varying PPH to model the complex ED system as a time-varying multi-server queue with shift-hour-dependent service rates. Validated using data from two Canadian EDs, the simulation results show that our model can accurately capture the time-of-day-dependent patient waiting times with a simple parameter estimation procedure, which is among the first in the literature. In contrast, the simulated waiting times under constant PPH rates deviate significantly from the data. Hence, it is important to explicitly consider time- varying service rates to obtain accurate and valuable models for ED operations. The essence of our model is dimension reduction by state aggregation. As a result, it also allows transient analysis through the uniformization of a continuous- time Markov chain, which can be integrated with off-the-shelf algorithms for physician staffing. Our case study using data from a Canadian ED shows that the new staffing schedule generated from our method can significantly improve the current schedule in practice.

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.

Original equipment manufacturers (OEMs) often source their components from two different suppliers to mitigate supply risks and enhance their bargaining power. However, a notable consequence of dual sourcing is heterogeneity in ex post quality among the final products from different suppliers. Such quality differences can cause peer-induced fairness concerns among consumers who receive products of lower quality: They feel unfair that they have paid the same price as others but receive inferior products. We characterize the effect of the ex post product quality heterogeneity induced by sourcing from different suppliers and the resulting consumer fairness concerns. We examine how the quality differences and fairness concerns affect an OEM’s sourcing strategy selection, the supplier’s wholesale pricing, and the OEM’s optimal ordering decision.

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.

This paper investigates the nature of social learning (SL) on content platforms and its impact on optimal content design. The content provider can choose low- or high-quality content and, in addition, may also polarize (as opposed to keeping it neutral) the content in favor of some consumers while opposing other consumers’ opinions/preferences. On the content platform, SL manifests as consumers’ inference about the unknown quality of content using the history of past consumption, based on which they make consumption decisions. We specify a behavioral model of SL that accounts for and illustrates the impact of false consensus effect (FCE) --- a cognitive bias wherein consumers project their own preference onto others --- on the SL outcome. In this environment, we find that whether the SL mechanism reveals the true quality of content depends largely on the interaction between content polarization and the degree of the FCE. Depending on the extent of the interaction, SL may be incomplete or even cursed in the sense that beliefs converge to a limit where history offers no information about quality. The optimal content design internalizes the SL dynamics and as a result, we find that quality and polarization can be used as substitutes by the content provider. In particular, content may be polarized to mask its low quality. Interestingly, we also find that SL increases the incentive for the content provider to increase quality compared to a benchmark without SL. Furthermore, we find parametric regimes in which SL may not be beneficial to consumers, but is in fact preferred by the content platform (and vice versa). In this sense, the value of SL may be misaligned between the platform and its consumers.