We are excited to host our Research Talk Series: CORS Practice Presentations.
📆 Wednesday, May 21
🕛 12:00 PM – 1:30 PM
đź“Ť MC331 (Mechanical Engineering Building), 5 King’s College Rd, Toronto.
This session features presentations from UTORG student members preparing for CORS Annual Meeting 2025:
🎤 Ali Daei Naby (PhD Candidate, Rotman)
Ttile: Inspect or Guess? Mechanism Design with Unobservable Inspection
Abstract: We study the problem of selling k units of an item to n unit-demand buyers to maximize revenue, where buyers’ values are independently (and not necessarily identically) distributed. The buyers’ values are initially unknown but can be learned at a cost through inspection sources. Motivated by applications in e-commerce, where the inspection is unobservable by the seller (i.e., buyers can externally inspect their values without informing the seller), we introduce a framework to find the optimal selling strategy when the inspection is unobservable with and without the seller’s ability to control inspection. For each setting, we fully characterize the optimal mechanism for selling to a single buyer, subject to an upper bound on the allocation probability. Building on this characterization and leveraging connections to the \emph{Prophet Inequality}, we design an approximation mechanism for selling k items to $n$ buyers that achieves $1-1/\sqrt{k+3}$ of the optimal revenue. Our mechanism is simple and sequential and achieves the same approximation bound in an online setting, remaining robust to the order of buyer arrivals. Additionally, in a setting with observable inspection, we leverage connections to cutoff-based search algorithms in \emph{Weitzman’s Pandora’s problem with non-obligatory inspection} and propose a new sequential mechanism for selling an item to n buyers that achieves (the improved bound of) $1-1/e \approx .63$ of the optimal revenue.
🎤 Forrest Mayer (Master’s Student, MIE)
Title: Assessing the Trade-offs Between Disease Mitigation and Health Equity Focused Vaccine Prioritization Strategies
Abstract: The COVID-19 pandemic disproportionately impacted socially disadvantaged populations’ health outcomes and worsened existing disparities. During the pandemic, governments had to allocate limited vaccines to the population. Limited budget vaccine allocation can be formulated as the critical node detection problem (CNDP), which identifies a set of critical nodes (people) in a graph (social contact network) that when removed (vaccinated), minimize a network connectivity metric. Previous implementations of CNDP for vaccination have not considered equitable distribution. We extend an optimal approximation algorithm for the distance-based critical node detection problem to prioritize social disadvantage while minimizing network connectivity. Our experiments demonstrate a majority focus on equity-based prioritization has a large impact on disadvantaged groups’ vaccination rates with a low negative impact on network connectivity and advantaged groups’ vaccination rates. These results demonstrate that policymakers can prioritize socially disadvantaged groups in vaccine allocation to improve health outcomes with little negative impact on disease mitigation.
🎤 Zhenghang Xu (PhD Candidate, Rotman)
Title: Bayesian Pricing for Impatient Customers with Unknown Valuation
Abstract: We study a Bayesian dynamic pricing problem for a revenue-maximizing, capacity-constrained service provider that learns unknown customer valuations through sequential pricing. Customers arrive sequentially, accept or reject the prevailing price, and those who accept occupy the server; arrivals are lost if the server is busy. Our contributions include: (1) A novel recursive algorithm to compute optimal prices that balance demand learning and revenue maximization; (2) theoretical identification of an adaptive explore-then-commit structure: when uncertainty resolves sufficiently, the firm prices at the valuation’s lower bound to guarantee sales; (3) a practical heuristic for delay-free settings, inspired by the Bayesian optimal policy, that numerically outperforms existing benchmarks and achieves the optimal regret order. By unifying Bayesian learning with pricing dynamics, we show how firms adaptively transition from exploration to exploitation. Results demonstrate how customer impatience and server constraints shape pricing strategies, offering actionable methods with provable guarantees for revenue management under uncertainty.
🎓 Whether you’re attending CORS or just curious about ongoing research in OR, we invite you to come, listen, and enjoy having lunch!
📝Please register using this link so we can prepare the right amount of food and seating. Don’t worry if you forget to register—you’re still welcome to join us!