Özlem Ergun | Searcharxiv

arXiv2024

Multimodal Transportation Pricing Alliance Design: Large-Scale Optimization for Rapid Gains

Transit agencies have the opportunity to outsource certain services to established Mobility-on-Demand (MOD) providers. Such alliances can improve service quality, coverage, and ridership; reduce public sector costs and vehicular emissions; and integrate the passenger experience. To amplify the effectiveness of such alliances, we develop a fare-setting model that jointly optimizes fares and discounts across a multimodal network. We capture commuters' travel decisions with a discrete choice model, resulting in a large-scale, mixed-integer, non-convex optimization problem. To solve this challenging problem, we develop a two-stage decomposition with the pricing decisions in the first stage and a mixed-integer linear optimization of fare discounts and passengers' travel decisions in the second stage. To solve the decomposition, we develop a new solution approach combining tailored coordinate descent, parsimonious second-stage evaluations, and interpolations using special ordered sets. This approach, enhanced by acceleration techniques based on slanted traversal, randomization and warm-start, significantly outperforms algorithmic benchmarks. Different alliance priorities result in qualitatively different fare designs: flat fares decrease the total vehicle-miles traveled, while geographically-informed discounts improve passenger happiness. The model responds appropriately to equity-oriented and passenger-centric priorities, improving system utilization and lowering prices for low-income and long-distance commuters. Our profit allocation mechanism improves outcomes for both types of operators, thus incentivizing profit-oriented MOD operators to adopt transit priorities.

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arXiv2023

When Should the FDA Inspect Pharmaceutical Manufacturing Facilities to Better Mitigate Drug Shortages?

Drug shortages have been a persistent problem in American healthcare for decades, and the resulting lack of access to necessary drugs has been disastrous to patient health. A majority of these shortages were caused by quality issues related to problems in the manufacturing process. More frequent inspections can reduce quality concerns, but deciding when to inspect is a complex problem; strict regulation enforcement can force low-profit facilities to close due to excessive maintenance costs, while lax enforcement allows for regulation violations to persist, both of which can cause drug shortages. We propose a novel model to assist the FDA in determining when to inspect manufacturing facilities. We formulate this problem as a finite-horizon partially observable Markov decision process (POMDP) based on the classifications the FDA assigns to each facility after inspection, as well two disruptive events: a manufacturing failure occurring or the facility closing for non-mandatory maintenance. We theoretically show that this problem can be reduced to only needing to consider whether or not to inspect immediately, which is independent of the time horizon. We additionally determine the sensitivity of the optimal inspection time on the penalty incurred for an unexpected disruptive event occurring. Our computational study demonstrates a quadratic relationship between the relative difference in average value accumulated between inspecting based on the optimal inspection time produced by our model and inspecting based on the expected time to an unexpected disruptive event, highlighting the importance of allocating more inspection resources to high-risk facilities that produce drugs that highly impact public health. We additionally find that optimal inspection time is more sensitive to changes in the penalty incurred from a disruptive event occurring the longer it has been since the last inspection.

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