ALLOCATING COVID TESTING STATIONS
The project is a course project for Optimization Methods, the final report is not published yet. You can access to the drafet version of the report and slides here: Final Report Final Slides
Keywords: COVID, Mixed Integer Optimization, Infrastructure and System Management, Spatial Analysis
This project, undertaken in the 6.7201 Optimization Methods course in collaboration with DUSP PhD student Liu Liu, addresses the strategic allocation of COVID-19 testing facilities in Shanghai during the 2022 lockdown. Our objective was to optimize the distribution of testing sites to ensure pedestrian accessibility within 10-15 minutes, while minimizing operational costs and adhering to site capacity constraints. Employing mixed integer optimization, our model, though simple in formulation, presented complexities when scaled to large city data.
Key challenges included the sparse population distribution in suburban areas and the need to balance between integer and linear solutions. Leveraging my experience in city information management, we utilized the grid method in urban spatial analysis to segment the dataset into regions, achieving an integer feasible optimal solution.
We are still optimizing the algorithm and plan to submit a paper in early 2024. Please follow this page for the latest developments.