This project aims to optimize the process of assigning volunteers to roadside assistance requests at the "Yedidim" organization, a non-profit that provides non-medical emergency help throughout Israel. The system uses a combination of data-driven methods, including the Hungarian algorithm, to automatically assign the most suitable volunteer based on factors such as geographical proximity, past performance, and availability. This will result in faster response times, higher satisfaction rates, and overall improved efficiency for the organization.
The primary goal of the project is to develop a solution that automates the assignment process, significantly reducing manual work and optimizing resource use. The project tackles the challenge of managing over 40,000 volunteers by implementing a scalable and efficient solution that can handle high-volume requests in real time. Technologies Used Languages: Python Libraries: Pandas (Data manipulation) NumPy (Numerical computations) Geopy (Geographical distance calculations) SciPy (Optimization and statistical computations) Streamlit (User interface and web app development) CiviCRM (For integrating with volunteer and request management) Algorithms: Hungarian algorithm (Task-to-volunteer optimization) T-Test, ANOVA (Statistical evaluation for performance improvements)