MediRouteSaver Solution

Inspiration

Our inspiration comes from the pressing need to optimize and streamline the transportation of medical samples, ensuring swift delivery while minimizing resource consumption. The challenge sparked our creativity to develop a solution that addresses the inefficiencies in the current system.

What it does

The MediRouteSaver Solution utilizes advanced optimization algorithms to intelligently assign journeys to vehicles, merging routes where possible. By combining routes and strategically allocating resources, it significantly reduces travel distance, the number of vehicles needed, and overall energy consumption. The system ensures timely and efficient delivery of medical samples from various doctor's offices to Southampton General Hospital.

How we built it

We built the solution using a combination of Python programming, streamlining the process with the help of libraries such as OR-Tools for optimization and Streamlit for the user interface. The system integrates data from pathology sample files, vehicle routes, and courier schedules to create a holistic approach to medical sample transport.

Challenges we ran into

One of the main challenges was integrating diverse datasets and ensuring seamless communication between different components of the solution. We also faced complexities in optimizing routes, considering varying capacities of vehicles and the dynamic nature of courier schedules.

What we learned

Through this project, we gained valuable insights into the complexities of healthcare logistics. We deepened our understanding of optimization algorithms, data integration, and the importance of balancing efficiency with resource conservation in transportation systems.

What's next for MediRouteSaver Solution

Some of the optimization code is incomplete eg. making time allowances for free time of courier vans. There are also issues with time-constraints that we would like to address.

Built With

• data-integration
• python
• streamlit