Inspiration
We work in healthcare engineering fields and endeavor to help and optimize in this area. There are many areas where healthcare can be improved by better coding and logistics is definitely one of them.
What it does
The program modeled the real scenario where ambulances from the hospital need to navigate through the city to the patient's home by the quickest path. Our program attempted to model that by simplifying the city into a 2D maze with walls.
How I built it
We used Python primarily for our project. We modeled the city as a 2D matrix, where the hospital and house had a coordinate and were represented as -2 and -1 on the matrix, respectively. 0 represents the wall. We then used the breath first search algorithm to find the path from the hospital to the house.
Challenges I ran into
It was really hard to find an optimal path given the map, so we only wrote the algorithm to find one of the ways from the hospital to the house. It was not necessarily the best path.
Accomplishments that I'm proud of
This is our very first hackathon, so being able to have a complete project is in itself a really good achievement! We are proud that we obtained some output that can actually path the ambulance to the home and were able to ideate and come up with custom solutions on the fly.
What I learned
The team gained a better understanding of object oriented programming and in map-solving algorithms. Really, our goal was to learn a bit about using python and quickly thinking of and iterating on a system model. This was a great exercise in learning these tasks and implementing something quick in practice. This flows well into understanding of iterative code improvements as throughout the process we kept imagining things we could do to improve the algorithm given more time and experience, so we may continue this as we learn more.
What's next for Ambulance Pathing Algorithm
Next would be the implementation of cost savings algorithms such as Uniform Cost Search to account for the time and gasoline that the ambulance uses to make it to a location and optimize for least cost and most efficient travel. This would include the distances traveled as well as the traffic situations at each node to represent some real-world parameters that will slow down or entirely impede the ambulance. This would also require running many iterations of the search to find and select optimal solutions.
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