Flock

Inspiration

Small and medium-sized remote enterprises may struggle to find the best locale to set up a meeting with employees based in various different countries.

What it does

It provides the optimum location for people to congregate at minimum expense, which is of special importance for SME companies, for whom profit margins may be tight and who have no or small headquarters in few and disparate cities.

How we built it

We made use of genetic search and optimization algorithms.

Challenges we ran into

Defining the problem in a formal way using mathematic notation, in order to convert to a appropriate representation for the genetic algorithms.

Accomplishments that we're proud of

Our solution was designed to be easily scalable, such that meetings involving larger groups would be handled with minimum additional effort.

What we learned

Application of genetic algorithms. The power of API as regards the quality of data available, is an influential element to the whole solution.

What's next for Flock

Enhancing it by introducing more parameters to the search function to increase to specificity and the power of the model and provide more detailed search results. Introducing hotel costs could give more accurate estimate of the true expense. The Skyscanner API has not the ability to show indirect routes between chosen airports, which limits the effectiveness of our model. Therefore the inclusion of this feature to the API could improve our solution.

Built With

• python
• skyscanner