The demand for table eggs is subject to strong seasonal fluctuations. To find the best schedule for hundreds of flocks manually is an impossible task. Here comes SupplySolver's algorithm, which is able to calculate the near best schedule out of billions.

Besides the obvious challenge to develop the mathematical algorithm, we faced a lot of other technical quests. We built an Excel like the angularJS application with a fast symfony2 backend. For each calculation, we use Python PulP to model the problem in MLP, fire up Google cloud instances, and finally, after a few hours of calculations, send the result back to our backend for visualizing it in Google Charts.

Symfony2, Agular.js, Python, Bootstrap, CBC MIP Solver, Google compute engine