Optimization with machine learning-based modeling: an application to humanitarian food aid

In this thesis, we propose a machine learning-based optimization methodology to build (part of) optimization models with a data-driven approach. This approach is useful whenever we have to model one or more relations between the decisions and their impact on the system. This kind of relationship can be challenging to model manually, and so machine learning is used to learn it through the use of data. We demonstrate the potential of this method through a case study in which a predictive model is used to approximate the palatability scoring function in a typical diet problem formulation. First, the performance of this approach is analyzed by embedding a Linear Regression model and then by embedding a Fully Connected Neural Network.