\begin{frame}{Incorporating advanced behavioral models in mixed integer linear optimization}




\item Discrete choice models are used for detailed representation of the "demand". However, their complexity makes mathematical formulations highly non convex in the explanatory variables. On the other hand, Mixed Integer Linear Programs (MILP) are optimization problems with discrete variables that are used in many applications to design and configure the "supply". In this project, we propose a new modeling framework that allows to include any random utility model in a mixed integer optimization formulation. The heterogeneity of demand is captured within the general choice model framework and the offers are tailored in a way that is beneficial for users as wells as providers. The main objective of the project is to obtain a framework that is both general, so that it can be applied in many applications, and operational, so that it can be used in practice.

\item Sponsor: Swiss National Science Foundation

\item April 01, 2016-March 31, 2019