Integrating advanced discrete choice models in mixed integer linear optimization

The integration of choice models in mixed integer linear programming (MILP) is appealing to operators because it provides a better understanding of the preferences of customers while planning for their systems. However, the complexity of choice models leads to mathematical formulations that are highly nonlinear and non convex in the variables of interest, and therefore difficult to be included in MILP. In this research, we present a general framework that overcomes these limitations by relying on simulation to integrate advanced discrete choice models in MILP formulations.