Integrating advanced discrete choice models in mixed integer linear optimization

In many transportation systems, a mismatch between the associated design and planning decisions demand is typically encountered. A tailored system not only appealing to operators, which could have better knowledge of their operational costs, but also users, since they would benefit from an increase in level service satisfaction. Hence, it important explicitly allow for interactions two model governing system. Discrete choice models (DCM) provide disaggregate representation that able capture impact on behavior these by taking into account heterogeneity tastes preferences as well subjective aspects related attitudes or perceptions. Despite advantages, expressions derived DCM are non-linear non-convex explanatory variables, restricts integration optimization problems. this paper, we overcome probabilistic nature relying simulation order specify directly terms utility functions (instead probabilities). This allows us define mixed-integer linear formulation characterizes preference structure behavioral assumption DCM, can then be embedded programming (MILP) model. We overview extent framework with illustrative MILP designed solve profit maximization problem parking services operator. The obtained results show potential proposed methodology adjust supply-related users.