A rolling horizon model to optimize aerodynamic efficiency of intermodal freight trains with uncertainty

Aerodynamic efficiency of intermodal freight trains can be significantly improved by minimizing the adjusted gap lengths between adjacent loads. This paper first develops a static model to optimize load placement on a sequence of intermodal trains that have scheduled departure times. This model applies when full information on all trains and loads is available. Then, we develop a dynamic model to account for the more realistic situation in which there is incomplete or uncertain information on future trains and incoming loads. This paper develops methodology to balance between (i) the advantage from optimizing multiple trains together and (ii) the risk of making suboptimal decisions due to incomplete future information. We propose a rolling horizon scheme to address this challenge, where exponentially decreasing weights are assigned to the objective functions of future trains. Numerical results based on empirical data show significant aerodynamic efficiency benefits from these optimization models.