Optimization of a Train Traffic Management Problem under Uncertainties and Disruptions

Abstract

In this paper, we consider a train traffic management problem. Our aim is to find an optimal schedule for a train traffic network where time duration uncertainties are considered. This problem was intensively studied with mixed integer linear models where trains moving duration are deterministic. In this paper, a new formulation of the problem as a classical one with scenario-based stochastic programming taking expected values as objective functions is presented. Then, new criterion is proposed to quantify scheduling robustness in the face of uncertainty. Besides, a novel control policy is elaborated to find quickly, a feasible train schedule when disruption or unexpected event occurs during scheduling execution.

Optimal behaviour prediction using a primitive-based data-driven model-free iterative learning control approach

“…The learning is expected to converge also under trial repeated disturbances and considering different o.f.s as well [37][38][39][40][41][42][43][44][45][46]. …”