Improved distributed model predictive control for rescheduling of railway traffic by manipulation of the cost functions

In this paper we introduce two distributed model predictive control (DMPC) approaches that significantly improve the quality of the solutions found compared to the DMPC approaches that were introduced by Kersbergen et al. [2014b] for the rescheduling of railway traffic, while the computation time only increased by a small fraction. In DMPC the global rescheduling problem is split up into several local problems that are solved by local model predictive controllers that communicate with each other to achieve a solution for the global rescheduling problem. We improve the solution found by the DMPC approaches by adjusting the weights in the local problems such that the delay propagation through the network is reduced. We compare the performance in terms of computation time and delay reduction of the different DMPC approaches with the global model predictive control approach for different lengths of the prediction horizon.