Distributed model predictive control methods for intermodal transport cooperative planning based on ADMM

The cooperative planning in intermodal transport networks can obtain the global optimal decision under premise of ensuring data privacy each role cooperation and avoiding massive transmission. For control container flow networks, distributed model predictive (DMPC) method effectively realize planning, but convergence speed existing DMPC methods is slow. Therefore, this study attempts to construct faster for planning. Jacobi proximal (JP-DMPC) dual consensus (DC-DMPC) are constructed based on two variants alternating direction multipliers (ADMM). simulation experiments prove that JP-DMPC DC-DMPC higher than state-of-the-art time cost interaction volume iteration do not change much, improves particularly significantly. This provides new has significance development synchromodal transport.