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

Abstract

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