Dynamic Multimodal Freight Routing Using a Co-Simulation Optimization Approach

Abstract

The complexity and dynamics of multimodal freight transportation networks make the optimum routing of freight demand a challenging task. Route decision-making in a dynamical and complex urban multimodal transportation environment aims to minimize a certain objective cost relying on the accurate prediction of the traffic network states and the estimation of the route costs that are not readily available. The purpose of this paper is to develop a methodology to be used by a central coordinator who generates individual routing decisions for shippers by minimizing overall cost, assuming that all participating shippers send their demands to this central coordinator. We propose, analyze, and evaluate a multimodal freight routing system with hard vehicle availability and capacity constraints based on a hierarchical Co-Simulation Optimization (COSMO) approach. The COSMO approach consists of a simulation layer that provides traffic state predictions and cost estimations to an upper optimization layer that incorporates a load balancing methodology to speed up the convergence of the optimization algorithm. A simulation test bed consisting of a road traffic simulation and a rail simulation model for the Los Angeles/Long Beach Ports regional area is developed and is used to demonstrate the efficiency of the proposed approach.