Solving synchromodal container transportation problem using a genetic algorithm

Abstract

This paper proposes a Genetic Algorithm(GA) to solve the synchromodal transportation problem. The objective is to find a feasible transportation path for container transportation while minimizing travel duration and $C{O}_2$ emissions. The transportation network is modeled in a multigraph and a novel chromosome encoding method, that takes into account the parallel edges is proposed, along with the GA operators. The parameters of the GA are set using Taguchi analysis. The model is validated on instances based on the Seine Axis in France while considering three modes of transport: Barge, Train, and Truck as well as a benchmark instance. The GA finds optimal solutions for small instances and provides good enough solutions with a low deviation from the best-known solution in larger instances.