A robust ant colony metaheuristic for urban freight transport scheduling using passenger rail network

Abstract

This paper considers a promising alternative for freight transport using urban passenger rail networks. It addresses a freight rail transport scheduling problem (FRTSP), which is one of the key operational issues to integrate freight into passenger transport in urban areas. In this paper, we develop a robust ant colony optimization (ACO) metaheuristic to tackle large-size instances. Taguchi method is used to set control parameters to make the algorithm robust. Computational results clearly highlight the proposed ACO algorithm’s capability of yielding near-optimal solutions in short computation times, even in large-size instances. They also show that the proposed algorithm dominates classic ACO variants, even when the parameters of these variants are also set using Taguchi method.