Algorithms for Freight Train Scheduling

Lucas Morais, Rodrigo Gonçalves, Alexandre Tazoniero, Fernando Gomide

2022

Abstract

This paper develops train scheduling algorithms for freight railroads using scheduling generation and genetic algorithms. First scheduling generation procedures developed in the realm of job shop manufacturing are tailored for freight railroad applications. The scheduling generation procedures are used to create feasible populations to start genetic algorithms. Next, it suggests a novel representation and encoding mechanism based on random keys and job permutation encoding inspired in flexible job shop scheduling. In freight railroads a common goal is to minimize overall maximum transit time, analogous to minimize the makespan in manufacturing systems. The genetic algorithms whose initial population are produced by the scheduling generation algorithms are compared with the random key-job permutation algorithm developed herein. An example rail line is used to evaluate the performance of the algorithms. The exact optimal solution for the example rail line, found using the OR Tools solver, is used as a baseline. The results suggest that all approaches may produce optimal solutions, but the random key-job permutation algorithm consistently performs best amongst the remaining ones.

Paper Citation

in Harvard Style

Morais L., Gonçalves R., Tazoniero A. and Gomide F. (2022). Algorithms for Freight Train Scheduling. In Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - Volume 1: ECTA; ISBN 978-989-758-611-8, SciTePress, pages 74-81. DOI: 10.5220/0011379500003332

in Bibtex Style

@conference{ecta22,
author={Lucas Morais and Rodrigo Gonçalves and Alexandre Tazoniero and Fernando Gomide},
title={Algorithms for Freight Train Scheduling},
booktitle={Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - Volume 1: ECTA},
year={2022},
pages={74-81},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011379500003332},
isbn={978-989-758-611-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - Volume 1: ECTA
TI - Algorithms for Freight Train Scheduling
SN - 978-989-758-611-8
AU - Morais L.
AU - Gonçalves R.
AU - Tazoniero A.
AU - Gomide F.
PY - 2022
SP - 74
EP - 81
DO - 10.5220/0011379500003332
PB - SciTePress