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Sustainable Rail Transport pp 159–186Cite as

Parallel Genetic Algorithm and High Performance Computing to Solve the Intercity Railway Alignment Optimization Problem

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Abstract

Despite the advances in solving the Railway Alignment Optimization (RAO) problem, the computational burden of the current algorithms to estimate new intercity connections of minimum cost is still an issue. This paper proposes a parallel Genetic Algorithm framework running on a high performance computing environment to solve the RAO problem while minimizing the costs of new railway alignments constrained by the geometric parameters required to run trains with different average speeds. The framework was applied to new connections between Brazilian cities and the results show that it is capable of providing accurate estimations compared to the international experience. From the computational aspect, the parallel computing approach drastically reduces the running times in the cases studied. However, scaling the computing infrastructure to more than 5 machines running in parallel may not be advantageous since the running times do not decrease significantly when more virtual machines are available.

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Acknowledgements

The authors thank the reviewers for providing helpful comments and suggestions that improved the presentation of the paper. They acknowledge the use of computational facilities of the Laboratory of Advanced Scientific Computation of the University of São Paulo (LCCA-USP) between 2014 and 2015. The first author also acknowledges Brazil’s CNPq grant 142417/2010-6 for the financial aid through a doctoral scholarship.

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Authors and Affiliations

  1. Transportation Engineering Department, Polytechnic School, University of São Paulo, Av. Prof. Almeida Prado, 83, Travessa 2, São Paulo, SP, Brazil

    Cassiano A. Isler

  2. Transportation Engineering Department, São Carlos School of Engineering, University of São Paulo, Av. Trabalhador São-Carlense, 400, São Carlos, SP, Brazil

    João A. Widmer

Authors
  1. Cassiano A. Isler
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  2. João A. Widmer
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Correspondence to Cassiano A. Isler .

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Editors and Affiliations

  1. Engg Systems & Mgmt ESM, Main Building, Aston University, Birmingham, UK

    Marin Marinov

  2. JP Research & Consulting, Port Lincoln SA, Australia

    Janene Piip

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Isler, C.A., Widmer, J.A. (2020). Parallel Genetic Algorithm and High Performance Computing to Solve the Intercity Railway Alignment Optimization Problem. In: Marinov, M., Piip, J. (eds) Sustainable Rail Transport. Lecture Notes in Mobility. Springer, Cham. https://doi.org/10.1007/978-3-030-19519-9_5

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  • DOI: https://doi.org/10.1007/978-3-030-19519-9_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-19518-2

  • Online ISBN: 978-3-030-19519-9

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