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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    Title :

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


    Contributors:


    Publication date :

    2019-07-25


    Size :

    28 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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