A binary cuckoo search big data algorithm applied to large-scale crew scheduling problems

José García, Francisco Altimiras, Alvaro Peña, Gino Astorga, Oscar Peredo

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

47 Citas (Scopus)

Resumen

The progress of metaheuristic techniques, big data, and the Internet of things generates opportunities to performance improvements in complex industrial systems. This article explores the application of Big Data techniques in the implementation of metaheuristic algorithms with the purpose of applying it to decision-making in industrial processes. This exploration intends to evaluate the quality of the results and convergence times of the algorithm under different conditions in the number of solutions and the processing capacity. Under what conditions can we obtain acceptable results in an adequate number of iterations? In this article, we propose a cuckoo search binary algorithm using the MapReduce programming paradigm implemented in the Apache Spark tool. The algorithm is applied to different instances of the crew scheduling problem. The experiments show that the conditions for obtaining suitable results and iterations are specific to each problem and are not always satisfactory.

Idioma originalInglés
Número de artículo8395193
PublicaciónComplexity
Volumen2018
DOI
EstadoPublicada - 2018
Publicado de forma externa

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