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

8Citations
Citations of this article
5Readers
Mendeley users who have this article in their library.

Abstract

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.

Cite

CITATION STYLE

APA

García, J., Altimiras, F., Peña, A., Astorga, G., & Peredo, O. (2018). A binary cuckoo search big data algorithm applied to large-scale crew scheduling problems. Complexity, 2018. https://doi.org/10.1155/2018/8395193

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free