A Binary Cuckoo Search Big Data Algorithm Applied to Large-Scale Crew Scheduling Problems. (30th July 2018)

• Record Type: Journal Article
• Authors: García, José; Altimiras, Francisco; Peña, Alvaro; Astorga, Gino; Peredo, Oscar
• Other Names: Koczy Laszlo T. Academic Editor.
• Is Part Of: Complexity. Volume 2018(2018)
• Publication Date: 2018-07-30
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2018/8395193
• Languages: English
• ISSNs: 1076-2787

Abstract

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.