An evolutionary clustering search for the total tardiness blocking flow shop problem

Marcelo Nagano (), Adriano Seiko Komesu and Hugo Hissashi Miyata
Additional contact information
Adriano Seiko Komesu: University of São Paulo
Hugo Hissashi Miyata: University of São Paulo

Journal of Intelligent Manufacturing, 2019, vol. 30, issue 4, No 21, 1843-1857

Abstract: Abstract In this paper we propose the evolutionary clustering search (ECS) algorithm combined with a variable neighbourhood search (VNS) for the m-machine blocking flow shop scheduling problem with total tardiness minimization. The proposed ECS uses NEH-based procedure to generate an initial solution, the genetic algorithm to generate solutions and VNS to improve the solutions. A set of experiments were carried out to adjust the parameter of the metaheuristic. ECS is compared with ILS by Ribas et al. (J Prod Res 51(17):5238–5252, 2013), known as the best metaheuristic for the problem. Computational tests show superiority of the new method for the set of problems evaluated. Finally, we update 67 new best values of best-know found by ECS.

Keywords: Blocking flow shop; Total tardiness; Clustering search; Variable neighborhood search (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://link.springer.com/10.1007/s10845-017-1358-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:30:y:2019:i:4:d:10.1007_s10845-017-1358-7

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-017-1358-7

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().