415
Views
10
CrossRef citations to date
0
Altmetric
Articles

Fuzzy-metaheuristic methods to solve a hybrid flow shop scheduling problem with pre-assignment

, , , &
Pages 3609-3624 | Received 19 May 2011, Accepted 26 Nov 2012, Published online: 06 Mar 2013
 

Abstract

This paper deals with a particular version of the hybrid flow shop scheduling problem inspired from a real application in the automotive industry. Specific constraints such as pre-assigned jobs, non-identical parallel machines and non-compatibility between certain jobs and machines are considered in order to minimise the total tardiness time. A mixed-integer programming model that incorporates these aspects is developed and solved using ILOG Cplex software. Thus, because of the computation time constraint, we propose approximate resolution methods based on genetic and particle swarm optimisation algorithms coupled or not with fuzzy logic control. The effectiveness of these methods is investigated via computational experiments based on theoretical and real case instances. The obtained results show that fuzzy logic control improves the performances of both genetic and particle swarm optimisation algorithms significantly.

Acknowledgements

This research was supported by Caillou company (France). The authors would like to express their sincere thanks to the anonymous referees for their valuable remarks, comments and suggestions that helped to improve the paper. Thanks are conveyed to Muriel Whitchurch who proofread the article.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 973.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.