351
Views
9
CrossRef citations to date
0
Altmetric
Original Articles

A hybrid genetic algorithm approach for solving an extension of assembly line balancing problem

, , &
Pages 504-519 | Received 15 Mar 2014, Accepted 31 May 2015, Published online: 16 Jul 2015
 

Abstract

The lively field of assembly line configuration and adjustment often have a significant impact on the performance of manufacturing systems. In this context, assembly line balancing problems (ALBPs) are widely cited in the literature. An ALBP consists of distributing the total product manufacturing workload among the stations along the manufacturing line. Previous research has focused on developing effective and fast solution methods for solving simple assembly line balancing problems (SALBP) and their various extensions. Each extension is motivated by several real-life applications and the need for solving precise practical problems. In this article, another interesting extension of SALBP (named in this work ‘Task Restrictions Assembly Line Balancing Problem’ of type 2 (TRALBP-2)) is focused on. In this situation, the number of stations is known and the objective is to minimise cycle time where both precedence and zoning constraints between tasks must be satisfied. For the resolution of such problem, an innovative hybrid genetic algorithm (HGA) scheme hybridised with a local search procedure is implemented. This genetic algorithm consists of a new representation scheme and a special genetic operator. The effectiveness of the proposed HGA is evaluated through various sets of instances which are (1) theoretically and randomly generated, (2) collected from the literature and (3) based on a real case study of an automotive cable manufacturer. Comparison of the proposed HGA results with CPLEX software for the TRALBP-2 demonstrates that, in a reasonable time, the proposed HGA generates consistent solutions that are very close to their optimal ones. Therefore, the proposed HGA approach is very effective and competitive.

Acknowledgements

The authors thank the editor and the three anonymous reviewers for their thought-provoking and insightful comments and corrections which have been very useful for improving the paper quality and presentation.

Disclosure statement

No potential conflict of interest was reported by the authors.

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 528.00 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.