Design of a genetic algorithm for bi-objective flow shop scheduling problems with re-entrant jobs

Ka Man Lee, Danping Lin, William Ho, Zhang Wu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

9 Citations (Scopus)

Abstract

This paper presents a simulated genetic algorithm (GA) model of scheduling the flow shop problem with re-entrant jobs. The objective of this research is to minimize the weighted tardiness and makespan. The proposed model considers that the jobs with non-identical due dates are processed on the machines in the same order. Furthermore, the re-entrant jobs are stochastic as only some jobs are required to reenter to the flow shop. The tardiness weight is adjusted once the jobs reenter to the shop. The performance of the proposed GA model is verified by a number of numerical experiments where the data come from the case company. The results show the proposed method has a higher order satisfaction rate than the current industrial practices.
Original languageEnglish
Pages (from-to)1105-1113
Number of pages9
JournalInternational Journal of Advanced Manufacturing Technology
Volume56
Issue number9-12
DOIs
Publication statusPublished - 1 Oct 2011
Externally publishedYes

Keywords

  • Bi-objective
  • Flow shop
  • Genetic algorithm
  • Re-entrant

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Mechanical Engineering
  • Computer Science Applications
  • Industrial and Manufacturing Engineering

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