Hybrid genetic algorithm for bi-objective flow shop scheduling problems with re-entrant jobs

Ka Man Lee, Danping Lin

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)

Abstract

This paper presents a simulated genetic algorithm model of scheduling the flow shop problems with re-entrant jobs. The objectives of this research are to minimize the weighted tardiness and makespan. The proposed model considers that the jobs with non-identical due dates are processed on the machines with 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 re-enter 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 industrial practices.
Original languageEnglish
Title of host publicationIEEM2010 - IEEE International Conference on Industrial Engineering and Engineering Management
Pages1240-1245
Number of pages6
DOIs
Publication statusPublished - 1 Dec 2010
Externally publishedYes
EventIEEE International Conference on Industrial Engineering and Engineering Management, IEEM2010 - Macao, China
Duration: 7 Dec 201010 Dec 2010

Conference

ConferenceIEEE International Conference on Industrial Engineering and Engineering Management, IEEM2010
CountryChina
CityMacao
Period7/12/1010/12/10

Keywords

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

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

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