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

Carman K.M. Lee, Danping Lin, William Ho, Zhang Wu

Research output: Contribution to journalArticle

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
Early online date5 Apr 2011
DOIs
Publication statusPublished - 1 Oct 2011

Fingerprint

Genetic algorithms
Scheduling
Industry
Experiments

Bibliographical note

© Springer-Verlag London Limited 2011. The final publication is available at Springer via http://dx.doi.org/10.1007/s00170-011-3251-4

Keywords

  • bi-objective
  • flow shop
  • genetic algorithm
  • re-entrant

Cite this

Lee, Carman K.M. ; Lin, Danping ; Ho, William ; Wu, Zhang. / Design of a genetic algorithm for bi-objective flow shop scheduling problems with re-entrant jobs. In: International Journal of Advanced Manufacturing Technology. 2011 ; Vol. 56, No. 9-12. pp. 1105-1113.
@article{2180b998b1474690ba9b06c8381624ac,
title = "Design of a genetic algorithm for bi-objective flow shop scheduling problems with re-entrant jobs",
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.",
keywords = "bi-objective, flow shop, genetic algorithm, re-entrant",
author = "Lee, {Carman K.M.} and Danping Lin and William Ho and Zhang Wu",
note = "{\circledC} Springer-Verlag London Limited 2011. The final publication is available at Springer via http://dx.doi.org/10.1007/s00170-011-3251-4",
year = "2011",
month = "10",
day = "1",
doi = "10.1007/s00170-011-3251-4",
language = "English",
volume = "56",
pages = "1105--1113",
journal = "International Journal of Advanced Manufacturing Technology",
issn = "0268-3768",
publisher = "Springer",
number = "9-12",

}

Design of a genetic algorithm for bi-objective flow shop scheduling problems with re-entrant jobs. / Lee, Carman K.M.; Lin, Danping; Ho, William; Wu, Zhang.

In: International Journal of Advanced Manufacturing Technology, Vol. 56, No. 9-12, 01.10.2011, p. 1105-1113.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Design of a genetic algorithm for bi-objective flow shop scheduling problems with re-entrant jobs

AU - Lee, Carman K.M.

AU - Lin, Danping

AU - Ho, William

AU - Wu, Zhang

N1 - © Springer-Verlag London Limited 2011. The final publication is available at Springer via http://dx.doi.org/10.1007/s00170-011-3251-4

PY - 2011/10/1

Y1 - 2011/10/1

N2 - 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.

AB - 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.

KW - bi-objective

KW - flow shop

KW - genetic algorithm

KW - re-entrant

UR - http://www.scopus.com/inward/record.url?scp=80053574512&partnerID=8YFLogxK

UR - http://link.springer.com/article/10.1007/s00170-011-3251-4

U2 - 10.1007/s00170-011-3251-4

DO - 10.1007/s00170-011-3251-4

M3 - Article

VL - 56

SP - 1105

EP - 1113

JO - International Journal of Advanced Manufacturing Technology

JF - International Journal of Advanced Manufacturing Technology

SN - 0268-3768

IS - 9-12

ER -