A fuzzy-based multi-term genetic algorithm for reentrant flow shop scheduling problem

I. Hsuan Huang, Shigeru Fujimura

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In semiconductor manufacturing factories, the process of wafer fabrication is the most technologically complex and capital intensive stage. This process is configured as a reentrant flow shop process with many machines and processing steps. It needs an efficient and effective scheduling method for large size process in order to increase the competitiveness. The reentrant flow shop problem (RFSP) means that all jobs have the same route through the shop machines and the same shop machine is used several times to complete a job. This research provides an effective fuzzy-based multi-term genetic algorithm to solving RFSP with the objective of minimizing the total turn around time (TTAT). The proposed method focuses on the critical point in scheduled solutions. The middle position of longest TAT is defined as the critical point. According to the critical point and current generation, fuzzy logic chooses the focused term of chromosome, then the genetic algorithm effects on this term. In each evolution, only corresponded part of chromosome is evolved by crossover and mutation while other parts of chromosome remain unchanged. Through computational experiments, the effectiveness of the fuzzy-based multi-term genetic algorithm is evaluated.

Original languageEnglish
Title of host publicationIEEE International Conference on Industrial Engineering and Engineering Management
PublisherIEEE Computer Society
Pages305-309
Number of pages5
ISBN (Print)9781479909865
DOIs
Publication statusPublished - 2014 Nov 18
Event2013 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2013 - Bangkok
Duration: 2013 Dec 102013 Dec 13

Other

Other2013 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2013
CityBangkok
Period13/12/1013/12/13

Fingerprint

Chromosomes
Machine shops
Genetic algorithms
Scheduling
Turnaround time
Fuzzy logic
Industrial plants
Semiconductor materials
Fabrication
Processing
Flow shop scheduling
Genetic algorithm
Experiments
Flow shop
Critical point

Keywords

  • Fonts
  • formatting
  • margins

ASJC Scopus subject areas

  • Business, Management and Accounting (miscellaneous)
  • Industrial and Manufacturing Engineering
  • Safety, Risk, Reliability and Quality

Cite this

Huang, I. H., & Fujimura, S. (2014). A fuzzy-based multi-term genetic algorithm for reentrant flow shop scheduling problem. In IEEE International Conference on Industrial Engineering and Engineering Management (pp. 305-309). [6962423] IEEE Computer Society. https://doi.org/10.1109/IEEM.2013.6962423

A fuzzy-based multi-term genetic algorithm for reentrant flow shop scheduling problem. / Huang, I. Hsuan; Fujimura, Shigeru.

IEEE International Conference on Industrial Engineering and Engineering Management. IEEE Computer Society, 2014. p. 305-309 6962423.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Huang, IH & Fujimura, S 2014, A fuzzy-based multi-term genetic algorithm for reentrant flow shop scheduling problem. in IEEE International Conference on Industrial Engineering and Engineering Management., 6962423, IEEE Computer Society, pp. 305-309, 2013 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2013, Bangkok, 13/12/10. https://doi.org/10.1109/IEEM.2013.6962423
Huang IH, Fujimura S. A fuzzy-based multi-term genetic algorithm for reentrant flow shop scheduling problem. In IEEE International Conference on Industrial Engineering and Engineering Management. IEEE Computer Society. 2014. p. 305-309. 6962423 https://doi.org/10.1109/IEEM.2013.6962423
Huang, I. Hsuan ; Fujimura, Shigeru. / A fuzzy-based multi-term genetic algorithm for reentrant flow shop scheduling problem. IEEE International Conference on Industrial Engineering and Engineering Management. IEEE Computer Society, 2014. pp. 305-309
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