A New Artificial Immune System Algorithm for Multiobjective Fuzzy Flow Shop Problems

Cengiz Kahraman*, Orhan Engin, Mustafa Kerim Yılmaz

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


In this paper a new artificial immune system (AIS) algorithm is proposed to solve multi objective fuzzy flow shop scheduling problems. A new mutation operator is also described for this AIS. Fuzzy sets are used to model processing times and due dates. The objectives are to minimize the average tardiness and the number of tardy jobs. The developed new AIS algorithm is tested on real world data collected at an engine cylinder liner manufacturing process. The feasibility and effectiveness of the proposed AIS is demonstrated by comparing it with genetic algorithms. Computational results demonstrate that the proposed AIS algorithm is more effective meta-heuristic for multi objective flow shop scheduling problems with fuzzy processing time and due date.

Original languageEnglish
Pages (from-to)236-247
Number of pages12
JournalInternational Journal of Computational Intelligence Systems
Issue number3
Publication statusPublished - Oct 2009

Bibliographical note

Publisher Copyright:
© 2009, the authors.


  • engine cylinder liner manufacturing process
  • Fuzzy flow shop
  • multi objective
  • new artificial immune system


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