Genetic algorithms for buffer size and work stations capacity in serial-parallel production lines

Abu Qudeiri Jaber, Rizauddin Ramli, Yamamoto Hidehiko

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

Abstract

Recently, many production lines that have complicated structures such as parallel, reworks, feed-forward, etc. are widely used in high volume industries. Among them, the serial-parallel production line (S-PPL) is one of the more common production styles in many modern industries. One of the methods used for studying the S-PPL design is through genetic algorithms (GA). One of the important jobs to use GA is how to express a chromosome. In this paper, we attempt to find the nearest optimal design of an S-PPL that will maximize production efficiency by optimizing the following 3 decision variables: buffer size between each pair of work stations, machine numbers in each of the work stations; and, machine types. In order to do this we present a new GA-simulation based method to find the nearest optimal design for our proposed S-PPL. For efficient use of GA, our GA methodology is based on a technique that is called gene family arrangement method (GFAM) which arranges the genes inside individuals. An application example shows that after a number of operations based on the proposed simulator, the nearest optimal design of S-PPL can be found.

Original languageEnglish
Title of host publicationProceedings of the 12th International Symposium on Artificial Life and Robotis, AROB 12th'07
Pages513-516
Number of pages4
Publication statusPublished - 2007
Externally publishedYes
Event12th International Symposium on Artificial Life and Robotics, AROB 12th'07 - Oita
Duration: 25 Jan 200727 Jan 2007

Other

Other12th International Symposium on Artificial Life and Robotics, AROB 12th'07
CityOita
Period25/1/0727/1/07

Fingerprint

Genetic algorithms
Genes
Chromosomes
Industry
Simulators
Optimal design

Keywords

  • Buffer size
  • Genetic algorithms
  • Serial-parallel production line
  • Throughput evaluation

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction

Cite this

Jaber, A. Q., Ramli, R., & Hidehiko, Y. (2007). Genetic algorithms for buffer size and work stations capacity in serial-parallel production lines. In Proceedings of the 12th International Symposium on Artificial Life and Robotis, AROB 12th'07 (pp. 513-516)

Genetic algorithms for buffer size and work stations capacity in serial-parallel production lines. / Jaber, Abu Qudeiri; Ramli, Rizauddin; Hidehiko, Yamamoto.

Proceedings of the 12th International Symposium on Artificial Life and Robotis, AROB 12th'07. 2007. p. 513-516.

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

Jaber, AQ, Ramli, R & Hidehiko, Y 2007, Genetic algorithms for buffer size and work stations capacity in serial-parallel production lines. in Proceedings of the 12th International Symposium on Artificial Life and Robotis, AROB 12th'07. pp. 513-516, 12th International Symposium on Artificial Life and Robotics, AROB 12th'07, Oita, 25/1/07.
Jaber AQ, Ramli R, Hidehiko Y. Genetic algorithms for buffer size and work stations capacity in serial-parallel production lines. In Proceedings of the 12th International Symposium on Artificial Life and Robotis, AROB 12th'07. 2007. p. 513-516
Jaber, Abu Qudeiri ; Ramli, Rizauddin ; Hidehiko, Yamamoto. / Genetic algorithms for buffer size and work stations capacity in serial-parallel production lines. Proceedings of the 12th International Symposium on Artificial Life and Robotis, AROB 12th'07. 2007. pp. 513-516
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