Genetic algorithm for buffer size and work station capacity in serial-parallel production lines

Jaber Abu Qudeiri, Hidehiko Yamamoto, Rizauddin Ramli, Anouar Jamali

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)

Abstract

Recently, many production lines that have complicated structures such as parallel, reworks, feed-forward, etc., have become 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 a genetic algorithm (GA). One of the important jobs in using a GA is how to express a chromosome. In this study, we attempt to find the nearest optimal design of a S-PPL that will maximize production efficiency by optimizing the following three 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 a GA, our GA methodology is based on a technique that is called the 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 a S-PPL can be found.

Original languageEnglish
Pages (from-to)102-106
Number of pages5
JournalArtificial Life and Robotics
Volume12
Issue number1-2
DOIs
Publication statusPublished - 2008
Externally publishedYes

Keywords

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

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Artificial Intelligence

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