• DocumentCode
    2096640
  • Title

    A Hybrid Genetic Algorithm for Machine Part Grouping

  • Author

    Tariq, Adnan ; Hussain, Iftikhar ; Ghafoor, Abdul

  • Author_Institution
    Dept. of Mech. Eng., Nat. Univ. of Sci. & Technol.
  • fYear
    2006
  • fDate
    13-14 Nov. 2006
  • Firstpage
    624
  • Lastpage
    629
  • Abstract
    Cellular manufacturing, which incorporates the flexibility of job shops and the high production rate of flow lines, has been seen as a promising alternative for batch type production. Although cellular manufacturing provides great benefits, the design of cellular manufacturing is complex for real life problems. The main problem in the design of a cellular manufacturing system is the formation of machine groups and corresponding part families. This paper aims at developing an approach that combines a local improvement strategy with genetic algorithm. The approach, after being tested on a number of problems from the literature, shows that it not only converges to the best solution very quickly but also produces solutions that are as accurate as any results reported so far in literature. Also, in certain cases the results produced by this technique are even better than the previously reported results
  • Keywords
    cellular manufacturing; genetic algorithms; group technology; job shop scheduling; cellular manufacturing; hybrid genetic algorithm; improvement strategy; machine part grouping; Artificial intelligence; Cellular manufacturing; Educational institutions; Genetic algorithms; Genetic engineering; Group technology; Job production systems; Manufacturing systems; Mechanical engineering; Testing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Emerging Technologies, 2006. ICET '06. International Conference on
  • Conference_Location
    Peshawar
  • Print_ISBN
    1-4244-0503-3
  • Electronic_ISBN
    1-4244-0503-3
  • Type

    conf

  • DOI
    10.1109/ICET.2006.335912
  • Filename
    4136880