• Title of article

    A Knowledge Based Design Methodology for manufacturing assembly lines

  • Author/Authors

    Asar Khan، نويسنده , , Andrew J. Day، نويسنده ,

  • Issue Information
    ماهنامه با شماره پیاپی سال 2001
  • Pages
    27
  • From page
    441
  • To page
    467
  • Abstract
    In assembly line design, the problem of balancing has received most attention from past researchers, and a number of algorithms have been devised for the analysis of single, multi- and mixed-product assembly lines [Int. J. Prod. Res. 27 (1989) 637]. In many cases, such algorithms seek a solution for the particular situation, which is under consideration and therefore have very little flexibility for generic application to assembly line design. Real life practical design issues include stochastic operation times, parallel workstation requirements, feasibility for workstation combining, and parallel line implementations, all of which are features which are ignored in many analyses. This paper presents a Knowledge Based Design Methodology (KBDM) for automated and manual assembly lines, which can be applied equally well to single, multi- and mixed-product assembly lines with either deterministic operation times or stochastic operation times. The methodology starts from a suitable assembly system selection and thereafter decides suitable cycle times, parallel workstation requirements, and parallel line implementation for the type of assembly system being selected. An economical number of workstations are decided with the aid of workstation combining options depending upon the factual information provided. The end result is the detailed design of a manufacturing assembly line. A case study from a practical assembly line is presented to illustrate how the KBDM works.
  • Keywords
    Manual , Assembly lines , Single , Multi-product , Mixed-Product , Deterministic , Stochastic , Task times , Assembly workstations , Automated
  • Journal title
    Computers & Industrial Engineering
  • Serial Year
    2001
  • Journal title
    Computers & Industrial Engineering
  • Record number

    926247