Biological computation of optimal task arrangement for a flexible machining cell

R. A. Bakar, J. Watada

    Research output: Contribution to journalArticle

    5 Citations (Scopus)

    Abstract

    A flexible manufacturing system (FMS) plays an important and central role in today's advanced manufacturing. It replaces human tasks (especially those that are highly dangerous ones), efficiently performed tasks, or crucially precise tasks. Considering the NP-hard nature of such computation when the numbers of parameters, robots, or/and tasks are increasing. The objective of this paper is to propose a super parallel computation method optimally to rearrange tasks of an FMS in a production line. A biological computing approach is presented to minimize the waiting time of machines and workstations, and maximize the usage of robots. Biological computing with powerful massive parallelism enables the generation of all feasible solutions at one time, as opposed to the limitation of conventional computing in reaching an optimal solution. The proposed method is illustrated using two different examples of single and multiple robots. Finally, solving an FMS problem is explained from a biological computing point of view.

    Original languageEnglish
    Pages (from-to)605-618
    Number of pages14
    JournalProceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering
    Volume222
    Issue number6
    DOIs
    Publication statusPublished - 2008

    Fingerprint

    Flexible manufacturing systems
    Machining
    Robots

    Keywords

    • Automated manufacturing
    • Biological computing
    • DNA computing
    • Flexible manufacturing
    • Multiple robot cell
    • Single robot cell

    ASJC Scopus subject areas

    • Mechanical Engineering
    • Control and Systems Engineering

    Cite this

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    abstract = "A flexible manufacturing system (FMS) plays an important and central role in today's advanced manufacturing. It replaces human tasks (especially those that are highly dangerous ones), efficiently performed tasks, or crucially precise tasks. Considering the NP-hard nature of such computation when the numbers of parameters, robots, or/and tasks are increasing. The objective of this paper is to propose a super parallel computation method optimally to rearrange tasks of an FMS in a production line. A biological computing approach is presented to minimize the waiting time of machines and workstations, and maximize the usage of robots. Biological computing with powerful massive parallelism enables the generation of all feasible solutions at one time, as opposed to the limitation of conventional computing in reaching an optimal solution. The proposed method is illustrated using two different examples of single and multiple robots. Finally, solving an FMS problem is explained from a biological computing point of view.",
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