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    Polymorphic uncertain nonlinear programming approach for maximizing the capacity of V-belt driving

    Access Status
    Fulltext not available
    Authors
    Wan, Z.
    Zhang, S.
    Teo, Kok Lay
    Date
    2014
    Type
    Journal Article
    
    Metadata
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    Citation
    Wan, Zhong and Zhang, Shaojun and Teo, Kok Lay. 2014. Polymorphic uncertain nonlinear programming approach for maximizing the capacity of V-belt driving. Optimization and Engineering. 15 (1): pp. 267-292.
    Source Title
    Optimization and Engineering
    DOI
    10.1007/s11081-012-9205-3
    ISSN
    1389-4420
    URI
    http://hdl.handle.net/20.500.11937/16852
    Collection
    • Curtin Research Publications
    Abstract

    In this paper, a polymorphic uncertain nonlinear programming (PUNP) approach is developed to formulate the problem of maximizing the capacity in a system of V-belt driving with uncertainties. The constructed optimization model is found to consist of a nonlinear objective function and some nonlinear constraints with some parameters which are of uncertain nature. These uncertain parameters are interval parameters, random interval parameters, fuzzy parameters or fuzzy interval parameters. To find a robust solution of the problem, a deterministic equivalent formulation (DEF) is established for the polymorphic uncertain nonlinear programming model. For a given satisfaction level, this DEF turns out to be a nonlinear programming involving only interval parameters. A solution method, called a sampling based interactive method, is developed such that a robust solution of the original model with polymorphic uncertainties is obtained by using standard smooth optimization techniques. The proposed method is applied into a real-world design of V-belt driving, and the results indicate that both the PUNP approach and the developed algorithm are useful to the optimization problem with polymorphic uncertainty.

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