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Mixed-integer linear programming approach for global discrete sizing optimization of frame structures

  • R. van Mellaert*
  • , K. Mela
  • , T. Tiainen
  • , M. Heinisuo
  • , G. Lombaert
  • , M. Schevenels
  • *Corresponding author for this work

    Research output: Contribution to journalArticleScientificpeer-review

    23 Citations (Scopus)

    Abstract

    This paper focuses on discrete sizing optimization of frame structures using commercial profile catalogs. The optimization problem is formulated as a mixed-integer linear programming (MILP) problem by including the equations of structural analysis as constraints. The internal forces of the members are taken as continuous state variables. Binary variables are used for choosing the member profiles from a catalog. Both the displacement and stress constraints are formulated such that for each member limit values can be imposed at predefined locations along the member. A valuable feature of the formulation, lacking in most contemporary approaches, is that global optimality of the solution is guaranteed by solving the MILP using branch-and-bound techniques. The method is applied to three design problems: a portal frame, a two-story frame with three load cases and a multiple-bay multiple-story frame. Performance profiles are determined to compare the MILP reformulation method with a genetic algorithm.

    Original languageEnglish
    Pages (from-to)579–593
    Number of pages15
    JournalStructural and Multidisciplinary Optimization
    Volume57
    Issue number2
    DOIs
    Publication statusPublished - 2018
    Publication typeA1 Journal article-refereed

    Keywords

    • Discrete optimization
    • Frame structures
    • Global optimization
    • Mixed-integer linear programming
    • Sizing optimization

    Publication forum classification

    • Publication forum level 1

    ASJC Scopus subject areas

    • Control and Systems Engineering
    • Software
    • Computer Science Applications
    • Computer Graphics and Computer-Aided Design
    • Control and Optimization

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