A hybrid approach for biobjective optimization

Thomas Jacob Riis Stidsen*, Kim Allan Andersen

*Corresponding author for this work

    Research output: Contribution to journalJournal articleResearchpeer-review

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    Abstract

    A large number of the real world planning problems which are today solved using Operations Research methods are actually multiobjective planning problems, but most of them are solved using singleobjective methods. The reason for converting, i.e. simplifying, multiobjective problems to singleobjective problems is that no standard multiobjective solvers exist and specialized algorithms need to be programmed from scratch.In this article we will present a hybrid approach, which operates both in decision space and in objective space. The approach enables massive efficient parallelization and can be used to a wide variety of biobjective Mixed Integer Programming models. We test the approach on the biobjective extension of the classic traveling salesman problem, on the standard datasets, and determine the full set of nondominated points. This has only been done once before (Florios and Mavrotas, 2014), and in our approach we do it in a fraction of the time.
    Original languageEnglish
    JournalDiscrete Optimization
    Volume28
    Pages (from-to)89-114
    ISSN1572-5286
    DOIs
    Publication statusPublished - 2018

    Keywords

    • Biobjective optimization
    • Branch-and-cut algorithm
    • Mixed integer programming
    • Traveling salesman problem

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