Unbiased approximation in multicriteria optimization

Algorithms generating piecewise linear approximations of the non-dominated set for general, convex and nonconvex, multicriteria programs are developed. Polyhedral distance functions are used to construct the approximation and evaluate its quality. The functions automatically adapt to the problem str...

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Bibliographic Details
Published inMathematical methods of operations research (Heidelberg, Germany) Vol. 56; no. 3; pp. 413 - 437
Main Authors KLAMROTH, Kathrin, TIND, Jørgen, WIECEK, Margaret M
Format Journal Article
LanguageEnglish
Published Heidelberg Physica 2002
Subjects
Applied sciences
Exact sciences and technology
Mathematical programming
Operational research and scientific management
Operational research. Management science
Polyhedron
Multicriteria analysis
Non dominated set
Multiobjective programming
Approximation algorithm
Distance
Piecewise linearization
Mathematical programming
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Summary:Algorithms generating piecewise linear approximations of the non-dominated set for general, convex and nonconvex, multicriteria programs are developed. Polyhedral distance functions are used to construct the approximation and evaluate its quality. The functions automatically adapt to the problem structure and scaling which makes the approximation process unbiased and self-driven. Decision makers preferences, if available, can be easily incorporated but are not required by the procedure.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
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ISSN:1432-2994
1432-5217