Artikel
Decision space robustness for multi-objective integer linear programming
In this article we introduce robustness measures in the context of multi-objective integer linear programming problems. The proposed measures are in line with the concept of decision robustness, which considers the uncertainty with respect to the implementation of a specific solution. An efficient solution is considered to be decision robust if many solutions in its neighborhood are efficient as well. This rather new area of research differs from robustness concepts dealing with imperfect knowledge of data parameters. Our approach implies a two-phase procedure, where in the first phase the set of all efficient solutions is computed, and in the second phase the neighborhood of each one of the solutions is determined. The indicators we propose are based on the knowledge of these neighborhoods. We discuss consistency properties for the indicators, present some numerical evaluations for specific problem classes and show potential fields of application.
- Language
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Englisch
- Bibliographic citation
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Journal: Annals of Operations Research ; ISSN: 1572-9338 ; Volume: 319 ; Year: 2021 ; Issue: 2 ; Pages: 1769-1791 ; New York, NY: Springer US
- Classification
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Allgemeines, Wissenschaft
- Subject
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Multi-objective integer programming
Decision space robustness
Connectedness of efficient solutions
Representation
Decision analysis
- Event
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Geistige Schöpfung
- (who)
-
Stiglmayr, Michael
Figueira, José Rui
Klamroth, Kathrin
Paquete, Luís
Schulze, Britta
- Event
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Veröffentlichung
- (who)
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Springer US
- (where)
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New York, NY
- (when)
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2021
- DOI
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doi:10.1007/s10479-021-04462-w
- Last update
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10.03.2025, 11:45 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Artikel
Associated
- Stiglmayr, Michael
- Figueira, José Rui
- Klamroth, Kathrin
- Paquete, Luís
- Schulze, Britta
- Springer US
Time of origin
- 2021
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