Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/287086 
Year of Publication: 
2021
Citation: 
[Journal:] Annals of Operations Research [ISSN:] 1572-9338 [Volume:] 319 [Issue:] 2 [Publisher:] Springer US [Place:] New York, NY [Year:] 2021 [Pages:] 1769-1791
Publisher: 
Springer US, New York, NY
Abstract: 
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.
Subjects: 
Multi-objective integer programming
Decision space robustness
Connectedness of efficient solutions
Representation
Decision analysis
Persistent Identifier of the first edition: 
Creative Commons License: 
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Document Type: 
Article
Document Version: 
Published Version

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