Robust optimization: Sensitivity to uncertainty in scalar and vector cases, with applications
| Autor: | Daishi Kuroiwa, Matteo Rocca, Giovanni Paolo Crespi |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Statistics and Probability
Mathematical optimization Control and Optimization Computer science Decision analysis Multiple objective programming Set optimization Uncertainty modelling Strategy and Management media_common.quotation_subject Scalar (mathematics) 0211 other engineering and technologies 02 engineering and technology Management Science and Operations Research Complete information Perception 0202 electrical engineering electronic engineering information engineering ddc:330 media_common 021103 operations research lcsh:Mathematics Feasible region Opinion leadership Robust optimization Decision maker lcsh:QA1-939 020201 artificial intelligence & image processing |
| Zdroj: | Operations Research Perspectives, Vol 5, Iss, Pp 113-119 (2018) |
| ISSN: | 2214-7160 |
| Popis: | The question we address is how robust solutions react to changes in the uncertainty set. We prove the location of robust solutions with respect to the magnitude of a possible decrease in uncertainty, namely when the uncertainty set shrinks, and convergence of the sequence of robust solutions.In decision making, uncertainty may arise from incomplete information about people’s (stakeholders, voters, opinion leaders, etc.) perception about a specific issue. Whether the decision maker (DM) has to look for the approval of a board or pass an act, they might need to define the strategy that displeases the minority. In such a problem, the feasible region is likely to unchanged, while uncertainty affects the objective function. Hence the paper studies only this framework. Keywords: Uncertainty modelling, Decision analysis, Multiple objective programming, Set optimization |
| Databáze: | OpenAIRE |
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