Artikel
Selection of discrete multiple criteria decision making methods in the presence of risk and uncertainty
This paper presents a new methodology to recommend the most suitable Multi-Criteria Decision Making (MCDM) method from a subset of candidate methods when risk and uncertainty are anticipated. A structured approach has been created based on an analysis of MCDM problems and methods characteristics. Outcomes of this analysis provide decision makers with a suggested group of candidate methods for their problem. Sensitivity analysis is applied to the suggested group of candidate methods to analyze the robustness of outputs when risk and uncertainty are anticipated. A MCDM method is automatically selected that delivers the most robust outcome. MCDM methods dealing with discrete sets of alternatives are considered. Numerical examples are presented where some MCDM methods are compared and recommended by calculating the minimum percentage change in criteria weights and performance measures required to alter the ranking of any two alternatives. A MCDM method will be recommended based on a best compromise in minimum percentage change required in inputs to alter the ranking of alternatives. Different cases are considered and some new propositions are presented based on potential generalized scenarios of MCDM problems.
- Sprache
-
Englisch
- Erschienen in
-
Journal: Operations Research Perspectives ; ISSN: 2214-7160 ; Volume: 5 ; Year: 2018 ; Pages: 357-370 ; Amsterdam: Elsevier
- Klassifikation
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Wirtschaft
- Thema
-
Multiple criteria analysis
Robustness
Sensitivity
Decision making
Criteria weights
Performance
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Haddad, Malik
Sanders, David
- Ereignis
-
Veröffentlichung
- (wer)
-
Elsevier
- (wo)
-
Amsterdam
- (wann)
-
2018
- DOI
-
doi:10.1016/j.orp.2018.10.003
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Haddad, Malik
- Sanders, David
- Elsevier
Entstanden
- 2018
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