On the Monte Carlo weights in multiple criteria decision analysis
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F47813059%3A19520%2F22%3AA0000290" target="_blank" >RIV/47813059:19520/22:A0000290 - isvavai.cz</a>
Result on the web
<a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0268950" target="_blank" >https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0268950</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
On the Monte Carlo weights in multiple criteria decision analysis
Original language description
In multiple-criteria decision making/aiding/analysis (MCDM/MCDA) weights of criteria constitute a crucial input for finding an optimal solution (alternative). A large number of methods were proposed for criteria weights derivation including direct ranking, point allocation, pairwise comparisons, entropy method, standard deviation method, and so on. However, the problem of correct criteria weights setting persists, especially when the number of criteria is relatively high. The aim of this paper is to approach the problem of determining criteria weights from a different perspective: we examine what weights’ values have to be for a given alternative to be ranked the best. We consider a space of all feasible weights from which a large number of weights in the form of n−tuples is drawn randomly via Monte Carlo method. Then, we use predefined dominance relations for comparison and ranking of alternatives, which are based on the set of generated cases. Further on, we provide the estimates for a sample size so the results could be considered robust enough. At last, but not least, we introduce the concept of central weights and the measure of its robustness (stability) as well as the concept of alternatives’ multi-dominance, and show their application to a real-world problem of the selection of the best wind turbine.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA21-03085S" target="_blank" >GA21-03085S: Supporting Decision Processes with Pairwise Comparisons and Data Mining</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
PLOS ONE
ISSN
1932-6203
e-ISSN
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Volume of the periodical
17
Issue of the periodical within the volume
10
Country of publishing house
US - UNITED STATES
Number of pages
18
Pages from-to
1-18
UT code for WoS article
000911414400001
EID of the result in the Scopus database
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