MAZUREK, Jiří and Dominik STRZALKA. On the Monte Carlo weights in multiple criteria decision analysis. PLOS ONE. 2022, vol. 17, No 10, p. 1-18. ISSN 1932-6203.
Other formats:   BibTeX LaTeX RIS
Basic information
Original name On the Monte Carlo weights in multiple criteria decision analysis
Authors MAZUREK, Jiří (203 Czech Republic, guarantor, belonging to the institution) and Dominik STRZALKA (616 Poland).
Edition PLOS ONE, 2022, 1932-6203.
Other information
Original language English
Type of outcome Article in a journal
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher United States of America
Confidentiality degree is not subject to a state or trade secret
WWW URL
RIV identification code RIV/47813059:19520/22:A0000290
Organization unit School of Business Administration in Karvina
UT WoS 000911414400001
Keywords in English Monte Carlo simulations; weights; multiple criteria decision making
Tags International impact, Reviewed
Links GA21-03085S, research and development project.
Changed by Changed by: Miroslava Snopková, učo 43819. Changed: 11/4/2023 11:18.
Abstract
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.
PrintDisplayed: 8/5/2024 19:53