Statistical Analysis of KEMIRA Type Weights Balancing Methods

Aleksandras Krylovas, Natalja Kosareva () and Edmundas Kazimieras Zavadskas
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Aleksandras Krylovas: Department of Mathematical Modelling, Vilnius Gediminas Technical University
Natalja Kosareva: Department of Mathematical Modelling, Vilnius Gediminas Technical University
Edmundas Kazimieras Zavadskas: Department of Construction Technology and Management, Vilnius Gediminas Technical University

Journal for Economic Forecasting, 2016, issue 3, 19-39

Abstract: The article analyzes Multiple Criteria Decision Making (MCDM) problem when there are two different groups of evaluating criteria. It was shown how criteria weights can be calculated according to weights balancing method by formulating optimization task. Case study of the small dimensions problem was solved by Kemeny Median Indicator Ranks Accordance (KEMIRA) method with options re-selection. Next, 8 various candidates sorting algorithms – 6 based on voting theory methods and 2 algorithms based on Kemeny median – were compared with each other. Monte Carlo experiments were conducted for the cases of 3-10 experts, 3-5 candidates and probability values of correct decision p=0.4-0.8. The highest percent of correct decisions and the lowest percent of failed voting procedures were demonstrated by algorithms based on Kemeny median.

Keywords: Multiple Criteria Decision Making; KEMIRA method; Kemeny median; Monte Carlo method; voting theory (search for similar items in EconPapers)
JEL-codes: C15 C61 D72 D81 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (1)

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