Voting-KEMIRA method for determining criteria priority and weights in solving multi-attribute decision making problems

Kosareva, Natalja; Krylovas, Aleksandras; Soltanifar, Mehdi

Use this URL to cite this publication: https://hdl.handle.net/20.500.14911/113167

Voting-KEMIRA method for determining criteria priority and weights in solving multi-attribute decision making problems

Details

Publication Type (CRIS)

Konferencijų tezės nerecenzuojamame leidinyje / Conference theses in non-peer-reviewed publication (T2)

Publication Type (eLABa)

Konferencijos pranešimo tezės / Conference presentation abstract (T2)

VILNIUS TECH Research Priorities and Topics

Fizinių, technologinių ir ekonominių procesų matematiniai modeliai / Mathematical models of physical, technological and economic processes (FM0101)

Lithuanian Intelligent Specialization

Nauji gamybos procesai, medžiagos ir technologijos / New production processes, materials and technologies (L104)

Author(s)

Author	Affiliation
Kosareva, Natalja	Matematinio modeliavimo katedra / Department of Mathematical Modelling (1023)
Krylovas, Aleksandras	Matematinio modeliavimo katedra / Department of Mathematical Modelling (1023)
Soltanifar, Mehdi	Islamic Azad University

Title

Voting-KEMIRA method for determining criteria priority and weights in solving multi-attribute decision making problems

[en]

Natalja Kosareva, Aleksandras Krylovas, Mehdi Soltanifar

Is part of

Mathematical Modelling and Analysis [MMA2022] : 25th international conference, May 30 - June 2, 2022, Druskininkai, Lithuania : abstracts

Published In

Year
2022

Publisher

Vilnius : Vilnius Gediminas Technical University, 2022

Extent

p. 7

URI

URI	Access Rights
https://hdl.handle.net/20.500.14911/113167
http://inga.vgtu.lt/~art/konf/2020/abstr/MMA_2022_abstracts.pdf	Dokumento santrauka/dalis

Cited references

Science / Art Area

Gamtos mokslai / Natural Sciences (N)

Field of Science / Art

Matematika / Mathematics (N001)

Abstract (en)

The KEmeny Median Indicator Ranks Accordance (KEMIRA) method [1] is one of the newest Multi-Attribute Decision Making (MADM) methods that is used for problems in which criteria are inherently divided into two or more categories. Increasing computational complexity in the face of large-scale problems as well as the inflexibility of the method in increasing the categories of criteria are among the disadvantages of this method. Preferential voting is a decision-making method based on a linear programming model with weight restrictions [2], which in combination with KEMIRA can significantly eliminate its shortcomings. This talk presents the new Voting-KEMIRA method to achieve this purpose, which is more flexible and has less computational complexity. The new hybrid method is implemented on a real–world problem and compared to the previous method.