Paper Info
Title
Simultaneous Evaluation Of Criteria And Alternatives (Seca) For Multi-Criteria Decision-Making
Abstract
In the discrete form of multi-criteria decision-making (MCDM) problems, we are usually confronted with a decision-matrix formed from the information of some alternatives on some criteria. In this study, a new method is proposed for simultaneous evaluation of criteria and alternatives (SECA) in an MCDM problem. For making this type of evaluation, a multi-objective non-linear programming model is formulated. The model is based on maximization of the overall performance of alternatives with consideration of the variation information of decision-matrix within and between criteria. The standard deviation is used to measure the within-criterion, and the correlation is utilized to consider the between-criterion variation information. By solving the multi-objective model, we can determine the overall performance scores of alternatives and the objective weights of criteria simultaneously. To validate the proposed method, a numerical example is used, and three analyses are made. Firstly, we analyse the objective weights determined by the method, secondly, the stability of the performance scores and ranking results are examined, and finally, the ranking results of the proposed method are compared with those of some existing MCDM methods. The results of the analyses show that the proposed method is efficient to deal with MCDM problems.
Year
DOI
Venue
2018
10.15388/Informatica.2018.167
INFORMATICA
Keywords
Field
DocType
multi-criteria decision-making (MCDM), criteria weight, performance evaluation, simultaneous evaluation of criteria and alternatives (SECA)
Computer science,Artificial intelligence,Machine learning
Journal
Volume
Issue
ISSN
29
2
0868-4952
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Mehdi Keshavarz Ghorabaee1573.62
Maghsoud Amiri2819.31
Edmundas Kazimieras Zavadskas350049.88
Zenonas Turskis418115.67
Jurgita Antucheviciene512113.99