An Analysis of Multi-Criteria Decision Making Methods
01 Jan 2013-
TL;DR: A literature review of MCDM methods can be found in this paper, which examines the advantages and disadvantages of the identified methods, and explains how their common applications relate to their relative strengths and weaknesses.
Abstract: Multi-Criteria Decision Making (MCDM) methods have evolved to accommodate various types of applications. Dozens of methods have been developed, with even small variations to existing methods causing the creation of new branches of research. This paper performs a literature review of common Multi-Criteria Decision Making methods, examines the advantages and disadvantages of the identified methods, and explains how their common applications relate to their relative strengths and weaknesses. The analysis of MCDM methods performed in this paper provides a clear guide for how MCDM methods should be used in particular situations.
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TL;DR: A survey of MaOEAs is reported and seven classes of many-objective evolutionary algorithms proposed are categorized into seven classes: relaxed dominance based, diversity-based, aggregation- based, indicator-Based, reference set based, preference-based and dimensionality reduction approaches.
Abstract: Multiobjective evolutionary algorithms (MOEAs) have been widely used in real-world applications. However, most MOEAs based on Pareto-dominance handle many-objective problems (MaOPs) poorly due to a high proportion of incomparable and thus mutually nondominated solutions. Recently, a number of many-objective evolutionary algorithms (MaOEAs) have been proposed to deal with this scalability issue. In this article, a survey of MaOEAs is reported. According to the key ideas used, MaOEAs are categorized into seven classes: relaxed dominance based, diversity-based, aggregation-based, indicator-based, reference set based, preference-based, and dimensionality reduction approaches. Several future research directions in this field are also discussed.
614 citations
TL;DR: The results indicate the proposed fuzzy BWM can not only obtain reasonable preference ranking for alternatives but also has higher comparison consistency than the BWM.
Abstract: Fuzzy best-worst method is proposed to solve the issues under fuzzy environment.A consistency ratio for fuzzy best-worst method is proposed for verification.The results indicate the fuzzy best-worst method outperforms best-worst method.The fuzzy best-worst method has a higher comparison consistency. Considering the vagueness frequently representing in decision data due to the lack of complete information and the ambiguity arising from the qualitative judgment of decision-makers, the crisp values of criteria may be inadequate to model the real-life multi-criteria decision-making (MCDM) issues. In this paper, the latest MCDM method, namely best-worst method (BWM) was extended to the fuzzy environment. The reference comparisons for the best criterion and for the worst criterion were described by linguistic terms of decision-makers, which can be expressed in triangular fuzzy numbers. Then, the graded mean integration representation (GMIR) method was employed to calculate the weights of criteria and alternatives with respect to different criteria under fuzzy environment. According to the concept of BWM, the nonlinearly constrained optimization problem was built for determining the fuzzy weights of criteria and alternatives with respect to different criteria. The fuzzy ranking scores of alternatives can be derived from the fuzzy weights of alternatives with respect to different criteria multiplied by fuzzy weights of the corresponding criteria, and then the crisp ranking score of alternatives can be calculated by employing GMIR method for optimal alternative selection. Meanwhile, the consistency ratio was proposed for fuzzy BWM to check the reliability of fuzzy preference comparisons. Three case studies were performed to illustrate the effectiveness and feasibility of the proposed fuzzy BWM. The results indicate the proposed fuzzy BWM can not only obtain reasonable preference ranking for alternatives but also has higher comparison consistency than the BWM.
534 citations
TL;DR: Comparison of three popular multi-criteria supplier selection methods in a fuzzy environment indicates that the three fuzzy methods arrive at identical supplier rankings, yet fuzzy GRA requires less computational complexity to generate the same results.
363 citations
TL;DR: In this article, three Multi-Criteria Decision-Making (MCDM) analysis techniques (VIKOR, TOPSIS and SAW) along with two machine learning methods (NBT and NB) were tested for their ability to model flood susceptibility in one of China's most flood-prone areas, the Ningdu Catchment.
342 citations
TL;DR: This second task force report provides emerging good-practice guidance on the implementation of multiple criteria decision analysis to support health care decisions and provides an overview of the skills and resources required to implement MCDA.
330 citations
Cites background from "An Analysis of Multi-Criteria Decis..."
...[13] Velasquezl M, Hester PT....
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...(For instance, see Belton and Stewart [11], Guitouni and Martel [12], Velasquez and Hester [13], De Montis et al....
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...(For instance, see Belton and Stewart [11], Guitouni and Martel [12], Velasquez and Hester [13], De Montis et al. [14,15], Getzner et al. [16], Keeney and von Winterfeldt [17], Keeney [18], Dodgson et al. [2], and Olson et al. [19])....
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References
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Book•
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01 Aug 1996
TL;DR: A separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
Abstract: A fuzzy set is a class of objects with a continuum of grades of membership. Such a set is characterized by a membership (characteristic) function which assigns to each object a grade of membership ranging between zero and one. The notions of inclusion, union, intersection, complement, relation, convexity, etc., are extended to such sets, and various properties of these notions in the context of fuzzy sets are established. In particular, a separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
52,705 citations
TL;DR: The Analytic Hierarchy Process (AHP) is a theory of measurement through pairwise comparisons and relies on the judgements of experts to derive priority scales that measure intangibles in relative terms.
Abstract: Decisions involve many intangibles that need to be traded off To do that, they have to be measured along side tangibles whose measurements must also be evaluated as to, how well, they serve the objectives of the decision maker The Analytic Hierarchy Process (AHP) is a theory of measurement through pairwise comparisons and relies on the judgements of experts to derive priority scales It is these scales that measure intangibles in relative terms The comparisons are made using a scale of absolute judgements that represents, how much more, one element dominates another with respect to a given attribute The judgements may be inconsistent, and how to measure inconsistency and improve the judgements, when possible to obtain better consistency is a concern of the AHP The derived priority scales are synthesised by multiplying them by the priority of their parent nodes and adding for all such nodes An illustration is included
6,787 citations
"An Analysis of Multi-Criteria Decis..." refers background in this paper
...AHP is “a theory of measurement through pairwise comparisons and relies on the judgments of experts to derive priority scales” (Saaty, 2008, p. 83)....
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Book•
01 Jan 1980
6,172 citations
Journal Article•
TL;DR: The Analytic Hierarchy Process (AHP) as discussed by the authors is a theory of measurement through pairwise comparisons and relies on the judgements of experts to derive priority scales, these scales are these scales that measure intangibles in relative terms.
5,663 citations
TL;DR: A state-of-the-art literature survey is conducted to taxonomize the research on TOPSIS applications and methodologies and suggests a framework for future attempts in this area for academic researchers and practitioners.
Abstract: Multi-Criteria Decision Aid (MCDA) or Multi-Criteria Decision Making (MCDM) methods have received much attention from researchers and practitioners in evaluating, assessing and ranking alternatives across diverse industries. Among numerous MCDA/MCDM methods developed to solve real-world decision problems, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) continues to work satisfactorily across different application areas. In this paper, we conduct a state-of-the-art literature survey to taxonomize the research on TOPSIS applications and methodologies. The classification scheme for this review contains 266 scholarly papers from 103 journals since the year 2000, separated into nine application areas: (1) Supply Chain Management and Logistics, (2) Design, Engineering and Manufacturing Systems, (3) Business and Marketing Management, (4) Health, Safety and Environment Management, (5) Human Resources Management, (6) Energy Management, (7) Chemical Engineering, (8) Water Resources Management and (9) Other topics. Scholarly papers in the TOPSIS discipline are further interpreted based on (1) publication year, (2) publication journal, (3) authors' nationality and (4) other methods combined or compared with TOPSIS. We end our review paper with recommendations for future research in TOPSIS decision-making that is both forward-looking and practically oriented. This paper provides useful insights into the TOPSIS method and suggests a framework for future attempts in this area for academic researchers and practitioners.
1,571 citations