Fuzzy-MACBETH Hybrid Method: Mathematical Treatment of a Qualitative Scale Using the Fuzzy Theory

被引:1
|
作者
Bastos, Tatiane Roldao [1 ]
Longaray, Andre Andrade [1 ]
Machado, Catia Maria dos Santos [1 ]
Ensslin, Leonardo [2 ]
Ensslin, Sandra Rolim [3 ]
Dutra, Ademar [2 ]
机构
[1] Univ Fed Rio Grande FURG, Campus Carreiros, BR-96203900 Rio Grande, RS, Brazil
[2] Univ Santa Catarina UNISUL, Programa Posgrad Adm, BR-88010010 Florianopolis, SC, Brazil
[3] Univ Fed Santa Catarina UFSC, Programa Posgrad Engn Prod, BR-88010970 Florianopolis, SC, Brazil
关键词
MACBETH method; Semantic scale; Cardinal scale; Fuzzy logic; Fuzzy-MACBETH; DECISION-MAKING; ALTERNATIVES; LOGISTICS; MODEL;
D O I
10.1007/s44196-023-00195-9
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper describes the research procedures adopted in developing a triangular fuzzy number scale based on the semantic scale of MACBETH (Measuring Attractiveness by a Categorical Based Evaluation Technique). The objective was to mathematically treat the uncertainty and subjectivity of linguistic variables used to assess a decision problem. A matrix was initially obtained based on a decision maker's assessment of a given context analysis. This decision matrix was then fuzzified based on a triangular Fuzzy numbers scale. Next, the inference process was performed using F-LP-MACBETH linear programming problem proposed here, resulting in a Fuzzy scale. This scale was then defuzzified using the centroid method, from which a crisp basic scale emerged, which was then cardinalized. The results show that the MACBETH Fuzzy method proposed here can overcome the classical method's cardinal inconsistency problem, which facilitates its application in complex contexts. Hence, the MACBETH Fuzzy Hybrid method generated numerical values based on the decision makers' semantically consistent assessments in a decision matrix, which by the classical method presents cardinal inconsistency. Therefore, the advantage of the proposed method consists in the possibility of obtaining a cardinal scale aligned to the decision makers' preferences without the need to reassess the context.
引用
收藏
页数:16
相关论文
共 50 条
  • [31] A new method for CBR prediction using fuzzy set theory
    Cuvalcioglu, Gokhan
    Taciroglu, Murat Vergi
    Bal, Arif
    CONSTRUCTION AND BUILDING MATERIALS, 2024, 447
  • [32] A hybrid design method of fuzzy systems
    Li, Y
    Zhao, RC
    Zhang, YNN
    2004 7TH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS, VOLS 1-3, 2004, : 1618 - 1621
  • [33] Comprehensive evaluation method on earthquake damage using fuzzy theory
    Song, B.
    Hao, S.
    Murakami, S.
    Sadohara, S.
    Journal of Urban Planning and Development, 1996, 122 (01): : 1 - 17
  • [34] ERP software selection using intuitionistic fuzzy and interval grey number-based MACBETH method
    Yurtyapan, Mustafa Said
    Aydemir, Erdal
    GREY SYSTEMS-THEORY AND APPLICATION, 2022, 12 (01) : 78 - 100
  • [35] Quantification of qualitative assessments using computing with words: in framework of fuzzy set theory
    Basaran, Murat Alper
    Simonetti, Biagio
    Basaran, Alparslan Abdurrahman
    SOFT COMPUTING, 2020, 24 (18) : 13565 - 13577
  • [36] Quantification of qualitative assessments using computing with words: in framework of fuzzy set theory
    Murat Alper Basaran
    Biagio Simonetti
    Alparslan Abdurrahman Basaran
    Soft Computing, 2020, 24 : 13565 - 13577
  • [37] Using L-fuzzy sets to introduce information theory into qualitative reasoning
    Prats, Francesc
    Rosello, Llorenc
    Sanchez, Monica
    Agell, Nuria
    FUZZY SETS AND SYSTEMS, 2014, 236 : 73 - 90
  • [38] A Numerical Method for Fuzzy Differential Equations and Hybrid Fuzzy Differential Equations
    Ivaz, K.
    Khastan, A.
    Nieto, Juan J.
    ABSTRACT AND APPLIED ANALYSIS, 2013,
  • [39] A Fuzzy Best-Worst Method Based on the Fuzzy Interval Scale
    Goldani, Nastaran
    Kazemi, Mostafa
    ADVANCES IN BEST-WORST METHOD, BWM2022, 2023, : 59 - 73
  • [40] A Mathematical Theory of Fuzzy Numbers Granular Computing Approach
    Lin, Tsau Young
    ROUGH SETS, FUZZY SETS, DATA MINING, AND GRANULAR COMPUTING, 2013, 8170 : 208 - 215