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Interval-valued Fuzzy Soft Decision Making Methods Based on MABAC, Similarity Measure and EDAS

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Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Interval-valued fuzzy soft decision making problems have obtained great popularity recently. Most of the current methods depend on level soft set that provide choice value of alternatives to be ranked. Such choice value always encounter the equal condition that the optimal alternative can't be gained. Most important of all, the current decision making procedure is not in accordance with the way that the decision makers think about the decision making problems. In this paper, we initiate a new axiomatic definition of interval-valued fuzzy distance measure and similarity measure, which is expressed by interval-valued fuzzy number (IVFN) that will reduce the information loss and keep more original information. Later, the objective weights of various parameters are determined via grey system theory, meanwhile, we develop the combined weights, which can show both the subjective information and the objective information. Then, we present three algorithms to solve interval-valued fuzzy soft decision making problems by Multi- Attributive Border Approximation area Comparison (MABAC), Evaluation based on Distance from Average Solution (EDAS) and new similarity measure. Three approaches solve some unreasonable conditions and promote the development of decision making methods. Finally, the effectiveness and feasibility of approaches are demonstrated by some numerical examples.
Wydawca
Rocznik
Strony
373--396
Opis fizyczny
Bibliogr. 38 poz., tab.
Twórcy
autor
  • School of Information Science and Engineering, Shaoguan University, Shaoguan, 512005, Guangdong, China
autor
  • School of Information Science and Engineering, Shaoguan University, Shaoguan, 512005, Guangdong, China
autor
  • School of Information Science and Engineering, Shaoguan University, Shaoguan, 512005, Guangdong, China
Bibliografia
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  • [21] Feng F, Lia YM, Leoreanu-Fotead V. Application of level soft sets in decision making based on intervalvalued fuzzy soft sets. Computers and Mathematics with Applications. 2010; 60(6):1756-1767. URL http://dx.doi.org/10.1016/j.camwa.2010.07.006.
  • [22] Mukherjee A, Sarkar S. Similarity measures of interval-valued fuzzy soft sets and their application in decision making problems. Annals of Fuzzy Mathematics and Informatics. 2014; 8(9):447-460. URL http://www.afmi.or.kr/papers/2014.
  • [23] Yuan F, Hu MJ. Application of interval-valued fuzzy soft sets in evaluation of teaching quality. Journal of Hunan Institute of Science and Technology(Natural Sciences). 2012; 25(1):28-30. URL http://en.cnki.com.cn/Article$\_$en/CJFDTOTAL-YYSF201201007.htm.
  • [24] Peng XD, Yang Y. Algorithms for interval-valued fuzzy soft sets in stochastic multi-criteria decision making based on regret theory and prospect theory with combined weight. Applied Soft Computing. doi:10.1016/j.asoc.2016.06.036.
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  • [29] Ghorabaee MK, Zavadskas EK, Olfat L, Turskis Z. Multi-Criteria Inventory Classification Using a New Method of Evaluation Based on Distance from Average Solution (EDAS). Informatica. 2015; 26(3):435-451. URL http://dx.doi.org/10.15388/Informatica.2015.57.
  • [30] Ghorabaee MK, Zavadskas EK, Amiri M, Turskis Z. Extended EDAS Method for Fuzzy Multi-criteria Decision-making: An Application to Supplier Selection. International Journal of Computers Communications and Control. 2016; 11(3):358-371. URL http://univagora.ro/jour/index.php/ijccc/article/view/2557.
  • [31] Peng XD, Liu C. Algorithms for neutrosophic soft decision making based on EDAS, new similarity measure and level soft set. Journal of Intelligent and Fuzzy Systems. 2017; 32:955-968. doi:10.3233/JIFS-161548.
  • [32] Pamucar D, Cirovic G. The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation area Comparison (MABAC). Expert Systems wtih Applications. 2015; 42(6):3016-3028. URL http://dx.doi.org/10.1016/j.eswa.2014.11.057.
  • [33] Peng XD, Yang Y. Pythagorean Fuzzy Choquet Integral Based MABAC Method for Multiple Attribute Group Decision Making. International Journal of Intelligent Systems. 2016; 31(10):989-1020. doi:10.1002/int.21814.
  • [34] Peng XD, Dai JG. Approaches to single-valued neutrosophic MADM based on MABAC, TOPSIS and new similarity measure with score function. Neural Computing and Applications. doi:10.1007/s00521-016-2607-y.
  • [35] Liu SF, Dang YG, Fang ZG. Grey systems theory and its applications. Beijing: Science Press, 2000.
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  • [38] Xu ZS, Yager RR. Some geometric aggregation operators based on intuitionistic fuzzy sets. International Journal of General Systems. 2006; 35(4):417-433. URL http://dx.doi.org/10.1080/03081070600574353.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-8cc63494-a361-4280-a352-f1d89cd2cf3b
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