多粒度支持直觉模糊粗糙集的多属性决策方法

doi:10.16451/j.cnki.issn1003-6059.201908001

Abstract
Figure/Table
References (28)
Related Citation (11)

Download: PDF (710 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract

Multiple contradictory attribute information makes it difficult for decision makers to make decisions in multi-attribute decision-making, and therefore the problem are studied from the perspective of support intuitionistic fuzzy sets in this paper. Firstly, on the basis of support intuitionistic fuzzy sets, two models of optimistic and pessimistic multi-granulation support intuitionistic fuzzy rough sets are constructed in combination with the theory of multi-granulation rough sets. The relationship between two models above is analyzed and the related properties are discussed. Then, the fitting function is defined by t-norm and t-conorm, and a multi-attribute decision-making solving method with multi-granulation support intuitionistic fuzzy rough sets is proposed. Meanwhile, a score function and a accuracy function are defined to sort decision results, the corresponding decision rules are extracted, and an algorithm is designed. Example analysis verifies that the method enables decision makers to select the optimal decision-making scheme according to actual demands while dealing with conflicting multi-attribute decision-making problems.

Key words： Support Intuitionistic Fuzzy Set Multi-granulation Rough Set Multi-attribute Decision-Making Fitting Function

Received: 13 February 2019

ZTFLH:

TP 181

Fund:

Supported by National Natural Science Foundation of China(No.61772176), Scientific and Technological Project of Henan Province(No. 182102210078,182102210362), Plan for Scientific Innovation Talent of Henan Province(No. 184100510003), Scientific and Technological Project of Xinxiang City(No. CXGG17002)

Corresponding Authors: XUE Zhan′ao(Corresponding author), Ph.D., professor. His research interests include basic theory of artificial intelligence, rough sets theory and three-way decision theory.

About author:: ZHAO Liping, master student. Her research interests include multi-granulation rough sets theory and intuitionistic fuzzy sets theory.ZHANG Min, master student. Her research interests include multi-granulation rough sets theory and three-way decision theory.HOU Haodong, master student. His research interests include three-way decision theory and intuitionistic fuzzy sets theory.

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	XUE Zhan′ao
	ZHAO Liping
	ZHANG Min
	HOU Haodong

Cite this article:

XUE Zhan′ao,ZHAO Liping,ZHANG Min等. Multi-attribute Decision-Making Method Based on Multi-granulation Support Intuitionistic Fuzzy Rough Sets[J]. , 2019, 32(8): 677-690.

URL:

http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201908001 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2019/V32/I8/677

[1] PAWLAK Z. Rough Set. International Journal of Computer and Information Sciences, 1982, 11(5): 341-356.
[2] YANG B, HU B Q. Communication between Fuzzy Information Systems Using Fuzzy Covering-Based Rough Sets. International Journal of Approximate Reasoning, 2018, 103: 414-436.
[3] PANG B, MI J S, XIU Z Y. L-Fuzzifying Approximation Operators in Fuzzy Rough Sets. Information Sciences, 2019, 480: 14-33.
[4] HAN S E. Roughness Measures of Locally Finite Covering Rough Sets. International Journal of Approximate Reasoning, 2019, 105: 368-385.
[5] SUN B Z, MA W M, QIAN Y H. Multi-granulation Fuzzy Rough Set over Two Universes and Its Application to Decision Making. Knowledge Based Systems, 2017, 123: 61-74.
[6] LIANG D C, XU Z S, LIU D. Three-Way Decisions with Intuitio-nistic Fuzzy Decision-Theoretic Rough Sets Based on Point Operators. Information Sciences, 2017, 375: 183-201.
[7] QIAN Y H, LIANG J Y, YAO Y Y, et al. MGRS: A Multi-granulation Rough Set. Information Sciences, 2010, 180(6): 949-970.
[8] QIAN Y H, LIANG J Y, DANG C Y. Incomplete Multigranulation Rough Set. IEEE Transactions on Systems, Man, and Cybernetics(Systems and Humans), 2010, 40(2): 420-431.
[9] QIAN Y H, LI S Y, LIANG J Y, et al. Pessimistic Rough Set Based Decisions: A Multigranulation Fusion Strategy. Information Sciences, 2014, 264: 196-210.
[10] 陈静雯,马福民,张腾飞,等.基于最大粒的悲观邻域多粒度粗糙集规则获取算法.模式识别与人工智能, 2017, 30(11): 1048-1056.
(CHEN J W, MA F M, ZHANG T F, et al. Rule Acquisition Algorithm for Neighborhood Multi-granularity Rough Sets Based on Maximal Granule. Pattern Recognition and Artificial Intelligence, 2017, 30(11): 1048-1056.)
[11] ATANASSOV K T. Intuitionistic Fuzzy Sets. Fuzzy Sets and Systems, 1986, 20(1): 87-96.
[12] ATANASSOV K T. More on Intuitionistic Fuzzy Sets. Fuzzy Sets and Systems, 1989, 33(1): 37-45.
[13] ZADEH L A. Fuzzy Sets. Information and Control, 1965, 8(3): 338-353.
[14] BORAN F E, GENC S, KURT M, et al. A Multi-criteria Intuitionistic Fuzzy Group Decision Making for Supplier Selection with TOPSIS Method. Expert Systems with Applications, 2009, 36(8): 11363-11368.
[15] WANG C Y, CHEN S M. Multiple Attribute Decision Making Based on Interval-Valued Intuitionistic Fuzzy Sets, Linear Programming Methodology, and the Extended TOPSIS Method. Information Sciences, 2017, 397/398: 155-167.
[16] WU T, LIU X W, LIU F. An Interval Type-2 Fuzzy TOPSIS Model for Large Scale Group Decision Making Problems with Social Network Information. Information Sciences, 2018, 432: 392-410.
[17] LIANG D C, LIU D. Deriving Three-Way Decisions from Intuitio-nistic Fuzzy Decision-Theoretic Rough Sets. Information Sciences, 2015, 300: 28-48.
[18] XU Z S, ZHAO N. Information Fusion for Intuitionistic Fuzzy Decision Making: An Overview. Information Fusion, 2016, 28: 10-23.
[19] HUANG B, LI H X, FENG G F, et al. Inclusion Measure-Based Multi-granulation Intuitionistic Fuzzy Decision-Theoretic Rough Sets and Their Application to ISSA. Knowledge-Based Systems, 2017, 138: 220-231.
[20] NGUYEN X T, VAN NGUYEN D. Support Intuitionistic Fuzzy Set: A New Concept For Soft Computing. International Journal of Intelligent Systems and Applications, 2015, 4: 11-16.
[21] 李佳,梁吉业,庞天杰.基于加权α优势关系的多属性决策排序方法.模式识别与人工智能, 2017, 30(8): 761-768.
(LI J, LIANG J Y, PANG T J. Sorting Method of Multi-attribute Decision Making Based on Weighted α Dominance Relation. Pa-ttern Recognition and Artificial Intelligence, 2017, 30(8): 761-768.)
[22] WAN S P, WANG F, DONG J Y. A Preference Degree for Intui-tionistic Fuzzy Values and Application to Multi-attribute Group Decision Making. Information Sciences, 2016, 370/371: 127-146.
[23] ZHANG L, ZHAN J M, XU Z S. Covering-Based Generalized IF Rough Sets with Applications to Multi-attribute Decision-Making. Information Sciences, 2019, 478: 275-302.
[24] SUN B Z, MA W M, QIAN Y H. Multi-granulation Fuzzy Rough Set over Two Universes and Its Application to Decision Making. Knowledge-Based Systems, 2017, 123: 61-74.
[25] SUN B Z, MA W M, CHEN X T, et al. Heterogeneous Multi-granulation Fuzzy Rough Set-Based Multiple Attribute Group Decision Making with Heterogeneous Preference Information. Compu-ters and Industrial Engineering, 2018, 122: 24-38.
[26] SUN B Z, MA W M, CHEN X T. Variable Precision Multi-granulation Rough Fuzzy Set Approach to Multiple Attribute Group Decision-Making Based on λ-Similarity Relation. Computers and Industrial Engineering, 2019, 127: 326-343.
[27] ZHAN J M, SUN B Z, ALCANTUD J C R. Covering Based Multi-granulation (I,T)-Fuzzy Rough Set Models and Applications in Multi-attribute Group Decision-Making. Information Sciences, 2019,
476: 290-318.
[28] 杨勇,梁晨成.Support-Intuitionistic模糊集集成算子及其决策应用.计算机工程, 2017, 43(1): 207-212.
(YANG Y, LIANG C C. Aggregation Operators of Support-In-tuitionistic Fuzzy Sets and Their Applications in Decision Making. Computer Engineering, 2017, 43(1): 207-212.)