Dynamic hesitant fuzzy linguistic group decision-making from a reliability perspective

doi:10.21629/JSEE.2018.05.12

Journal of Systems Engineering and Electronics ›› 2018, Vol. 29 ›› Issue (5): 1009-1021.doi: 10.21629/JSEE.2018.05.12

• Systems Engineering • Previous Articles Next Articles

Dynamic hesitant fuzzy linguistic group decision-making from a reliability perspective

Zhenzhen MA¹(), Kumaraswamy PONNAMBALAM²(), Jianjun ZHU^1,*(), Shitao ZHANG³()

¹ College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
² Department of Systems Design Engineering, University of Waterloo, Waterloo N2L 3G1, Canada
³ School of Mathematics & Physics Science and Engineering, Anhui University of Technology, Ma’anshan 243002, China

Received:2017-11-13 Online:2018-10-26 Published:2018-11-14
Contact: Jianjun ZHU E-mail:zhenzhen5886@163.com;ponnu@uwaterloo.ca;zhujianjun@nuaa.edu.cn;zhangshitao1980@126.com
About author:MA Zhenzhen was born in 1988. She is a Ph.D. candidate in management science and engineering from Nanjing University of Aeronautics and Astronautics, Nanjing, China. She studied as a visiting scholar with the Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada from 2016 to 2017. Her current research interests include group decision-making, soft computing, multicriteria decision analysis and applications. E-mail: zhenzhen5886@163.com|PONNAMBALAM Kumaraswamy was born in 1958. He received his Ph.D. degree from University of Toronto, Toronto, ON, Canada, in 1987. He is a professor with the Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada. His research interests include the development of design optimization under uncertainty, decision analysis and its applications. E-mail: ponnu@uwaterloo.ca|ZHU Jianjun was born in 1976. He received his Ph.D degree in systems engineering, Northeastern University, Shenyang, China, in 2005. He is a professor with the College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, China. His current research interests include the multiple-criteria decision analysis and its applications, decision support systems, and supply chain coordination and modeling. E-mail: zhujianjun@nuaa.edu.cn|ZHANG Shitao was born in 1980. He received his Ph.D. degree in management science and engineering from Nanjing University of Aeronautics and Astronautics, China, in 2017. He is a lecturer at the School of Mathematics and Physics, Anhui University of Technology, China. His current research interests include group decision making, consensus, computing with words, and decision support systems. E-mail: zhangshitao1980@126.com
Supported by:
the National Natural Science Foundation of China(71171112);the National Natural Science Foundation of China(71502073);the National Natural Science Foundation of China(71601002);the Scientific Innovation Research of College Graduates in Jiangsu Province(KYZZ15_0094);the Anhui Provincial Natural Science Foundation(1708085MG168);This work was supported by the National Natural Science Foundation of China (71171112; 71502073; 71601002), the Scientific Innovation Research of College Graduates in Jiangsu Province (KYZZ15_0094), and the Anhui Provincial Natural Science Foundation (1708085MG168)

Abstract

Abstract:

A dynamic hesitant fuzzy linguistic group decisionmaking (DHFLGDM) problem is studied from the perspective of information reliability based on the theory of hesitant fuzzy linguistic term sets (HFLTSs). First, an approach is applied to transform the dynamic HFLTSs (DHFLTSs) into a set of proportional linguistic terms to eliminate the time dimension. Second, expert reliability is measured by considering both group similarity and degree of certainty, and an optimization method is employed to quantify the linguistic terms by maximizing the group similarity. Third, through computing the attribute stability as well as its reliability, a combination rule which considers both reliability and weight is proposed to aggregate the information, and then the aggregated grade values and degree of stability are used to make a selection. Finally, the application and feasibility of the proposed method are verified through a case study and method comparison.

Key words: dynamic decision-making, hesitant fuzzy linguistic term sets (HFLTSs), proportional linguistic terms, group similarity, degree of certainty

Zhenzhen MA, Kumaraswamy PONNAMBALAM, Jianjun ZHU, Shitao ZHANG. Dynamic hesitant fuzzy linguistic group decision-making from a reliability perspective[J]. Journal of Systems Engineering and Electronics, 2018, 29(5): 1009-1021.

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References 47

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Stage	Alternative	Design and manufacturing capability c₁			Financial capability c₂			Cooperative service capability c₃
Stage	Alternative	e¹	e²	e³	e¹	e²	e³	e¹	e²	e³
t₁	a₁	s₄	s₄	(s₃, s₄)	s₄	(s₄, s₅)	s₄	s₅	(s₄, s₅)	s₄
	a₂	s₄	s₄	s₃	s₄	s₄	(s₃, s₄)	(s₄, s₅)	s₄	s₄
	a₃	(s₃, s₄)	s₄	s₃	s₄	s₄	(s₃, s₄)	s₄	s₄	(s₃, s₄)
t₂	a₁	(s₄, s₅)	(s₄, s₅)	s₄	s₅	s₅	s₄	s₅	s₅	s₄
	a₂	s₄	(s₄, s₅)	s₄	s₅	s₅	(s₄, s₅)	(s₄, s₅)	s₅	s₄
	a₃	(s₃, s₄)	s₄	s₃	s₄	s₄	(s₃, s₄)	(s₄, s₅)	(s₄, s₅)	s₄
t₃	a₁	(s₄, s₅)	s₅	s₄	s₅	(s₅, s₆)	s₄	s₅	s₅	s₄
	a₂	s₅	s₅	(s₄, s₅)	(s₄, s₅)	(s₅, s₆)	s₄	(s₅, s₆)	s₅	(s₄, s₅)
	a₃	s₄	(s₄, s₅)	(s₃, s₄)	(s₅, s₆)	s₅	s₄	s₅	s₅	(s₃, s₄)
t₄	a₁	s₅	s₅	(s₃, s₄)	(s₅, s₆)	(s₅, s₆)	(s₄, s₅)	s₅	s₅	s₄
	a₂	s₅	s₅	s₄	(s₅, s₆)	(s₅, s₆)	s₅	(s₅, s₆)	s₅	(s₄, s₅)
	a₃	s₄	s₅	(s₃, s₄)	(s₅, s₆)	s₅	s₅	s₅	s₅	s₄

Alternative	s₀	s₁	s₂	s₃	s₄	s₅	s₆
a₁	0	0	0	0.1	0.59	0.31	0
a₂	0	0	0	0	0.4	0.45	0.15
a₃	0	0	0	0	0.4	0.6	0

Method	Group similarity	Ranking order
Chen’s method [18]	$SM_2 = 0.943$	$a_2 \succ a_1 \succ a_3 $
	$SM_2 = 0.961$
	$SM_3 = 0.931$
Wang’s method [36]	$SM_1 = 0.95$	$a_3 \succ a_1 \succ a_2 $
	$SM_2 = 0.951$
	$SM_3 = 0.9$
Wu’s method [28]	$SM_1 = 0.894$	$a_2 \succ a_1 \succ a_3 $
	$SM_2 = 0.912$
	$SM_3 = 0.898$
Proposed method	$SM_1 = 0.963$	$a_2 \succ a_1 \succ a_3 $
	$SM_2 = 0.974$
	$SM_3 = 0.966$

Comparative item	The proposed method	Wang’s method [36]
Initial information forms	HFLTSs	Linguistic terms
Linguistic quantification methods	Linguistic granulation optimization method based on group similarity	Trapezoidal fuzzy number
Generation of the attribute weight	Reliability (group similarity and degree of certainty) and prior subjective information	Prior subjective information
Generation of the expert weight	Reliability (degree of certainty) and prior subjective information	Degree of grey relationship and the Euclid distance
Generation of the stage weight	Not required	A model by maximizing the distance among alternatives and some subjective information
Ranking reference criteria	Grade assessments and degree of certainty	Closeness coefficient

Dynamic hesitant fuzzy linguistic group decision-making from a reliability perspective

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