Research Article Multiple Attribute Decision Making Based...
Transcript of Research Article Multiple Attribute Decision Making Based...
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Research ArticleMultiple Attribute Decision Making Based onHesitant Fuzzy Einstein Geometric Aggregation Operators
Xiaoqiang Zhou1,2 and Qingguo Li1
1 College of Mathematics and Econometrics, Hunan University, Changsha 410082, China2 College of Mathematics, Hunan Institute of Science and Technology, Yueyang 414006, China
Correspondence should be addressed to Qingguo Li; [email protected]
Received 25 June 2013; Accepted 12 November 2013; Published 16 January 2014
Academic Editor: Yi-Chi Wang
Copyright Β© 2014 X. Zhou and Q. Li. This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
We first define an accuracy function of hesitant fuzzy elements (HFEs) and develop a new method to compare two HFEs. Then,based on Einstein operators, we give some new operational laws on HFEs and some desirable properties of these operations. Wealso develop several new hesitant fuzzy aggregation operators, including the hesitant fuzzy Einstein weighted geometric (HFEWG
π)
operator and the hesitant fuzzy Einstein ordered weighted geometric (HFEWGπ) operator, which are the extensions of the
weighted geometric operator and the ordered weighted geometric (OWG) operator with hesitant fuzzy information, respectively.Furthermore, we establish the connections between the proposed and the existing hesitant fuzzy aggregation operators and discussvarious properties of the proposed operators. Finally, we apply the HFEWG
πoperator to solve the hesitant fuzzy decision making
problems.
1. Introduction
Atanassov [1, 2] introduced the concept of intuitionistic fuzzyset (IFS) characterized by a membership function and a non-membership function. It is more suitable to deal with fuzzi-ness and uncertainty than the ordinary fuzzy set proposedby Zadeh [3] characterized by one membership function.Information aggregation is an important research topic inmany applications such as fuzzy logic systems and multiat-tribute decisionmaking as discussed byChen andHwang [4].Research on aggregation operators with intuitionistic fuzzyinformation has received increasing attention as shown inthe literature. Xu [5] developed some basic arithmetic aggre-gation operators based on intuitionistic fuzzy values (IFVs),such as the intuitionistic fuzzy weighted averaging operatorand intuitionistic fuzzy ordered weighted averaging operator,while Xu and Yager [6] presented some basic geometricaggregation operators for aggregating IFVs, including theintuitionistic fuzzy weighted geometric operator and intu-itionistic fuzzy ordered weighted geometric operator. Basedon these basic aggregation operators proposed in [6] and [5],
many generalized intuitionistic fuzzy aggregation operatorshave been investigated [5β30]. Recently, Torra and Narukawa[31] and Torra [32] proposed the hesitant fuzzy set (HFS),which is another generalization form of fuzzy set. The char-acteristic of HFS is that it allows membership degree to havea set of possible values.Therefore, HFS is a very useful tool inthe situationswhere there are somedifficulties in determiningthe membership of an element to a set. Lately, research onaggregation methods and multiple attribute decision makingtheories under hesitant fuzzy environment is very active,and a lot of results have been obtained for hesitant fuzzyinformation [33β43]. For example, Xia et al. [38] developedsome confidence induced aggregation operators for hesitantfuzzy information. Xia et al. [37] gave several series of hesitantfuzzy aggregation operators with the help of quasiarithmeticmeans. Wei [35] explored several hesitant fuzzy prioritizedaggregation operators and applied them to hesitant fuzzydecision making problems. Zhu et al. [43] investigated thegeometric Bonferroni mean combining the Bonferroni meanand the geometric mean under hesitant fuzzy environment.Xia and Xu [36] presented some hesitant fuzzy operational
Hindawi Publishing CorporationJournal of Applied MathematicsVolume 2014, Article ID 745617, 14 pageshttp://dx.doi.org/10.1155/2014/745617
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2 Journal of Applied Mathematics
laws based on the relationship between the HFEs and theIFVs. They also proposed a series of aggregation operators,such as hesitant fuzzy weighted geometric (HFWG) operatorand hesitant fuzzy ordered weighted geometric (HFOWG)operator. Furthermore, they applied the proposed aggrega-tion operators to solve the multiple attribute decisionmakingproblems.
Note that all aggregation operators introduced previouslyare based on the algebraic product and algebraic sum of IFVs(orHFEs) to carry out the combination process. However, thealgebraic operations include algebraic product and algebraicsum, which are not the unique operations that can be used toperform the intersection and union.There aremany instancesof various t-norms and t-conorms families which can bechosen tomodel the corresponding intersections and unions,among which Einstein product and Einstein sum are goodalternatives for they typically give the same smooth approxi-mation as algebraic product and algebraic sum, respectively.For intuitionistic fuzzy information,Wang and Liu [10, 11, 44]and Wei and Zhao [30] developed some new intuitionisticfuzzy aggregation operators with the help of Einstein oper-ations. For hesitant fuzzy information, however, it seems thatin the literature there is little investigation on aggregationtechniques using the Einstein operations to aggregate hesitantfuzzy information. Therefore, it is necessary to develop somehesitant fuzzy information aggregation operators based onEinstein operations.
The remainder of this paper is structured as follows.In Section 2, we briefly review some basic concepts andoperations related to IFS andHFS. we also define an accuracyfunction of HFEs to distinguish the two HFEs having thesame score values, based on which we give the new com-parison laws on HFEs. In Section 3, we present some newoperations for HFEs and discuss some basic properties of theproposed operations. In Section 4, we develop some novelhesitant fuzzy geometric aggregation operators with the helpof Einstein operations, such as theHFEWG
πoperator and the
HFEOWGπoperator, and we further study various properties
of these operators. Section 5 gives an approach to solve themultiple attribute hesitant fuzzy decision making problemsbased on the HFEOWG
πoperator. Finally, Section 6 con-
cludes the paper.
2. Preliminaries
In this section, we briefly introduce Einstein operations andsome notions of IFS and HFS. Meantime, we define anaccuracy function of HFEs and redefine the comparison lawsbetween two HFEs.
2.1. Einstein Operations. Since the appearance of fuzzy settheory, the set theoretical operators have played an importantrole and received more and more attention. It is well knownthat the t-norms and t-conorms are the general conceptsincluding all types of the specific operators, and they satisfythe requirements of the conjunction and disjunction opera-tors, respectively. There are various t-norms and t-conormsfamilies that can be used to perform the corresponding inter-sections and unions. Einstein sum β
πand Einstein product
βπare examples of t-conorms and t-norms, respectively.They
are called Einstein operations and defined as [45]
π₯βππ¦ =
π₯ β π¦
1 + (1 β π₯) β (1 β π¦), π₯β
ππ¦ =
π₯ + π¦
1 + π₯ β π¦,
βπ₯, π¦ β [0, 1] .
(1)
2.2. Intuitionistic Fuzzy Set. Atanassov [1, 2] generalizedthe concept of fuzzy set [3] and defined the concept ofintuitionistic fuzzy set (IFS) as follows.
Definition 1. Let π be fixed an IFSπ΄ on π is given by;
π΄ = {β¨π₯, ππ΄(π₯) , ]
π΄(π₯)β© | π₯ β π} , (2)
where ππ΄
: π β [0, 1] and ]π΄
: π β [0, 1], with thecondition 0 β€ π
π΄(π₯) + ]
π΄(π₯) β€ 1 for all π₯ β π. Xu [5] called
π = (ππ, ]π) an IFV.
For IFVs, Wang and Liu [11] introduced some operationsas follows.
Let π > 0, π1= (ππ1
, ]π1
) and π2= (ππ2
, ]π2
) be two IFVs;then
(1) π1βππ2= (
ππ1
+ ππ2
1 + ππ1
ππ2
,]π1
]π2
1 + (1 β ]π1
) (1 β ]π2
))
(2) π1βππ2= (
ππ1
ππ2
1 + (1 β ππ1
) (1 β ππ2
),]π1
+ ]π2
1 + ]π1
]π2
)
(3) πβ§ππ
1= (
2]ππ1
(2 β ]π1
)π
+ ]ππ1
,(1 + π
π1
)π
β (1 β ππ1
)π
(1 + ππ1
)π
+ (1 β ππ1
)π
) .
(3)
2.3. Hesitant Fuzzy Set. As another generalization of fuzzyset, HFS was first introduced by Torra and Narukawa [31, 32].
Definition 2. Letπ be a reference set; anHFS onπ is in termsof a function that when applied toπ returns a subset of [0, 1].
To be easily understood, Xia and Xu use the followingmathematical symbol to express the HFS:
π» = {βπ»(π₯)
π₯| π₯ β π} , (4)
where βπ»(π₯) is a set of some values in [0, 1], denoting the
possible membership degrees of the element π₯ β π to the setπ». For convenience, Xu and Xia [40] called β
π»(π₯) a hesitant
fuzzy element (HFE).Let β be an HFE, ββ = min{πΎ | πΎ β β}, and β+ = max{πΎ |
πΎ β β}. Torra and Narukawa [31, 32] define the IFV π΄env(β)as the envelope of β, where π΄env(β) = (β
β, 1 β β+).
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LetπΌ > 0, β1and β2be twoHFEs. Xia andXu [36] defined
some operations as follows:
(4) β1β¨β2= βπΎ1ββ1,πΎ2ββ2
{πΎ1+ πΎ2β πΎ1πΎ2}
(5) β1β¨β2= βπΎ1ββ1,πΎ2ββ2
{πΎ1πΎ2}
(6) πΌβ = βπΎββ
{πΎπΌ
}
(7) βπΌ
= βπΎββ
{1 β (1 β πΎ)πΌ
} .
(5)
In [36], Xia and Xu defined the score function of anHFE β to compare the HFEs and gave the comparison laws.
Definition 3. Let β be anHFE; π (β) = (1/π(β))βπΎββ
πΎ is calledthe score function of β, where π(β) is the number of values ofβ. For two HFEs β
1and β
2, if π (β
1) > π (β
2), then β
1> β2; if
π (β1) = π (β
2), then β
1= β2.
From Definition 3, it can be seen that all HFEs areregarded as the same if their score values are equal. In hesitantfuzzy decision making process, however, we usually need tocompare two HFEs for reordering or ranking. In the casewhere two HFEs have the same score values, they can notbe distinguished by Definition 3. Therefore, it is necessary todevelop a new method to overcome the difficulty.
For an IFV, Hong and Choi [46] showed that the relationbetween the score function and the accuracy function is sim-ilar to the relation between mean and variance in statistics.From Definition 3, we know that the score value of HFE β isjust themean of the values in β.Motivated by the idea ofHongand Choi [46], we can define the accuracy function of HFE βby using the variance of the values in β.
Definition 4. Let β be an HFE; π(β) = 1 ββ(1/π(β))β
πΎββ(πΎ β π (β))
2 is called the accuracy function ofβ, where π(β) is the number of values in β and π (β) is thescore function of β.
It is well known that an efficient estimator is a measureof the variance of an estimateβs sampling distribution instatistics: the smaller the variance, the better the performanceof the estimator. Motivated by this idea, it is meaningful andappropriate to stipulate that the higher the accuracy degreeof HFE, the better the HFE. Therefore, in the following, wedevelop a new method to compare two HFEs, which is basedon the score function and the accuracy function, defined asfollows.
Definition 5. Let β1and β
2be two HFEs and let π (β ) and
π(β ) be the score function and accuracy function of HFEs,respectively. Then
(1) if π (β1) < π (β
2), then β
1is smaller than β
2, denoted by
β1βΊ β2;
(2) if π (β1) = π (β
2), then
(i) if π(β1) < π(β
2), then β
1is smaller than β
2,
denoted by β1βΊ β2;
(ii) if π(β1) = π(β
2), then β
1and β
2represent
the same information, denoted by β1β β2. In
particular, if πΎ1= πΎ2for any πΎ
1β β1and πΎ2β β2,
then β1is equal to β
2, denoted by β
1= β2.
Example 6. Let β1= {0.5}, β
2= {0.1, 0.9}, β
3= {0.3, 0.7},
β4
= {0.1, 0.3, 0.7, 0.9}, β5
= {0.2, 0.4, 0.6, 0.8}, and β6
=
{0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9}; then π (β1) = π (β
2) =
π (β3) = π (β
4) = π (β
5) = π (β
6) = 0.5, π(β
1) = 1, π(β
2) = 0.6,
π(β3) = 0.8, π(β
4) = 0.6838, π(β
5) = 0.7764, and π(β
6) =
0.7418. By Definition 5, we have β1β» β3β» β5β» β6β» β4β» β2.
3. Einstein Operations of Hesitant Fuzzy Sets
In this section, we will introduce the Einstein operationson HFEs and analyze some desirable properties of theseoperations.Motivated by the operational laws (1)β(3) on IFVsand based on the interconnection between HFEs and IFVs,we give some new operations of HFEs as follows.
Let πΌ > 0, β, β1, and β
2be three HFEs; then
(8) β1βπβ2= βπΎ1ββ1,πΎ2ββ2
{πΎ1+ πΎ2
1 + πΎ1πΎ2
} ,
(9) β1βπβ2= βπΎ1ββ1,πΎ2ββ2
{πΎ1πΎ2
1 + (1 β πΎ1) (1 β πΎ
2)} ,
(10) ββ§ππΌ
= βπΎββ
{2πΎπΌ
(2 β πΎ)πΌ
+ πΎπΌ} .
(6)
Proposition 7. Let πΌ > 0, πΌ1> 0, πΌ
2> 0, β, β
1and β2be three
HFEs; then
(1) β1βπβ2= β2βπβ1,
(2) (β1βπβ2)βπβ3= β1βπ(β2βπβ3),
(3) (β1βπβ2)β§ππΌ
= ββ§ππΌ
1βπββ§ππΌ
2,
(4) (ββ§ππΌ1)β§ππΌ2 = ββ§π(πΌ1πΌ2);
(5) π΄ env(ββ§ππΌ) = (π΄ env(β))
β§ππΌ,
(6) π΄ env(β1βπβ2) = π΄ env(β1)βππ΄ env(β2).
Proof. (1) It is trivial.(2) By the operational law (9), we have
(β1βπβ2) βπβ3
= βπΎ1ββ1,πΎ2ββ2,πΎ3ββ3
{ ((πΎ1πΎ2/ (1 + (1 β πΎ
1) (1 β πΎ
2))) πΎ3)
Γ (1 + (1 β (πΎ1πΎ2/ (1 + (1 β πΎ
1) (1 β πΎ
2))))
Γ (1 β πΎ3))β1
}
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4 Journal of Applied Mathematics
= βπΎ1ββ1,πΎ2ββ2,πΎ3ββ3
{ (πΎ1πΎ2πΎ3) Γ (1 + (1 β πΎ
1) (1 β πΎ
2)
+ (1 β πΎ1) (1 β πΎ
3) + (1 β πΎ
2)
Γ (1 β πΎ3))β1
}
= βπΎ1ββ1,πΎ2ββ2,πΎ3ββ3
{ (πΎ1(πΎ2πΎ3/ (1 + (1 β πΎ
2) (1 β πΎ
3))))
Γ (1 + (1 β πΎ1)
Γ (1 β (πΎ2πΎ3/ (1 + (1 β πΎ
2) (1 β πΎ
3)))))β1
}
= β1βπ(β2βπβ3) .
(7)
(3) Let β = β1βπβ2; then β = β
1βπβ2
=
βπΎ1ββ1,πΎ2ββ2
{πΎ1πΎ2/(1 + (1 β πΎ
1)(1 β πΎ
2))}
(β1βπβ2)β§ππΌ
= ββ§ππΌ
= βπΎββ
{2πΎπΌ
(2 β πΎ)πΌ
+ πΎπΌ}
= βπΎ1ββ1,πΎ2ββ2
{ (2(πΎ1πΎ2/ (1 + (1 β πΎ
1) (1 β πΎ
2)))πΌ
)
Γ ((2 β (πΎ1πΎ2/ (1 + (1 β πΎ
1) (1 β πΎ
2))))πΌ
+ (πΎ1πΎ2/ (1 + (1 β πΎ
1) (1 β πΎ
2)))πΌ
)β1
}
= βπΎ1ββ1,πΎ2ββ2
{2(πΎ1πΎ2)πΌ
(4 β 2πΎ1β 2πΎ2+ πΎ1πΎ2)πΌ
+ (πΎ1πΎ2)πΌ} ,
= βπΎ1ββ1,πΎ2ββ2
{2(πΎ1πΎ2)πΌ
(2 β πΎ1)πΌ
(2 β πΎ2)πΌ
+ (πΎ1πΎ2)πΌ} .
(8)
Since ββ§ππΌ1
= βπΎ1ββ{2πΎπΌ1/((2 β πΎ
1)πΌ
+ πΎπΌ1)} and ββ§ππΌ
2=
βπΎ2ββ{2πΎπΌ
2/((2 β πΎ
2)πΌ
+ πΎπΌ
2)}, then
ββ§ππΌ
1βπββ§ππΌ
2
= βπΎ1ββ1,πΎ2ββ2
{ ((2πΎπΌ
1/ ((2 β πΎ
1)πΌ
+ πΎπΌ
1))
β (2πΎπΌ
2/ ((2 β πΎ
2)πΌ
+ πΎπΌ
2)))
Γ (1 + (1 β (2πΎπΌ
1/ ((2 β πΎ
1)πΌ
+ πΎπΌ
1)))
Γ (1 β (2πΎπΌ
2/ ((2 β πΎ
2)πΌ
+ πΎπΌ
2))))β1
}
= βπΎ1ββ1,πΎ2ββ2
{2(πΎ1πΎ2)πΌ
(2 β πΎ1)πΌ
(2 β πΎ2)πΌ
+ (πΎ1πΎ2)πΌ} .
(9)
Thus (β1βπβ2)β§ππΌ
= ββ§ππΌ
1βπββ§ππΌ
2.
(4) Since ββ§ππΌ1 = βπΎββ
{2πΎπΌ1/((2 β πΎ)πΌ1 + πΎπΌ1)}, then
(ββ§ππΌ1)β§ππΌ2
= βπΎββ
{ (2(2πΎπΌ1/ ((2 β πΎ)
πΌ1 + πΎπΌ1))πΌ2
)
Γ ((2 β (2πΎπΌ1/ ((2 β πΎ)πΌ1 + πΎπΌ1)))
πΌ2
+ (2πΎπΌ1/ ((2 β πΎ)πΌ1 + πΎπΌ1))
πΌ2
)β1
}
= βπΎββ
{2πΎ(πΌ1πΌ2)
(2 β πΎ)(πΌ1πΌ2)
+ πΎ(πΌ1πΌ2)}
= ββ§π(πΌ1πΌ2).
(10)
(5) By the definition of the envelope of an HFE and theoperation laws (3) and (10), we have
(π΄env (β))β§ππΌ
= (ββ
, 1 β β+
)β§ππΌ
= (2(ββ)
πΌ
(2 β ββ)πΌ
+ (ββ)πΌ,[1 + (1 β β+)]
πΌ
β [1 β (1 β β+)]πΌ
[1 + (1 β β+)]πΌ
+ [1 β (1 β β+)]πΌ)
= (2(ββ)
πΌ
(2 β ββ)πΌ
+ (ββ)πΌ,(2 β β+)
πΌ
β (β+)πΌ
(2 β β+)πΌ
+ (β+)πΌ) .
π΄env (ββ§ππΌ
)
= π΄env (βπΎββ
{2πΎπΌ
(2 β πΎ)πΌ
+ πΎπΌ})
= (2(ββ)
πΌ
(2 β ββ)πΌ
+ (ββ)πΌ, 1 β
2(β+)πΌ
(2 β β+)πΌ
+ (β+)πΌ)
= (2(ββ)
πΌ
(2 β ββ)πΌ
+ (ββ)πΌ,(2 β β+)
πΌ
β (β+)πΌ
(2 β β+)πΌ
+ (β+)πΌ) .
(11)
Thus, π΄env(ββ§ππΌ
) = (π΄env(β))β§ππΌ.
(6) By the definition of the envelope of an HFE and theoperation laws (2) and (9), we have
π΄env (β1) βππ΄env (β2)
= (ββ
1, 1 β β
+
1) βπ(ββ
2, 1 β β
+
2)
= (ββ1ββ2
1 + (1 β ββ1) (1 β ββ
2),(1 β β+
1) + (1 β β+
2)
1 + (1 β β+1) (1 β β+
2))
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Journal of Applied Mathematics 5
π΄env (β1βπβ2)
= π΄env ( βπΎ1ββ1,πΎ2ββ2
{πΎ1πΎ2
1 + (1 β πΎ1) (1 β πΎ
2)})
= (ββ1ββ2
1 + (1 β ββ1) (1 β ββ
2), 1 β
β+1β+2
1 + (1 β β+1) (1 β β+
2))
= (ββ1ββ2
1 + (1 β ββ1) (1 β ββ
2),(1 β β+
1) + (1 β β+
2)
1 + (1 β β+1) (1 β β+
2)) .
(12)
Thus, π΄env(β1βπβ2) = π΄env(β1)βππ΄env(β2).
Remark 8. Let πΌ1> 0, πΌ
2> 0, and β be an HFE. It is worth
noting that ββ§ππΌ1βπββ§ππΌ2 β ββ§π(πΌ1+πΌ2) does not hold necessarily
in general. To illustrate that, an example is given as follows.
Example 9. Let β = (0.3, 0.5), πΌ1
= πΌ2
= 1; thenββ§ππΌ1β
πββ§ππΌ2 = ββ
πβ = β
πΎπββ,πΎπββ,(π,π=1,2)
{πΎππΎπ/(1 + (1 β
πΎπ)(1 β πΎ
π))} = (0.0604, 0.1111, 0.2), and ββ§π(πΌ1+πΌ2) =
ββ§π2 = βπΎββ
{2πΎ2/((2 β πΎ)2
+ πΎ2)} = (0.0604, 0.2). Clearly,π (ββ§ππΌ1β
πββ§ππΌ2) = 0.1238 < 0.1302 = π (ββ§π(πΌ1+πΌ2)). Thus
ββ§ππΌ1βπββ§ππΌ1 βΊ ββ§π(πΌ1+πΌ2).
However, if the number of the values in β is only one, thatis, HFE β is reduced to a fuzzy value, then the above resultholds.
Proposition 10. Let πΌ1> 0, πΌ
2> 0, and β be an HFE, in
which the number of the values is only one, that is, β = {πΎ};then ββ§ππΌ1β
πββ§ππΌ2 = ββ§π(πΌ1+πΌ2).
Proof. Since ββ§ππΌ1 = βπΎββ
{2πΎπΌ1/((2 β πΎ)πΌ1 + πΎπΌ1)} and ββ§ππΌ2 =
βπΎββ
{2πΎπΌ2/((2 β πΎ)πΌ2 + πΎπΌ2)}, then
ββ§ππΌ1βπββ§ππΌ1
= βπΎββ
{ ((2πΎπΌ1/ ((2 β πΎ)
πΌ1 + πΎπΌ1))
β (2πΎπΌ2/ ((2 β πΎ)
πΌ2 + πΎπΌ2)))
Γ (1 + (1 β (2πΎπΌ1/ ((2 β πΎ)
πΌ1 + πΎπΌ1)))
Γ (1 β (2πΎπΌ2/ ((2 β πΎ)
πΌ2 + πΎπΌ2))))β1
}
= βπΎββ
{ (2πΎπΌ1 β 2πΎπΌ2) Γ ([(2 β πΎ)
πΌ1 + πΎπΌ1] β [(2 β πΎ)
πΌ2 + πΎπΌ2]
+ [(2 β πΎ)πΌ1 β πΎπΌ1]
β [(2 β πΎ)πΌ2 β πΎπΌ2])β1
}
= βπΎββ
{2πΎπΌ1+πΌ2
(2 β πΎ)πΌ1+πΌ2 + πΎπΌ1+πΌ2
}
= ββ§π(πΌ1+πΌ2)
.
(13)
Proposition 10 shows that it is consistent with the result(iii) in Theorem 2 in the literature [11].
4. Hesitant Fuzzy Einstein GeometricAggregation Operators
The weighted geometric operator [47] and the orderedweighted geometric operator [48] are two of the most com-mon and basic aggregation operators. Since their appearance,they have received more and more attention. In this section,we extend them to aggregate hesitant fuzzy information usingEinstein operations.
4.1. Hesitant Fuzzy Einstein Geometric Weighted AggregationOperator. Based on the operational laws (5) and (7) onHFEs,Xia and Xu [36] developed some hesitant fuzzy aggregationoperators as listed below.
Let βπ(π = 1, 2, . . . , π) be a collection of HFEs; then.
(1) the hesitant fuzzy weighted geometric (HFWG) oper-ator
HFWG (β1, β2, . . . , β
π) =
π
β¨π=1
βπ
ππ
= βπΎ1ββ1,πΎ2ββ2,...,πΎπββπ
{
{
{
π
βπ=1
πΎπ
ππ
}
}
}
,
(14)
where π = (π1, π2, . . . , π
π)π is the weight vector of β
π(π =
1, 2, . . . , π) with ππβ [0, 1] and βπ
π=1ππ= 1.
(2) the hesitant fuzzy ordered weighted geometric(HFOWG) operator
HFOWG (β1, β2, . . . , β
π)
=
π
β¨π=1
π€π
βπ(π)
= βπΎπ(1)ββπ(1),πΎπ(2)ββπ(2),...,πΎπ(π)ββπ(π)
{
{
{
π
βπ=1
πΎπ€π
π(π)
}
}
}
,
(15)
where π(1), π(2), . . . , π(π) is a permutation of 1, 2, . . . , π,such that β
π(πβ1)> βπ(π)
for all π = 2, . . . , π and π€ =(π€1, π€2, . . . , π€
π)π is aggregation-associated vector with π€
πβ
[0, 1] and βππ=1
π€π= 1.
For convenience, letπ» be the set of all HFEs. Based on theproposed Einstein operations onHFEs, we develop some newaggregation operators for HFEs and discuss their desirableproperties.
-
6 Journal of Applied Mathematics
Definition 11. Let βπ(π = 1, 2, . . . , π) be a collection of HFEs.
A hesitant fuzzy Einstein weighted geometric (HFEWGπ)
operator of dimension π is a mapping HFEWGπ: π»π β π»
defined as follows:
HFEWGπ(β1, β2, . . . , β
π)
=
π
β¨π
π=1
ββ§πππ
π
= βπΎ1ββ1,πΎ2ββ2,...,πΎπββπ
{
{
{
2βπ
π=1πΎππ
π
βπ
π=1(2 β πΎ
π)ππ
+βπ
π=1πΎππ
π
}
}
}
,
(16)
where π€ = (π€1, π€2, . . . , π€
π)π is the weight vector of β
π(π =
1, 2, . . . , π) and π€π> 0,βπ
π=1π€π= 1. In particular, when π€
π=
1/π, π = 1, 2, . . . , π, the HFEWGπoperator is reduced to the
hesitant fuzzy Einstein geometric (HFEGπ) operator:
HFEGπ(β1, β2, . . . , β
π)
= βπΎ1ββ1,πΎ2ββ2,...,πΎπββπ
{
{
{
2βπ
π=1πΎ1/π
π
βπ
π=1(2 β πΎ
π)1/π
+βπ
π=1πΎ1/π
π
}
}
}
.(17)
From Proposition 10, we easily get the following result.
Corollary 12. If all βπ(π = 1, 2, . . . , π) are equal and the
number of values in βπis only one, that is, β
π= β = {πΎ} for
all π = 1, 2, . . . , π, then
HFEWGπ(β1, β2, . . . , β
π) = β. (18)
Note that the HFEWGπoperator is not idempotent in
general; we give the following example to illustrate this case.
Example 13. Let β1
= β2
= β3
= β = (0.3, 0.7), π€ =(0.4, 0.25, 0.35)
π; then HFEWGπ(β1, β2, β3) = {0.3, 0.4137,
0.3782, 0.5126, 0.4323, 0.579, 0.5342, 0.7}. ByDefinition 3, wehave π (HFEWG
π(β1, β2, β3)) = 0.4812 < 0.5 = π (β). Hence
HFEWGπ(β1, β2, β3) βΊ β.
Lemma 14 (see [18, 49]). Let πΎπ> 0, π€
π> 0, π = 1, 2, . . . , π,
and βππ=1
π€π= 1. Then
π
βπ=1
πΎπ€π
πβ€
π
βπ=1
π€ππΎπ (19)
with equality if and only if πΎ1= πΎ2= β β β = πΎ
π.
Theorem 15. Let βπ(π = 1, 2, . . . , π) be a collection of HFEs
and π€ = (π€1, π€2, . . . , π€
π)π the weight vector of β
π(π =
1, 2, . . . , π) with π€πβ [0, 1] and βπ
π=1π€π= 1. Then
HFEWGπ(β1, β2, . . . , β
π) βͺ° HFWG (β
1, β2, . . . , β
π) , (20)
where the equality holds if only if all βπ(π = 1, 2, . . . , π) are
equal and the number of values in βπis only one.
Proof. For any πΎπβ βπ(π = 1, 2, . . . , π), by Lemma 14, we have
π
βπ=1
(2 β πΎπ)π€π
+
π
βπ=1
πΎπ€π
πβ€
π
βπ=1
π€π(2 β πΎ
π) +
π
βπ=1
π€ππΎπ= 2. (21)
Then
2βπ
π=1πΎππ
π
βπ
π=1(2 β πΎ
π)ππ
+βπ
π=1πΎππ
π
β₯
π
βπ=1
πΎππ
π. (22)
It follows that π (βπ
π
π=1ββ§πππ
π) β₯ π (β
π
π
π=1βππ
π), which completes
the proof of Theorem 15.
Theorem 15 tells us the result that the HFEWGπoperator
shows the decision makerβs more optimistic attitude than theHFWA operator proposed by Xia and Xu [36] (i.e., (15)) inaggregation process. To illustrate that, we give an exampleadopted from Example 1 in [36] as follows.
Example 16. Let β1= (0.2, 0.3, 0.5), β
2= (0.4, 0.6) be two
HFEs, and let π€ = (0.7, 0.3)π be the weight vector of βπ(π =
1, 2); then by Definition 11, we have
HFEWGπ(β1, β2) = βπΎ1ββ1,πΎ2ββ2
{
{
{
2β2
π=1πΎππ
π
β2
π=1(2 β πΎ
π)ππ
+β2
π=1πΎππ
π
}
}
}
= {0.2482, 0.2856, 0.3276, 0.3744,
0.4683, 0.5288} .
(23)
However, Xia and Xu [36] used the HFWG operator toaggregate the β
π(π = 1, 2) and got
HFEG (β1, β2)
= βπΎ1ββ1,πΎ2ββ2
{
{
{
2
βπ=1
πΎπ€π
π
}
}
}
= {0.2462, 0.2781, 0.3270, 0.3693, 0.4676, 0.5281} .
(24)
It is clear that π (HFEWGπ(β1, β2)) = 0.3722 > 0.3694 =
π (HFEG(β1, β2)). Thus HFEWG
π(β1, β2) β» HFEG(β
1, β2).
Based onDefinition 11 and the proposed operational laws,we can obtain the following properties onHFEWG
πoperator.
Theorem 17. Let πΌ > 0, βπ(π = 1, 2, . . . , π), be a collection of
HFEs and π€ = (π€1, π€2, . . . , π€
π)π the weight vector of β
π(π =
1, 2, . . . , π) with π€πβ [0, 1] and βπ
π=1π€π= 1. Then
HFEWGπ(ββ§ππΌ
1, ββ§ππΌ
2, . . . , β
β§ππΌ
π)
= (HFEWGπ(β1, β2, . . . , β
π))β§ππΌ
.
(25)
-
Journal of Applied Mathematics 7
Proof. Since ββ§ππΌπ
= βπΎββπ
{2πΎπΌπ/((2 β πΎ
π)πΌ
+ πΎπΌπ)} for all π =
1, 2, . . . , π, by the definition of HFEWGπ, we have
HFEWGπ(ββ§ππΌ
1, ββ§ππΌ
2, . . . , β
β§ππΌ
π)
= βπΎ1ββ1,πΎ2ββ2,...,πΎπββπ
{
{
{
(2
π
βπ=1
(2πΎπΌ
π/ ((2 β πΎ
π)πΌ
+ πΎπΌ
π))ππ
)
Γ (
π
βπ=1
(2 β (2πΎπΌ
π/ ((2 β πΎ
π)πΌ
+ πΎπΌ
π)))ππ
+
π
βπ=1
(2πΎπΌ
π/ ((2 β πΎ
π)πΌ
+ πΎπΌ
π))ππ
)
β1
}
}
}
= βπΎ1ββ1,πΎ2ββ2,...,πΎπββπ
{
{
{
2βπ
π=1πΎπΌππ
π
βπ
π=1(2 β πΎ
π)πΌππ
+βπ
π=1πΎπΌππ
π
}
}
}
.
(26)
Since HFEWGπ(β1, β2, . . . , β
π) =
βπΎ1ββ1,πΎ2ββ2,...,πΎπββπ
{2βπ
π=1πΎππ
π/(βπ
π=1(2 β πΎ
π)ππ + β
π
π=1πΎππ
π)},
then
(HFEWGπ(β1, β2, . . . , β
π))β§ππΌ
= βπΎ1ββ1,πΎ2ββ2,...,πΎπββπ
{
{
{
(2(2
π
βπ=1
πΎππ
π/(
π
βπ=1
(2 β πΎπ)ππ
+
π
βπ=1
πΎππ
π))
πΌ
)
Γ ((2 β (2
π
βπ=1
πΎππ
π/(
π
βπ=1
(2 β πΎπ)ππ
+
π
βπ=1
πΎππ
π)))
πΌ
+ (2
π
βπ=1
πΎππ
π/(
π
βπ=1
(2 β πΎπ)ππ
+
π
βπ=1
πΎππ
π))
πΌ
)
β1
}
}
}
= βπΎ1ββ1,πΎ2ββ2,...,πΎπββπ
{
{
{
2(βπ
π=1πΎππ
π)πΌ
(βπ
π=1(2 β πΎ
π)ππ
)πΌ
+ (βπ
π=1πΎπΌππ
π)πΌ
}
}
}
= βπΎ1ββ1,πΎ2ββ2,...,πΎπββπ
{
{
{
2βπ
π=1πΎπΌππ
π
βπ
π=1(2 β πΎ
π)πΌππ
+βπ
π=1πΎπΌππ
π
}
}
}
.
(27)
Theorem 18. Let β be anπ»πΉπΈ, βπ(π = 1, 2, . . . , π) a collection
of HFEs, and π€ = (π€1, π€2, . . . , π€
π)π the weight vector of β
π
(π = 1, 2, . . . , π) with π€πβ [0, 1] and βπ
π=1π€π= 1. Then
HFEWGπ(β1βπβ, β2βπβ, . . . , β
πβπβ)
= HFEWGπ(β1, β2, . . . , β
π) βπβ.
(28)
Proof. By the definition of HFEWGπand Einstein product
operator of HFEs, we have
HFEWGπ(β1, β2, . . . , β
π) βπβ
= βπΎββ,πΎ
πββπ,π=1,...,π
{
{
{
(2βπ
π=1πΎππ
π/ (βπ
π=1(2 β πΎ
π)ππ
+βπ
π=1πΎππ
π)) β πΎ
1 + (1 β (2βπ
π=1πΎππ
π/ (βπ
π=1(2 β πΎ
π)ππ
+βπ
π=1πΎππ
π))) (1 β πΎ)
}
}
}
= βπΎββ,πΎ
πββπ,π=1,...,π
{
{
{
2πΎβπ
π=1πΎππ
π
(2 β πΎ)βπ
π=1(2 β πΎ
π)ππ
+ πΎβπ
π=1πΎππ
π
}
}
}
.
(29)
Since βπβπβ = β
πΎπββπ,πΎββ
{πΎππΎ/(1 + (1 β πΎ
π)(1 β πΎ))} for all π =
1, 2, . . . , π, by the definition of HFEWGπ, we have
HFEWGπ(β1βπβ, β2βπβ, . . . , β
πβπβ)
= βπΎββ,πΎ
πββπ,π=1,...,π
{
{
{
(2
π
βπ=1
(πΎππΎ/ (1 + (1 β πΎ
π) (1 β πΎ)))
ππ
)
Γ (
π
βπ=1
(2 β (πΎππΎ/ (1 + (1 β πΎ
π)
Γ (1 β πΎ))))ππ
+
π
βπ=1
(πΎππΎ/ (1 + (1 β πΎ
π)
Γ (1 β πΎ)))π€π)
β1
}
}
}
= βπΎββ,πΎ
πββπ,π=1,...,π
{
{
{
2βπ
π=1(πΎππΎ)ππ
βπ
π=1((2 β πΎ
π) (2 β πΎ))
ππ
+βπ
π=1(πΎππΎ)ππ
}
}
}
-
8 Journal of Applied Mathematics
= βπΎββ,πΎ
πββπ,π=1,...,π
{
{
{
(2
π
βπ=1
πΎππ β
π
βπ=1
πΎππ
π)
Γ (
π
βπ=1
(2 β πΎ)ππ β
π
βπ=1
(2 β πΎπ)ππ
+
π
βπ=1
πΎππ
β
π
βπ=1
πΎππ
π)
β1
}
}
}
= βπΎββ,πΎ
πββπ,π=1,...,π
{
{
{
(2πΎβπ
π=1ππ β
π
βπ=1
πΎππ
π)
Γ ((2 β πΎ)βπ
π=1ππ β
π
βπ=1
(2 β πΎπ)ππ
+ πΎβπ
π=1ππ β
π
βπ=1
πΎππ
π)
β1
}
}
}
= βπΎββ,πΎ
πββπ,π=1,...,π
{
{
{
2πΎβπ
π=1πΎππ
π
(2 β πΎ)βπ
π=1(2 β πΎ
π)ππ
+ πΎβπ
π=1πΎππ
π
}
}
}
.
(30)
Based onTheorems 17 and 18, the following property canbe obtained easily.
Theorem 19. Let πΌ > 0, β be an HFE, let βπ(π = 1, 2, . . . , π)
be a collection of HFEs, and let π€ = (π€1, π€2, . . . , π€
π)π be the
weight vector of βπ(π = 1, 2, . . . , π) with π€
πβ [0, 1] and
βπ
π=1π€π= 1. Then
HFEWGπ(ββ§ππΌ
1βπβ, ββ§ππΌ
1βπβ, . . . , β
β§ππΌ
πβπβ)
= (HFEWGπ(β1, β2, . . . , β
π) βπβ)β§ππΌ
.
(31)
Theorem 20. Let βπand βπ(π = 1, 2, . . . , π) be two collections
of HFEs andπ€ = (π€1, π€2, . . . , π€
π)π the weight vector of β
π(π =
1, 2, . . . , π) with π€πβ [0, 1] and βπ
π=1π€π= 1. Then
HFEWGπ(β1βπβ
1, β2βπβ
2, . . . , β
πβπβ
π)
= HFEWGπ(β1, β2, . . . , β
π) βπHFEWG
π(β
1, β
2, . . . , β
π) .
(32)
Proof. By the definition of HFEWGπand Einstein product
operator of HFEs, we have
HFEWGπ(β1, β2, . . . , β
π) βπHFEWG
π(β
1, β
2, . . . , β
π)
= β
πΎπββπ,πΎ
πββ
π,π=1,...,π
{
{
{
(2βπ
π=1πΎππ
π/ (βπ
π=1(2 β πΎ
π)ππ
+βπ
π=1πΎππ
π)) β (2β
π
π=1πΎπ
ππ
/ (βπ
π=1(2 β πΎ
π)ππ
+βπ
π=1πΎπ
ππ
))
1 + (1 β (2βπ
π=1πΎππ
π/ (βπ
π=1(2 β πΎ
π)ππ
+βπ
π=1πΎππ
π))) (1 β (2β
π
π=1πΎπ
ππ/ (β
π
π=1(2 β πΎ
π)ππ
+βπ
π=1πΎπ
ππ)))
}
}
}
= β
πΎπββπ,πΎ
πββ
π,π=1,...,π
{
{
{
2βπ
π=1πΎππ
πβ βπ
π=1πΎπ
ππ
βπ
π=1(2 β πΎ
π)ππ
β βπ
π=1(2 β πΎ
π)ππ
+βπ
π=1πΎππ
πβ βπ
π=1πΎπ
ππ
}
}
}
.
(33)
Since βπβπβπ= βπΎπββπ,πΎ
πββ
π
{πΎππΎπ/(1 + (1 β πΎ
π)(1 β πΎ
π))} for all
π = 1, 2, . . . , π, by the definition of HFEWGπ, we have
HFEWGπ(β1β¨π
β
1, β2β¨π
β
2, . . . , β
πβ¨π
β
π)
= β
πΎπββπ,πΎ
πββ
π,π=1,...,π
{
{
{
(2
π
βπ=1
(πΎππΎ
π/ (1 + (1 β πΎ
π) (1 β πΎ
π)))ππ
)
Γ (
π
βπ=1
(2 β (πΎππΎ
π/ (1 + (1 β πΎ
π)
Γ (1 β πΎ
π))))ππ
+
π
βπ=1
(πΎππΎ
π/ (1 + (1 β πΎ
π)
Γ (1 β πΎ
π)))ππ
)
β1
}
}
}
= β
πΎπββπ,πΎ
πββ
π,π=1,...,π
{
{
{
(2
π
βπ=1
(πΎππΎ
π)ππ
)
Γ (
π
βπ=1
[(2 β πΎπ) (2 β πΎ
π)]ππ
+
π
βπ=1
(πΎππΎ
π)ππ
)
β1
}
}
}
= β
πΎπββπ,πΎ
πββ
π,π=1,...,π
{
{
{
(2
π
βπ=1
πΎππ
πβ
π
βπ=1
πΎ
π
ππ
)
-
Journal of Applied Mathematics 9
Γ (
π
βπ=1
(2 β πΎπ)ππ
β
π
βπ=1
(2 β πΎ
π)ππ
+
π
βπ=1
πΎππ
πβ
π
βπ=1
πΎ
π
ππ
)
β1
}
}
}
.
(34)
Theorem 21. Let βπ(π = 1, 2, . . . , π) be a collection of HFEs,
ββmin = minπ{ββ
π| ββπ= min{πΎ
πβ βπ}}, and β+max = maxπ{β
+
π|
β+π= max{πΎ
πβ βπ}}, and let π€ = (π€
1, π€2, . . . , π€
π)π be the
weight vector of βπ(π = 1, 2, . . . , π) with π€
πβ [0, 1] and
βπ
π=1π€π= 1. Then
ββ
min βͺ― HFEWGπ (β1, β2, . . . , βπ) βͺ― β+
max, (35)
where the equality holds if only if all βπ(π = 1, 2, . . . , π) are
equal and the number of values in βπis only one.
Proof. Let π(π‘) = (2 β π‘)/π‘, π‘ β [0, 1]. Then π(π‘) = β2/π‘2 < 0.Hence π(π‘) is a decreasing function. Since ββmin β€ β
β
πβ€ πΎπβ€
β+πβ€ β+max for any πΎπ β βπ (π = 1, 2, . . . , π), then π(β
+
max) β€
π(πΎπ) β€ π(ββmin); that is, (2 β β
+
max)/β+
max β€ (2 β πΎπ)/πΎπ β€
(2βββmin)/ββ
min. Then for any πΎπ β βπ (π = 1, 2, . . . , π), we have
π
βπ=1
(2 β β+maxβ+max
)
π€π
β€
π
βπ=1
(2 β πΎπ
πΎπ
)
π€π
β€
π
βπ=1
(1 β ββmin1 + ββmin
)
π€π
ββ (2 β β+maxβ+max
)
βπ
π=1π€π
β€
π
βπ=1
(2 β πΎπ
πΎπ
)
π€π
β€ (1 β ββmin1 + ββmin
)
βπ
π=1π€π
ββ (2 β β+maxβ+max
)
β€
π
βπ=1
(2 β πΎπ
πΎπ
)
π€π
β€ (1 β ββmin1 + ββmin
) ββ2
β+max
β€
π
βπ=1
(2 β πΎπ
πΎπ
)
π€π
+ 1 β€2
ββminββ
ββ
min2
β€1
βπ
π=1((2 β πΎ
π) /πΎπ)π€π
+ 1β€ (
β+max2
) ββ ββ
min
β€2
βπ
π=1((2 β πΎ
π) /πΎπ)π€π
+ 1β€ β+
max ββ ββ
min
β€2βπ
π=1πΎππ
π
βπ
π=1(2 β πΎ
π)ππ
+βπ
π=1πΎππ
π
β€ β+
max.
(36)
It follows that ββmin β€ π (HFEWGπ(β1, β2, . . . , βπ)) β€ β+
max.Thus we have ββmin βͺ― HFEWGπ(β1, β2, . . . , βπ) βͺ― β
+
max.
Remark 22. Let βπand β
π(π = 1, 2, . . . , π) be two collections
ofHFEs, and βπβΊ β
πfor all π; thenHFEWG
π(β1, β2, . . . , β
π) βΊ
HFEWGπ(β1, β2, . . . , β
π) does not hold necessarily in general.
To illustrate that, an example is given as follows.
Example 23. Let β1
= (0.45, 0.6), β2
= (0.6, 0.7), β3
=
(0.5, 0.6), β1= (0.2, 0.9), β
2= (0.45, 0.95), β
3= (0.35, 0.8),
and π€ = (0.5, 0.3, 0.2)π; then HFEWGπ(β1, β2, β3) = {0.5024,
0.5215, 0.5286, 0.5483, 0.5791, 0.6, 0.6077, 0.6291} andHFEWG
π(β1, β2, β3) = {0.2778, 0.3372, 0.3835, 0.4595,
0.6088, 0.7099, 0.7833, 0.8947}. By Definition 3, we haveπ (HFEWG
π(β1, β2, β3)) = 0.5646 and π (HFEWG
π(β1, β2,
β3)) = 0.5568. It follows that HFEWG
π(β1, β2, β3) β»
HFEWGπ(β1, β2, β3). Clearly, β
πβΊ βπfor π = 1, 2, 3, but
HFEWGπ(β1, β2, β3) β» HFEWG
π(β
1, β
2, β
3).
4.2. Hesitant Fuzzy Einstein Ordered Weighted AveragingOperator. Similar to the HFOWG operator introduced byXia and Xu [36] (i.e., (15)), in what follows, we developan (HFEOWG
π) operator, which is an extension of OWA
operator proposed by Yager [50].
Definition 24. For a collection of the HFEs βπ(π =
1, 2, . . . , π), a hesitant fuzzy Einstein ordered weighted aver-aging (HFEOWG
π) operator is a mapping HFEWG
π: π»π
β
π» such that
HFEOWGπ(β1, β2, . . . , β
π)
=
π
β¨π
π=1
ββ§ππ€π
π(π)
= βπΎπ(1)ββπ(1),πΎπ(2)ββπ(2),...,πΎπ(π)ββπ(π)
{
{
{
(2
π
βπ=1
πΎπ€π
π(π))
Γ (
π
βπ=1
(2 β πΎπ(π)
)π€π
+
π
βπ=1
πΎπ€π
π(π))
β1
}
}
}
,
(37)
where (π(1), π(2), . . . , π(π)) is a permutation of (1, 2, . . . , π),such that β
π(πβ1)β» β
π(π)for all π = 2, . . . , π and
π€ = (π€1, π€2, . . . , π€
π)π is aggregation-associated vector with
π€π
β [0, 1] and βππ=1
π€π
= 1. In particular, if π€ =(1/π, 1/π, . . . , 1/π)
π, then the HFEOWGπoperator is reduced
to the HFEAπoperator of dimension π (i.e., (17)).
-
10 Journal of Applied Mathematics
Note that the HFEOWGπweights can be obtained similar
to the OWA weights. Several methods have been introducedto determine the OWA weights in [20, 21, 50β53].
Similar to the HFEWGπoperator, the HFEOWG
πopera-
tor has the following properties.
Theorem 25. Let βπ(π = 1, 2, . . . , π) be a collection of HFEs
and π€ = (π€1, π€2, . . . , π€
π)π the weight vector of β
π(π =
1, 2, . . . , π) with π€πβ [0, 1] and βπ
π=1π€π= 1. Then
HFEOWGπ(β1, β2, . . . , β
π) βͺ° HFOWG (β
1, β2, . . . , β
π) ,
(38)
where the equality holds if only if all βπ(π = 1, 2, . . . , π) are
equal and the number of values in βπis only one.
From Theorem 25, we can conclude that the valuesobtained by the HFEOWG
πoperator are not less than the
ones obtained by the HFOWA operator proposed by Xiaand Xu [36]. To illustrate that, let us consider the followingexample.
Example 26. Let β1
= (0.1, 0.4, 0.7), β2
= (0.3, 0.5), andβ3
= (0.2, 0.6) be three HFEs and suppose that π€ =(0.2, 0.45, 0.35)
π is the associated vector of the aggregationoperator.
By Definitions 3 and 4, we calculate the score values andthe accuracy values of β
1, β2, and β
3as follows, respectively:
π (β1) = π (β
2) = π (β
3) = 0.5, π(β
1) = 0.7551, π(β
2) = 0.9,
π(β3) = 0.8.According to Definition 5, we have β
2βΊ β3βΊ β1. Then
βπ(1)
= β2, βπ(2)
= β3, βπ(3)
= β1.
By the definition of HFEOWGπ, we have
HFEOWGπ(β1, β2, β3)
=
3
β¨π
π=1
ββ§ππ€π
π(π)
= βπΎπ(1)ββπ(1),πΎπ(2)ββπ(2),πΎπ(3)ββπ(3)
{{
{{
{
2β3
π=1πΎπ€π
π(π)
β3
π=1(2 β πΎ
π(π))π€π
+β3
π=1πΎπ€π
π(π)
}}
}}
}
= {0.1716, 0.2787, 0.3495, 0.2939, 0.4582, 0.5598, 0.1926,
0.3106, 0.3877, 0.3272, 0.5047, 0.6125} .
(39)
If we use the HFOWA operator, which was given by Xia andXu [36] (i.e., (15)), to aggregate the HFEs β
π(π = 1, 2, 3), then
we have
HFOWG (β1, β2, . . . , β
π)
=
3
β¨π=1
βπ€π
π(π)= βπΎπ(1)ββπ(1),πΎπ(2)ββπ(2),πΎπ(3)ββπ(3)
{
{
{
3
βπ=1
πΎπ€π
π(π)
}
}
}
= {0.1702, 0.2764, 0.3363, 0.2790, 0.4532, 0.5513,
0.1885, 0.3062, 0.3724, 0.3090, 0.5020, 0.6106} .
(40)
Clearly, π (HFEOWGπ(β1, β2, β3)) = 0.3706 > 0.3629 =
π (HFOWG(β1, β2, . . . , β
π)). By Definition 3, we have
HFEOWGπ(β1, β2, β3) β» HFOWG(β
1, β2, β3).
Theorem 27. Let πΌ > 0, β be an HFE, let βπand β
π
(π = 1, 2, . . . , π) be two collection of HFEs, and let π€ =(π€1, π€2, . . . , π€
π)π be an aggregation-associated vector with
π€πβ [0, 1] and βπ
π=1π€π= 1. Then
(1) HFEWGπ(ββ§ππΌ
1, ββ§ππΌ
2, . . . , ββ§ππΌ
π) =
(HFEWGπ(β1, β2, . . . , β
π))β§ππΌ,
(2) HFEWGπ(β1βπβ, β2βπβ, . . . , β
πβπβ) = HFEWG
π(β1,
β2, . . . , β
π)βπβ,
(3) HFEWGπ(ββ§ππΌ
1βπβ, ββ§ππΌ
1βπβ, . . . , ββ§ππΌ
πβπβ) =
(HFEWGπ(β1, β2, . . . , β
π)βπβ)β§ππΌ,
(4) HFEWGπ(β1βπβ1, β2βπβ2, . . . , β
πβπβπ) =
HFEWGπ(β1, β2, . . . , β
π)βπHFEWG
π(β1, β2, . . . , β
π).
Theorem 28. Let βπ(π = 1, 2, . . . , π) be a collection of HFEs
and let π€ = (π€1, π€2, . . . , π€
π)π be an aggregation-associated
vector with π€πβ [0, 1] and βπ
π=1π€π= 1. Then
ββ
min βͺ― HFEOWGπ (β1, β2, . . . , βπ) βͺ― β+
max, (41)
where ββmin = minπ{ββ
π| ββπ= min{πΎ
πβ βπ}} and β+max =
maxπ{β+π| β+π= max{πΎ
πβ βπ}}.
Besides the above properties, we can get the followingdesirable results on the HFOWG
πoperator.
Theorem 29. Let βπ(π = 1, 2, . . . , π) be a collection of HFEs,
and let π€ = (π€1, π€2, . . . , π€
π)π be an aggregation-associated
vector with π€πβ [0, 1] and βπ
π=1π€π= 1. Then
HFEOWGπ(β1, β2, . . . , β
π) = HFEOWG
π(β
1, β
2, . . . , β
π) ,
(42)
where (β1, β2, . . . , β
π) is any permutation of (β
1, β2, . . . , β
π).
Proof. Let HFEOWGπ(β1, β2, . . . , β
π) = β
π
π
π=1ββ§ππ€π
π(π)
and HFEOWGπ(β1, β2, . . . , β
π) = β
π
π
π=1βπ(π)
β§ππ€π . Since
(β1, β2, . . . , β
π) is any permutation of (β
1, β2, . . . , β
π),
then we have βπ(π)
= βπ(π)
(π = 1, 2, . . . , π). ThusHFEOWG
π(β1, β2, . . . , β
π) = HFEOWG
π(β1, β2, . . . , β
π).
Theorem 30. Let βπ(π = 1, 2, . . . , π) be a collection of HFEs,
and let π€ = (π€1, π€2, . . . , π€
π)π be an aggregation-associated
vector with π€πβ [0, 1] and βπ
π=1π€π= 1. Then
(1) if π€ = (0, 0, . . . , 1), then HFOWGπ(β1, β2, . . . , β
π)=
min{β1, β2, . . . , β
π};
-
Journal of Applied Mathematics 11
(2) if π€ = (1, 0, . . . , 0), then HFOWGπ(β1, β2, . . .,
βπ)=max{β
1, β2, . . . , β
π};
(3) if π€π= 1 and π€
π= 0 (π ΜΈ= π), then HFOWG
π(β1, β2, . . .,
βπ) = β
π(π), where β
π(π)is the πth largest of β
π(π =
1, 2, . . . , π).
5. An Application in HesitantFuzzy Decision Making
In this section, we apply the HFEWGπand HFEOWG
π
operators to multiple attribute decision making with hesitantfuzzy information.
For hesitant fuzzy multiple attribute decision makingproblems, let π = {π
1, π2, . . . , π
π} be a discrete set of
alternatives, let π΄ = {π΄1, π΄2, . . . , π΄
π} be a collection of
attributes, and let π = (π1, π2, . . . , π
π)π be the weight vector
of π΄π(π = 1, 2, . . . , π) with π
πβ₯ 0, π = 1, 2, . . . , π, and
βπ
π=1ππ= 1. If the decision makers provide several values for
the alternative ππ(π = 1, 2, . . . , π) under the attribute π΄
π(π =
1, 2, . . . , π)with anonymity, these values can be considered asan HFE β
ππ. In the case where two decision makers provide
the same value, the value emerges only once in βππ. Suppose
that the decision matrix π» = (βππ)πΓπ
is the hesitant fuzzydecision matrix, where β
ππ(π = 1, 2, . . . , π, π = 1, 2, . . . , π) are
in the form of HFEs.To get the best alternative, we can utilize the HFEWG
π
operator or the HFEOWGπoperator; that is,
βπ= HFEWG
π(βπ1, βπ2, . . . , β
ππ)
= βπΎπ1ββπ1,πΎπ2ββπ2,...,πΎππββππ
{
{
{
2βπ
π=1πΎππ
ππ
βπ
π=1(2 β πΎ
ππ)ππ
+βπ
π=1πΎππ
ππ
}
}
}
(43)
or
βπ= HFEOWG
π(βπ1, βπ2, . . . , β
ππ)
= βπΎππ(π)ββππ(π),π=1,2,...,π
{{
{{
{
2βπ
π=1πΎπ€π
ππ(π)
βπ
π=1(2 β πΎ
ππ(π))π€π
+βπ
π=1πΎπ€π
ππ(π)
}}
}}
}(44)
to derive the overall value βπof the alternatives π
π(π =
1, 2, . . . , π), whereπ€ = (π€1, π€2, . . . , π€
π)π is the weight vector
related to the HFEOWAπoperator, such that π€
πβ₯ 0, π =
1, 2, . . . , π, and βππ=1
π€π= 1, which can be obtained by the
normal distribution based method [20].Then by Definition 3, we compute the scores π (β
π) (π =
1, 2, . . . , π) of the overall values βπ(π = 1, 2, . . . , π) and use
the scores π (βπ) (π = 1, 2, . . . , π) to rank the alternatives
π = {π1, π2, . . . , π
π} and then select the best one (note that
if there is no difference between the two scores βπand β
π,
thenwe need to compute the accuracy degrees π(βπ) and π(β
π)
of the overall values βπand β
πby Definition 4, respectively,
and then rank the alternatives ππand π
πin accordance with
Definition 5).In the following, an example on multiple attribute deci-
sion making problem involving a customer buying a car,
which is adopted from Herrera and Martinez [54], is givento illustrate the proposed method using the HFEOWG
π
operator.
Example 31. Consider that a customer wants to buy a car,which will be chosen from five types π
π(π = 1, 2, . . . , 5).
In the process of choosing one of the cars, four factors areconsidered:π΄
1is the consumption petrol,π΄
2is the price,π΄
3
is the degree of comfort, and π΄4is the safety factor. Suppose
that the characteristic information of the alternatives ππ(π =
1, 2, . . . , 5) can be represented by HFEs βππ(π = 1, 2, . . . , 5; π =
1, 2, . . . , 4), and the hesitant fuzzy decision matrix is given inTable 1.
To use HFEOWGπoperator, we first reorder the β
ππ(π =
1, 2, . . . , 4) for each alternative ππ(π = 1, 2, . . . , 5). According
to Definitions 3 and 4, we compute the score values andaccuracy degrees of π (β
ππ) (π = 1, 2, . . . , 5; π = 1, 2, . . . , 4) as
follows:
π (β11) = 0.45, π (β
12) = 0.75, π (β
13) = 0.3,
π (β14) = 0.3, π (β
13) = 0.9184, π (β
14) = 0.9;
π (β21) = 0.5, π (β
22) = 0.7, π (β
23) = 0.7,
π (β24) = 0.5, π (β
21) = 0.7551, π (β
24) = 0.8129,
π (β22) = 0.8367, π (β
23) = 0.9;
π (β31) = 0.85, π (β
32) = 0.4, π (β
33) = 0.35,
π (β34) = 0.4, π (β
32) = 0.8367, π (β
34) = 0.7764;
π (β41) = 0.6, π (β
42) = 0.6, π (β
43) = 0.3,
π (β44) = 0.4, π (β
41) = 0.772, π (β
42) = 0.8367;
π (β51) = 0.5, π (β
52) = 0.3, π (β
53) = 0.5,
π (β54) = 0.35, π (β
51) = 0.8367, π (β
53) = 0.8129.
(45)
Then by Definition 5, we have
β1π(1)
= β12, β1π(2)
= β11, β1π(3)
= β13, β1π(4)
= β14;
β2π(1)
= β23, β2π(2)
= β22, β2π(3)
= β24, β2π(4)
= β21;
β3π(1)
= β31, β3π(2)
= β32, β3π(3)
= β34, β3π(4)
= β33;
β4π(1)
= β42, β4π(2)
= β41, β4π(3)
= β44, β4π(4)
= β43;
β5π(1)
= β51, β5π(2)
= β53, β5π(3)
= β54, β5π(4)
= β52.
(46)
Suppose that π€ = (0.1835, 0.3165, 0.3165, 0.1835)π is theweighted vector related to the HFEOWA
πoperator and it
is derived by the normal distribution based method [20].Thenwe utilize theHFEOWA
πoperator to obtain the hesitant
-
12 Journal of Applied Mathematics
Table 1: Hesitant fuzzy decision making matrix.
π΄1
π΄2
π΄3
π΄4
π1
{0.4, 0.5} {0.7, 0.8} {0.2, 0.3, 0.4} {0.2, 0.4}
π2
{0.2, 0.5, 0.8} {0.5, 0.7, 0.9} {0.6, 0.8} {0.2, 0.5, 0.6, 0.7}
π3
{0.8, 0.9} {0.2, 0.4, 0.6} {0.2, 0.3, 0.4, 0.5} {0.1, 0.3, 0.5, 0.7}
π4
{0.3, 0.4, 0.6, 0.8, 0.9} {0.4, 0.6, 0.8} {0.1, 0.2, 0.4, 0.5} {0.2, 0.3, 0.5, 0.6}
π5
{0.3, 0.5, 0.7} {0.2, 0.3, 0.4} {0.2, 0.5, 0.6, 0.7} {0.1, 0.3, 0.4, 0.6}
fuzzy elements βπ(π = 1, 2, 3, 4, 5) for the alternatives π
π
(π = 1, 2, 3, 4, 5). Take alternativeπ1for an example; we have
β1= HFEOWG
π(β11, β12, . . . , β
14)
= βπΎ1π(π)ββ1π(π),π=1,2,3,4
{{
{{
{
2β4
π=1πΎπ€π
1π(π)
β4
π=1(2 β πΎ
1π(π))π€π
+β4
π=1πΎπ€π
1π(π)
}}
}}
}
= {0.3220, 0.3642, 0.3635, 0.4099, 0.3974, 0.4470,
0.3473, 0.3921, 0.3914, 0.4403, 0.4272, 0.4794,
0.3327, 0.3760, 0.3753, 0.4228, 0.4101, 0.4607,
0.3587, 0.4046, 0.4039, 0.4539, 0.4405, 0.4938} .
(47)
The results can be obtained similarly for the other alterna-tives; here we will not list them for vast amounts of data. ByDefinition 3, the score values π (β
π) of βπ(π = 1, 2, 3, 4, 5) can
be computed as follows:
π (β1) = 0.4048, π (β
2) = 0.5758, π (β
3) = 0.4311,
π (β4) = 0.4479, π (β
5) = 0.3620.
(48)
According to the scores π (βπ) of the overall hesitant fuzzy
values βπ(π = 1, 2, 3, 4, 5), we can rank all the alternatives π
π:
π2β» π4β» π3β» π1β» π5. Thus the optimal alternative is
π2.If we use the HFWG operator introduced by Xia and Xu
[36] to aggregate the hesitant fuzzy values, then
π (β1) = 0.3960, π (β
2) = 0.5630, π (β
3) = 0.4164,
π (β4) = 0.4344, π (β
5) = 0.3548.
(49)
By Definition 5, we haveπ2β» π4β» π3β» π1β» π5.
Note that the rankings are the same in such two cases, butthe overall values of alternatives by the HFEOWG
πoperator
are not smaller than the ones by the HFOWG operator.It shows that the attitude of the decision maker using theproposed HFEOWG
πoperator is more optimistic than the
one using the HFOWG operator introduced by Xia andXu [36] in aggregation process. Therefore, according to thedecision makersβ optimistic (or pessimistic) attitudes, thedifferent hesitant fuzzy aggregation operators can be used toaggregate the hesitant fuzzy information in decision makingprocess.
6. Conclusions
The purpose of multicriteria decision making is to select theoptimal alternative from several alternatives or to get theirranking by aggregating the performances of each alternativeunder some attributes, which is the pervasive phenomenonin modern life. Hesitancy is the most common problemin decision making, for which hesitant fuzzy set can beconsidered as a suitable means allowing several possibledegrees for an element to a set. Therefore, the hesitant fuzzymultiple attribute decision making problems have receivedmore and more attention. In this paper, an accuracy functionof HFEs has been defined for distinguishing between thetwo HFEs having the same score values, and a new orderrelation between two HFEs has been provided. Some Ein-stein operations on HFEs and their basic properties havebeen presented. With the help of the proposed operations,several new hesitant fuzzy aggregation operators includingthe HFEWG
πoperator and HFEOWG
πoperator have been
developed, which are extensions of the weighted geometricoperator and the OWGoperator with hesitant fuzzy informa-tion, respectively. Moreover, some desirable properties of theproposed operators have been discussed and the relationshipsbetween the proposed operators and the existing hesitantfuzzy aggregation operators introduced by Xia and Xu [36]have been established. Finally, based on the HFEOWG
π
operator, an approach of hesitant fuzzy decision making hasbeen given and a practical example has been presented todemonstrate its practicality and effectiveness.
Conflict of Interests
The authors declared that there is no conflict of interestsregarding the publication of this paper.
Acknowledgments
The authors are very grateful to the editors and the anony-mous reviewers for their insightful and constructive com-ments and suggestions that have led to an improved version ofthis paper. This work was supported by the National NaturalScience Foundation of China (nos. 11071061 and 11101135)and the National Basic Research Program of China (no.2011CB311808).
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