# Some Similarity Measures for Triangular Fuzzy Number and Their Applications in Multiple Criteria Group Decision-Making

Authors: Liyuan Zhang; Xuanhua Xu; Li Tao
Journal: Journal of Applied Mathematics
 (2013)

---

## Abstract

We propose some similarity measures between two
triangular fuzzy numbers (TFNs) based on the vector similarity measures in
vector space, which can be used to aggregate the decision information with
TFNs. A methodology for multiple criteria group decision-making (MCGDM)
problems with triangular fuzzy information is proposed; the criteria values take
the form of linguistic values, which can be converts to TFNs. According to the
weighted similarity measures between each alternative and ideal alternative,
it is easy to rank alternatives and select the most desirable alternative. Finally,
we apply the proposed methods to an illustrative example of MCGDM;
the numerical results show that our method is effective and practical. For
comparison, we also apply our similarity measures method to solve the fuzzy
decision-making problem in Wei (2011); our method has simpler computation and gets
the same results more rapidly than the FLOWHM method.


---

## Body

## 1. Introduction

Fuzzy multiple criteria decision-making (FMCDM) is the process of ranking the feasible alternatives and selecting the best one by considering multiple criteria, in which the alternatives and criteria values are carried by fuzzy sets [1], intuitionistic fuzzy set [2, 3], triangular fuzzy number [4], triangular intuitionistic fuzzy number [5], interval-valued triangular fuzzy number [6], trapezoidal fuzzy number [7, 8], intuitionistic trapezoidal fuzzy number [9], internal-valued trapezoidal fuzzy number [10], and so on. As an important part of the modern decision science, some related methods have been successfully applied to fuzzy decision-making problems, for example, ordered weighted aggregation operators  [11], weighted geometric aggregation operators [10], TOPSIS method [12–14], analytic hierarchy process method [15], grey relational analysis method [16, 17], and similarity measures [3, 18], and so forth. Over the last decades, many studies have been done on the concepts of similarity measures between two intuitionistic fuzzy sets. On the one hand, the similarity measures were defined based on distance models, such as the Hamming distance similarity method [19], the Hausdorff distance similarity measure [20], and the Euclidean distance similarity measure [3]. On the other hand, the intuitionistic fuzzy set was seen as a vector containing some elements, by using the vector similarity measures to define the similarity measures between two intuitionistic fuzzy sets, for example, the cosine similarity measures [21] and so forth. However, as an effective method and a wide range of application in various fields, very few researchers worked on similarity measures between two triangular fuzzy numbers and applied them to triangular fuzzy multiple criteria group decision-making.Because the triangular fuzzy number (TFN) is intuitive, easy to use, computationally simple, and useful in promoting representation and information processing in a fuzzy environment, it was usefully applied to solve FMCDM problems, in which the criteria values are carried by the TFNs. Many methodologys have been proposed to solve FMCDM with TFNs in the literature; Fu [22] presented a fuzzy optimization method based on the concept of ideal and anti-ideal points to solve FMCDM problems with TFNs. A case study of reservoir flood control operation was given to demonstrate the proposed method’s effectiveness. Wei [23] proposed fuzzy induced ordered weighted harmonic mean operator to solve fuzzy group decision-making problems. In [24], Wei et al. investigated the multiple attribute decision-making problems with triangular fuzzy information and developed an generalized triangular fuzzy correlated averaging operator; numerical results showed the method was applicable. The above literatures mainly focus on the group decision-making with unknown information on criterion weight and on group member weight. However, in some actual decision-making process, the information about criterion weight and on group member weight are important. Therefore, with the defects of the methods in the literatures, we develop some similarity measures to solve FMCGDM problems with known information on criterion weight and on group member weight. In order to do so, the rest of this paper is set out as follows. In the next section, we introduce some basic concepts and operational laws of TFNs; three similarity measures between two TFNs are also defined. In Section 3, we present a triangular FMCGDM method based on similarity measures. Section 4 gives two illustrated examples to verify the developed approach; all numerical results show that our method is feasible and applicable. The paper ends with conclusion in Section 5.


## 2. Preliminaries

We consider the following well-know description of a triangular fuzzy numberα.Definition 1 (see [25]).
A triangular fuzzy numberα can be defined by a triplet (a1, a2, and a3). The membership function μα(x) is
(1)μα(x)={0,x<a1,x-a1a2-a1,a1≤x≤a2,x-a3a2-a3,a2≤x≤a3,0,a3<x,
where 0≤a1≤a2≤a3≤1, a1 and a3 stand for the lower and upper values of the support of α, respectively, and a2 stands for the modal values. The membership functions can be seen in Figure 1.Figure 1
Membership functions of TFNs.Letα=(a1,a2,a3) and β=(b1,b2,b3) be two TFNs and γ a positive scalar number; the basic operational laws related to TFNs are shown as
(2)α⊕β=(a1+b1,a2+b2,a3+b3),α⊗β=(a1b1,a2b2,a3b3),γ⊗α=(γa1,γa2,γa3),1α=(1a1,1a2,1a3).In the vector space, there are some similarity measures between two vectors, which successfully apply to various fields, such as pattern recognition, description and classification of complex structured objects, faculty recruitment, and fuzzy assignment problems. In this section, we introduce three important vector similarity measures.LetX=(x1,x2,…,xn) and Y=(y1,y2,…,yn) be two vectors of length n, where all the coordinates are positive; three important similarity measures are defined as
(3)J(Y,Z)=XY∥X∥22+∥Y∥22-XYJ(Y,Z)=∑i=1nxiyi∑i=1nxi2+∑i=1nyi2-∑i=1nxiyi,E(Y,Z)=2XY∥X∥22+∥Y∥22=2∑i=1nxiyi∑i=1nxi2+∑i=1nyi2,C(Y,Z)=XY∥X∥2∥Y∥2=∑i=1nxiyi∑i=1nxi2∑i=1nyi2.The three parametersai (i=1,2,and3) in TFN α=(a1,a2,anda3) can be considered as one vector representation with three elements. Based on the extension of the similarity measures in vector space, the similarity measures between two TFNs are shown in Definition 2.Definition 2.
Letα=(a1,a2,a3) and β=(b1,b2,b3) be two TFNs, where 0≤a1≤a2≤a3≤1, 0≤b1≤b2≤b3≤1; the similarity measures between two TFNs can be defined as follows:
(4)SJ(α,β)=∑i=13aibi∑i=13ai2+∑i=13bi2-∑i=13aibi,(5)SE(α,β)=2∑i=13aibi∑i=13ai2+∑i=13bi2,(6)SC(α,β)=∑i=13aibi∑i=13ai2∑i=13bi2.
The three similarity measures between two TFNs in Definition2 satisfies the following properties, as in Theorem 3.Theorem 3.
The similarity satisfies the following properties:(1)
0≤S(α,β)≤1,(2)
S(α,β)=S(β,α),(3)
ifα=β, that is, ai=bi, i=1,2, and 3. S(α,β)=1.Proof.
Firstly, we proveSJ(α,β) satisfies the above properties, as follows.(P1)
It is obvious thatSJ(α,β)≥0. Thus, we only need to prove SJ(α,β)≤1.By using the basic mathematical equations,(7)2aibi≤ai2+bi2,we get(8)∑i=13‍ai2+∑i=13‍bi2-∑i=13‍aibi≥2aibi-aibi≥aibi.Taking (8) in (4), we get SJ(α,β)≤1.(P2)
It is obvious that the equation is true.(P3)
Whenα=β, that is, ai=bi, for i=1,2, and 3,
(9)SJ(α,β)∑i=13aiai∑i=13ai2+∑i=13ai2-∑i=13aiai=1.
In the same way, we can proveSE(α,β) and SC(α,β) satisfy the properties in Theorem 3.
Therefore, we have finished the proofs.


## 3. Triangular Fuzzy Multiple Criteria Group Decision-Making Method Based on Similarity Measures

In this section, we present a handling method for TFNs multiple criteria group decision-making problems.LetC={C1,C2,…,Cn} be a set of criteria and A={A1,A2,…,Am} a set of alternatives. Suppose we invite p experts to make the judgement, and let G={G1,G2,…,Gp} be a set of experts. They are expected to give the linguistic value of TFNs (see Table 1). The vectors of alternative Ai given by the expert Gk are represented by following TFNs:
(10)vik={〈C1,(ai11k,ai12k,ai13k)〉,〈C2,(ai21k,ai22k,ai23k)〉,…,〈Cn,(ain1k,ain2k,ain3k)〉},
for i=1,2,…,m, k=1,2,…,p.Table 1
Linguistic values of TFNs.
Linguistic term
Linguistic values of TFN
Absolutely poor (AP)
(
0.0,0.0,0.1
)
Very poor (VP)
(
0.0,0.1,0.2
)
Poor (P)
(
0.1,0.2,0.3
)
Medium poor (MP)
(
0.3,0.4,0.5
)
Medium (M)
(
0.4,0.5,0.6
)
Medium good (MG)
(
0.5,0.6,0.7
)
Good (G)
(
0.7,0.8,0.9
)
Very good (VG)
(
0.8,0.9,1.0
)
Absolutely good (AG)
(
0.9,1.0,1.0
)The weight vector of the experts isλ=(λ1,λ2,…,λp); for each alternative Ai, the group preference vector can be calculated by
(11)Vi={〈C1,(∑k=1p‍λkai11k,∑k=1p‍λkai12k,∑k=1p‍λkai13k)〉,〈C2,(∑k=1p‍λkai21k,∑k=1p‍λkai22k,∑k=1p‍λkai23k)〉,…,〈Cn,(∑k=1p‍λkain1k,∑k=1p‍λkain2k,∑k=1p‍λkain3k)〉}.For the different criteraCj(j=1,2,…,n), the weight of a criterion is a triangular fuzzy weight, which is obtained by the decision maker, as wj=(aj1,aj2,aj3)(j=1,2,…,n). The expected weight value wj(j=1,2,…,n) for a triangular fuzzy weight is obtained by the following equation:
(12)EV(wj)=aj1+aj2+aj33.
Then we normalize the expected weight value wj(j=1,2,…,n) by the following formula:
(13)w~j=EV(wj)∑i=1mEV(wj).In FMCGDM environments, the concept of ideal point has been used to determine the best alternative in the decision set. Although the ideal alternative does not exist in real world, it does provide a useful theoretical construct to evaluate alternatives (Ye [26]).Thus three weighted similarity measures between an alternativeAi and the ideal alternative AP represented by the TFNs are defined as follows:
(14)SJ(Ai,AP)=∑j=1n‍wj∑l=13aijlbpjl∑l=13aijl2+∑l=13bpjl2-∑l=13aijlbpjl,SE(Ai,AP)=∑j=1n‍wj2∑l=13aijlbpjl∑l=13aijl2+∑l=13bpjl2,SC(Ai,AP)=∑j=1n‍wj∑l=13aijlbpjl∑l=13aijl2∑l=13bpjl2.The decision procedure for the proposed method can be summarized as follows.Step 1.
Give the characteristic of each alternative and criteria by the linguistic values of TFNs.Step 2.
Using (11), we can obtain the group preference vector for each alternative Ai(i=1,2,…,m).Step 3.
Calculate the expected weight valuewj for a criterion Cj(j=1,2,…,n) by using (12) and (13).Step 4.
From formula (14), we can get three weighted similarity measures for an alternative Ai(i=1,2,…,m).Step 5.
Rank the alternatives, and select the best one(s) in accordance with the weighted similarity measure.


## 4. Illustrative Example

### 4.1. Example 1: Personnel Selection

In this section, a numerical example is presented to illustrate the application of the proposed method. Suppose that the human resources department of a company desires to hire a competent. After initial screening, four candidates (i.e., alternatives)A1, A2, A3, and A4 remain for further evaluation. In order to select the most suitable candidate, the decision maker invite five experts, who take into account the following five criteria: (1) emotional steadiness (C1); (2) oral communication skill (C2); (3) education experience (C3); (4) work experience (C4); (5) personality and self-confidence (C5). Assume that the five experts provide his/her preference information on candidates with regard to criteria by using a linguistic variable, as listed in Table 1.The decision procedure for the above decision-making problem is as follows.Step 1.
Five experts provide his/her preference information on candidates with linguistic terms; we convert the linguistic variables to TFNs, as depicted in Table2.Table 2
Preference values of alternative given by 5 experts for TFNs.
A
M
C
1
C
2
C
3
C
4
C
5
1
(
0.0,0.0,0.1
)
(
0.4,0.5,0.6
)
(
0.5,0.6,0.7
)
(
0.8,0.9,1.0
)
(
0.5,0.6,0.7
)
2
(
0.1,0.2,0.3
)
(
0.3,0.4,0.5
)
(
0.4,0.5,0.6
)
(
0.5,0.6,0.7
)
(
0.9,1.0,1.0
)
A
1
3
(
0.4,0.5,0.6
)
(
0.4,0.5,0.6
)
(
0.1,0.2,0.3
)
(
0.3,0.4,0.5
)
(
0.0,0.0,0.1
)
4
(
0.3,0.4,0.5
)
(
0.1,0.2,0.3
)
(
0.0,0.1,0.2
)
(
0.5,0.6,0.7
)
(
0.7,0.8,0.9
)
5
(
0.5,0.6,0.7
)
(
0.7,0.8,0.9
)
(
0.2,0.3,0.4
)
(
0.0,0.0,0.1
)
(
0.5,0.6,0.7
)
1
(
0.4,0.5,0.6
)
(
0.8,0.9,1.0
)
(
0.5,0.6,0.7
)
(
0.4,0.5,0.6
)
(
0.0,0.1,0.2
)
2
(
0.1,0.2,0.3
)
(
0.4,0.5,0.6
)
(
0.5,0.6,0.7
)
(
0.8,0.9,1.0
)
(
0.1,0.2,0.3
)
A
2
3
(
0.7,0.8,0.9
)
(
0.5,0.6,0.7
)
(
0.3,0.4,0.5
)
(
0.1,0.2,0.3
)
(
0.0,0.1,0.2
)
4
(
0.5,0.6,0.7
)
(
0.7,0.8,0.9
)
(
0.3,0.4,0.5
)
(
0.9,1.0,1.0
)
(
0.0,0.0,0.1
)
5
(
0.5,0.6,0.7
)
(
0.3,0.4,0.5
)
(
0.4,0.5,0.6
)
(
0.0,0.1,0.2
)
(
0.8,0.9,1.0
)
1
(
0.3,0.4,0.5
)
(
0.1,0.2,0.3
)
(
0.7,0.8,0.9
)
(
0.4,0.5,0.6
)
(
0.5,0.6,0.7
)
2
(
0.1,0.2,0.3
)
(
0.5,0.6,0.7
)
(
0.3,0.4,0.5
)
(
0.0,0.0,0.1
)
(
0.9,1.0,1.0
)
A
3
3
(
0.9,1.0,1.0
)
(
0.4,0.5,0.6
)
(
0.5,0.6,0.7
)
(
0.0,0.1,0.2
)
(
0.3,0.4,0.5
)
4
(
0.1,0.2,0.3
)
(
0.0,0.0,0.1
)
(
0.5,0.6,0.7
)
(
0.4,0.5,0.6
)
(
0.0,0.1,0.2
)
5
(0.4, 0.5, 0.6)
(
0.4,0.5,0.6
)
(
0.3,0.4,0.5
)
(
0.0,0.1,0.2
)
(
0.7,0.8,0.9
)
1
(0.5, 0.6, 0.7)
(
0.5,0.6,0.7
)
(
0.4,0.5,0.6
)
(
0.3,0.4,0.5
)
(
0.8,0.9,1.0
)
2
(
0.4,0.5,0.6
)
(
0.3,0.4,0.5
)
(
0.5,0.6,0.7
)
(
0.0,0.1,0.2
)
(
0.4,0.5,0.6
)
A
4
3
(
0.3,0.4,0.5
)
(
0.3,0.4,0.5
)
(
0.4,0.5,0.6
)
(
0.5,0.6,0.7
)
(
0.7,0.8,0.9
)
4
(
0.1,0.2,0.3
)
(
0.0,0.1,0.2
)
(
0.5,0.6,0.7
)
(
0.3,0.4,0.5
)
(
0.1,0.2,0.3
)
5
(
0.7,0.8,0.9
)
(
0.4,0.5,0.6
)
(
0.1,0.2,0.3
)
(
0.3,0.4,0.5
)
(
0.3,0.4,0.5
)Step 2.
The weight vector of five experts isλ=(0.1,0.2,0.3,0.2,and0.2), and using (11), we can obtain the group preference vector for each alternative Ai(i=1,2,3,4), as follows:
(15)V1={〈C1,(0.13,0.17,0.22)〉,〈C2,(0.36,0.46,0.56)〉,〈C3,(0.39,0.54,0.69)〉,〈C4,(0.42,0.50,0.60)〉,〈C5,(0.52,0.60,0.60)〉};V2={〈C1,(0.22,0.27,0.32)〉,〈C2,(0.54,0.64,0.74)〉,〈C3,(0.60,0.75,0.90)〉,〈C4,(0.44,0.54,0.62)〉,〈C5,(0.18,0.26,0.26)〉};V3={〈C1,(0.18,0.23,0.27)〉,〈C2,(0.28,0.36,0.46)〉,〈C3,(0.69,0.84,0.99)〉,〈C4,(0.16,0.24,0.34)〉,〈C5,(0.48,0.58,0.58)〉};V4={〈C1,(0.21,0.26,0.31)〉,〈C2,(0.34,0.42,0.52)〉,〈C3,(0.57,0.72,0.87)〉,〈C4,(0.28,0.38,0.48)〉,〈C5,(0.46,0.56,0.56)〉}.Step 3.
The weight value of criteriaCi is represented by the following TFNs:
(16)w1=〈C1,(0.3,0.4,0.5)〉,w2=〈C2,(0.5,0.6,0.7)〉,w3=〈C3,(0.1,0.2,0.3)〉,w4=〈C4,(0.9,1.0,1.0)〉,w5=〈C5,(0.4,0.5,0.7)〉.
The expected weight values can be obtained by (12). Then we normalize the expected weight value by (13) and get the weight vector W=(0.1481,0.2222,0.0741,0.3580,and0.1975).Step 4.
The ideal alternative is given by the decision maker as(17)Ap={〈C1,(0.40,0.50,0.60)〉,〈C2,(0.70,0.80,0.90)〉,〈C3,(0.80,0.90,1.00)〉,〈C4,(0.70,0.80,0.90)〉,〈C5,(0.50,0.60,0.70)〉}.
From formula (14), we can get three weighted similarity measure for an alternative Ai(i=1,2,…,4), as listed in Table 3.Table 3
Decision results of three weighted similarity measures for TFNs.
S
J
S
E
S
C
A
1
0.7869
0.8707
0.9974
A
2
0.7976
0.8775
0.9988
A
3
0.6367
0.7546
0.9918
A
4
0.7528
0.8525
0.9964
Ranking
A
2
≻
A
1
≻
A
4
≻
A
3
A
2
≻
A
1
≻
A
4
≻
A
3
A
2
≻
A
1
≻
A
4
≻
A
3
Best
A
2
A
2
A
2Step 5.
From Table3, we can see that all the proposed method have the same decision results. The decision results of different similarity measures demonstrate that the proposed method for FMCGDM problem is effective.
Let us discuss the different weightW*=(0.2,0.1,0.3,0.20,and0.2), and using the above 4 steps, we can get the decision results as follows.
In Table4, it is easy to see that the three similarity measures get the same decision results; the best alternative is A4. From Tables 3 and 4, for the different weights of the criteria, the decision results are different.Table 4
Decision results for different weights in the same numerical example.
S
J
S
E
S
C
A
1
0.7674
0.8561
0.9959
A
2
0.8022
0.8793
0.9971
A
3
0.7567
0.8381
0.9950
A
4
0.8180
0.8928
0.9984
Ranking
A
4
≻
A
2
≻
A
1
≻
A
3
A
4
≻
A
2
≻
A
1
≻
A
3
A
4
≻
A
2
≻
A
1
≻
A
3
Best
A
4
A
4
A
4


### 4.2. Example 2: Investment Case Selection

Using the illustrative example in [23], an investment company wants to invest a sum of money in the best option. There is a panel with five possible alternatives to invest the money: A1 is a car company; A2 is a food company; A3 is a computer company; A4 is an arms company; A5 is a TV company. The three experts (G1, G2, and G3) must take a decision according to the following four attributes: C1 is the risk analysis; C2 is the growth analysis; C3 is the social-political impact analysis; C4 is the environmental impact analysis.Using our methods in Section3, the decision procedure is as follows.Step 1.
The TFNs preference vector of the three experts is shown in Table5.Table 5
The TFNs preference vector of the three experts.
A
M
C
1
C
2
C
3
C
4
1
(0.80, 0.85, 0.90)
(0.72, 0.76, 0.80)
(0.91, 0.93, 0.96)
(
0.62,0.65,0.68
)
A
1
2
(0.65, 0.70, 0.75)
(0.57, 0.61, 0.65)
(0.76, 0.78, 0.81)
(
0.47,0.50,0.53
)
3
(0.42, 0.47, 0.52)
(0.34, 0.38, 0.42)
(0.53, 0.55, 0.58)
(
0.24,0.27,0.30
)
1
(0.88, 0.90, 0.93)
(0.67, 0.77, 0.83)
(0.60, 0.67, 0.70)
(
0.69,0.72,0.75
)
A
2
2
(0.73, 0.75, 0.78)
(0.52, 0.62, 0.68)
(0.42, 0.52, 0.50)
(
0.54,0.57,0.60
)
3
(0.50, 0.52, 0.55)
(0.29, 0.39, 0.45)
(0.22, 0.39, 0.32)
(
0.31,0.34,0.37
)
1
(0.95, 0.97, 0.98)
(0.90, 0.93, 0.95)
(0.77, 0.79, 0.82)
(
0.93,0.95,0.96
)
A
3
2
(0.80, 0.82, 0.83)
(0.75, 0.78, 0.80)
(0.62, 0.64, 0.67)
(
0.78,0.80,0.81
)
3
(0.57, 0.59, 0.60)
(0.52, 0.55, 0.57)
(0.39, 0.41, 0.44)
(
0.55,0.57,0.58
)
1
(0.82, 0.85, 0.88)
(0.97, 0.98, 1.00)
(0.98, 0.99, 1.00)
(
0.97,0.99,1.00
)
A
4
2
(0.67, 0.70, 0.73)
(0.82, 0.83, 0.85)
(0.83, 0.84, 0.85)
(
0.82,0.84,0.85
)
3
(0.44, 0.47, 0.50)
(0.59, 0.60, 0.62)
(0.60, 0.61, 0.62)
(
0.59,0.61,0.62
)
1
(0.78, 0.79, 0.81)
(0.78, 0.79, 0.81)
(0.83, 0.85, 0.88)
(
0.94,0.97,0.99
)
A
5
2
(0.63, 0.64, 0.66)
(0.63, 0.64, 0.60)
(0.68, 0.70, 0.76)
(
0.79,0.82,0.80
)
3
(0.40, 0.41, 0.43)
(0.40, 0.41, 0.43)
(0.45, 0.47, 0.50)
(
0.56,0.59,0.61
)Step 2.
The weight vector of three experts isλ=(0.2,0.5,and0.3), and using (11), we can obtain the group preference vector for each alternative Ai(i=1,2,3,4,5) as follows:
(18)V1={〈C1,(0.611,0.661,0.711)〉,〈C2,(0.531,0.571,0.611)〉,〈C3,(0.721,0.741,0.771)〉,〈C4,(0.431,0.461,0.491)〉};V2={〈C1,(0.691,0.711,0.741)〉,〈C2,(0.481,0.581,0.641)〉,〈C3,(0.396,0.511,0.486)〉,〈C4,(0.501,0.531,0.561)〉};V3={〈C1,(0.761,0.781,0.791)〉,〈C2,(0.711,0.741,0.761)〉,〈C3,(0.581,0.601,0.631)〉,〈C4,(0.741,0.761,0.771)〉};V4={〈C1,(0.631,0.661,0.691)〉,〈C2,(0.781,0.791,0.811)〉,〈C3,(0.791,0.801,0.811)〉,〈C4,(0.781,0.801,0.811)〉};V5={〈C1,(0.591,0.601,0.621)〉,〈C2,(0.591,0.601,0.591)〉,〈C3,(0.641,0.661,0.706)〉,〈C4,(0.751,0.781,0.781)〉}.Step 3.
From [23], the weight value of criteria vector Ci is W=(0.2,0.1,0.1,0.4).Step 4.
The ideal alternative is given by the decision maker as(19)Ap={〈C1,(0.44,0.47,0.50)〉,〈C2,(0.79,0.82,0.80)〉,〈C3,(0.97,0.98,1.00)〉,〈C4,(0.83,0.85,0.88)〉}.
From formula (14), we can get three weighted similarity measures for an alternative Ai(i=1,2,…,5), as listed in Table 6. For comparison, we also list the decision results of FIOWHM operator method in Table 6.Table 6
Decision results of different methods in the same numerical example.
S
J
S
E
S
C
FIOWHM operator
A
1
0.6486
0.7141
0.7997
1.3310
A
2
0.6465
0.7136
0.7988
0.6690
A
3
0.7329
0.7630
0.7998
2.5000
A
4
0.7730
0.7859
0.7999
4.5000
A
5
0.7624
0.7803
0.7998
3.5000
Ranking
A
4
≻
A
5
≻
A
3
≻
A
1
≻
A
2
A
4
≻
A
5
≻
A
3
≻
A
1
≻
A
2
A
4
≻
A
5
≻
A
3
≻
A
1
≻
A
2
A
4
≻
A
5
≻
A
3
≻
A
1
≻
A
2
Best
A
4
A
4
A
4
A
4Step 5.
From Table6, we can see that all the proposed methods have the same decision results, but our method has simpler computation and gets the same results more rapidly than the FLOWHM method.
The decision results of different methods demonstrate that the proposed method for FMCGDM problem is effective.


## 4.1. Example 1: Personnel Selection

In this section, a numerical example is presented to illustrate the application of the proposed method. Suppose that the human resources department of a company desires to hire a competent. After initial screening, four candidates (i.e., alternatives)A1, A2, A3, and A4 remain for further evaluation. In order to select the most suitable candidate, the decision maker invite five experts, who take into account the following five criteria: (1) emotional steadiness (C1); (2) oral communication skill (C2); (3) education experience (C3); (4) work experience (C4); (5) personality and self-confidence (C5). Assume that the five experts provide his/her preference information on candidates with regard to criteria by using a linguistic variable, as listed in Table 1.The decision procedure for the above decision-making problem is as follows.Step 1.
Five experts provide his/her preference information on candidates with linguistic terms; we convert the linguistic variables to TFNs, as depicted in Table2.Table 2
Preference values of alternative given by 5 experts for TFNs.
A
M
C
1
C
2
C
3
C
4
C
5
1
(
0.0,0.0,0.1
)
(
0.4,0.5,0.6
)
(
0.5,0.6,0.7
)
(
0.8,0.9,1.0
)
(
0.5,0.6,0.7
)
2
(
0.1,0.2,0.3
)
(
0.3,0.4,0.5
)
(
0.4,0.5,0.6
)
(
0.5,0.6,0.7
)
(
0.9,1.0,1.0
)
A
1
3
(
0.4,0.5,0.6
)
(
0.4,0.5,0.6
)
(
0.1,0.2,0.3
)
(
0.3,0.4,0.5
)
(
0.0,0.0,0.1
)
4
(
0.3,0.4,0.5
)
(
0.1,0.2,0.3
)
(
0.0,0.1,0.2
)
(
0.5,0.6,0.7
)
(
0.7,0.8,0.9
)
5
(
0.5,0.6,0.7
)
(
0.7,0.8,0.9
)
(
0.2,0.3,0.4
)
(
0.0,0.0,0.1
)
(
0.5,0.6,0.7
)
1
(
0.4,0.5,0.6
)
(
0.8,0.9,1.0
)
(
0.5,0.6,0.7
)
(
0.4,0.5,0.6
)
(
0.0,0.1,0.2
)
2
(
0.1,0.2,0.3
)
(
0.4,0.5,0.6
)
(
0.5,0.6,0.7
)
(
0.8,0.9,1.0
)
(
0.1,0.2,0.3
)
A
2
3
(
0.7,0.8,0.9
)
(
0.5,0.6,0.7
)
(
0.3,0.4,0.5
)
(
0.1,0.2,0.3
)
(
0.0,0.1,0.2
)
4
(
0.5,0.6,0.7
)
(
0.7,0.8,0.9
)
(
0.3,0.4,0.5
)
(
0.9,1.0,1.0
)
(
0.0,0.0,0.1
)
5
(
0.5,0.6,0.7
)
(
0.3,0.4,0.5
)
(
0.4,0.5,0.6
)
(
0.0,0.1,0.2
)
(
0.8,0.9,1.0
)
1
(
0.3,0.4,0.5
)
(
0.1,0.2,0.3
)
(
0.7,0.8,0.9
)
(
0.4,0.5,0.6
)
(
0.5,0.6,0.7
)
2
(
0.1,0.2,0.3
)
(
0.5,0.6,0.7
)
(
0.3,0.4,0.5
)
(
0.0,0.0,0.1
)
(
0.9,1.0,1.0
)
A
3
3
(
0.9,1.0,1.0
)
(
0.4,0.5,0.6
)
(
0.5,0.6,0.7
)
(
0.0,0.1,0.2
)
(
0.3,0.4,0.5
)
4
(
0.1,0.2,0.3
)
(
0.0,0.0,0.1
)
(
0.5,0.6,0.7
)
(
0.4,0.5,0.6
)
(
0.0,0.1,0.2
)
5
(0.4, 0.5, 0.6)
(
0.4,0.5,0.6
)
(
0.3,0.4,0.5
)
(
0.0,0.1,0.2
)
(
0.7,0.8,0.9
)
1
(0.5, 0.6, 0.7)
(
0.5,0.6,0.7
)
(
0.4,0.5,0.6
)
(
0.3,0.4,0.5
)
(
0.8,0.9,1.0
)
2
(
0.4,0.5,0.6
)
(
0.3,0.4,0.5
)
(
0.5,0.6,0.7
)
(
0.0,0.1,0.2
)
(
0.4,0.5,0.6
)
A
4
3
(
0.3,0.4,0.5
)
(
0.3,0.4,0.5
)
(
0.4,0.5,0.6
)
(
0.5,0.6,0.7
)
(
0.7,0.8,0.9
)
4
(
0.1,0.2,0.3
)
(
0.0,0.1,0.2
)
(
0.5,0.6,0.7
)
(
0.3,0.4,0.5
)
(
0.1,0.2,0.3
)
5
(
0.7,0.8,0.9
)
(
0.4,0.5,0.6
)
(
0.1,0.2,0.3
)
(
0.3,0.4,0.5
)
(
0.3,0.4,0.5
)Step 2.
The weight vector of five experts isλ=(0.1,0.2,0.3,0.2,and0.2), and using (11), we can obtain the group preference vector for each alternative Ai(i=1,2,3,4), as follows:
(15)V1={〈C1,(0.13,0.17,0.22)〉,〈C2,(0.36,0.46,0.56)〉,〈C3,(0.39,0.54,0.69)〉,〈C4,(0.42,0.50,0.60)〉,〈C5,(0.52,0.60,0.60)〉};V2={〈C1,(0.22,0.27,0.32)〉,〈C2,(0.54,0.64,0.74)〉,〈C3,(0.60,0.75,0.90)〉,〈C4,(0.44,0.54,0.62)〉,〈C5,(0.18,0.26,0.26)〉};V3={〈C1,(0.18,0.23,0.27)〉,〈C2,(0.28,0.36,0.46)〉,〈C3,(0.69,0.84,0.99)〉,〈C4,(0.16,0.24,0.34)〉,〈C5,(0.48,0.58,0.58)〉};V4={〈C1,(0.21,0.26,0.31)〉,〈C2,(0.34,0.42,0.52)〉,〈C3,(0.57,0.72,0.87)〉,〈C4,(0.28,0.38,0.48)〉,〈C5,(0.46,0.56,0.56)〉}.Step 3.
The weight value of criteriaCi is represented by the following TFNs:
(16)w1=〈C1,(0.3,0.4,0.5)〉,w2=〈C2,(0.5,0.6,0.7)〉,w3=〈C3,(0.1,0.2,0.3)〉,w4=〈C4,(0.9,1.0,1.0)〉,w5=〈C5,(0.4,0.5,0.7)〉.
The expected weight values can be obtained by (12). Then we normalize the expected weight value by (13) and get the weight vector W=(0.1481,0.2222,0.0741,0.3580,and0.1975).Step 4.
The ideal alternative is given by the decision maker as(17)Ap={〈C1,(0.40,0.50,0.60)〉,〈C2,(0.70,0.80,0.90)〉,〈C3,(0.80,0.90,1.00)〉,〈C4,(0.70,0.80,0.90)〉,〈C5,(0.50,0.60,0.70)〉}.
From formula (14), we can get three weighted similarity measure for an alternative Ai(i=1,2,…,4), as listed in Table 3.Table 3
Decision results of three weighted similarity measures for TFNs.
S
J
S
E
S
C
A
1
0.7869
0.8707
0.9974
A
2
0.7976
0.8775
0.9988
A
3
0.6367
0.7546
0.9918
A
4
0.7528
0.8525
0.9964
Ranking
A
2
≻
A
1
≻
A
4
≻
A
3
A
2
≻
A
1
≻
A
4
≻
A
3
A
2
≻
A
1
≻
A
4
≻
A
3
Best
A
2
A
2
A
2Step 5.
From Table3, we can see that all the proposed method have the same decision results. The decision results of different similarity measures demonstrate that the proposed method for FMCGDM problem is effective.
Let us discuss the different weightW*=(0.2,0.1,0.3,0.20,and0.2), and using the above 4 steps, we can get the decision results as follows.
In Table4, it is easy to see that the three similarity measures get the same decision results; the best alternative is A4. From Tables 3 and 4, for the different weights of the criteria, the decision results are different.Table 4
Decision results for different weights in the same numerical example.
S
J
S
E
S
C
A
1
0.7674
0.8561
0.9959
A
2
0.8022
0.8793
0.9971
A
3
0.7567
0.8381
0.9950
A
4
0.8180
0.8928
0.9984
Ranking
A
4
≻
A
2
≻
A
1
≻
A
3
A
4
≻
A
2
≻
A
1
≻
A
3
A
4
≻
A
2
≻
A
1
≻
A
3
Best
A
4
A
4
A
4


## 4.2. Example 2: Investment Case Selection

Using the illustrative example in [23], an investment company wants to invest a sum of money in the best option. There is a panel with five possible alternatives to invest the money: A1 is a car company; A2 is a food company; A3 is a computer company; A4 is an arms company; A5 is a TV company. The three experts (G1, G2, and G3) must take a decision according to the following four attributes: C1 is the risk analysis; C2 is the growth analysis; C3 is the social-political impact analysis; C4 is the environmental impact analysis.Using our methods in Section3, the decision procedure is as follows.Step 1.
The TFNs preference vector of the three experts is shown in Table5.Table 5
The TFNs preference vector of the three experts.
A
M
C
1
C
2
C
3
C
4
1
(0.80, 0.85, 0.90)
(0.72, 0.76, 0.80)
(0.91, 0.93, 0.96)
(
0.62,0.65,0.68
)
A
1
2
(0.65, 0.70, 0.75)
(0.57, 0.61, 0.65)
(0.76, 0.78, 0.81)
(
0.47,0.50,0.53
)
3
(0.42, 0.47, 0.52)
(0.34, 0.38, 0.42)
(0.53, 0.55, 0.58)
(
0.24,0.27,0.30
)
1
(0.88, 0.90, 0.93)
(0.67, 0.77, 0.83)
(0.60, 0.67, 0.70)
(
0.69,0.72,0.75
)
A
2
2
(0.73, 0.75, 0.78)
(0.52, 0.62, 0.68)
(0.42, 0.52, 0.50)
(
0.54,0.57,0.60
)
3
(0.50, 0.52, 0.55)
(0.29, 0.39, 0.45)
(0.22, 0.39, 0.32)
(
0.31,0.34,0.37
)
1
(0.95, 0.97, 0.98)
(0.90, 0.93, 0.95)
(0.77, 0.79, 0.82)
(
0.93,0.95,0.96
)
A
3
2
(0.80, 0.82, 0.83)
(0.75, 0.78, 0.80)
(0.62, 0.64, 0.67)
(
0.78,0.80,0.81
)
3
(0.57, 0.59, 0.60)
(0.52, 0.55, 0.57)
(0.39, 0.41, 0.44)
(
0.55,0.57,0.58
)
1
(0.82, 0.85, 0.88)
(0.97, 0.98, 1.00)
(0.98, 0.99, 1.00)
(
0.97,0.99,1.00
)
A
4
2
(0.67, 0.70, 0.73)
(0.82, 0.83, 0.85)
(0.83, 0.84, 0.85)
(
0.82,0.84,0.85
)
3
(0.44, 0.47, 0.50)
(0.59, 0.60, 0.62)
(0.60, 0.61, 0.62)
(
0.59,0.61,0.62
)
1
(0.78, 0.79, 0.81)
(0.78, 0.79, 0.81)
(0.83, 0.85, 0.88)
(
0.94,0.97,0.99
)
A
5
2
(0.63, 0.64, 0.66)
(0.63, 0.64, 0.60)
(0.68, 0.70, 0.76)
(
0.79,0.82,0.80
)
3
(0.40, 0.41, 0.43)
(0.40, 0.41, 0.43)
(0.45, 0.47, 0.50)
(
0.56,0.59,0.61
)Step 2.
The weight vector of three experts isλ=(0.2,0.5,and0.3), and using (11), we can obtain the group preference vector for each alternative Ai(i=1,2,3,4,5) as follows:
(18)V1={〈C1,(0.611,0.661,0.711)〉,〈C2,(0.531,0.571,0.611)〉,〈C3,(0.721,0.741,0.771)〉,〈C4,(0.431,0.461,0.491)〉};V2={〈C1,(0.691,0.711,0.741)〉,〈C2,(0.481,0.581,0.641)〉,〈C3,(0.396,0.511,0.486)〉,〈C4,(0.501,0.531,0.561)〉};V3={〈C1,(0.761,0.781,0.791)〉,〈C2,(0.711,0.741,0.761)〉,〈C3,(0.581,0.601,0.631)〉,〈C4,(0.741,0.761,0.771)〉};V4={〈C1,(0.631,0.661,0.691)〉,〈C2,(0.781,0.791,0.811)〉,〈C3,(0.791,0.801,0.811)〉,〈C4,(0.781,0.801,0.811)〉};V5={〈C1,(0.591,0.601,0.621)〉,〈C2,(0.591,0.601,0.591)〉,〈C3,(0.641,0.661,0.706)〉,〈C4,(0.751,0.781,0.781)〉}.Step 3.
From [23], the weight value of criteria vector Ci is W=(0.2,0.1,0.1,0.4).Step 4.
The ideal alternative is given by the decision maker as(19)Ap={〈C1,(0.44,0.47,0.50)〉,〈C2,(0.79,0.82,0.80)〉,〈C3,(0.97,0.98,1.00)〉,〈C4,(0.83,0.85,0.88)〉}.
From formula (14), we can get three weighted similarity measures for an alternative Ai(i=1,2,…,5), as listed in Table 6. For comparison, we also list the decision results of FIOWHM operator method in Table 6.Table 6
Decision results of different methods in the same numerical example.
S
J
S
E
S
C
FIOWHM operator
A
1
0.6486
0.7141
0.7997
1.3310
A
2
0.6465
0.7136
0.7988
0.6690
A
3
0.7329
0.7630
0.7998
2.5000
A
4
0.7730
0.7859
0.7999
4.5000
A
5
0.7624
0.7803
0.7998
3.5000
Ranking
A
4
≻
A
5
≻
A
3
≻
A
1
≻
A
2
A
4
≻
A
5
≻
A
3
≻
A
1
≻
A
2
A
4
≻
A
5
≻
A
3
≻
A
1
≻
A
2
A
4
≻
A
5
≻
A
3
≻
A
1
≻
A
2
Best
A
4
A
4
A
4
A
4Step 5.
From Table6, we can see that all the proposed methods have the same decision results, but our method has simpler computation and gets the same results more rapidly than the FLOWHM method.
The decision results of different methods demonstrate that the proposed method for FMCGDM problem is effective.


## 5. Conclusion

The similarity measure is an effective method to solve the FMCDM problem, but it rarely applies to triangular FMCDM problem. In this paper, we propose three similarity measures and three weighted similarity measures between two TFNs based on the vector similarity measures. Using the similarity measures model, we establish a method for FMCGDM with known information on criterion weight and group weight, in which the alternatives and criteria are given by triangular fuzzy information. Finally, a practical example is given to select the most suitable candidate in human resource evaluation system, by the weighted similarity measures between each alternative and ideal alternative; the ranking order of all alternatives can be determined, and the best one(s) can be easily identified as well. For comparison, we also apply our method to solve the triangular FMCGDM problem in [23]. The different methods have the same decision results, which show that our proposed method in this paper is applicable and effective.



---

*Source: 538261-2013-03-28.xml*