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Choosing Project Leader Based on Interval Linguistic TOPSIS and Social Network Technology

Chen-Tung Chen Department of Information Management

National United University Miaoli, Taiwan

[email protected]

Wei-Zhan Hung Department of International Businesss

National ChiNan University, NanTou, Taiwan,

[email protected]

Abstract—“Internal promotion” is not only a good tool to attract and reserve a person of talent but also can reduce the training cost because the worker had already worked for the enterprise many years. In the matrix organization, the project leader is not easy to acquire the engineers who he/she prefers for forming a team. A better project leader must try to cooperate with each engineer as easily as possible. In this study, Interval linguistic TOPSIS is combined with maximizing deviation method to evaluate the ability of each employee. Based on the importance of project leaders’ cooperation experiment with other member in the enterprise, this study modifies degree centrality (the index of social network analysis) to calculate the cooperation capacity of each employee. Project leader is selected by considering employees’ ability and their cooperation capacity simultaneously. For reader understanding proposed method, a numerical example will be implemented. Finally, a brief conclusion will be discussed.

Keywords—Interval Linguistic TOPSIS, Maximizing Deviation Method, Social Network Analysis, Project Management. Human Resource Management.

I. INTRODUCTION

Human resource is the enterprise most important asset especially in research enterprise. Every enterprise must invest a lot of time and money for a new employee to adapt a work to his/her job. However, potential and outstanding employees do not satisfy their position and usually require a promotion opportunity when they outperform their colleague. It is useless to say that the outstanding employees in the enterprise can easily be moved to competitors if they provide a better welfare and high level position for them. “Internal promotion” not only is a better tool to save a person of talent but also can reduce the training cost because the worker had already worked for the enterprise many years [1].

The organization of research department in the high technology enterprise is usually organized as matrix organization instead of traditional hierarchy organization. Every engineer may be a member who belongs to many projects. So, a project leader must compete to acquire the work time of each engineer for completing his/her project. Because every engineer’s work time is limited, project leader usually can’t acquire the engineer who he/she wants. A better

project leader must try to cooperate with each engineer as easily as possible.

There are some literatures about using Multi criteria decision technology to handle personnel selection problem. Chien and Chen developed an effective data mining approach based on rough set theory to analyze human resource data for personnel selection [2]. Chien and Chen developed a data mining framework based on decision tree and association rules to generate useful rules for personnel selection [3]. Gungor et al., considered both quantitative and qualitative criteria and used fuzzy AHP to cope with personnel selection problem [4]. Celik et al., used Fuzzy AHP and Fuzzy TOPSIS to recruit academic personnel in maritime human resources [5]. Fan considered individual information of members and the collaborative information between members and use a bi-objective 0-1 programming model to select the desired members [6]. Feng built a multi-objective 0–1 programming model and improved non-dominated sorting genetic algorithm II (INSGA-II) based on the individual and collaborative performances of candidate to deal with the selection problem of project member [7]. Zhang and Liu combined both subjective and objective information and used intuitionistic fuzzy weighted averaging (IFWA) operator to aggregate individual opinions of each decision maker. And then, they used intuitionistic fuzzy entropy to acquire the weight of each criterion and developed a grey relational based MCDM method for coping with personnel selection problem [8].

Although there are so many multi criteria decision technology in copying with personnel problem, a few of them are suitable for selecting research project leader because above literatures lack the mechanism about evaluating the cooperation relation of each employee which is important to measure the employees’ leading capacity.

Social network analysis extended from graph theory which tries to describe the structure of member relation in the network and is developed relative index to descript the characteristic of the network [9]. The concept of centrality of members in the network is an earliest research issue in social network analysis and can measure the importance of each member [10]. Degree centrality measure how many nodes the member connects [9]. Because each node represents a relation of two members, degree centrality can judge the This work was supported by National Science Council “99-2410-H-239-

009-MY2”.

Proceedings of 2012 International Conference on Fuzzy Theory and Its Applications National Chung Hsing University, Taichung, Taiwan, Nov.16-18, 2012

310

importance of each member. The employees’ leading capacity can be measured by degree centrality.

Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) which is proposed by Hwang and Yoon is one kind of multi criteria decision technology for making decision [11]. TOPSIS had already used in many management fields such as human resources management, investment portfolio selection, project selection and quality control etc. The concept of TOPSIS is to choose the best alternative by simultaneously consider the shortest distance from the positive ideal solution (PIS) and the farthest from the negative ideal solution (NIS) in each alternative [12]. TOPSIS not only is a simple computation process that is easy to be programmed [13] but also is an effective method to acquire the total ranking order of each alternative [12].

Practically, crisp value is not a suitable tool to describe the real-life environment. On account of experts’ subject opinion, preference and judgment are usually vague and uncertainty, it is not easy to express them by exact numerical value. A more practically solution is to use linguistic assessments instead of numerical values. The 2-tuple linguistic representation model is one kind of linguistic variable and is based on the concept of symbolic translation [14]. Interval linguistic variable can let experts exactly express their opinions because expert can express their preference between two 2-tuple linguistic values. Experts apply interval linguistic variables to express their opinions and can obtain the final evaluation result with appropriate linguistic variable. The advantage of interval linguistic variable is that it can reduce the mistakes of information translation and avoid information loss through computing with words [15].

The maximizing deviation method is first developed by Wang for computing the weight of each criterion in multiple attribute decision making (MADM) problems with numerical information [16]. If some criterion makes the performance values among all the alternatives have obvious differences, such a criterion plays a more important role in choosing the best alternative. The distinguish ability and objectivity of the maximizing deviation method is better than AHP which is based on subjective opinions of experts. [17].

The goal of this research is to develop a reasonable process to handle personnel promotion selection problem for research department in enterprise. Interval linguistic TOPSIS is combined with maximizing deviation method to evaluate the ability of each employee. Based on the importance of project leaders’ cooperation experiment with other member in the enterprise, this study modifies degree centrality (the index of social network analysis) to calculate the cooperation capacity of each employee. Finally, project leader is selected by considering employees’ ability and their cooperation capacity simultaneously.

This study is organized as follows. In section 2, the basic notation and operation of interval linguistic variable is simply introduced. The main executing process of selecting project leader including ability evaluating and cooperation capacity measuring is presented in section 3. And then, a

numerical example is executed for reader understanding proposed method. Finally, a brief conclusion is discussed.

II. INTERVALSE LINGUISTIC VARIABLE

Let },...,,,{ 210 gssssS = be a finite and totally ordered

linguistic term set. An interval linguistic variable can be expressed as ( )( )11,

~+−= ttt ssS αα , where ts , 1+ts is the

central value of t-th and t+1-th linguistic term separately in S and α ( ( )1,0∈α ) is a numerical value representing the

ratio of the central value of t-th linguistic term ts and the

central value of t+1-th linguistic term 1+ts [18].

The linguistic transfer function Θ can translate linguistic variable into crisp value β (β∈ [0, 1]) [19].

( )1−

=Θg

tst (1)

where g is the scale of linguistic term and t=1,2,…,g-1.

The symbolic translation function Δ is to translate crisp value β (β∈ [0, 1]) into an interval linguistic variable [20].

( ) ( )( )( ) ( )( ) ( )( )

−Θ−=Θ≤≤Θ

−=Δ

+

+

+

βαβααβ

1

1

1

*1

1,

t

tt

tt

sg

sswith

ss

(2)

The reverse symbolic translation function 1−Δ is to translate an interval linguistic variable into crisp value β [20].

( ) ( )( )( ) ( ) ( ) βαα

αα=Θ−+Θ=

−Δ=Δ

+

+−−

1

111

*1*

1,~

tt

ttt

ss

ssS (3)

TABLE 1. DIFFERENT TYPES OF LINGUISTIC VARIABLE

Linguistic variable Type I:

performanceExtremely Poor )( 5

0s , Poor )( 51s , Fair )( 5

2s ,

Good )( 53s , Extremely Good )( 5

4s

Type II: performance

Extremely Poor )( 70s , Poor )( 7

1s , Medium

Poor )( 72s , Fair )( 7

3s , Medium Good )( 74s ,

Good )( 75s , Extremely Good )( 7

6s

Type III: performance

Extremely Poor )( 90s ,Very Poor )( 9

1s , Poor )( 92s ,

Medium Poor )( 93s , Fair )( 9

4s , Medium Good )( 95s ,

Good )( 96s , Very Good )( 9

7s , Extremely Good )( 98s

Figure . 1. Membership functions of linguistic variables at type 1 (t=1) 0 1

1)( 5

1s )( 52s )( 5

3s )( 54s)( 5

0s


311

)( 70s )( 7

1s )( 72s )( 7

3s )( 74s )( 7

5s )( 76s

0 1

Figure . 2. Membership functions of linguistic variables at type 2 (t=2)

Figure .3. Membership functions of linguistic variables at type 3 (t=3)

III. PROPOSED METHOD

The notation of project leader selection problem includes:

(1) A set of promotion project decision-makers { }pDDDD ,,...,, 21= .

(2) A set of employees { }mEEEE ,,...,, 21= .

(3) A set of evaluation criteria { }nCCCC ,,...,, 21= .

(4) A set of performance ratings of employees with respect to evaluation criteria [ ]

mnijxX = , mi ,...,2,1= ,

nj ,...,2,1= . (Refer to formula 4)

=

mnmm

n

n

m

n

xxx

xxx

xxx

E

E

ECCC

X

~...~~............

~...~~

~...~~

...

...

21

22221

11211

2

1

21

(4)

(5) A set of importance degree respect to each evaluation criterion { }mWWWW ,,...,, 21= .

(6) A matrix of employee relation [ ]ijrR = , mi ,...,2,1=

mj ,...,2,1= . (Refer to formula 5)

=

mnmm

n

n

m

m

rrr

rrr

rrr

E

E

EEEE

R

...

............

...

...

...

...

21

22221

11211

2

1

21

(5)

The performance of the i-th employee with respect to the j-th evaluation criterion decided by the k-th decision maker can be represented as an interval linguistic variable

( )( )11,~ +−= tijkijk

tijkijkijk ssx αα .

The formula of aggregating decision makers’ opinion about the performance of the i-th employee with respect to the j-th evaluation criterion is as follows.

( )( )

−ΔΔ==

+−p

k

tijkijk

tijkijkij pssx

1

11 /1,~ αα (6)

According to the maximizing deviation method, the weight of criterion jC can be calculated as [21]

( ) ( )( )( ) ( )( ) Δ−Δ

Δ−Δ=

= = =

−−

=

= =

−−

=m

j

n

i

n

l

klj

kij

K

kk

n

i

n

l

klj

kij

K

kk

jxx

xx

w

1 1 1

211

1

1 1

211

1

~~

~~

λ

λ (7)

where kλ represents the weight of decision maker kD .

The interval linguistic weighted matrix can be computed as

njmiv nm ,...,2,1,,...,2,1,]~[=V~

ij ==× (8)

where ( )( )jijij wxv *~~ 1−ΔΔ= .

Thus, the positive ideal employee (PIE) and negative ideal employee (NIE) can be represented as

)~,...,~,~(E **2

*1

*nvvv= and )~,...,~,~(E 21

−−−− = nvvv , where

( )( )

ΔΔ= −

iji

v~maxv~ 1*j and ( )( )

ΔΔ= −

iji

v~minv~ 1-j .

The distance between employee iE and the positive

ideal employee ( *E ) can be calculated as

( )( ) ( )=

−−

Δ−Δ==n

jijij

iii vvdd

1

211** ~~max)A,A( (9)

The distance between employee ( iE ) and the negative

ideal employee ( -E ) can be calculated as

( )( ) ( )=

−−−−

Δ−Δ==n

jijij

iii vvdd

1

211 ~~min)A,A( (10)

And then, the closeness coefficient of each employee

iE ( iCC ) can be computed as

( ) midd

dCC

ii

ii ,...,2,1,

*=

+= −

− (11)

The employee familiar degree matrix can be computed as

mjmis mm ,...,2,1,,...,2,1,][=S ij ==× (12)

where ijij rs ^8.01−= . ijs represents the familiar degree

between employee i and employee j.

The familiar degree is a monotone increasing function. So, the familiar degree increases when the cooperation time

0 1

1 )( 9

0s )( 91s )( 9

2s )( 93s )( 9

4s )( 95s )( 9

6s )( 97s )( 9

8s


312

between two employees increases. But, the net familiar increasable degree decreases when the cooperation time between two employees increases.

The enterprise employee cooperation capacity (EECC) of employee iE can be computed as

( )( ) −+=

≠=

n

ijj

jiji CCppsEECC1

*1* (13)

where p represent the ratio of an employee basic importance.

The project leader comprehensive index (PLCI) of employee iE can be computed as

ii

ii

ii

ii EECC

EECCqCC

CC

CCqPLCI

max*)1(

max* −+= (14)

where q represent the ratio of project leader ability preference.

IV. NUMERICAL EXAMPLE

Suppose that a research enterprise wants to promote an employee as a project manager. The research enterprise possesses ten basic level employees. In the board of directors, the general manager decide to employee three experts as decision maker ( 1D , 2D and 3D ) to decide the

fittest employee as project manager. 1C Research

experiment, 2C Profession skill ability, 3C English ability,

4C Work attribute and 5C . Emotion control ability is five criteria considered to evaluate the ability of each employee. Because the organization of research enterprise is matrix organization, the cooperation capacity of employee is important index for selecting project manager.

The executing step of selecting project manager is as follows.

Step 1. Each decision maker chooses his/her preferable interval linguistic variable based on his/her knowledge, experiment. Decision maker 1D choose type 1, 2D choose

type 2 and 3D choose type 3 of interval linguistic variable.

Step 2. Each decision maker expresses his/her opinions about the performance of each employee (Refer to Table 2).

Step 3. Transform the interval linguistic variable into the interval linguistic variables of type 2 and aggregate the interval linguistic variable of each employee with respect to each criterion.

Step 4. Using maximizing deviation method (equation 7) to calculate the weigh of each criterion (Refer to Table 3). The weight of each decision maker all sets up 3/1

(3

1321 === λλλ ).

Step 5. Calculate interval linguistic weighted matrix.

Step 6. Calculate positive ideal employee and negative ideal employee and negative ideal employee and negative ideal employee.

Step 7. Calculate the distance between employee and the positive ideal employee, the negative ideal employee and closeness coefficient (Refer to Table 4).

Step 8. Collect past cooperation record between each employee as employee relation matrix (Refer to Table 5).

Step 9. Transfer employee relation matrix to employee familiar degree matrix by equation 12 (Refer to Table 6).

Step 10. Set the ratio of an employee basic importance p as 0.5. Calculate enterprise employee cooperation capacity of each employee (Refer to Table 7).

Step 11. Set the ratio of project leader ability preference q as 0.5. Calculate the project leader comprehensive index of each employee (Refer to Table 7).

The rank of each employee is

18610453729 EEEEEEEEEE >>>>>>>>> .

This study also executes the sensitive analysis. We display the changed trend of employee rank when parameter p is adjusted and q is constant (q=0.5) in Fig 4. And the changed trend of employee rank is also be showed in Fig 5 when parameter q is adjusted and p is constant (p=0.5).

V. CONCLUSION AND FUTURE RESEARCH

In this research, we develop a personnel promotion selection process which is suitable in the matrix organization of research development in the enterprise. Following by proposed method, enterprise can select a project leader which not only possess a better work ability but also is easily to be cooperated with each employee in the research development. Unlike selecting a new personnel who just needs to consider his/her ability, the ability of the employee is one measurement dimension of choosing a manager. Most importantly, the relation of the employee is the key index for a manager to be qualified his/her job because he/she must execute the inter-departmental cooperation and inter-disciplinary communication.

There are some useful tool such as betweenness centrality, structure hole etc which come from social network analysis field can be used in selecting suitable talent in different department in the enterprise. In the future, the different kinds of social network analysis tool will combined with multi criteria decision technology for dealing with personnel selection problem.

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TABLE 2. THE PERFORMANCE OF EACH EMPLOYEE

1D 2D 3D

1C ( )51

50 7.0,3.0 ss ( )7

271 1.0,9.0 ss ( )9

190 9.0,1.0 ss

2C ( )52

51 9.0,1.0 ss ( )7

473 3.0,7.0 ss ( )9

291 3.0,7.0 ss

1E

3C ( )53

52 1.0,9.0 ss ( )7

170 4.0,6.0 ss ( )9

594 5.0,5.0 ss

4C ( )53

52 6.0,4.0 ss ( )7

372 6.0,4.0 ss ( )9

493 4.0,6.0 ss

5C ( )54

53 2.0,8.0 ss ( )7

372 5.0,5.0 ss ( )9

594 9.0,1.0 ss

1C ( )53

52 5.0,5.0 ss ( )7

574 6.0,4.0 ss ( )9

796 2.0,8.0 ss

2C ( )53

52 9.0,1.0 ss ( )7

271 4.0,6.0 ss ( )9

695 5.0,5.0 ss

3C ( )53

52 4.0,6.0 ss ( )7

372 3.0,7.0 ss ( )9

897 9.0,1.0 ss

4C ( )53

52 6.0,4.0 ss ( )7

170 7.0,3.0 ss ( )9

594 7.0,3.0 ss

2E

5C ( )53

52 7.0,3.0 ss ( )7

372 9.0,1.0 ss ( )9

594 3.0,7.0 ss

1C ( )53

52 7.0,3.0 ss ( )7

372 9.0,1.0 ss ( )9

695 9.0,1.0 ss

2C ( )52

51 7.0,3.0 ss ( )7

372 2.0,8.0 ss ( )9

594 4.0,6.0 ss

3C ( )54

53 8.0,2.0 ss ( )7

271 9.0,1.0 ss ( )9

594 2.0,8.0 ss

4C ( )51

50 5.0,5.0 ss ( )7

372 8.0,2.0 ss ( )9

796 6.0,4.0 ss

3E

5C ( )53

52 9.0,1.0 ss ( )7

372 5.0,5.0 ss ( )9

897 7.0,3.0 ss

1C ( )53

52 2.0,8.0 ss ( )7

372 2.0,8.0 ss ( )9

594 8.0,2.0 ss

2C ( )53

52 6.0,4.0 ss ( )7

574 7.0,3.0 ss ( )9

291 8.0,2.0 ss

3C ( )53

52 3.0,7.0 ss ( )7

372 3.0,7.0 ss ( )9

594 9.0,1.0 ss

4C ( )52

51 7.0,3.0 ss ( )7

372 9.0,1.0 ss ( )9

392 5.0,5.0 ss

4E

5C ( )54

53 8.0,2.0 ss ( )7

271 9.0,1.0 ss ( )9

392 3.0,7.0 ss

1C ( )53

52 7.0,3.0 ss ( )7

372 3.0,7.0 ss ( )9

493 9.0,1.0 ss

2C ( )54

53 9.0,1.0 ss ( )7

372 9.0,1.0 ss ( )9

594 8.0,2.0 ss

3C ( )53

52 5.0,5.0 ss ( )7

372 5.0,5.0 ss ( )9

291 4.0,6.0 ss

4C ( )51

50 4.0,6.0 ss ( )7

372 2.0,8.0 ss ( )9

594 6.0,4.0 ss

5E

5C ( )53

52 6.0,4.0 ss ( )7

574 8.0,2.0 ss ( )9

594 8.0,2.0 ss

1C ( )53

52 7.0,3.0 ss ( )7

170 7.0,3.0 ss ( )9

796 1.0,9.0 ss

2C ( )53

52 7.0,3.0 ss ( )7

372 6.0,4.0 ss ( )9

897 7.0,3.0 ss

3C ( )53

52 7.0,3.0 ss ( )7

271 6.0,4.0 ss ( )9

897 5.0,5.0 ss

4C ( )53

52 7.0,3.0 ss ( )7

271 9.0,1.0 ss ( )9

594 1.0,9.0 ss

6E

5C ( )52

51 8.0,2.0 ss ( )7

372 9.0,1.0 ss ( )9

392 3.0,7.0 ss

1C ( )52

51 2.0,8.0 ss ( )7

372 2.0,8.0 ss ( )9

594 8.0,2.0 ss

2C ( )53

52 6.0,4.0 ss ( )7

372 9.0,1.0 ss ( )9

695 9.0,1.0 ss

3C ( )53

52 5.0,5.0 ss ( )7

372 3.0,7.0 ss ( )9

695 4.0,6.0 ss

4C ( )51

50 5.0,5.0 ss ( )7

675 4.0,6.0 ss ( )9

594 4.0,6.0 ss

7E

5C ( )53

52 9.0,1.0 ss ( )7

372 9.0,1.0 ss ( )9

493 6.0,4.0 ss

1C ( )53

52 7.0,3.0 ss ( )7

574 7.0,3.0 ss ( )9

594 9.0,1.0 ss

2C ( )53

52 3.0,7.0 ss ( )7

170 9.0,1.0 ss ( )9

392 3.0,7.0 ss

3C ( )54

53 8.0,2.0 ss ( )7

675 6.0,4.0 ss ( )9

190 7.0,3.0 ss

4C ( )52

51 4.0,6.0 ss ( )7

372 5.0,5.0 ss ( )9

392 5.0,5.0 ss

8E

5C ( )53

52 6.0,4.0 ss ( )7

271 4.0,6.0 ss ( )9

594 4.0,6.0 ss

1C ( )51

50 2.0,8.0 ss ( )7

675 7.0,3.0 ss ( )9

796 9.0,1.0 ss

2C ( )54

53 4.0,6.0 ss ( )7

675 3.0,7.0 ss ( )9

897 7.0,3.0 ss

9E

3C ( )51

50 6.0,4.0 ss ( )7

271 7.0,3.0 ss ( )9

594 9.0,1.0 ss


314

4C ( )54

53 5.0,5.0 ss ( )7

574 8.0,2.0 ss ( )9

897 5.0,5.0 ss

5C ( )51

50 1.0,9.0 ss ( )7

170 4.0,6.0 ss ( )9

190 4.0,6.0 ss

1C ( )53

52 7.0,3.0 ss ( )7

473 5.0,5.0 ss ( )9

594 3.0,7.0 ss

2C ( )52

51 4.0,6.0 ss ( )7

372 4.0,6.0 ss ( )9

190 4.0,6.0 ss

3C ( )53

52 2.0,8.0 ss ( )7

271 5.0,5.0 ss ( )9

594 9.0,1.0 ss

4C ( )51

50 3.0,7.0 ss ( )7

574 8.0,2.0 ss ( )9

392 6.0,4.0 ss

10E

5C ( )52

51 6.0,4.0 ss ( )7

170 6.0,4.0 ss ( )9

897 5.0,5.0 ss

TABLE 3. THE WEIGHT OF EACH CRITERION

1C 2C 3C 4C 5C

weight 0.187 0.228 0.197 0.212 0.176

TABLE 4. THE DISTANCE BETWEEN EMPLOYEE AND THE POSITIVE IDEAL EMPLOYEE, THE NEGATIVE IDEAL EMPLOYEE

AND CLOSENESS COEFFICIENT

1E 2E 3E 4E 5E

Distance between employee and PIE

0.189 0.137 0.145 0.155 0.145

Distance between employee and NIE

0.101 0.157 0.129 0.118 0.123

closeness coefficient 0.348 0.535 0.471 0.433 0.458

6E 7E 8E 9E 10E

Distance between employee and PIE

0.126 0.124 0.169 0.118 0.178

Distance between employee and NIE

0.125 0.125 0.123 0.199 0.110

closeness coefficient 0.498 0.501 0.421 0.629 0.381

TABLE 5. EMPLOYEE RELATION MATRIX

1E 2E 3E 4E 5E 6E 7E 8E 9E 10E

1E - 3 2 4 3 2 2 3 5 7

2E 3 - 1 5 3 1 4 7 4 6

3E 2 1 - 4 4 5 3 4 4 7

4E 4 5 4 - 3 3 3 4 4 6

5E 3 3 4 3 - 2 5 3 5 6

6E 2 1 5 3 2 - 3 3 6 4

7E 2 4 3 3 5 3 - 2 4 5

8E 3 7 4 4 3 3 2 - 1 3

9E 5 4 4 4 5 6 4 1 - 2

10E 7 6 7 6 6 4 5 3 2 -

TABLE 6. EMPLOYEE FAMILIAR DEGREE MATRIX

1E 2E 3E 4E 5E 6E 7E 8E 9E 10E

1E - 0.49 0.36 0.59 0.49 0.36 0.36 0.49 0.67 0.79

2E 0.49 - 0.20 0.67 0.49 0.20 0.59 0.79 0.59 0.74

3E 0.36 0.20 - 0.59 0.59 0.67 0.49 0.59 0.59 0.79

4E 0.59 0.67 0.59 - 0.49 0.49 0.49 0.59 0.59 0.74

5E 0.49 0.49 0.59 0.49 - 0.36 0.67 0.49 0.67 0.74

6E 0.36 0.20 0.67 0.49 0.36 - 0.49 0.49 0.74 0.59

7E 0.36 0.59 0.49 0.49 0.67 0.49 - 0.36 0.59 0.67

8E 0.49 0.79 0.59 0.59 0.49 0.49 0.36 - 0.20 0.49

9E 0.67 0.59 0.59 0.59 0.67 0.74 0.59 0.20 - 0.36

10E 0.79 0.74 0.79 0.74 0.74 0.59 0.67 0.49 0.36 -

TABLE 7. THE ENTERPRISE EMPLOYEE COOPERATION

CAPACITY AND PROJECT LEADER COMPREHENSIVE INDEX OF EACH EMPLOYEE

1E 2E 3E 4E 5E enterprise employee cooperation capacity

3.098 3.651 3.583 3.752 3.634

project leader comprehensive index

0.656 0.873 0.814 0.805 0.810

6E 7E 8E 9E 10E

enterprise employee cooperation capacity

3.284 3.534 3.186 4.075 4.077

project leader comprehensive index

0.799 0.832 0.726 1.000 0.803

Figure . 4. The ranking order of employees with different p when q=0.5

Figure . 5. The ranking order of employees with different q when p=0.5


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