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J. Appl. Environ. Biol. Sci., 4(11)112-124, 2014
© 2014, TextRoad Publication
ISSN: 2090-4274
Journal of Applied Environmental
and Biological Sciences www.textroad.com
*Corresponding Author: Behzad Babakhani, Department of Industrial Engineering, Islamic Azad University of Arak.
E-mail: Behzad.babakhani1982 @ gmail.com
Application of Linear Goal Programming (LGP), the Fuzzy Analytic Hierarchy
Process (FAHP), the Prioritization of Factors
Case Study: Kermanshah Province Gas Company
Behzad Babakhani1*
, Emad Roghanian2
1Department of Industrial Engineering, Islamic Azad University of Arak
2Assistant Professor, Department of Industrial Engineering, Khajeh Nasirodin Toosi University
Received: May 27, 2014
Accepted: September 30, 2014
ABSTRACT
Analytical Hierarchy Process (AHP), to prioritize applications such as weighting factors have been used in various
fields. Among the methods used to cause weight gain, fuzzy pair wise comparison matrix in analytic hierarchy
process, the method of least squares is logarithmic. Approach to complex logarithmic least squares method, and other
methods, this study has tried, through a simple process, rather than the existing methods used. In this approach, a
linear goal programming model to obtain the relative importance of factors and minimize the inconsistency in the
fuzzy pair wise comparison matrix is used. Superiority of this method over other existing methods, the simplicity of
computation and minimize the amount of deviation of the matrices are incompatible. Authentication method used for
prioritization factors, the empowerment of employees, the gas company of Kermanshah Province, is investigated.
KEYWORDS: Analytical Hierarchy Process fuzzy Multi Attribute Decision Making, linear goal programming,
fuzzy normal weight, empowerment.
1 INTRODUCTION
Analytical Hierarchy Process, the first time was presented by [1]. In many areas, such as design and
development, decision-making, forecasting, etc. were used [2]. The traditional analytic hierarchy process, a certain
number of judgments expressed by the decision maker uses, but the numbers are uncertain because of the uncertainty,
a lot of stuff is not available, or if the desired results are achieved not there. To overcome this problem, the method of
AHP, fuzzy judgments, and a fuzzy analytic hierarchy process is used [3, 4, 5 and 6].
The key issue in the application of fuzzy AHP, the decision of how to calculate the relative weight vector of
paired comparisons matrix phase. Several methods of determining the relative weights for the fuzzy AHP proposed.
For instance van Laarhoven and colleagues [7], triangular fuzzy numbers are used instead of absolute numbers, the
logarithmic least squares method for fuzzy AHP proposed. Boender and colleagues [8], referring to one of the
drawbacks of Laarhoven, normal weight gain phase, a modified normalization method presented. Wang et al [9] also
showed an improved method for normalization is not true. Xu and Zhai [10], a logarithmic least squares method for
fuzzy judgment matrix based on Euclidean distance is presented. Weights obtained in this way, the characters range
from t different levels.
Buckley [11], to obtain the relative weight of the matrix of paired comparisons using the geometric mean
method. Chang [12] developed a method of analysis to obtain the final weights, the proposed fuzzy matrix of paired
comparisons. Csutora and Buckley [13], the Landa- Max (����
) to get the fuzzy weights. This method could only
non-fuzzy weight gain, some of which were very far from reality.
Among the methods mentioned above, the method of analysis developed, the computational approach is simple,
it is mostly used. While this method may provide answers wrong and unrealistic [14]. The method used in this paper,
the method proposed by Wang and Chen [15] is. The method to obtain the relative weights normalized triangular
fuzzy, fuzzy pair wise comparison matrix of a linear goal programming model is used. This method can, with relative
weights of fuzzy approach is much simpler than the above mentioned methods, such as least squares logarithmic gain.
Case Study: This article is a gas company in Kermanshah Province. Directors of the company, in line with measures
taken for the benefit of expert personnel, and high efficiency are concerned, the factors affecting the empowerment of
its staff, prioritize, and how important each of these factors, determine. Due to the aforementioned advantages of this
method over other methods, in this paper, the linear goal programming approach to priorities in the analytic hierarchy
process are used.
2- Stage fuzzy analytic hierarchy process (FAHP), based on a linear goal programming (LGP)
2.1The first step:
The fuzzy analytic hierarchy process model (FAHP), utilizes a linear goal programming (LGP), as follows:
112
Babakhani and Roghanian, 2014
Step 1: Understand the problem;
Step 2: Determine the purpose;
Step 3: Determine the criteria at this stage of the decision criteria, are identified. n standard library and researches
studies have been conducted, and the researchers are identified1 2
( , , ..., )n
C C C C= .
Step 4: Determine objects, at this stage of the decision-m options, are set. 1 2
( , , ..., )m
A A A A=
Step 5: The tree hierarchy, then the set objective standards objects decision, in the form of a hierarchy decision tree
technology. This tree includes three objectives, decision criteria and decision options professionalism.
Figure 1: Decision tree hierarchy
Step 6: formation of paired comparisons matrix phase;
At this point, the opinion of experts, the preference of each criterion relative to other criteria, according to the
purpose of the issue, as well as the preferred option compared to other options, according to the criteria in the form of
triangular fuzzy numbers, are determined. So objects these matrices, fuzzy numbers up occurs. A comparison matrix
of criteria relative to target the problem and the number of criteria, Matrixes paired comparisons between options,
compared to the standards established by the fuzzy objects screw. Paired comparisons matrix elements of triangular
fuzzy numbers, the form ����=(���,���,���)are.
If a fuzzy number to express a judgment about the priority criteria (option) i have, compared to the standard (option) j
I decide to by k ����=(���,���,���)is used, it can be a way out geometry [16] as:
M = (�∏ �������� �
�
�, �∏ �������� �
�
� ,�∏ �������� �
�
� )
We used to gather expert opinions.
2.2 Second, the use of linear goal programming (LGP)
Paired comparison matrices obtained from the previous steps to consider:
Table 1: Paired comparison matrix
Where we have:
)1(
The main objective
Criterion 1 Criterion2
NSelectionCri
teria
1Options Options2 mOptions
...
The first
level
The second
level
The third
level
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J. Appl. Environ. Biol. Sci., 4(11)112-124, 2014
Paired comparison matrix above can be converted into non-negative definite matrix with number.
Table 2: Matrix leg left, center and right
For each paired comparison matrix, a vector normal weight, is as follows:
The weight vector according to the following formula, paired comparison matrixA� is close to [9].
Fuzzy weight vector is normalif and only if:
Which is equivalent to:
Relationship between matrix elements, paired comparisons in Table(3-1), and the fuzzy weight vector can be written
as:
So it can be paired comparison matrix�� , we can write:
Table3: Paired comparison matrix
)5(
114
Babakhani and Roghanian, 2014
According to sharing rules, the triangular fuzzy number arithmetic operations, which it wrote:
According to the principle of division of the triangular fuzzy numbers, paired comparison matrix�� in Table 3, we can
write:
Table 4: Paired comparison matrix
The matrices, non-negative definite matrix into three sub-indicators are:
And can be easily fixed, the relationship between the three Matrix, and the weight vector is:
)9(
In the above equation ، و�, the three vectors are a definite weight
( ), which constitute the fuzzy weight vector.
Also non-negative definite matrix element of the three above, we can conclude the following relationships:
These conditions, compatibility conditions for fuzzy triangular matrix of paired comparisons, respectively.
However, because of the subjective judgments and uncertainties in the actual decision-makers subjective judgment,
can not always be 100% sure. In other words, these equations due to the uncertainty of subjective judgment, not
always be used, thus to solve this problem, we introduce the following error vectors.
E = (� � �) � – (n�1)
)12((�� � �) – (n�1) � =�
= (� � ��)
Where I is a unit matrix is n × n and �� و �� و �� (i = 1,2, ..., n), all the variables are wrong. It is desirable that the
absolute values of the deviation variables, the size may be considered small, so we can model the nonlinear goal
programming (NGP) below, to obtain the fuzzy weight vector W build.
Minimize J = ∑ �|��| � |��| � |��|�����
Subject to (� � �) � – (n�1) –� = 0.
)13. ( (�� � �) – (n�1) � –� = 0
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J. Appl. Environ. Biol. Sci., 4(11)112-124, 2014
(�� − ��)��– ∆= 0.
���
+ ∑ ����
���,�� ≥ 1, i= 1,…,n
���
+ ∑ ����
���,�� ≤ 1, i= 1,…,n
∑ ����
��� = 1.
�� − �� ≥ 0.
�� − �� ≥ 0. , �� ≥ 0.
The first three constraints, obtained from equation 12, and three central limits are �� fuzzy weight vector
normalization restrictions. The last three constraints, the requirement to be�� non-negative.
Considering the time we know the eigenvectors for each paired comparison ��matrix disruption, there will be a���
special vector, ��� n ≥ ��
� ��Thus the error vector Δ can always be nonnegative, that is 0 ≤ Δ. However there is no
guarantee that the deviation vectors E and are also non-negative.
So you:
)14 ( i = 1, 2 ,…, n,= ��� |��|
��
,= ��� |��|
���
)15 (i = 1, 2 ,…, n,= ��� |��|
�
,= ��� |��|
��
As a result:
0 ≥(�, … , �)� =0 , ≥(��, … , ��)� =�0 , ≥(��, … , ��)� =�0 , ≥(��
�, … , ���)� =��
Based on the definitions���
and ��
, as well as �� and |��|, can be written as:
)16 (, i = 1, 2 ,…, n. ��
- ���
=��
)17. (i = 1, 2 ,…, n, ��
+���
=|��|
= 0. .The.i = 1, 2, ..., n so on, and can be expressed as:
)18 (, i = 1, 2 ,…, n. �
- ��
=�
)19 ( .i = 1, 2 ,…, n, �
+��
=|�|
That 0=��
. ���
Begin. i = 1, 2, ..., n with the results of the above equation, a non linear goal programming, � and
|�|can berewrittenas follows:
Minimize J = ∑ �� + ��
� + � + �
� + ��� = ��(�� + � + � + + ∆)����
Subject to (�� − �)�� – (n-1)�� –�� +� = 0.
)20( .(�� − �)�� – (n-1)�� –� + = 0
(�� − ��)��– ∆= 0.
���
+ ∑ ����
���,�� ≥ 1, i= 1,…,n
���
+ ∑ ����
���,�� ≤ 1, i= 1,…,n
∑ ����
��� = 1.
�� − �� ≥ 0.
�� − �� ≥ 0. , ��,��,�,�,, ∆ ≥ 0.
The Nonlinear goal programming, a linear goal programming, and = (1, ..., 1) and ��And���
and ��
and can not, at
the same time as the basic variables in the simplex method selected.
It developed a model for fuzzy AHP (FAHP) Such a method employing linear programming model ideal for weight
gain, fuzzy, fuzzy pair of matrices compeer as an ideal linear programming (LGP) suggested it is. For each triangular
fuzzy comparison matrices, is quite "consistent, objective function�∗ values are always zero.
116
Babakhani and Roghanian, 2014
2-3 – third step, final weights and fuzzy, and non fuzzy
In a hierarchical structure, so that the partial fuzzy weights to the criteria in the second level and third level options,
using ideal linear programming (LGP) were optimized using fractional weights obtained, the final fuzzy weights
obtains.
Normalized triangular fuzzy weights to criteria�� (���,��
�,���
), and normalized triangular fuzzy weights for the
option�� (���� ,���
�,����
), we consider (j = 1, ..., m; i = 1, ..., n). Final fuzzy weights can be fleshed out using a
simple sum (SAW), the multi attribute decision obtained.
)21 ( ���� = ∑ (��
�,���,��
�)(���� ,���
�,����)�
���
Where we have: = (��� � = (���
�, ���
� , ���
�)) (equation (3-27)). So the mathematics of fuzzy numbers, the above
equation can be written as:
)22( ���
� = ∑ �����
��� ���
, ���
� = ∑ �����
��� ���
, ���
� = ∑ �����
��� ���
Final fuzzy weights more precisely, you can use the following equations and linear programming models obtained.
)23 (∑ �����
��� ���
= ���
�
)24 (���
� = ∑ �����
��� �� Minimize
���
≤ �� ≤ ���
Subject to
.∑ ������ = 1
, j = 1,…, m
)25 ( ���
� = ∑ �����
��� �� Maximize
, j = 1,…, m w��
≤ w� ≤ w��
Subject to
.∑ ������ = 1
To simplify the comparison of the numbers, the final decision will be final weights Non fuzzy phase, and to a certain
number of turns. The formula used in this study for Non fuzzy the fuzzy numbers is:
������ ��� = 1
3����
� + ���
� + ���
��, � = 1, … ,�
The������ ��� non-fuzzy weight option. Non fuzzy based weights, we can make choices to compare and prioritize up
[15].
2.4 Case Study prioritization factors, the gas company employee empowerment
Step 1: Understand the problem;
Empowering employees of the gas company preferences of Kermanshah province, in the context of human
resource management. Empowerment and the factors affecting it are the issues of interest to corporate executives, and
the influencing factors on the evaluation and Rankings empowering employees and as a result is known in the
organization.
Step 2: Determine the purpose;
The aim of the present study, prioritize the most important factors, the empowerment of employees, the gas
company of Kermanshah Province.
Step 3: Determine the criteria;
According to studies conducted library, empowerment of employees, many felt the five criteria of merit (��), the
feeling of having a choice (� ), perceived effectiveness (��), a sense of significance (��), and a sense of confidence
(��) as standards, empowering employees, have been considered. In this research, these criteria as the main criteria
for the analysis of hierarchical structure, are considered.
Step 4: Determine objects;
9 important factors have been identified, using Pareto charts, as the options in the hierarchical analysis, are
used. The nine factors are:
Use of meritocracy (��), performance-based bonuses (� ), education (��), Delegate (��), personal motivation
(��), personal skills (��), sense of responsibility, (��), education level (��), and experiments (��).
Step 5: The tree hierarchy;
117
J. Appl. Environ. Biol. Sci., 4(11)112-124, 2014
Step 6: formation of paired comparisons matrix phase;
At this stage, to obtain the relationship between options and decision criteria, using a questionnaire that included
questions on paired comparisons between alternatives with respect to criteria for decision-making, as well as paired
comparisons between the decision criteria, the goal is to decide, human resources experts from the four gas companies
of Kermanshah province, located. In the questionnaire, as in the following examples, the respondents are asked about
the options listed towards the decision criteria as the quality of speech.
Example) The following options can be paired to the competency criteria to compare.
Absolutely "More important Very important More Relatively "More important Equal
importance Relatively "More important More Very important Absolutely "More important
Use of meritocracy Bonuses based on performance
Figure2: Comparison of paired two options
In step 6, the respondents 9 option decision, compared to the five decision criteria, the couple, and then the decision
criteria as well, towards the objective of the decision, compare, and questionnaires were completed, which contains
information quality, the researchers are doing. Since Thursday, paired comparison matrix, decision criteria, and a
paired comparison matrix, based on objective decision comes as a result of responses per respondent, for up to 6
paired comparison matrix is used. For example, first responders, 9 options, make decisions, to measure competency,
using a paired comparison questionnaire, and the results will be available to researchers, with putting Quality answers
in paired comparison matrix, the matrix the following is obtained:
Prioritize the factors
affecting employee
empowerment
Merit)��( Choice)��
(
Effective)
��(
Significan
t)��(
Having
confidence
Meritoc
racy
)��(
Rew
ard
)��(
Educ
ation
)��(
Deleg
ation
)��(
Motiv
ation
)��(
Skills
)�(
Respo
nsibilit
y
)�(
Educat
ion)
��(
Experi
ences
)��(
Figure 3: Decision tree showing the three levels
118
Babakhani and Roghanian, 2014
Table 6: Paired comparison matrix with linguistic values
As you can see, the information obtained from pair wise comparison matrix, as their language. To convert qualitative
data into numerical values phase, we use the following table [15]
Table 7: Variable fuzzy language and equivalents Variableloss Fuzzyvaluesof
Equalimportance 1�= (1,1,2)
Relatively"more 3�= (2,3,4)
More important 5�= (4,5,6)
Veryimportant 7�= (6,7,8)
Quiteimportant 9�= (8,9,9)
The median valuesare2�= (1,2,3),4�= (3,4,5),6�= (5,6,7),8�= (7,8,9)
Using the above table, the values of qualitative comparison matrix pair are converted into numerical values phase.
Each pair comparison matrix element that has the quality to become fuzzy number, and the element, it is symmetric
about the main diagonal inverse of the fuzzy number, is placed. Due to the mentioned issues, the expert opinion is:
119
J. Appl. Environ. Biol. Sci., 4(11)112-124, 2014
Competency Use
ofmerit
ocracy
Bonusesba
sed on
performan
ce
Educatio
n
Delegation Theremoti
vatestaff
Interperson
alskills
Sense
ofresponsibil
ity
Level of
Educatio
n
Experiences
1A
2A
3A
4A
5A
6A
7A
8A
9A
Use ofmeritocracy
1A
l 1 4.1195 3.7224 0.6389 1.0746 1 1
1.5197
2.8284
m 1 5.2068 4.7867 0.6687 1.3151 1.2359 1.0878 2.0062 3.3437
u 1 6.2603 5.8259 1.1892 1.8612 1.5651 1.6818 2.4495 4.5590
Bonusesbased on
performance 2A
l 0.1597 1 3.7224 0.4855 0.9036 0.4518 0.5373 2 1.0746
m 0.1921 1 4.7867 0.5886 1.1583 0.5774 0.6687 2.5900 1.3161
u 0.2427 1 5.8259 0.8409 1.6818 0.7825 1 3.7224 1.8612
Education
3A
l 0.1716 0.1716 1 0.1716 0.3195 1.1892 0.3799 0.5000 1.1892
m 0.2089 0.2089 1 0.2089 0.3861 1.4316 0.5081 0.5774 1.3161
u 0.2686 0.2686 1 0.2686 0.5946 2 0.7071 1 2.3784
Delegation
4A
l 0.8409 1.1892 3.7224 1 2 2.0598 0.9036 2.6321 2
m 1.4953 1.6990 4.7867 1 2.2361 2.6457 1.0878 3.2011 2.2361
u 1.5651 2.0598 5.8259 1 3.4641 3.3636 1.3161 4.4267 3.4641
Theremotivatestaff
5A
l 0.5373 0.5946 1.6818 0.2887 1 1.1892 0.6389 0.7598 2.6321
m 0.7598 0.8633 2.5900 0.4472 1 1.4316 0.6687 0.9573 3.2011
u 0.9306 1.1067 3.1302 0.5000 1 2 1.1892 1.1892 4.4267
Interpersonalskills
6A
l 0.6389 1.2779 0.5000 0.2973 0.5000 1 0.3433 1.1892 1.8612
m 0.8091 1.7321 0.6985 0.3780 0.6985 1 0.4111 1.3161 2.1407
u 1 2.2134 0.8409 0.4855 0.8409 1 0.5000 2.3784 3.3636
Sense ofresponsibility
7A
l 0.5946 1 1.4142 0.7598 0.8409 2 1 2.7108 2.6321
m 0.9193 1.4953 1.9680 0.9193 1.4935 2.4323 1 3.2700 3.2011
u 1 1.8612 2.6321 1.1067 1.5651 2.9130 1 4 4.4267
Level of Education
8A
l 0.4082 0.2686 1 0.2260 0.8409 0.4204 0.1768 1 1
m 0.4984 0.3860 1.7321 0.3124 1.0450 0.7598 0.3058 1 1.1362
u 0.6580 0.5000 2 0.3799 1.3161 0.8409 0.3689 1 1.8612
Experiences
9A
l 0.2193 0.5373 0.4204 0.2887 0.2259 0.2973 0.2259 0.5373 1
m 0.2991 0.7598 0.7598 0.4472 0.3124 0.4671 0.3124 0.8801 1
u 0.3536 0.9306 0.8409 0.5000 0.3799 0.5373 0.3799 1 1
Competency
Use of
meritocr
acy
Bonuses
based on
perform
ance
Educatio
n
Delegati
on
There
motivate
staff
Interper
sonal
skills
Sense of
responsi
bility
Level of
Educatio
n
Experien
ces
1A
2A
3A
4A
5A
6A
7A
8A
9A
Use of
meritocracy 1A
(1,1,1)
(6,7,8)
(4,5,6)
(1,1,2)
(�
�,�
�,�
�)
(�
�,�
�,�
�)
(�
�,�
�,�
�)
(8,9,9)
(4,5,6)
Bonuses based
on
performance
2A
(�
�,�
�,�
�) (1,1,1) (4,5,6) (
�
�,�
�,�
�) (
�
�,�
�,�
�) (
�
�,�
�,�
�) (
�
�,�
�,�
�) (4,5,6) (
,
�,
�)
Education 3
A
(�
�,�
�,�
�)
(�
�,�
�,�
�) (1,1,1) (
�
�,�
�,�
�) (
�
�,�
�,�
�) (6,7,8) (
�
�,�
�,�
�) (
�
�,�
�,�
�) (1,1,2)
Delegation 4
A
(�
�,1,1)
(4,5,6) (4,5,6) (1,1,1) (4,5,6) (6,7,8)
(�
�,�
�,�
�) (6,7,8) (1,1,2)
There
motivate staff 5A
(4,5,6) (4,5,6) (4,5,6) (�
�,�
�,�
�) (1,1,1) (6,7,8) (1,1,2) (6,7,8) (6,7,8)
Interpersonal
skills 6A
(4,5,6)
(2,3,4) (�
�,�
�,�
�) (
�
�,�
�,�
�) (
�
�,�
�,�
�) (1,1,1) (
�
�,�
�,�
�) (1,1,2) (6,7,8)
Sense of
responsibility 7A
(4,5,6) (4,5,6) (4,5,6) (4,5,6) (�
�,1,1) (4,5,6) (1,1,1) (6,7,8) (6,7,8)
Level of
Education 8A
(�
,�
,�
�)
(�
�,�
�,�
�) (2,3,4) (
�
�,�
�,�
�) (
�
�,�
�,�
�) (
�
�,1,1) (
�
�,�
�,�
�) (1,1,1) (1,1,2)
Experiences
9A
(
,
�,
�)
(4,5,6) (
�,1,1) (
�,1,1) (
�,
�,
) (
�,
�,
) (
�,
�,
) (
�,1,1) (1,1,1)
120
Babakhani and Roghanian, 2014
The matrix, the matrix of paired comparisons between factors affecting employee empowerment, to measure
competency, which, according to information obtained from questionnaires completed by the first respondent, and
transforming them into fuzzy numbers, is obtained. Similarly, can be paired comparison matrices options than the
standard competency, to acquire other experts, and then using the geometric mean method, integration experts, and the
final phase paired comparison matrix for each criterion obtained.
Similarly, what criteria do we feel merit, can be paired comparison matrix between factors affecting
empowerment, empowerment than other criteria are also obtained. The analytic hierarchy process, then the paired
comparisons between options, decisions, decisions were made about the criteria should then paired comparisons
among the criteria for the decision, rather than deciding the main purpose of this research. "prioritization factors,
based empowerment ", is also carried out. At the end of step 6, all paired comparisons matrices for use in later stages
of the fuzzy analytic hierarchy process, the obtained. The six paired comparison matrices, as an input to the next phase
of the research will be used.
Priority ideal method of linear programming (LGP), a fuzzy AHP (FAHP)
After forming the matrix of paired comparisons, the next phase of this study is to calculate the normalized weight
vector elements in the matrix of paired comparisons, using linear programming priorities Armani (LGP), a fuzzy AHP
(FAHP), we . To calculate the weight vector (relative weight), the matrix of paired comparisons in this method, the
original phase matrix into three matrix with non-negative definite numbers, analyze them. For example, the paired
comparisons matrix, options, based on the competency criteria into three definite matrix with non-negative integers,
we decompose. The three matrices, respectively, are the matrix elements of the left leg fuzzy numbers, matrix
elements of the middle leg and the right leg of fuzzy numbers. After analyzing the fuzzy matrix of paired
comparisons, the three matrices left foot, mid-foot and right foot, and put the matrices in equation (20), and using the
software LINGO 11, the weights of the paired comparisons matrix, obtained come.
Relative weightsobtainedcompetency-based criteria )�∗�( Factors
affectingempowerment
(�.���� ,�.���� ,�.����)
(�.��� ,�.��� ,�.���)
(�.��� ,�.��� ,�.���)
(�.�� ,�.��� ,�.����)
(�.���� ,�.���� ,�.����)
(�.�� ,�.��� ,�.���)
(�.���� ,�.���� ,�.����)
(�.���� ,�.��� ,�.���)
(�.���� ,�.�� ,�.����)
�
�
�
�
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choice) ∗�(
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121
J. Appl. Environ. Biol. Sci., 4(11)112-124, 2014
Relative weights obtained, according to the sense of
significance) ∗�(
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empowerment
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empowerment
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122
Babakhani and Roghanian, 2014
And so on, using a linear goal programming (LGP), which measures the relative weight of the paired comparisons
matrix, we get:
Calculation of the fuzzy weights and the weights Non fuzzy
After calculating the relative weight options, and criteria for paired comparisons matrix, linear goal programming
method, the final weight factors, the empowerment of employees (making choices), using the existing relationship
obtains. After calculating the weights of the items, in order to facilitate the comparison of fuzzy weights, converted to
absolute numbers, and then we prioritize options.
Table 16: Final fuzzy weights for the nine factors affecting employee empowerment, and rank them
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The
relative
weights
of the
criteria
Criteria
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�0.05790.0668
0.0703
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�0.14490.1642
0.1642
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�0.08680.0936
0.1053
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�0.10020.1060
0.1120
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�0.18300.1993
0.2194
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�0.02350.0345
0.0668
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�0.10390.1039
0.1180
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�0.19240.1924
0.2170
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�0.20050.2005
0.2413
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�0.03420.0524
0.0531
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�0.09150.1055
0.1070
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�0.06970.0776
0.0827
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�0.07240.0743
0.0825
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�0.32970.3489
0.3489
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�0.00000.0109
0.0507
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�0.11180.1126
0.1126
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�0.13910.1555
0.1642
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�0.12490.1694
0.2146
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�0.03980.0464
0.0520
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�0.06280.0735
0.0780
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�0.05170.0574
0.0644
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�0.08510.0932
0.1008
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�0.22750.2578
0.2578
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�0.00060.0227
0.0512
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�0.17330.1851
0.2036
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�0.19860.2148
0.2629
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�0.09050.1190
0.1682
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�0.01310.0298
0.0423
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�0.10120.1256
0.1256
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�0.04210.0474
0.0539
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�0.14580.1698
0.1815
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�0.17570.2012
0.2143
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�0.03740.0393
0.0477
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�0.13100.1517
0.1643
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�0.18950.1936
0.2379
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0.2750
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�0.01160.0256
0.0320
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�0.06200.0799
0.0825
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�0.03700.0416
0.0460
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�0.09410.1100
0.1128
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�0.20940.2453
0.2453
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�0.03470.0371
0.0453
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�0.19000.2195
0.2347
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�0.18530.2035
0.2298
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0.2361
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0.0484
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0.0677
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0.1194
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0.0519
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0.1602
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0.2174
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Finalfuzzyweights
0.0404 0.0396 0.1058 0.0614 0.1125 0.2330 0.0346 0.1507 0.1961
Non-fuzzy
weights.
Relative weights obtained, based on objective criteria enabling
prioritization factors) ∗�(
Empowerment
criteria
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123
J. Appl. Environ. Biol. Sci., 4(11)112-124, 2014
Final ranking factors, the empowerment approach using FAHP-LGP
Table17: Final ranking factors on employee empowerment Rating Enablers Weight
1 entrusting 0.2330
2 Use meritocracy 0.1961
3 bonuses based on performance 0.1507
4 motivate staff 0.1125
5 a sense of responsibility 0.1058
6 Individual Skills 0.0614
7 experiences 0.0404
8 levels of education 0.0396
9 education 0.0346
3. Conclusions and Recommendations
Fuzzy analytic hierarchy process, a technique widely used in various sciences. The huge volume of articles, in
which Fuzzy Analytic Hierarchy Process is used, the logarithmic methods such as least squares, total single weighty,
development, analysis, and other methods to obtain the relative weights of fuzzy, fuzzy pair wise comparison matrix
used, while some of the methods, procedures are complex, and some results are far from reality, and often provide
inaccurate. To use properly, and the Fuzzy Analytic Hierarchy successful, a linear goal programming approach, we use.
This study was conducted in Kermanshah Province Gas Company. And using the above mentioned factors on
the empowerment of employees in the company were prioritized. The results show that the method can be linear goal
programming, Fuzzy relative weights precise, perfectly adapted for paired comparisons matrix, and the optimal
weights normalized fuzzy, with the lowest variance for paired comparisons matrix inconsistent obtained. This case
study shows that, this method factors prioritize the empowerment of employees, as well as answers.
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