 Open Access
 Total Downloads : 8
 Authors : P. Bharathi
 Paper ID : IJERTCONV3IS33007
 Volume & Issue : RACMS – 2015 (Volume 3 – Issue 33)
 Published (First Online): 24042018
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
A Method to Solve Multi Criteria Decision Making Problems based on Fuzzy Numbers
P. Bharathi,
Asst.Prof Of Mathematics
Sri Sarada Niketan College for Women Amaravathipudur, Sivaganga Dist, TamilNadu
Abstract: MultiCriteria decision making (MCDM) method is a technique where alternatives or options are assessed based on a set of criteria. Most important decisions in organizations are finalized by group of experts. Human judgments including preferences are often vague and cannot be estimated in exact numerical values. This paper proposes a fuzzy approach under the linguistic frame work to obtain optimal solution for Multi Criteria Decision Making problems. To accomplish this, an aggregatedeviation method based on triangular fuzzy numbers is proposed. A fuzzy decision matrix plays an important role in our research problem.
Keywords: Multi Criteria Decision Making, Triangular fuzzy numbers, Linguistic Variables, Aggregation Operators, Fuzzy Decision Matrix.

INTRODUCTION
Decision Making
Decision making can be defined as a process of specifying a problem, identifying and evaluating criteria or alternatives and selecting a preferred alternative among possible ones (Chen, 2005).
Multicriteria Decision Making (MCDM)
Multicriteria decision making (MCDM) method is a technique where alternatives or options are assessed based on a set of criteria and it is one of the most widely used methods in decision making (Hwang & Yoon, 1981). MCDM methods have been employed in many areas such as engineering, agricultural, banking, energy, forestry, health services and education. General form of MCDM problem with m alternatives and n criteria can be illustrated in matrix format as follows:
/ 1
1
. . .
. . .
Fuzzy MCDM
In real life, decision makers often make evaluation based on a set of criteria which are normally vague and imprecise. Due to this, fuzzy set was introduced particularly in representing the vague information or criteria. Fuzzy set theory was first utilized in solving decision making problem by Bellman and Zadeh in 1970. The key concept of fuzzy set theory is that its elements have a varying grade of membership, ranging from 0 to 1. The boundaries of these fuzzy sets are not sharp or imprecise. The individual membership in a fuzzy set is represented by the degree of compatibility (Klir et.al, 1997) and fuzzy sets are used to describe linguistic values for example "very good," "good," "fair," "poor," and "very poor". Instead of using exact numbers as input values, fuzzy numbers were utilized in representing these linguistic terms.
The introduction of fuzzy set theory also motivates many researchers in integrating the theory with some of the classical MCDM methods. Pioneer work in incorporating fuzzy element into decision making was done by introducing an algorithm for rating and ranking multiple aspects of alternatives using fuzzy sets. Decision makers' opinions can be expressed in terms of linguistic variables.

PRELIMINARIES
Fuzzy Set :
A fuzzy set is a pair (A, f) where A is a set and f:A[0,1]. For each x in A the value f(x) is called the grade of membership of x in (A, f).
Triangular fuzzy number:
Let l, m, u in R , l< < . The fuzzy number t:R[0,1] denoted by
0 <
Evaluation table
t =
t =
{ 0 > }
is called a triangular fuzzy number. Example: [1, 2, 3]
Linguistic Variable[5]:
A linguistic variable is a variable whose values are words or sentences in a natural or artificial language. These linguistic variables can be expressed in positive triangular fuzzy numbers.

PROPOSED METHOD

Collect the evaluation of alternatives by expert decision makers with respect to all criteria in terms of linguistic variables and we can form a decision matrix.

Replace each linguistic variable by corresponding fuzzy number.

Aggregate the fuzzy numbers in column wise based on criteria C1, C2 ,..

Aggregate the fuzzy numbers column wise based on decision makers P1, P2 ,..

Find the deviation of each triangular fuzzy number.
C1
C2
Cm
P1
A1
L1
L2
L3
..
..
..
..
..
..
..
.
.
An
P2
.
.
Pk
A1
..
..
.
.
An
C1
C2
Cm
P1
A1
L1
L2
L3
..
..
..
..
..
..
..
.
.
An
P2
.
.
Pk
A1
..
..
.
.
An
Here C1, C2,. are the criteria. A1, A2, A3. are the alternatives. P1,P2,. are the decision makers.L1, L2, are the linguistic variables.
STEP 2
To construct a fuzzy decision matrix replace each linguistic variable by corresponding fuzzy number.
STEP 3
Aggregate the fuzzy numbers in column wise based on criteria C1, C2,.. by using the formula [7],
1 1

The fuzzy number with minimum deviation comes first in ranking order [ascending].
Lag =Mag –
1 , Mag = [1 ] &
1 1 []

COMPUTATIONAL ASPECTS
Suppose group of expert decision makers want to select a most suitable candidate from several alternatives based on some criteria.
STEP 1
Evaluation of alternatives by expert decision makers with respect to all criteria in terms of linguistic variables.
Linguistic frame work [5]
Uag = Mag + 1 for all fuzzy numbers (li,mi,ui).
1
1
1 1 [ ]
..
P1
P2..
..Pk
A1
Fag 1
Fag 2
.
.
.
.
.
.
Am
P1
P2..
..Pk
A1
Fag 1
Fag 2
.
.
.
.
.
.
Am
..
Now we have the set of fuzzy numbers(Lag , Mag , Uag). and we can form the table,
Very Poor
VP
(0,0,1)
Poor
P
(0,1,3)
Medium Poor
MP
(1,3,5)
Fair
F
(3,5,7)
Medium Good
MG
(5,7,9)
Good
G
(7,9,10)
Very Good
VG
(9,10,10)
Very Poor
VP
(0,0,1)
Poor
P
(0,1,3)
Medium Poor
MP
(1,3,5)
Fair
F
(3,5,7)
Medium Good
MG
(5,7,9)
Good
G
(7,9,10)
Very Good
VG
(9,10,10)
Where F
STEP 4
ag 1,
Fag 2
are fuzzy numbers.
Aggregate fuzzy numbers column wise using the same formula based on decision makers P , P ,..
1 2
We get
A1
Fag 1
A2
Fag 2
.
.
Am
Fag m
STEP 5
Find the deviation in triangular fuzzy number by using the
STEP 3
Aggregate the fuzzy numbers in column wise based on
formula Df =( ) +
3+
where (l,m,u) is triangular
criteria C1, C2 ,..
fuzzy number.
STEP 6 [CONCLUSION]
The fuzzy number with minimum deviation (Df) comes first in ranking order [ascending].

NUMERICAL EXAMPLE
Suppose 3 expert decision makers want to select a most suitable computer programmer from 3 alternatives based on 5 criteria which are attitude, communication skills, hardworking, general knowledge, and programming knowledge.
STEP 1
Collect the evaluation of alternatives by expert decision makers with respect to all criteria in terms of linguistic variables and we can form a decision matrix[6].
C1
C2
C3
C4
C5
A
1
A2
A
3
A1
A2
A3
A
1
A2
A3
A1
A2
A3
A1
A2
A3
P
1
M G
G
V G
G
V G
M G
F
V G
G
V G
V G
V G
F
V G
G
P
2
G
G
G
M G
V G
G
G
V G
M G
G
V G
V G
F
M G
G
P
3
M G
M G
F
F
V G
V G
G
G
V G
V G
V G
M G
F
G
M G
STEP 2
Replace each linguistic variable by corresponding fuzzy number.
C1
C2
C3
C4
C5
Aggregation
P1
A1
(5,7,9)
(7,9,10)
(3,5,7)
(9,10,10
)
(3,5,7)
(6.57,6.91,10.1
2)
A2
(7,9,10
)
(9,10,10
)
(9,10,10
)
(9,10,10
)
(9,10,1
0)
(8.82, 9.79, 10)
A3
(9,10,1
0)
(5,7,9)
(7,9,10)
(7,9,10)
(7,9,10
)
(6.71, 8.74,
9.66)
P2
A1
(7,9,10
)
(5,7,9)
(7,9,10)
(7,9,10)
(3,5,7)
(6.39, 7.6,
9.24)
A2
(7,9,10
)
(9,10,10
)
(9,10,10
)
(9,10,10
)
(5,7,9)
8.69,
9.12,9.71)
A3
(7,9,10
)
(7,9,10)
(5,7,9)
(9,10,10
)
(7,9,10
)
(6.71,8.74,9.66
)
P 3
A1
(5,7,9)
(3,5,7)
(7,9,10)
(9,10,10
)
(3,5,7)
(3.97,6.91,8.05
)
A2
(5,7,9)
(9,10,10
)
(7,9,10)
(9,10,10
)
(7,9,10
)
(6.71, 8.93,
9.55)
A3
(3,5,7)
(9,10,10
)
(9,10,10
)
(5,7,9)
(5,7,9)
(5.27, 7.55,
9.02)
We get
P1
P2
P3
A1
(6.57,6.91,10.12)
(6.39, 7.6, 9.24)
(3.97,6.91,8.05)
A2
(8.82, 9.79, 10)
8.69, 9.12,9.71)
(6.71, 8.93, 9.55)
A3
(6.71, 8.74, 9.66)
(6.71,8.74,9.66)
(5.27, 7.55, 9.02)
STEP 4
Aggregate the fuzzy numbers column wise based on decision makers P1, P2 ,..
Total evaluation
A1
(6.07, 7.02, 8.93)
A2
(8.46, 9.07, 9.73)
A3
(6.73, 8.15, 9.40)
STEP 5
Find the deviation in triangular fuzzy number by using
FUZZY DECISION MATRIX
C1
C1
C3
C4
C5
P1
A1
(5,7,9)
(7,9,10)
(3,5,7)
(9,10,10)
(3,5,7)
A2
(7,9,10)
(9,10,10)
(9,10,10)
(9,10,10)
(9,10,10)
A3
9,10,10
(5,7,9)
(7,9,10)
(7,9,10)
(7,9,10)
P2
A1
(7,9,10)
(5,7,9)
(7,9,10)
(7,9,10)
p>(3,5,7) A2
(7,9,10)
(9,10,10)
(9,10,10)
(9,10,10)
(5,7,9)
A3
(7,9,10)
(7,9,10)
(5,7,9)
(9,10,10)
(7,9,10)
P3
A1
(5,7,9)
(3,5,7)
(7,9,10)
(9,10,10)
(3,5,7)
A2
(5,7,9)
(9,10,10)
(7,9,10)
(9,10,10)
(7,9,10)
A3
(3,5,7)
(9,10,10)
(9,10,10)
(5,7,9)
(5,7,9)
FUZZY DECISION MATRIX
C1
C1
C3
C4
C5
P1
A1
(5,7,9)
(7,9,10)
(3,5,7)
(9,10,10)
(3,5,7)
A2
(7,9,10)
(9,10,10)
(9,10,10)
(9,10,10)
(9,10,10)
A3
9,10,10
(5,7,9)
(7,9,10)
(7,9,10)
(7,9,10)
P2
A1
(7,9,10)
(5,7,9)
(7,9,10)
(7,9,10)
(3,5,7)
A2
(7,9,10)
(9,10,10)
(9,10,10)
(9,10,10)
(5,7,9)
A3
(7,9,10)
(7,9,10)
(5,7,9)
(9,10,10)
(7,9,10)
P3
A1
(5,7,9)
(3,5,7)
(7,9,10)
(9,10,10)
(3,5,7)
A2
(5,7,9)
(9,10,10)
(7,9,10)
(9,10,10)
(7,9,10)
A3
(3,5,7)
(9,10,10)
(9,10,10)
(5,7,9)
(5,7,9)
Df =( ) + .
3+
For A1 ( l=6.07,m=7.02,u=8.93) Df=2.96 For A2 ( l=8.46,m=9.07,u=9.73) Df=1.32 For A3 ( l=6.73, m=8.15, u=9.40) Df=2.82
STEP 6
The fuzzy number A2 with minimum deviation Df=1.32 comes first in ranking order [ascending]. The final ranking order is A2,A3,A1.

REFERENCES


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