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International Journal of Research in Engineering and Science (IJRES)
ISSN (Online): 2320-9364, ISSN (Print): 2320-9356
www.ijres.org Volume 1 Issue 2 ǁJune. 2013 ǁPP.05-12
www.ijres.org 5 | Page
Notes on TOPSIS Method
Zlatko Pavić1
, Vedran Novoselac2
1,2
(Mechanical Engineering Faculty in Slavonski Brod, University of Osijek, Croatia)
ABSTRACT: The paper demonstrates the application of TOPSIS method using two selected examples. In the
first example, it is shown that the best TOPSIS solution is neither closest to the positive ideal solution nor the
farthest from the negative ideal solution. In many works on TOPSIS method stands as follows: "The basic
principle is that the chosen alternative should have the shortest distance from the positive ideal solution and the
longest distance from the negative ideal solution".
Keywords: decision matrix, negative ideal solution, positive ideal solution
I. INTRODUCTION
Multi-criteria decision making.
In a general sense, it is the aspiration of human being to make "calculated" decision in a position of
multiple selection. In scientific terms, it is the intention to develop analytical and numerical methods that take into
account multiple alternatives with multiple criteria.
TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) is one of the numerical
methods of the multi-criteria decision making. This is a broadly applicable method with a simple mathematical
model. Furthermore, relying on computer support, it is very suitable practical method. The method is applied in
the last three decades (on the history of TOPSIS see [4], [3]), and there are many papers on its applications (see
[11], [8], [9]).
Description of the problem.
Given m options (alternatives) iA , each of which depends on n parameters (criteria) jx whose
values are expressed with positive real numbers ijx . The best option should be selected.
Mathematical model of the problem.
Initially, the parameter values ijx should be balanced according to the procedure of normalization.
Suppose that ija are the normalized parameter values. Then each option iA is expressed as the point
1( , , ) n
i i inA a a R . Selecting the most optimal value 1{ , , }j j mja a a
 for every parameter jx , we
determine the positive ideal solution 1= ( , , )nA a a  
. The opposite is the negative ideal solution
1= ( , , )nA a a  
. The positive and negative ideal solution are also denoted by A
and A
. The decision on
the order of options is made respecting the order of numbers
( , ) 1
= = .
( , ) ( , ) ( , ) / ( , ) 1
i
i
i i i i
d A A
D
d A A d A A d A A d A A


   
 
(1)
The option
1iA is the best solution if 1 2 1
max{ , , , } =m iD D D D   , and the option
2iA is the worst solution
if 1 2 2
min{ , , , } =m iD D D D    . The other options are between these two extremes. The maximum distance
=1, ,= maxi m iD D 
is usually called TOPSIS metric.
Geometrical image of the problem.
Fig. 1 shows the initial arrangement of alternatives in TOPSIS method for = 2n . Parameter 1 1=x x
has a monotonically increasing preference, and parameter 2 2=x x
has a monotonically decreasing preference.
The positive A
and negative A
ideal solution are located at diagonally opposite positions. The best solution
Notes on TOPSIS Method
www.ijres.org 6 | Page
is the alternative 7A .
Figure 1. Geometrical representation of TOPSIS method
TOPSIS is a compensatory method. These kinds of methods allow the compromise between different
criteria, where a bad result in one criterion can be compensated by a good result in another criterion. An
assumption of TOPSIS method is that each criterion has either a monotonically increasing or decreasing
preference. Due to the possibility of criteria modelling, compensatory methods, certainly including TOPSIS, are
widely used in various sectors of multi-criteria decision making (see [10], [2], [1]).
II. COMPUTATIONAL PROCEDURE FOR TOPSIS METHOD
Problem.
We examine m alternatives 1, , mA A . Each alternative iA respects n criteria 1, , nx x which are
expressed with positive numbers ijx . The criteria 1, , kx x are benefit (monotonically increasing preference),
and criteria 1, ,k nx x are non-benefit (monotonically decreasing preference). Weights jw of the criteria jx
are given so that =1
=1
n
jj
w . It is necessary to select the most optimal alternative.
Initial Table and Decision Matrix.
For better visibility, the given alternatives, criteria and its weights are placed in the table (see Table 1).
Table 1. Initial table for TOPSIS method
Notes on TOPSIS Method
www.ijres.org 7 | Page
The given numbers ijx and their matrix
11 12 1
21 22 2
1 2
=
n
n
m m mn
x x x
x x x
x x x
 
 
 
 
 
 
  
X (2)
must be balanced, since the numbers ijx present values of different criteria with different measuring units. One
must also take into account the given weights jw of the criteria jx . First, the measuring numbers ijx of the
criteria jx are replaced with the normalized or relative numbers
2
=1
=
ij
ij m
ij
i
x
r
x
(3)
belonging to the open interval 0,1  . Then, according to the share j jw x of the criteria jx , the normalized
numbers ijr are replaced with the weighted normalized numbers
2
=1
= =
ij
ij j ij j m
ij
i
x
a w r w
x
(4)
belonging to 0,1  . The further data processing uses the weighted normalized decision matrix
11 12 1
21 22 2
1 2
= .
n
n
m m mn
a a a
a a a
a a a
 
 
 
 
 
 
  
A (5)
If all weights jw are mutually equal, in which case = 1/jw n, the numbers ijr can be applied in the matrix
A as the numbers ija .
Working Table.
The weighted normalized decision matrix A and all the data that will be calculated, we try to write in
one table.
Table 2. Working table for TOPSIS method
Notes on TOPSIS Method
www.ijres.org 8 | Page
The coordinates ja
of the positive ideal solution 1 2= ( )nA a a a   
are chosen using the formula
for =1, ,max
= for = 1, , .min
ij
i
j ij
i
a j k
a a j k n





(6)
If some alternative
0iA is equal to A
, then it is obvious that the alternative
0iA is the best solution. If it is not,
then we continue the procedure.
The coordinates ja
of the negative ideal solution 1 2= ( )nA a a a   
are chosen applying the
formula
for =1, ,min
= for = 1, , .max
ij
i
j ij
i
a j k
a a j k n





(7)
The numbers id 
of the column 1 2= ( )md d d d    
are the distances from the points iA to the
point A
, which is calculated by the formula
 
2
=1
= ( , ) = .
n
i i ij j
j
d d A A a a  
 (8)
The numbers id 
of the column 1 2= ( )md d d d    
are the distances from the points iA to the
point A
, which is calculated by the formula
 
2
=1
= ( , ) = .
n
i i ij j
j
d d A A a a  
 (9)
The numbers iD
of the column 1 2= ( )mD D D D    
are the relative distances of the points iA
respecting the points A
and A
, which is expressed by the formula
( , )
= = .
( , ) ( , )
i i
i
i i i i
d d A A
D
d d d A A d A A
 

   
 
(10)
If 1 2 1
max{ , , , } =m iD D D D    , then we accept the alternative
1iA as the best solution. If
1 2 2
min{ , , , } =m iD D D D    , then we accept the alternative
2iA as the worst solution.
Notes on TOPSIS Method
www.ijres.org 9 | Page
III. TWO EXAMPLES OF USING TOPSIS METHOD
Example 1. Four alternatives with three criteria are given in Table 3. The criteria 1x and 2x are benefit, and the
criterion 3x is non-benefit. The weights of the criteria are equal. Decide which alternative is the best.
Table 3. Initial table for Example 1
Since the weights are equal, we can use the normalized decision matrix A with the elements
4
2
=1
=
ij
ij
ij
i
x
a
x
(11)
for =1,2,3,4i and =1,2,3j . In this case, the matrix A reads as follows:
0,535 0,441 0,407
0,688 0,441 0,488
= .0,382 0,515 0,732
0,306 0,588 0,244
 
 
 
 
 
 
  
A (12)
Relying on the matrix A , we have to determine two rows ( A
, A
) and three columns ( d
, d
, D
) in the
working table.
Table 4. Working table for Example 1
According to the formula in (6), the positive ideal solution 1 2 3= ( )A a a a   
contains the greatest
numbers of the first and second column of A , and the smallest number of the third column of A .
According to the formula in (7), the negative ideal solution 1 2 3= ( )A a a a   
contains the smallest
numbers of the first and second column of A , and the greatest number of the third column of A .
The distances 1 2 3 4= ( )d d d d d     
, from the alternatives iA to the positive ideal solution A
, are calculated
Notes on TOPSIS Method
www.ijres.org 10 | Page
by the formula in (8) with = 3n , so
 
3 2
=1
= ( , ) = .i i ij j
j
d d A A a a  
 (13)
The distances 1 2 3 4= ( )d d d d d     
, from the alternatives iA to the negative ideal solution A
, are
calculated by the formula in (9) with = 3n , so
 
3 2
=1
= ( , ) = .i i ij j
j
d d A A a a  
 (14)
The relative distances 1 2 3 4= ( )D D D D D     
of the alternatives iA respecting the positive ideal
solution A
and negative ideal solution A
are calculated using the formula in (10), so
= .i
i
i i
d
D
d d


 

(15)
Applying the last tree columns of the Table 4 we have the following three preferred orders of alternatives:
2 1 4 3A A A A by the column of D
from the largest to smallest number
4 2 1 3A A A A by the column of d
from the largest to smallest number
1 2 4 3A A A A by the column of d
from the smallest to largest number
TOPSIS method prefers the first order respecting the column of D
.
In the following example we have the most desirable combination: d
, d
and D
point to one and
the same best solution.
Example 2. 1Six alternatives with five criteria and their weights are given in Table 5. The criteria 1 2 3, ,x x x are
benefit, and the criteria 4 5,x x are non-benefit. Select the best alternative.
Table 5. Initial table for Example 2
Weighted normalized decision matrix A with the elements
6
2
=1
=
ij
ij j
ij
i
x
a w
x
(16)
for =1, ,6i and =1, ,5j stands as follows:
Notes on TOPSIS Method
www.ijres.org 11 | Page
0,118 0,049 0,057 0,118 0,033
0,158 0,035 0,043 0,103 0,088
0,097 0,028 0,113 0,044 0,077
= .
0,079 0,063 0,099 0,088 0,055
0,178 0,028 0,071 0,103 0,066
0,059 0,028 0,085 0,133 0,022
 
 
 
 
 
 
 
 
 
A (17)
The positive ideal solution 1 2 3 4 5= ( )A a a a a a     
contains the greatest numbers of the first, second and
third column of A , and the smallest numbers of the fourth and fifth column of A .
The negative ideal solution 1 2 3 4 5= ( )A a a a a a     
contains the smallest numbers of the first, second
and third column of A , and the greatest numbers of the fourth and fifth column of A .
The distances 1 2 3 4 5 6= ( )d d d d d d d       
, from the alternatives iA to the positive ideal solution A
,
are calculated applying the distance formulas in (8) with = 5n .
The distances 1 2 3 4 5 6= ( )d d d d d d d       
, from the alternatives iA to the negative ideal solution
A
, are calculated applying the distance formulas in (9) with = 5n .
The relative distances 1 2 3 4 5 6= ( )D D D D D D D       
of the alternatives iA respecting the positive
ideal solution A
and negative ideal solution A
are determined using the quotient formulas in (10).
Table 6. Working table for Example 2
Applying the last tree columns of the Table 6 we have the following three preferred orders of
alternatives:
5 3 2 4 1 6A A A A A A by the column of D
from the largest to smallest number
5 3 2 4 1 6A A A A A A by the column of d
from the largest to smallest number
5 3 1 4 2 6A A A A A A by the column of d
from the smallest to largest number
The first order is preferred by TOPSIS method.
Notes on TOPSIS Method
www.ijres.org 12 | Page
IV. CONCLUSION
In the method presenting it is important to find examples that adequately show its meaning and
application. After that, the generalizations of the method can be implemented to extend its applications. In the case
of TOPSIS method the first generalization refers to the processing of insufficiently precise data, namely, fuzzy
data (see [6], [5], [1]).
The second generalization applies to the norm and metric. Let 1p  be a real number. Using the p
-norm in the normalization procedure, we get
1
=1
= = .
|| ( , , ) ||
| |
ij ij
ij m
j mj p pp
ij
i
x x
r
x x
x
(18)
Then, using the p -metric in the distance calculation, we have
=1
= ( , ) = | | .
n
p
p
i p i ij j
j
d d A A a a  
 (19)
The max-norm and max-metric can also be applied in the computational procedure of TOPSIS method .
REFERENCES
[1] A. Balin, P. Alcan, and H. Basligil, Co performance comparison on CCHP systems using different fuzzy
multi criteria decision making models for energy sources, Fuelling the Future, pp. 591-595, 2012.
[2] I. B. Huang, J. Keisler, and I. Linkov, Multi-criteria decision analysis in environmental science: ten years of
applications and trends, Science of the Total Environment 409, pp. 3578-3594, 2011.
[3] C. L. Hwang, Y. J. Lai, and T. Y. Liu, A new approach for multiple objective decision making, Computers
and Operational Research 20, pp. 889-899, 1983.
[4] C. L. Hwang, and K. Yoon, Multiple Attribute Decision Making: Methods and Applications, Berlin
Heidelberg New York, Springer-Verlag, 1981.
[5] G. R. Jahanshahloo, F. Hosseinzadeh Lotfi, and M. Izadikhah, Extension of the TOPSIS method for
decision-making problems with fuzzy data, Applied Mathematics and Computation, pp. 1544-1551, 2006.
[6] Y. J. Lai, T. Y. Liu, and C. L. Hwang, Fuzzy Mathematical Programming: Methods and Applications, Berlin
Heidelberg New York, Springer-Verlag, 1992.
[7] Y. J. Lai, T. Y. Liu, andC. L. Hwang, TOPSIS for MODM, European Journal of Operational Research 76,
pp. 486-500, 1994.
[8] G. H. Tzeng, and J. J. Huang, Multiple Attribute Decision Making: Methods and Applications, New York,
CRC Press, 2011.
[9] J. Xu, and Z. Tao, Rough Multiple Objective Decision Making, New York, CRC Press, 2012.
[10]K. A. Yoon, A reconciliation among discrete compromise situations, Journal of Operational Research
Society 38, pp. 277-286, 1987.
[11]K. P. Yoon, and C. Hwang, Multiple Attribute Decision Making: An Introduction, California, SAGE
Publications, 1995.
[12]E. K. Zavadskas, A. Zakarevicius, and J. Antucheviciene, Evaluation of ranking accuracy in multi-criteria
decisions, Informatica 17, pp. 601-618, 2006.

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  • 1. International Journal of Research in Engineering and Science (IJRES) ISSN (Online): 2320-9364, ISSN (Print): 2320-9356 www.ijres.org Volume 1 Issue 2 ǁJune. 2013 ǁPP.05-12 www.ijres.org 5 | Page Notes on TOPSIS Method Zlatko Pavić1 , Vedran Novoselac2 1,2 (Mechanical Engineering Faculty in Slavonski Brod, University of Osijek, Croatia) ABSTRACT: The paper demonstrates the application of TOPSIS method using two selected examples. In the first example, it is shown that the best TOPSIS solution is neither closest to the positive ideal solution nor the farthest from the negative ideal solution. In many works on TOPSIS method stands as follows: "The basic principle is that the chosen alternative should have the shortest distance from the positive ideal solution and the longest distance from the negative ideal solution". Keywords: decision matrix, negative ideal solution, positive ideal solution I. INTRODUCTION Multi-criteria decision making. In a general sense, it is the aspiration of human being to make "calculated" decision in a position of multiple selection. In scientific terms, it is the intention to develop analytical and numerical methods that take into account multiple alternatives with multiple criteria. TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) is one of the numerical methods of the multi-criteria decision making. This is a broadly applicable method with a simple mathematical model. Furthermore, relying on computer support, it is very suitable practical method. The method is applied in the last three decades (on the history of TOPSIS see [4], [3]), and there are many papers on its applications (see [11], [8], [9]). Description of the problem. Given m options (alternatives) iA , each of which depends on n parameters (criteria) jx whose values are expressed with positive real numbers ijx . The best option should be selected. Mathematical model of the problem. Initially, the parameter values ijx should be balanced according to the procedure of normalization. Suppose that ija are the normalized parameter values. Then each option iA is expressed as the point 1( , , ) n i i inA a a R . Selecting the most optimal value 1{ , , }j j mja a a  for every parameter jx , we determine the positive ideal solution 1= ( , , )nA a a   . The opposite is the negative ideal solution 1= ( , , )nA a a   . The positive and negative ideal solution are also denoted by A and A . The decision on the order of options is made respecting the order of numbers ( , ) 1 = = . ( , ) ( , ) ( , ) / ( , ) 1 i i i i i i d A A D d A A d A A d A A d A A         (1) The option 1iA is the best solution if 1 2 1 max{ , , , } =m iD D D D   , and the option 2iA is the worst solution if 1 2 2 min{ , , , } =m iD D D D    . The other options are between these two extremes. The maximum distance =1, ,= maxi m iD D  is usually called TOPSIS metric. Geometrical image of the problem. Fig. 1 shows the initial arrangement of alternatives in TOPSIS method for = 2n . Parameter 1 1=x x has a monotonically increasing preference, and parameter 2 2=x x has a monotonically decreasing preference. The positive A and negative A ideal solution are located at diagonally opposite positions. The best solution
  • 2. Notes on TOPSIS Method www.ijres.org 6 | Page is the alternative 7A . Figure 1. Geometrical representation of TOPSIS method TOPSIS is a compensatory method. These kinds of methods allow the compromise between different criteria, where a bad result in one criterion can be compensated by a good result in another criterion. An assumption of TOPSIS method is that each criterion has either a monotonically increasing or decreasing preference. Due to the possibility of criteria modelling, compensatory methods, certainly including TOPSIS, are widely used in various sectors of multi-criteria decision making (see [10], [2], [1]). II. COMPUTATIONAL PROCEDURE FOR TOPSIS METHOD Problem. We examine m alternatives 1, , mA A . Each alternative iA respects n criteria 1, , nx x which are expressed with positive numbers ijx . The criteria 1, , kx x are benefit (monotonically increasing preference), and criteria 1, ,k nx x are non-benefit (monotonically decreasing preference). Weights jw of the criteria jx are given so that =1 =1 n jj w . It is necessary to select the most optimal alternative. Initial Table and Decision Matrix. For better visibility, the given alternatives, criteria and its weights are placed in the table (see Table 1). Table 1. Initial table for TOPSIS method
  • 3. Notes on TOPSIS Method www.ijres.org 7 | Page The given numbers ijx and their matrix 11 12 1 21 22 2 1 2 = n n m m mn x x x x x x x x x                X (2) must be balanced, since the numbers ijx present values of different criteria with different measuring units. One must also take into account the given weights jw of the criteria jx . First, the measuring numbers ijx of the criteria jx are replaced with the normalized or relative numbers 2 =1 = ij ij m ij i x r x (3) belonging to the open interval 0,1  . Then, according to the share j jw x of the criteria jx , the normalized numbers ijr are replaced with the weighted normalized numbers 2 =1 = = ij ij j ij j m ij i x a w r w x (4) belonging to 0,1  . The further data processing uses the weighted normalized decision matrix 11 12 1 21 22 2 1 2 = . n n m m mn a a a a a a a a a                A (5) If all weights jw are mutually equal, in which case = 1/jw n, the numbers ijr can be applied in the matrix A as the numbers ija . Working Table. The weighted normalized decision matrix A and all the data that will be calculated, we try to write in one table. Table 2. Working table for TOPSIS method
  • 4. Notes on TOPSIS Method www.ijres.org 8 | Page The coordinates ja of the positive ideal solution 1 2= ( )nA a a a    are chosen using the formula for =1, ,max = for = 1, , .min ij i j ij i a j k a a j k n      (6) If some alternative 0iA is equal to A , then it is obvious that the alternative 0iA is the best solution. If it is not, then we continue the procedure. The coordinates ja of the negative ideal solution 1 2= ( )nA a a a    are chosen applying the formula for =1, ,min = for = 1, , .max ij i j ij i a j k a a j k n      (7) The numbers id  of the column 1 2= ( )md d d d     are the distances from the points iA to the point A , which is calculated by the formula   2 =1 = ( , ) = . n i i ij j j d d A A a a    (8) The numbers id  of the column 1 2= ( )md d d d     are the distances from the points iA to the point A , which is calculated by the formula   2 =1 = ( , ) = . n i i ij j j d d A A a a    (9) The numbers iD of the column 1 2= ( )mD D D D     are the relative distances of the points iA respecting the points A and A , which is expressed by the formula ( , ) = = . ( , ) ( , ) i i i i i i i d d A A D d d d A A d A A          (10) If 1 2 1 max{ , , , } =m iD D D D    , then we accept the alternative 1iA as the best solution. If 1 2 2 min{ , , , } =m iD D D D    , then we accept the alternative 2iA as the worst solution.
  • 5. Notes on TOPSIS Method www.ijres.org 9 | Page III. TWO EXAMPLES OF USING TOPSIS METHOD Example 1. Four alternatives with three criteria are given in Table 3. The criteria 1x and 2x are benefit, and the criterion 3x is non-benefit. The weights of the criteria are equal. Decide which alternative is the best. Table 3. Initial table for Example 1 Since the weights are equal, we can use the normalized decision matrix A with the elements 4 2 =1 = ij ij ij i x a x (11) for =1,2,3,4i and =1,2,3j . In this case, the matrix A reads as follows: 0,535 0,441 0,407 0,688 0,441 0,488 = .0,382 0,515 0,732 0,306 0,588 0,244                A (12) Relying on the matrix A , we have to determine two rows ( A , A ) and three columns ( d , d , D ) in the working table. Table 4. Working table for Example 1 According to the formula in (6), the positive ideal solution 1 2 3= ( )A a a a    contains the greatest numbers of the first and second column of A , and the smallest number of the third column of A . According to the formula in (7), the negative ideal solution 1 2 3= ( )A a a a    contains the smallest numbers of the first and second column of A , and the greatest number of the third column of A . The distances 1 2 3 4= ( )d d d d d      , from the alternatives iA to the positive ideal solution A , are calculated
  • 6. Notes on TOPSIS Method www.ijres.org 10 | Page by the formula in (8) with = 3n , so   3 2 =1 = ( , ) = .i i ij j j d d A A a a    (13) The distances 1 2 3 4= ( )d d d d d      , from the alternatives iA to the negative ideal solution A , are calculated by the formula in (9) with = 3n , so   3 2 =1 = ( , ) = .i i ij j j d d A A a a    (14) The relative distances 1 2 3 4= ( )D D D D D      of the alternatives iA respecting the positive ideal solution A and negative ideal solution A are calculated using the formula in (10), so = .i i i i d D d d      (15) Applying the last tree columns of the Table 4 we have the following three preferred orders of alternatives: 2 1 4 3A A A A by the column of D from the largest to smallest number 4 2 1 3A A A A by the column of d from the largest to smallest number 1 2 4 3A A A A by the column of d from the smallest to largest number TOPSIS method prefers the first order respecting the column of D . In the following example we have the most desirable combination: d , d and D point to one and the same best solution. Example 2. 1Six alternatives with five criteria and their weights are given in Table 5. The criteria 1 2 3, ,x x x are benefit, and the criteria 4 5,x x are non-benefit. Select the best alternative. Table 5. Initial table for Example 2 Weighted normalized decision matrix A with the elements 6 2 =1 = ij ij j ij i x a w x (16) for =1, ,6i and =1, ,5j stands as follows:
  • 7. Notes on TOPSIS Method www.ijres.org 11 | Page 0,118 0,049 0,057 0,118 0,033 0,158 0,035 0,043 0,103 0,088 0,097 0,028 0,113 0,044 0,077 = . 0,079 0,063 0,099 0,088 0,055 0,178 0,028 0,071 0,103 0,066 0,059 0,028 0,085 0,133 0,022                   A (17) The positive ideal solution 1 2 3 4 5= ( )A a a a a a      contains the greatest numbers of the first, second and third column of A , and the smallest numbers of the fourth and fifth column of A . The negative ideal solution 1 2 3 4 5= ( )A a a a a a      contains the smallest numbers of the first, second and third column of A , and the greatest numbers of the fourth and fifth column of A . The distances 1 2 3 4 5 6= ( )d d d d d d d        , from the alternatives iA to the positive ideal solution A , are calculated applying the distance formulas in (8) with = 5n . The distances 1 2 3 4 5 6= ( )d d d d d d d        , from the alternatives iA to the negative ideal solution A , are calculated applying the distance formulas in (9) with = 5n . The relative distances 1 2 3 4 5 6= ( )D D D D D D D        of the alternatives iA respecting the positive ideal solution A and negative ideal solution A are determined using the quotient formulas in (10). Table 6. Working table for Example 2 Applying the last tree columns of the Table 6 we have the following three preferred orders of alternatives: 5 3 2 4 1 6A A A A A A by the column of D from the largest to smallest number 5 3 2 4 1 6A A A A A A by the column of d from the largest to smallest number 5 3 1 4 2 6A A A A A A by the column of d from the smallest to largest number The first order is preferred by TOPSIS method.
  • 8. Notes on TOPSIS Method www.ijres.org 12 | Page IV. CONCLUSION In the method presenting it is important to find examples that adequately show its meaning and application. After that, the generalizations of the method can be implemented to extend its applications. In the case of TOPSIS method the first generalization refers to the processing of insufficiently precise data, namely, fuzzy data (see [6], [5], [1]). The second generalization applies to the norm and metric. Let 1p  be a real number. Using the p -norm in the normalization procedure, we get 1 =1 = = . || ( , , ) || | | ij ij ij m j mj p pp ij i x x r x x x (18) Then, using the p -metric in the distance calculation, we have =1 = ( , ) = | | . n p p i p i ij j j d d A A a a    (19) The max-norm and max-metric can also be applied in the computational procedure of TOPSIS method . REFERENCES [1] A. Balin, P. Alcan, and H. Basligil, Co performance comparison on CCHP systems using different fuzzy multi criteria decision making models for energy sources, Fuelling the Future, pp. 591-595, 2012. [2] I. B. Huang, J. Keisler, and I. Linkov, Multi-criteria decision analysis in environmental science: ten years of applications and trends, Science of the Total Environment 409, pp. 3578-3594, 2011. [3] C. L. Hwang, Y. J. Lai, and T. Y. Liu, A new approach for multiple objective decision making, Computers and Operational Research 20, pp. 889-899, 1983. [4] C. L. Hwang, and K. Yoon, Multiple Attribute Decision Making: Methods and Applications, Berlin Heidelberg New York, Springer-Verlag, 1981. [5] G. R. Jahanshahloo, F. Hosseinzadeh Lotfi, and M. Izadikhah, Extension of the TOPSIS method for decision-making problems with fuzzy data, Applied Mathematics and Computation, pp. 1544-1551, 2006. [6] Y. J. Lai, T. Y. Liu, and C. L. Hwang, Fuzzy Mathematical Programming: Methods and Applications, Berlin Heidelberg New York, Springer-Verlag, 1992. [7] Y. J. Lai, T. Y. Liu, andC. L. Hwang, TOPSIS for MODM, European Journal of Operational Research 76, pp. 486-500, 1994. [8] G. H. Tzeng, and J. J. Huang, Multiple Attribute Decision Making: Methods and Applications, New York, CRC Press, 2011. [9] J. Xu, and Z. Tao, Rough Multiple Objective Decision Making, New York, CRC Press, 2012. [10]K. A. Yoon, A reconciliation among discrete compromise situations, Journal of Operational Research Society 38, pp. 277-286, 1987. [11]K. P. Yoon, and C. Hwang, Multiple Attribute Decision Making: An Introduction, California, SAGE Publications, 1995. [12]E. K. Zavadskas, A. Zakarevicius, and J. Antucheviciene, Evaluation of ranking accuracy in multi-criteria decisions, Informatica 17, pp. 601-618, 2006.