Intuitionistic Fuzzy Aggregation Techniques - Intuitionistic Fuzzy Aggregation and Clustering - page 79

by which we obtain

y 6 y 4 y 5 y 2 y 3

y 1

In Step 2, if we let p = q = r = 2, then we have

r 1 = ( 0 . 2132 , 0 . 4666 , 0 . 3202 ), r 2 = ( 0 . 3316 , 0 . 3516 , 0 . 3168 )

r 3 = ( 0 . 2708 , 0 . 3771 , 0 . 3521 ), r 4 = ( 0 . 3140 , 0 . 2664 , 0 . 4196 )

r 5 = ( 0 . 3278 , 0 . 3259 , 0 . 3463 ), r 5 = ( 0 . 3547 , 0 . 2992 , 0 . 3461 )

Then we calculate the scores of all the alternatives:

S ( r 1 ) =− 0 . 2534 , S ( r 2 ) =− 0 . 0200 , S ( r 3 ) =− 0 . 1064

S ( r 4 ) = 0 . 0475 , S ( r 5 ) = 0 . 0019 , S ( r 6 ) = 0 . 0555

and thus,

y 6 y 4 y 5 y 2 y 3 y 1

In Step 2, if we use the GIFWBGM to aggregate the performances of the alterna-
tives (here, we let p = q = r = 1 ) , then

r 1 = ( 0 . 2401 , 0 . 4773 , 0 . 2826 ), r 2 = ( 0 . 3111 , 0 . 3542 , 0 . 3347 )

r 3 = (

. , . , . ),

r 4 = (

. , . , . )

0 2546 0 3879 0 3575 0 2929 0 2681 0 4390

r 5 = (

. , . , . ),

r 6 = (

. , . , . )

0 3268 0 3313 0 3419 0 3404 0 3000 0 3596

from which we calculate the scores of all the alternatives:

S ( r 1 ) =− 0 . 2732 , S ( r 2 ) =− 0 . 0431 , S ( r 3 ) =− 0 . 1333

S ( r 4 ) = 0 . 0248 , S ( r 5 ) =− 0 . 0045 , S ( r 6 ) = 0 . 0404

and thus,

y 6 y 4 y 5 y 2 y 3 y 1

If we let p = q = r = 2, then

r 1 = ( 0 . 2029 , 0 . 4861 , 0 . 3110 ), r 2 = ( 0 . 3052 , 0 . 3577 , 0 . 3371 )

r 3 = ( 0 . 2514 , 0 . 4004 , 0 . 3482 ), r 4 = ( 0 . 2875 , 0 . 2712 , 0 . 4413 )

r 5 = ( 0 . 3265 , 0 . 3395 , 0 . 3340 ), r 5 = ( 0 . 3357 , 0 . 3016 , 0 . 3627 )

and the scores of all the alternatives are:

S ( r 1 ) =− 0 . 2832 , S ( r 2 ) =− 0 . 0525 , S ( r 3 ) =− 0 . 1490

S ( r 4 ) = 0 . 0163 , S ( r 5 ) =− 0 . 0129 , S ( r 6 ) = 0 . 0341