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R
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ed
May
24
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2
0
2
4
R
ev
is
ed
Oct
9
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2
0
2
4
Acc
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ted
Oct
30
,
2
0
2
4
Crit
e
ria
we
ig
h
ti
n
g
m
e
th
o
d
s
in
d
e
c
isio
n
su
p
p
o
rt
sy
ste
m
(
DSS
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fa
c
e
v
a
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u
s
c
h
a
ll
e
n
g
e
s
a
n
d
li
m
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t
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t
th
e
ir
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c
c
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ra
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y
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n
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li
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it
y
.
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e
o
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th
e
m
a
in
c
h
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ll
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e
s
is
su
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ti
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it
y
,
th
is
su
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jec
ti
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e
a
ss
e
ss
m
e
n
t
c
a
n
re
d
u
c
e
th
e
o
b
jec
ti
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it
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n
d
c
o
n
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n
c
y
o
f
re
su
lt
s.
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e
m
a
in
o
b
jec
ti
v
e
o
f
th
e
n
e
w
we
ig
h
ti
n
g
m
e
th
o
d
g
re
y
g
e
o
m
e
tri
c
m
e
a
n
(
G
2
M
)
w
e
ig
h
ti
n
g
is
t
o
p
ro
v
id
e
m
o
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o
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ti
v
e
a
n
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o
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st
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rit
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ria
we
ig
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ts
u
n
d
e
r
c
o
n
d
it
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o
n
s
o
f
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n
c
e
rt
a
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ty
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n
d
in
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o
m
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lete
d
a
ta.
Th
e
n
e
w
G
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e
ig
h
ti
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g
a
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ro
a
c
h
h
a
s
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sig
n
ifi
c
a
n
t
p
o
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ti
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l
imp
a
c
t
o
n
th
e
DS
S
fi
e
ld
,
it
h
a
s
t
h
e
p
o
ten
ti
a
l
to
g
e
n
e
ra
te
m
o
re
e
ffe
c
ti
v
e
a
n
d
e
fficie
n
t
d
e
c
isio
n
s,
wh
ic
h
c
a
n
imp
r
o
v
e
o
rg
a
n
iza
ti
o
n
a
l
p
e
rfo
rm
a
n
c
e
,
re
d
u
c
e
risk
a
n
d
o
p
ti
m
ize
o
u
tco
m
e
s.
P
e
a
rso
n
c
o
rre
latio
n
tes
t
re
su
lt
s
o
f
two
se
ts
o
f
ra
n
k
i
n
g
s
g
e
n
e
ra
ted
b
y
DSS
m
e
th
o
d
s
n
a
m
e
ly
g
re
y
re
latio
n
a
l
a
n
a
l
y
sis
(
G
RA
)
,
s
imp
le
a
d
d
it
i
v
e
we
ig
h
ti
n
g
(
S
A
W
)
,
m
u
lt
i
-
a
tt
rib
u
t
iv
e
id
e
a
l
-
re
a
l
c
o
m
p
a
ra
ti
v
e
a
n
a
ly
sis
(M
AIRCA
),
we
ig
h
te
d
p
ro
d
u
c
t
(W
P
),
c
o
m
b
in
e
d
c
o
m
p
r
o
m
ise
so
lu
ti
o
n
(COCO
S
O),
v
lse
k
riter
ij
u
ms
k
a
o
p
ti
miz
a
c
ij
a
i
k
o
mp
r
o
misn
o
re
se
n
je
(VIK
OR),
a
n
d
a
n
e
w
a
d
d
i
ti
v
e
ra
ti
o
a
ss
e
ss
m
e
n
t
(ARA
S)
th
a
t
t
h
e
re
is
a
stro
n
g
p
o
sit
iv
e
c
o
rre
lati
o
n
b
e
t
we
e
n
th
e
two
m
e
th
o
d
s
u
sin
g
G
2
M
w
e
ig
h
ti
n
g
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rit
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ria.
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e
h
i
g
h
c
o
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n
v
a
lu
e
in
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ica
tes
t
h
a
t
t
h
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ra
n
k
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n
g
s
o
f
t
h
e
m
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th
o
d
s
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se
d
ten
d
t
o
m
o
v
e
to
g
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t
h
e
r,
g
iv
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n
g
c
o
n
fid
e
n
c
e
in
th
e
c
o
n
siste
n
c
y
a
n
d
v
a
li
d
it
y
o
f
th
e
re
su
l
ti
n
g
ra
n
k
in
g
re
su
lt
s.
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is
g
iv
e
s
c
o
n
fid
e
n
c
e
t
h
a
t
b
o
t
h
m
e
th
o
d
s
c
a
n
b
e
u
se
d
sim
u
lt
a
n
e
o
u
sly
o
r
in
terc
h
a
n
g
e
a
b
ly
with
c
o
n
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n
t
re
su
lt
s.
T
h
e
u
se
o
f
G
2
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w
e
ig
h
t
in
g
i
n
t
h
e
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m
e
th
o
d
u
se
d
c
a
n
su
p
p
o
rt
b
e
tt
e
r
d
e
c
isio
n
-
m
a
k
in
g
b
y
p
ro
v
id
i
n
g
c
o
n
siste
n
t
i
n
fo
rm
a
ti
o
n
a
n
d
v
a
li
d
it
y
o
f
ra
n
k
i
n
g
re
su
lt
s.
K
ey
w
o
r
d
s
:
C
r
iter
ia
weig
h
tin
g
Dec
is
io
n
G2
M
weig
h
tin
g
New
ap
p
r
o
ac
h
Ob
jectiv
e
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Setiawan
s
y
ah
Dep
ar
tm
en
t o
f
I
n
f
o
r
m
atics,
Facu
lty
o
f
E
n
g
in
ee
r
i
n
g
an
d
C
o
m
p
u
ter
Scien
ce
Un
iv
er
s
itas
T
ek
n
o
k
r
at
I
n
d
o
n
e
s
ia
B
an
d
ar
L
am
p
u
n
g
,
I
n
d
o
n
esia
E
m
ail: setiawa
n
s
y
ah
@
tek
n
o
k
r
at.
ac
.
id
1.
I
NT
RO
D
UCT
I
O
N
Dec
is
io
n
s
u
p
p
o
r
t
s
y
s
tem
s
(
DSS)
ar
e
ess
en
tial
as
th
ey
ass
is
t
o
r
g
an
izatio
n
s
a
n
d
in
d
iv
id
u
als
in
m
ak
in
g
m
o
r
e
in
f
o
r
m
ativ
e
an
d
tim
ely
d
ec
is
io
n
s
th
r
o
u
g
h
c
o
m
p
lex
d
ata
an
aly
s
is
[
1
]
,
[
2
]
.
DSS
u
tili
ze
s
in
f
o
r
m
atio
n
tech
n
o
lo
g
y
to
co
llect,
p
r
o
ce
s
s
,
an
d
an
aly
ze
d
ata,
r
esu
ltin
g
in
r
eliab
le
r
ec
o
m
m
en
d
atio
n
s
.
I
t
is
h
ig
h
ly
b
en
ef
icial
in
v
ar
io
u
s
s
ec
to
r
s
,
in
clu
d
in
g
b
u
s
in
ess
,
h
ea
lth
c
ar
e,
g
o
v
er
n
m
en
t,
ed
u
ca
tio
n
,
an
d
f
in
a
n
ce
.
I
n
th
e
h
ea
lth
ca
r
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
8
,
No
.
1
,
Ap
r
il
20
2
5
:
4
0
3
-
4
1
6
404
s
ec
to
r
,
h
u
m
an
r
eso
u
r
ce
s
(
HR
)
s
u
p
p
o
r
t
th
e
d
ia
g
n
o
s
is
an
d
tr
e
atm
en
t
p
lan
n
in
g
o
f
p
atien
ts
b
y
an
aly
zin
g
m
ed
ical
d
ata.
Go
v
e
r
n
m
e
n
ts
u
s
e
DSS
to
f
o
r
m
u
late
m
o
r
e
e
f
f
ec
tiv
e
p
o
licies
b
ased
o
n
s
o
cial
a
n
d
ec
o
n
o
m
i
c
d
ata
.
B
y
im
p
r
o
v
i
n
g
t
h
e
ac
cu
r
ac
y
,
e
f
f
icien
cy
a
n
d
s
p
ee
d
o
f
d
ec
is
io
n
-
m
ak
in
g
,
DSS
en
a
b
les
o
r
g
a
n
izatio
n
s
to
b
e
m
o
r
e
r
esp
o
n
s
iv
e
to
ch
an
g
es
an
d
c
h
a
llen
g
es,
an
d
s
u
p
p
o
r
ts
co
n
tin
u
o
u
s
in
n
o
v
atio
n
an
d
d
ev
elo
p
m
e
n
t.
W
ith
th
e
ab
ilit
y
to
in
teg
r
ate
m
u
ltip
le
d
ata
s
o
u
r
ce
s
an
d
p
r
o
v
id
e
ac
cu
r
ate
an
al
y
s
is
,
DSS
im
p
r
o
v
es
th
e
ef
f
icien
cy
,
r
eliab
ilit
y
an
d
ac
cu
r
ac
y
o
f
d
ec
is
io
n
s
,
m
ak
i
n
g
it
an
ess
en
tial
to
o
l
f
o
r
o
p
e
r
atio
n
al
an
d
s
tr
ateg
ic
s
u
cc
ess
in
v
a
r
io
u
s
f
ield
s
.
T
h
e
p
r
o
ce
s
s
o
f
d
eter
m
in
in
g
c
r
iter
ia
weig
h
ts
in
a
DSS
i
s
an
im
p
o
r
tan
t
s
t
ep
th
at
en
s
u
r
es
th
at
ea
ch
cr
iter
io
n
in
d
ec
is
io
n
-
m
ak
in
g
h
as
th
e
ap
p
r
o
p
r
iate
in
f
l
u
en
ce
ac
co
r
d
in
g
to
its
lev
el
o
f
im
p
o
r
tan
ce
.
T
h
is
p
r
o
ce
s
s
u
s
u
ally
s
tar
ts
with
th
e
id
en
tific
atio
n
o
f
r
elev
an
t
cr
iter
ia
b
ased
o
n
th
e
o
b
jectiv
e
o
f
th
e
d
ec
is
io
n
to
b
e
m
ad
e
.
Nex
t,
v
a
r
io
u
s
m
eth
o
d
s
ar
e
u
s
ed
to
d
eter
m
in
e
th
e
weig
h
t
o
f
ea
ch
cr
iter
io
n
,
in
clu
d
in
g
s
u
b
jectiv
e
tech
n
iq
u
es
s
u
ch
as
ex
p
e
r
t
in
ter
v
iews
an
d
th
e
Delp
h
i
m
eth
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d
,
as
we
ll
as
o
b
jectiv
e
tech
n
iq
u
es
s
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ch
as
th
e
a
n
aly
tic
h
ier
ar
ch
y
p
r
o
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s
s
(
AHP)
an
d
th
e
en
tr
o
p
y
m
eth
o
d
.
Su
b
j
ec
ti
v
e
m
eth
o
d
s
r
ely
o
n
ex
p
er
t
ju
d
g
m
en
t
a
n
d
ex
p
er
ien
ce
to
ass
ig
n
weig
h
ts
,
wh
ile
o
b
jectiv
e
m
eth
o
d
s
u
s
e
q
u
an
titativ
e
d
ata
to
ass
ess
th
e
r
elativ
e
im
p
o
r
tan
ce
o
f
ea
ch
cr
iter
io
n
.
Acc
u
r
ate
an
d
f
air
d
eter
m
in
atio
n
o
f
th
e
we
ig
h
ts
is
cr
itical
as
th
ey
af
f
ec
t
t
h
e
f
in
al
o
u
tc
o
m
e
o
f
th
e
DSS,
en
s
u
r
in
g
th
at
th
e
d
ec
is
io
n
s
tak
en
r
ef
lect
th
e
r
ea
l
p
r
i
o
r
ities
an
d
n
ee
d
s
o
f
th
e
s
itu
atio
n
at
h
a
n
d
.
C
r
iter
ia
weig
h
tin
g
m
eth
o
d
s
in
DSS
f
ac
e
v
ar
io
u
s
c
h
allen
g
es
an
d
lim
itatio
n
s
th
at
ca
n
a
f
f
ec
t
th
eir
ac
cu
r
ac
y
a
n
d
r
eliab
ilit
y
[
3
]
,
[
4
]
.
On
e
o
f
th
e
m
ain
c
h
allen
g
es
is
th
e
s
u
b
jectiv
ity
in
weig
h
t
d
eter
m
in
atio
n
wh
e
n
u
s
in
g
ex
p
er
t
-
b
ased
m
eth
o
d
s
,
s
u
ch
as
in
ter
v
iews
o
r
th
e
Del
p
h
i
m
eth
o
d
.
T
h
ese
s
u
b
jectiv
e
ju
d
g
m
en
ts
ca
n
b
e
in
f
lu
en
ce
d
b
y
th
e
p
e
r
s
o
n
al
p
r
ef
er
en
ce
s
,
ex
p
er
ien
ce
s
,
o
r
i
n
ter
ests
o
f
th
e
in
d
iv
id
u
als
in
v
o
lv
ed
,
wh
ich
ca
n
r
ed
u
ce
th
e
o
b
jectiv
ity
a
n
d
co
n
s
is
ten
cy
o
f
th
e
r
esu
lts
[
5
]
–
[
7
]
.
T
h
ese
co
m
p
le
x
p
r
o
ce
s
s
e
s
ca
n
r
eq
u
ir
e
s
p
ec
ialized
s
k
ills
in
d
ata
a
n
aly
s
is
an
d
an
in
-
d
e
p
th
u
n
d
er
s
tan
d
in
g
o
f
th
e
m
eth
o
d
o
lo
g
y
u
s
ed
,
wh
ich
c
an
tak
e
s
ig
n
if
ican
t
tim
e
an
d
r
eso
u
r
ce
s
.
Fu
r
th
er
m
o
r
e,
in
d
y
n
am
ic
co
n
te
x
ts
wh
er
e
cr
iter
ia
an
d
p
r
io
r
ities
ca
n
ch
an
g
e
r
a
p
id
ly
,
tr
ad
itio
n
al
m
eth
o
d
s
m
a
y
lack
th
e
f
lex
ib
ilit
y
to
ad
ju
s
t
cr
iter
ia
weig
h
ts
in
r
ea
l
-
tim
e
o
r
b
e
r
esp
o
n
s
iv
e
t
o
ch
an
g
in
g
s
itu
atio
n
s
.
T
h
is
co
u
ld
r
esu
lt
in
in
ap
p
r
o
p
r
iate
o
r
i
r
r
elev
an
t
d
ec
is
io
n
s
if
th
e
weig
h
ts
ar
e
n
o
t
u
p
d
ated
r
eg
u
lar
ly
.
Gr
e
y
n
u
m
b
er
is
a
co
n
ce
p
t
in
g
r
ey
s
y
s
tem
s
th
eo
r
y
th
at
is
u
s
ed
to
h
an
d
le
u
n
ce
r
ta
in
ty
an
d
in
co
m
p
lete
in
f
o
r
m
atio
n
in
d
ec
is
io
n
m
ak
i
n
g
.
Un
lik
e
ex
ac
t
n
u
m
b
er
s
,
g
r
ey
n
u
m
b
er
s
r
ep
r
esen
t
v
alu
es
th
at
f
all
with
in
a
ce
r
tain
r
a
n
g
e,
with
u
p
p
er
an
d
lo
wer
b
o
u
n
d
s
th
at
a
r
e
n
o
t
k
n
o
wn
with
ce
r
tain
ty
.
T
h
is
allo
w
s
f
o
r
m
o
r
e
f
lex
i
b
le
an
aly
s
es
in
co
n
d
itio
n
s
wh
e
r
e
f
u
ll
o
r
p
r
ec
is
e
d
ata
is
n
o
t
a
v
ailab
le.
I
n
g
r
ey
s
y
s
tem
ap
p
licatio
n
s
,
g
r
e
y
n
u
m
b
er
s
ar
e
o
f
ten
u
s
ed
in
m
et
h
o
d
s
s
u
c
h
as
g
r
ey
r
elatio
n
al
an
aly
s
is
(
GR
A)
to
ev
alu
ate
r
elatio
n
s
h
ip
s
b
etwe
en
v
ar
iab
les
with
p
ar
tial o
r
im
p
e
r
f
ec
t d
ata,
th
u
s
aid
in
g
d
ec
is
io
n
-
m
ak
in
g
i
n
co
m
p
le
x
an
d
am
b
ig
u
o
u
s
en
v
ir
o
n
m
en
ts
.
T
h
e
m
ain
o
b
jectiv
e
o
f
th
e
n
ew
weig
h
tin
g
m
eth
o
d
,
g
r
e
y
g
eo
m
etr
ic
m
ea
n
weig
h
tin
g
(
G2
M
w
eig
h
tin
g
)
is
to
o
v
er
co
m
e
th
e
lim
itatio
n
s
an
d
ch
allen
g
es
th
at
ex
is
t
in
tr
ad
itio
n
al
cr
iter
ia
weig
h
tin
g
m
eth
o
d
s
b
y
co
m
b
in
in
g
th
e
s
tr
en
g
th
s
o
f
g
r
ey
s
y
s
tem
an
aly
s
is
an
d
g
eo
m
e
tr
ic
m
ea
n
ca
lcu
latio
n
[
8
]
–
[
1
0
]
.
G2
M
weig
h
tin
g
is
d
esig
n
ed
to
p
r
o
v
id
e
m
o
r
e
o
b
jectiv
e
an
d
r
o
b
u
s
t
cr
iter
ia
weig
h
ts
u
n
d
er
c
o
n
d
itio
n
s
o
f
u
n
ce
r
tain
ty
an
d
in
co
m
p
lete
d
ata,
o
f
ten
e
n
co
u
n
ter
ed
in
v
ar
io
u
s
DSS
ap
p
licatio
n
s
.
T
h
is
m
et
h
o
d
aim
s
to
im
p
r
o
v
e
t
h
e
ac
cu
r
ac
y
an
d
co
n
s
is
ten
cy
o
f
weig
h
tin
g
b
y
m
in
im
izin
g
s
u
b
jectiv
e
p
e
r
c
ep
tio
n
s
an
d
u
tili
zin
g
a
q
u
an
tit
ativ
e
ap
p
r
o
ac
h
th
at
is
m
o
r
e
tr
a
n
s
p
ar
en
t,
an
d
ea
s
y
to
im
p
lem
en
t
[
1
1
]
.
As
s
u
c
h
,
G2
M
weig
h
tin
g
s
ee
k
s
to
im
p
r
o
v
e
th
e
r
eliab
ilit
y
an
d
ef
f
ec
tiv
en
ess
o
f
G2
M
i
n
g
en
er
atin
g
d
ec
is
io
n
s
th
at
ar
e
m
o
r
e
in
f
o
r
m
ativ
e
an
d
a
d
ap
tiv
e
to
ch
an
g
in
g
s
itu
atio
n
s
,
s
u
p
p
o
r
tin
g
m
o
r
e
i
n
f
o
r
m
e
d
an
d
r
esp
o
n
s
iv
e
d
ec
i
s
io
n
-
m
ak
in
g
i
n
v
ar
io
u
s
s
ec
to
r
s
.
G2
M
w
eig
h
tin
g
aim
s
to
o
f
f
er
g
r
ea
ter
f
lex
ib
ilit
y
th
an
tr
ad
itio
n
al
m
et
h
o
d
s
,
all
o
win
g
f
o
r
d
y
n
a
m
ic
ad
ju
s
tm
en
t
o
f
cr
iter
ia
weig
h
ts
ac
co
r
d
in
g
to
ch
a
n
g
in
g
co
n
tex
t
s
an
d
p
r
io
r
ities
.
B
y
u
s
in
g
g
r
ey
s
y
s
tem
an
aly
s
is
,
th
e
m
eth
o
d
c
an
b
etter
co
p
e
with
d
ata
u
n
ce
r
tai
n
ty
,
wh
ile
t
h
e
u
s
e
o
f
g
eo
m
etr
ic
m
ea
n
e
n
s
u
r
es
th
at
th
e
r
esu
ltin
g
cr
iter
ia
weig
h
ts
ar
e
m
o
r
e
p
r
o
p
o
r
tio
n
al
an
d
b
ala
n
ce
d
.
T
h
e
m
eth
o
d
is
also
d
esig
n
ed
to
r
ed
u
ce
th
e
an
aly
tical
b
u
r
d
e
n
o
n
n
o
n
-
tech
n
ical
u
s
er
s
,
m
ak
in
g
it
m
o
r
e
u
s
er
-
f
r
i
en
d
ly
a
n
d
m
o
r
e
e
f
f
icien
tly
im
p
lem
en
ted
.
B
y
in
teg
r
atin
g
th
e
s
e
ap
p
r
o
ac
h
es,
G
2
M
weig
h
tin
g
s
ee
k
s
to
s
tr
en
g
th
en
th
e
ab
ilit
y
o
f
HR
to
p
r
o
v
id
e
m
o
r
e
ac
cu
r
ate
an
d
r
ele
v
a
n
t
r
ec
o
m
m
en
d
atio
n
s
,
s
u
p
p
o
r
tin
g
m
o
r
e
s
tr
ateg
ic
an
d
ef
f
ec
tiv
e
d
ec
is
io
n
-
m
a
k
in
g
i
n
a
v
ar
iety
o
f
d
iv
er
s
e
s
itu
atio
n
s
a
n
d
en
v
i
r
o
n
m
e
n
ts
.
T
h
e
n
ew
ap
p
r
o
ac
h
o
f
G2
M
w
eig
h
tin
g
h
as
a
s
ig
n
if
ican
t
p
o
ten
tial
im
p
ac
t
o
n
th
e
DSS
f
ield
.
B
y
o
v
er
co
m
in
g
th
e
lim
itatio
n
s
o
f
tr
ad
itio
n
al
m
eth
o
d
s
,
G2
M
w
eig
h
tin
g
ca
n
im
p
r
o
v
e
th
e
q
u
ality
o
f
d
ec
is
io
n
s
g
en
er
ated
b
y
a
DSS,
esp
ec
ial
ly
in
ter
m
s
o
f
ac
cu
r
ac
y
,
co
n
s
i
s
ten
cy
,
an
d
tim
elin
ess
.
T
h
is
h
as
th
e
p
o
ten
tial
to
g
en
er
ate
m
o
r
e
ef
f
ec
tiv
e
an
d
ef
f
icien
t
d
ec
is
io
n
s
,
w
h
ich
i
n
tu
r
n
ca
n
im
p
r
o
v
e
o
r
g
a
n
izatio
n
al
p
e
r
f
o
r
m
an
ce
,
r
ed
u
ce
r
is
k
s
,
an
d
o
p
tim
ize
o
u
tco
m
es.
I
n
ad
d
itio
n
,
G2
M
w
eig
h
tin
g
ca
n
also
ex
p
an
d
th
e
s
co
p
e
o
f
DSS
ap
p
licatio
n
s
,
with
its
ab
il
ity
t
o
m
an
ag
e
u
n
ce
r
tai
n
a
n
d
co
m
p
lex
d
ata,
an
d
in
teg
r
ate
v
ar
i
o
u
s
r
elev
an
t
f
ac
to
r
s
.
T
h
is
ap
p
r
o
ac
h
ca
n
p
a
v
e
th
e
way
f
o
r
th
e
d
ev
elo
p
m
en
t
o
f
m
o
r
e
ad
ap
tiv
e,
r
esp
o
n
s
iv
e
an
d
h
ig
h
ly
co
m
p
etitiv
e
GI
S,
r
ein
f
o
r
cin
g
th
e
r
o
le
o
f
DSS
as
a
v
ital
to
o
l
in
d
ec
is
io
n
-
m
ak
in
g
ac
r
o
s
s
s
ec
to
r
s
an
d
s
ca
les.
A
n
o
th
er
p
o
ten
tial im
p
ac
t o
f
th
e
G2
M
w
eig
h
tin
g
ap
p
r
o
ac
h
is
in
cr
ea
s
ed
tr
an
s
p
ar
en
cy
an
d
ac
co
u
n
tab
ilit
y
in
th
e
d
ec
is
io
n
-
m
ak
in
g
p
r
o
ce
s
s
.
B
y
p
r
o
v
i
d
in
g
a
m
o
r
e
r
o
b
u
s
t
b
asis
f
o
r
c
r
iter
ia
weig
h
tin
g
,
G
2
M
w
eig
h
tin
g
ca
n
ass
is
t
in
ex
p
lain
in
g
an
d
ju
s
tify
in
g
th
e
d
ec
is
io
n
s
tak
en
b
y
th
e
DSS.
T
h
is
ca
n
in
cr
ea
s
e
u
s
er
s
’
an
d
s
tak
eh
o
ld
er
s
’
co
n
f
id
en
ce
i
n
th
e
r
esu
ltin
g
d
ec
is
io
n
s
,
as
well
a
s
f
ac
ilit
ate
m
o
r
e
ef
f
ec
tiv
e
c
o
m
m
u
n
icatio
n
b
etwe
en
v
a
r
io
u
s
s
tak
eh
o
ld
er
s
.
I
n
a
d
d
itio
n
,
b
y
f
o
cu
s
in
g
o
n
o
b
jectiv
ity
an
d
f
ai
r
n
ess
in
weig
h
tin
g
,
G2
M
w
eig
h
tin
g
ca
n
p
r
o
m
o
te
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
G2
M w
eig
h
tin
g
:
a
n
ew a
p
p
r
o
a
ch
b
a
s
ed
o
n
m
u
lti
-
o
b
jective
a
s
s
es
s
men
t d
a
ta
…
(
N
ir
w
a
n
a
Hen
d
r
a
s
tu
ty
)
405
f
air
er
an
d
m
o
r
e
s
u
s
tain
ab
le
d
ec
is
io
n
-
m
ak
in
g
,
wh
ich
co
m
p
lies
with
th
e
p
r
in
cip
les
o
f
eth
ics
an
d
s
o
cial
r
esp
o
n
s
ib
ilit
y
[
1
2
]
,
[
1
3
]
.
T
h
er
ef
o
r
e,
th
is
ap
p
r
o
ac
h
h
as
th
e
p
o
ten
tial
to
cr
ea
te
a
p
o
s
itiv
e
im
p
ac
t
in
im
p
r
o
v
in
g
m
an
ag
em
en
t
an
d
g
o
v
er
n
a
n
ce
in
v
ar
io
u
s
o
r
g
an
izatio
n
al
co
n
tex
ts
,
as
well
a
s
s
tr
en
g
th
en
in
g
th
e
r
o
le
o
f
PR
as
a
to
o
l th
at
s
u
p
p
o
r
ts
s
u
s
tain
ab
le
an
d
im
p
ac
tf
u
l d
ec
is
io
n
-
m
a
k
in
g
.
G2
M
w
eig
h
ti
n
g
f
ills
a
g
ap
in
th
e
liter
atu
r
e
b
y
p
r
esen
tin
g
a
h
o
lis
tic
an
d
in
teg
r
ate
d
a
p
p
r
o
ac
h
t
o
cr
iter
ia
weig
h
tin
g
in
DSS.
I
t
o
v
er
co
m
es
th
e
lim
itatio
n
s
o
f
t
r
ad
itio
n
al
m
eth
o
d
s
b
y
in
co
r
p
o
r
atin
g
g
r
e
y
s
y
s
tem
an
aly
s
is
an
d
g
e
o
m
etr
ic
m
ea
n
ca
lcu
latio
n
s
,
wh
ich
h
av
e
n
o
t
b
ee
n
w
id
ely
ex
p
lo
r
ed
in
p
r
ev
i
o
u
s
liter
atu
r
e
[
1
4
]
,
[
1
5
]
.
T
h
e
em
p
h
asis
o
n
m
an
ag
i
n
g
d
ata
u
n
ce
r
tain
ty
an
d
d
ec
is
io
n
co
m
p
le
x
ity
m
ak
es
G2
M
w
eig
h
tin
g
a
v
alu
a
b
le
co
n
tr
ib
u
tio
n
in
d
ev
elo
p
i
n
g
m
o
r
e
ad
ap
tiv
e
an
d
r
esp
o
n
s
iv
e
weig
h
tin
g
m
eth
o
d
s
.
I
n
ad
d
itio
n
,
th
e
f
o
cu
s
o
n
o
b
jectiv
ity
an
d
tr
an
s
p
a
r
en
cy
in
weig
h
tin
g
also
f
ills
a
g
ap
in
th
e
liter
atu
r
e
r
e
g
ar
d
in
g
t
h
e
n
ee
d
f
o
r
a
m
o
r
e
eth
ical
an
d
ac
co
u
n
tab
le
ap
p
r
o
ac
h
to
d
ec
is
io
n
-
m
ak
i
n
g
[
1
6
]
–
[
1
8
]
.
G2
M
W
eig
h
tin
g
m
ak
es
a
m
ea
n
in
g
f
u
l
co
n
tr
ib
u
tio
n
in
ex
p
an
d
in
g
o
u
r
u
n
d
e
r
s
tan
d
in
g
o
f
cr
iter
ia
weig
h
tin
g
in
HR
,
as
well
as
id
en
tify
in
g
n
ew
d
ir
ec
tio
n
s
f
o
r
f
u
t
u
r
e
r
esear
ch
an
d
d
ev
elo
p
m
en
t.
G2
M
w
eig
h
tin
g
also
f
ills
a
g
ap
in
th
e
liter
atu
r
e
b
y
o
f
f
er
in
g
a
n
ap
p
r
o
ac
h
th
at
is
ea
s
ier
f
o
r
n
o
n
-
tech
n
ical
u
s
er
s
to
u
n
d
er
s
tan
d
an
d
ap
p
ly
.
T
h
e
m
eth
o
d
is
d
esig
n
ed
to
m
in
im
ize
th
e
co
m
p
lex
ity
o
f
th
e
an
aly
s
is
an
d
ca
lcu
latio
n
s
,
th
u
s
allo
win
g
u
s
er
s
f
r
o
m
d
if
f
er
en
t
b
ac
k
g
r
o
u
n
d
s
to
u
s
e
th
e
m
eth
o
d
m
o
r
e
ef
f
icien
tly
.
T
h
is
cr
ea
tes
r
o
o
m
f
o
r
m
o
r
e
r
esear
ch
o
n
th
e
ap
p
licatio
n
o
f
DSS
in
v
ar
io
u
s
p
r
ac
tical
co
n
tex
ts
,
as
G2
M
w
eig
h
tin
g
c
an
s
er
v
e
as
a
m
o
d
el
f
o
r
m
o
r
e
u
s
er
-
f
r
ien
d
l
y
weig
h
tin
g
m
eth
o
d
s
.
B
y
b
r
o
a
d
en
in
g
th
e
s
co
p
e
o
f
DSS
ac
ce
s
s
ib
ilit
y
,
G2
M
w
eig
h
tin
g
p
la
y
s
an
im
p
o
r
tan
t
r
o
le
in
p
r
o
m
o
tin
g
t
h
e
ad
o
p
tio
n
o
f
th
is
tech
n
o
lo
g
y
ac
r
o
s
s
d
if
f
er
en
t se
cto
r
s
an
d
o
r
g
a
n
izatio
n
s
,
as we
ll a
s
en
r
ich
in
g
th
e
liter
atu
r
e
with
a
m
o
r
e
in
clu
s
iv
e
an
d
p
r
ac
tical
p
e
r
s
p
ec
ti
v
e.
G2
M
w
eig
h
tin
g
o
v
e
r
co
m
es
t
h
e
lim
itatio
n
s
o
f
tr
ad
itio
n
al
weig
h
tin
g
m
eth
o
d
s
b
y
in
te
g
r
atin
g
g
r
e
y
s
y
s
tem
an
aly
s
is
to
h
an
d
le
u
n
c
er
tain
ty
an
d
in
c
o
m
p
lete
d
ata,
an
d
u
s
in
g
g
e
o
m
etr
ic
m
ea
n
ca
l
cu
latio
n
to
p
r
o
d
u
ce
m
o
r
e
p
r
o
p
o
r
tio
n
al
an
d
b
alan
ce
d
weig
h
ts
.
Gr
e
y
s
y
s
tem
an
aly
s
is
allo
ws
G2
M
w
eig
h
tin
g
to
b
etter
m
a
n
ag
e
u
n
ce
r
tain
a
n
d
p
ar
tial
d
ata,
p
r
o
v
id
in
g
f
lex
ib
ilit
y
i
n
d
ea
lin
g
with
d
y
n
am
ic
s
itu
atio
n
s
.
I
n
a
d
d
itio
n
,
t
h
e
u
s
e
o
f
g
eo
m
etr
ic
a
v
er
ag
in
g
en
s
u
r
es
th
at
th
e
ex
tr
e
m
e
in
f
l
u
en
ce
o
f
o
u
tlier
d
ata
is
m
in
im
ized
,
m
ak
in
g
th
e
r
es
u
ltin
g
cr
iter
ia
weig
h
ts
m
o
r
e
s
tab
le
an
d
r
ep
r
esen
tativ
e.
T
h
e
m
eth
o
d
is
also
d
esig
n
ed
to
b
e
ea
s
ier
to
im
p
lem
en
t
an
d
u
n
d
er
s
tan
d
b
y
n
o
n
-
tech
n
ical
u
s
er
s
,
r
ed
u
cin
g
co
m
p
lex
ity
an
d
an
aly
tical
lo
ad
s
.
As
s
u
ch
,
G2
M
w
eig
h
tin
g
o
f
f
er
s
a
m
o
r
e
ad
ap
tiv
e,
ac
cu
r
ate,
an
d
u
s
er
-
f
r
ie
n
d
ly
ap
p
r
o
ac
h
,
o
v
er
co
m
in
g
th
e
s
u
b
jectiv
e
p
r
ef
er
en
ce
s
an
d
d
ata
lim
itatio
n
s
th
at
o
f
ten
h
am
p
er
co
n
v
en
tio
n
al
weig
h
tin
g
m
eth
o
d
s
,
an
d
im
p
r
o
v
in
g
th
e
r
eliab
ilit
y
an
d
ef
f
ec
tiv
en
ess
o
f
DSS.
B
y
o
v
e
r
co
m
in
g
th
ese
lim
itatio
n
s
,
G2
M
w
eig
h
tin
g
m
ak
es
a
s
ig
n
if
ic
an
t
co
n
t
r
ib
u
tio
n
to
im
p
r
o
v
in
g
th
e
q
u
ality
o
f
d
ec
is
io
n
-
m
ak
in
g
in
v
ar
io
u
s
c
o
n
t
ex
ts
.
Fo
r
ex
a
m
p
le,
in
in
d
u
s
tr
y
,
th
is
m
eth
o
d
ca
n
ass
is
t
co
m
p
an
ies
in
id
en
tify
in
g
m
o
r
e
ef
f
ec
tiv
e
s
tr
ateg
ies
b
y
co
n
s
id
er
in
g
v
ar
io
u
s
f
ac
t
o
r
s
p
r
o
p
o
r
tio
n
ally
.
I
n
h
ea
lth
ca
r
e,
G2
M
w
eig
h
tin
g
ca
n
b
e
u
s
ed
to
o
p
tim
ize
r
eso
u
r
ce
allo
ca
tio
n
b
y
tak
in
g
i
n
to
ac
co
u
n
t
a
m
o
r
e
b
alan
ce
d
p
r
io
r
ity
o
f
cr
iter
ia.
I
n
an
ac
ad
em
ic
s
ettin
g
,
th
is
m
eth
o
d
ca
n
ass
is
t
r
esear
ch
er
s
in
d
ete
r
m
in
in
g
th
e
m
o
s
t
in
f
lu
en
tial
v
a
r
iab
les
in
th
eir
r
esear
ch
.
T
h
u
s
,
G2
M
w
eig
h
tin
g
m
ak
es
a
m
ea
n
in
g
f
u
l
co
n
tr
ib
u
tio
n
i
n
im
p
r
o
v
in
g
t
h
e
d
ec
is
io
n
q
u
ality
an
d
e
f
f
icien
cy
o
f
d
ec
is
io
n
-
m
ak
in
g
p
r
o
ce
s
s
es
in
v
ar
i
o
u
s
s
ec
to
r
s
,
m
ak
i
n
g
it
o
n
e
o
f
th
e
p
r
o
m
is
in
g
a
p
p
r
o
ac
h
es in
th
e
d
e
v
elo
p
m
e
n
t o
f
m
o
r
e
s
o
p
h
is
ticated
an
d
r
eliab
le
DSS.
2.
M
E
T
H
O
D
T
h
e
r
esear
ch
co
n
ce
p
tu
al
f
r
a
m
ewo
r
k
is
a
th
eo
r
etica
l
s
tr
u
ctu
r
e
th
at
d
escr
ib
es
a
n
d
e
x
p
lain
s
th
e
r
elatio
n
s
h
ip
b
etwe
en
v
ar
ia
b
les
s
tu
d
ied
in
a
s
tu
d
y
.
T
h
e
co
n
ce
p
tu
al
f
r
am
ewo
r
k
p
r
o
v
id
es
a
s
o
lid
b
asis
f
o
r
h
y
p
o
th
esis
f
o
r
m
u
latio
n
,
ass
is
ts
r
esear
ch
er
s
in
d
esig
n
in
g
a
p
p
r
o
p
r
iate
m
eth
o
d
o
l
o
g
ies,
an
d
d
ir
ec
ts
d
ata
an
aly
s
is
an
d
in
ter
p
r
etatio
n
o
f
r
esu
lts
.
W
ith
a
co
n
ce
p
tu
al
f
r
am
ewo
r
k
,
r
esear
ch
b
ec
o
m
es
m
o
r
e
d
ir
ec
ted
,
s
y
s
tem
atic,
an
d
ab
le
to
m
ak
e
a
s
ig
n
if
ican
t
th
eo
r
etica
l
co
n
tr
ib
u
tio
n
to
th
e
f
ield
o
f
s
cien
ce
b
ein
g
s
tu
d
ied
.
T
h
e
co
n
ce
p
t
u
al
f
r
am
ewo
r
k
is
a
k
ey
elem
en
t
i
n
th
e
r
esear
ch
p
r
o
ce
s
s
th
at
n
o
t
o
n
ly
g
u
id
es
p
r
ac
tical
s
tep
s
,
b
u
t
also
d
ir
ec
ts
th
e
d
ev
elo
p
m
e
n
t
o
f
b
r
o
ad
e
r
th
e
o
r
y
a
n
d
k
n
o
wled
g
e.
Fig
u
r
e
1
is
th
e
c
o
n
ce
p
tu
al
f
r
am
ew
o
r
k
o
f
t
h
e
r
esear
ch
co
n
d
u
cte
d
in
G2
M
w
eig
h
tin
g
.
Fig
u
r
e
1
.
G2
M
weig
h
tin
g
c
o
n
ce
p
t f
r
am
ewo
r
k
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
8
,
No
.
1
,
Ap
r
il
20
2
5
:
4
0
3
-
4
1
6
406
T
h
e
co
n
ce
p
tu
al
f
r
am
ewo
r
k
o
f
Fig
u
r
e
1
is
th
e
p
r
o
ce
s
s
ca
r
r
ied
o
u
t
i
n
o
b
tain
in
g
th
e
weig
h
t
o
f
th
e
cr
iter
ia
u
s
in
g
G2
M
w
eig
h
tin
g
,
s
tar
tin
g
f
r
o
m
c
o
llectin
g
d
ata
b
ased
o
n
th
e
ca
s
e
s
tu
d
y
co
n
d
u
cted
.
T
h
e
ass
ess
m
en
t
d
ata
o
b
tain
ed
is
th
en
m
ad
e
in
th
e
f
o
r
m
o
f
a
d
ec
is
io
n
m
atr
ix
an
d
im
m
ed
i
ately
ca
lcu
lates
th
e
g
eo
m
etr
ic
m
ea
n
v
al
u
e.
Nex
t,
n
o
r
m
alize
th
e
m
atr
ix
an
d
ca
lcu
late
th
e
g
r
ey
v
alu
e,
f
i
n
ally
ca
lcu
latin
g
th
e
weig
h
t
v
alu
e
o
f
ea
c
h
cr
iter
io
n
.
2
.
1
.
Da
t
a
c
o
llect
io
n
Data
co
llectio
n
is
a
cr
itical
s
tag
e
in
an
ef
f
ec
tiv
e
an
d
in
f
o
r
m
ativ
e
d
ec
is
io
n
-
m
a
k
in
g
p
r
o
ce
s
s
.
T
h
e
p
r
im
ar
y
o
b
jectiv
e
o
f
d
ata
co
llectio
n
is
to
g
ath
er
r
elev
an
t,
ac
cu
r
ate,
an
d
tim
ely
in
f
o
r
m
atio
n
r
eq
u
ir
e
d
f
o
r
d
ec
is
io
n
an
aly
s
is
[
1
9
]
,
[
2
0
]
.
T
h
e
d
ata
c
o
llectio
n
p
r
o
ce
s
s
in
v
o
lv
es
id
en
tify
i
n
g
r
elev
an
t
c
r
iter
ia
f
o
r
ev
alu
atio
n
,
d
ev
elo
p
in
g
ap
p
r
o
p
r
iate
co
llec
tio
n
m
eth
o
d
s
,
an
d
ac
q
u
ir
in
g
d
ata
f
r
o
m
a
v
ar
iety
o
f
s
o
u
r
ce
s
th
at
m
ay
in
cl
u
d
e
h
is
to
r
ical
d
ata,
s
u
r
v
ey
s
,
in
ter
v
iews,
o
r
o
th
er
d
ata
s
o
u
r
ce
s
.
I
t
is
im
p
o
r
tan
t
to
en
s
u
r
e
th
at
th
e
d
ata
co
llected
i
s
co
m
p
lete,
co
n
s
is
ten
t
an
d
r
elia
b
le,
an
d
th
at
ap
p
licab
le
p
r
iv
a
cy
an
d
eth
ical
p
o
licies
ar
e
o
b
s
er
v
ed
.
W
ith
g
o
o
d
d
ata,
d
ec
is
io
n
-
m
ak
er
s
ca
n
p
r
o
d
u
ce
b
etter
an
aly
s
es a
n
d
m
ak
e
m
o
r
e
in
f
o
r
m
e
d
an
d
in
f
o
r
m
e
d
d
ec
is
io
n
s
.
T
h
e
d
ata
co
llectio
n
p
r
o
ce
s
s
also
in
v
o
lv
es
d
ata
v
alid
atio
n
an
d
v
er
if
ica
tio
n
to
en
s
u
r
e
th
e
ac
cu
r
ac
y
an
d
r
eliab
ilit
y
o
f
th
e
in
f
o
r
m
atio
n
co
llected
.
T
h
is
ca
n
in
v
o
lv
e
tech
n
iq
u
es
s
u
ch
as
cr
o
s
s
-
ch
ec
k
i
n
g
d
ata
wit
h
o
th
e
r
s
o
u
r
c
es
o
r
co
n
d
u
ctin
g
f
ield
tr
ials
to
v
er
if
y
th
e
v
er
ac
ity
o
f
th
e
d
ata.
I
n
ad
d
itio
n
,
ef
f
ec
tiv
e
d
ata
co
llec
tio
n
also
co
n
s
id
er
s
th
e
m
eth
o
d
s
o
f
s
to
r
i
n
g
,
m
a
n
a
g
in
g
an
d
an
aly
zin
g
th
e
d
ata
t
h
at
will
b
e
u
s
ed
in
th
e
d
ec
is
io
n
-
m
ak
i
n
g
p
r
o
ce
s
s
.
B
y
p
ay
in
g
atten
tio
n
to
th
ese
asp
ec
ts
,
d
ata
co
llectio
n
ca
n
p
r
o
v
id
e
a
s
o
lid
b
asis
f
o
r
ac
cu
r
ate
an
al
y
s
is
an
d
ef
f
ec
tiv
e
d
ec
is
io
n
-
m
a
k
in
g
.
2
.
2
.
Cre
a
t
ing
a
decisi
o
n m
a
t
rix
C
r
ea
tin
g
a
d
ec
is
io
n
m
atr
ix
is
an
im
p
o
r
tan
t
s
tep
in
DSS
d
ev
elo
p
m
en
t,
a
d
ec
is
io
n
m
atr
i
x
is
a
tab
l
e
u
s
ed
to
o
r
g
an
ize
a
n
d
c
o
m
p
a
r
e
v
ar
io
u
s
d
ec
is
io
n
alter
n
ati
v
es
b
ased
o
n
p
r
e
d
eter
m
in
ed
cr
iter
ia
[
2
1
]
–
[
2
3
]
.
T
h
e
f
ir
s
t
s
tep
in
cr
ea
tin
g
a
d
ec
is
io
n
m
atr
ix
is
to
id
en
tify
all
p
o
s
s
ib
le
alter
n
ativ
es
an
d
th
e
r
elev
an
t
cr
iter
ia
f
o
r
ev
alu
atin
g
th
o
s
e
alter
n
ativ
es.
Af
ter
th
at,
th
e
m
atr
ix
is
f
illed
with
v
alu
es
th
at
d
escr
ib
e
th
e
e
x
ten
t
to
wh
ich
ea
c
h
alter
n
ativ
e
m
ee
ts
ea
c
h
cr
iter
i
o
n
.
W
ith
th
is
d
ec
is
io
n
m
atr
i
x
,
d
ec
is
io
n
m
ak
e
r
s
ca
n
clea
r
ly
s
ee
an
d
co
m
p
ar
e
th
e
v
alu
es
ass
o
ciate
d
with
ea
ch
al
ter
n
ativ
e,
wh
ich
ca
n
h
elp
in
m
ak
in
g
i
n
f
o
r
m
ed
a
n
d
a
p
p
r
o
p
r
ia
te
d
ec
is
io
n
s
.
I
n
(
1
)
is
th
e
f
o
r
m
o
f
th
e
d
ec
i
s
io
n
m
at
r
ix
in
th
e
G2
M
w
eig
h
tin
g
m
eth
o
d
.
=
[
11
⋯
1
⋮
⋱
⋮
1
⋯
]
(
1
)
T
h
e
m
ain
o
b
jectiv
e
in
cr
ea
ti
n
g
a
d
ec
is
io
n
m
atr
ix
is
t
o
s
i
m
p
lify
th
e
d
ec
is
io
n
-
m
ak
in
g
p
r
o
ce
s
s
b
y
o
r
g
an
izin
g
co
m
p
lex
in
f
o
r
m
ati
o
n
in
t
o
a
f
o
r
m
at
th
at
is
ea
s
ier
to
u
n
d
er
s
tan
d
[
2
4
]
,
[
2
5
]
.
T
h
e
d
ec
is
io
n
m
atr
ix
h
elp
s
in
id
en
tify
in
g
th
e
b
est
alter
n
ativ
e
b
ased
o
n
p
r
e
d
ef
in
ed
cr
iter
ia,
an
d
p
r
o
v
id
es
a
clea
r
b
asis
f
o
r
co
m
p
ar
in
g
an
d
ev
alu
atin
g
ea
ch
alter
n
ati
v
e.
I
n
a
d
d
itio
n
,
t
h
e
d
ec
is
io
n
m
atr
ix
also
h
elp
s
in
u
n
d
er
s
tan
d
in
g
th
e
im
p
ac
t
o
f
ea
ch
d
ec
is
io
n
tak
e
n
,
as
well
as
ac
co
u
n
tin
g
f
o
r
th
e
d
if
f
e
r
en
t
p
r
e
f
er
en
ce
s
an
d
p
r
i
o
r
ities
o
f
s
tak
eh
o
ld
e
r
s
.
T
h
e
d
ec
is
io
n
m
at
r
ix
is
an
im
p
o
r
tan
t
to
o
l
in
s
u
p
p
o
r
tin
g
a
s
y
s
tem
atic,
s
tr
u
ctu
r
ed
a
n
d
i
n
f
o
r
m
ed
d
ec
is
io
n
-
m
a
k
in
g
p
r
o
ce
s
s
.
2
.
3
.
Ca
lcula
t
e
g
eo
m
et
ric
m
e
a
n v
a
lues
Geo
m
etr
ic
m
ea
n
v
alu
es
a
r
e
t
h
e
g
eo
m
etr
ic
m
ea
n
v
al
u
es
o
f
a
s
et
o
f
d
ata
u
s
ed
in
v
ar
i
o
u
s
an
aly
s
is
co
n
tex
ts
,
in
clu
d
in
g
cr
iter
ia
w
eig
h
tin
g
i
n
DSS.
Geo
m
etr
ic
m
ea
n
v
al
u
es
ar
e
ca
lc
u
lated
b
y
m
u
ltip
ly
i
n
g
all
th
e
d
ata
v
alu
es,
th
en
m
u
ltip
ly
in
g
th
e
r
esu
lt
b
y
th
e
to
tal
n
u
m
b
er
o
f
d
ata
[
2
6
]
,
[
2
7
]
.
Geo
m
et
r
ic
m
ea
n
v
alu
es
ar
e
u
s
ef
u
l b
ec
au
s
e
th
ey
p
r
o
v
id
e
a
b
etter
r
ep
r
esen
tatio
n
o
f
u
n
s
y
m
m
etr
ical
d
ata
d
is
tr
ib
u
tio
n
s
,
es
p
ec
ially
wh
en
th
er
e
ar
e
s
ig
n
if
ican
t
d
if
f
er
en
ce
s
b
et
wee
n
d
ata
v
alu
es.
I
n
th
e
co
n
t
ex
t
o
f
DSS,
g
eo
m
etr
ic
m
ea
n
v
alu
es
ar
e
u
s
ed
to
c
alcu
late
th
e
r
elativ
e
weig
h
t
o
f
ea
ch
cr
iter
io
n
b
ased
o
n
t
h
e
g
iv
en
d
ata,
t
h
u
s
m
ak
in
g
an
im
p
o
r
tan
t
co
n
tr
ib
u
tio
n
in
p
r
o
d
u
ci
n
g
m
o
r
e
in
f
o
r
m
ed
a
n
d
p
r
ec
is
e
d
ec
is
io
n
s
.
I
n
(
2
)
is
th
e
ca
lcu
latio
n
f
o
r
g
e
o
m
etr
ic
m
ea
n
v
al
u
es
in
th
e
G2
M
w
eig
h
tin
g
m
eth
o
d
.
=
(
∏
=
1
)
1
⁄
(
2
)
T
h
e
m
ain
p
u
r
p
o
s
e
o
f
u
s
in
g
g
eo
m
etr
ic
m
ea
n
v
alu
es
is
to
p
r
o
v
id
e
a
m
o
r
e
ac
cu
r
ate
r
e
p
r
esen
tatio
n
o
f
d
ata
th
at
h
as
lar
g
e
d
if
f
e
r
en
ce
s
in
m
a
g
n
itu
d
e
[
2
8
]
–
[
3
0
]
.
Ge
o
m
etr
ic
m
ea
n
v
alu
es
g
iv
e
g
r
ea
t
er
weig
h
t
to
s
m
aller
v
alu
es,
th
u
s
allo
win
g
u
n
s
y
m
m
etr
ical
d
ata
to
b
e
in
ter
p
r
eted
in
a
m
o
r
e
b
alan
ce
d
m
an
n
e
r
.
I
n
th
e
co
n
tex
t
o
f
cr
iter
ia
weig
h
tin
g
,
g
eo
m
etr
ic
m
ea
n
v
alu
es
ar
e
u
s
ed
t
o
ca
lcu
late
th
e
weig
h
ts
o
f
c
r
iter
ia
o
r
f
ac
to
r
s
in
th
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
G2
M w
eig
h
tin
g
:
a
n
ew a
p
p
r
o
a
ch
b
a
s
ed
o
n
m
u
lti
-
o
b
jective
a
s
s
es
s
men
t d
a
ta
…
(
N
ir
w
a
n
a
Hen
d
r
a
s
tu
ty
)
407
d
ec
is
io
n
-
m
ak
in
g
p
r
o
ce
s
s
.
B
y
g
iv
in
g
p
r
o
p
o
r
tio
n
al
atten
tio
n
to
ea
ch
v
alu
e
in
th
e
d
ata
s
et,
g
eo
m
etr
ic
m
ea
n
v
alu
es
h
elp
m
in
im
ize
d
is
to
r
tio
n
s
th
at
ca
n
ar
is
e
d
u
e
to
d
if
f
er
e
n
ce
s
in
s
ca
le
o
r
m
ag
n
itu
d
e
b
et
wee
n
v
alu
es,
th
u
s
im
p
r
o
v
in
g
ac
cu
r
ac
y
an
d
co
n
s
is
ten
cy
in
d
ec
is
io
n
an
al
y
s
is
.
2
.
4
.
No
r
m
a
liza
t
io
n o
f
m
a
t
rix
Ma
tr
ix
n
o
r
m
aliza
tio
n
is
th
e
p
r
o
ce
s
s
o
f
tr
an
s
f
o
r
m
in
g
t
h
e
v
al
u
es
in
a
m
atr
ix
o
n
t
o
a
co
m
m
o
n
s
ca
le
to
allo
w
f
o
r
f
air
er
an
d
m
o
r
e
ac
c
u
r
ate
co
m
p
ar
is
o
n
s
b
etwe
en
d
if
f
er
en
t
elem
en
ts
[
3
1
]
,
[
3
2
]
.
M
atr
ix
n
o
r
m
aliza
tio
n
is
ess
en
tial
b
ec
au
s
e
d
if
f
er
en
t
cr
iter
ia
m
ay
h
a
v
e
d
if
f
er
en
t
u
n
its
o
f
m
ea
s
u
r
em
en
t
o
r
wid
ely
v
ar
y
in
g
r
an
g
es
o
f
v
alu
es.
T
h
e
n
o
r
m
aliza
tio
n
p
r
o
ce
s
s
u
s
u
ally
in
v
o
lv
es
c
o
n
v
er
tin
g
th
e
v
alu
es
in
to
a
p
r
o
p
o
r
tio
n
al
f
o
r
m
.
W
ith
n
o
r
m
aliza
tio
n
,
ea
c
h
cr
it
er
io
n
in
t
h
e
d
ec
is
io
n
m
atr
ix
ca
n
b
e
m
ea
s
u
r
ed
o
n
th
e
s
am
e
s
ca
le,
s
o
th
at
th
e
r
elativ
e
in
f
lu
en
ce
o
f
ea
c
h
cr
i
ter
io
n
ca
n
b
e
co
m
p
ar
ed
d
ir
ec
tly
an
d
f
air
l
y
.
T
h
is
im
p
r
o
v
es
th
e
ac
cu
r
ac
y
an
d
r
eliab
ilit
y
o
f
th
e
a
n
aly
s
is
r
esu
lts
,
h
elp
in
g
d
ec
is
io
n
m
ak
er
s
to
m
ak
e
m
o
r
e
i
n
f
o
r
m
ed
an
d
o
b
jectiv
e
ch
o
ices.
I
n
(
3
)
is
th
e
f
o
r
m
o
f
m
atr
ix
n
o
r
m
aliza
tio
n
in
th
e
G2
M
w
eig
h
tin
g
m
eth
o
d
.
=
(
3
)
T
h
e
p
u
r
p
o
s
e
o
f
m
atr
ix
n
o
r
m
al
izatio
n
is
to
eq
u
alize
th
e
s
ca
le
o
f
v
alu
es
in
a
m
atr
ix
,
t
h
u
s
allo
win
g
f
air
an
d
ac
cu
r
ate
co
m
p
ar
is
o
n
s
b
etwe
en
elem
en
ts
th
at
m
ay
h
av
e
d
if
f
er
en
t
u
n
its
o
r
r
an
g
es
o
f
v
alu
es
[
3
3
]
.
Ma
tr
ix
n
o
r
m
aliza
tio
n
r
em
o
v
es
th
e
p
r
ef
er
en
ce
th
at
ca
n
ar
is
e
f
r
o
m
d
if
f
er
en
ce
s
in
s
ca
le,
en
s
u
r
in
g
th
at
ea
ch
cr
iter
io
n
o
r
v
ar
iab
le
h
as
p
r
o
p
o
r
tio
n
ate
in
f
l
u
en
ce
in
th
e
d
ec
is
io
n
an
al
y
s
is
.
B
y
tr
an
s
f
o
r
m
in
g
v
alu
es
in
to
a
co
n
s
is
ten
t
s
ca
le,
n
o
r
m
aliza
tio
n
f
ac
ilit
ates
th
e
in
teg
r
atio
n
a
n
d
an
al
y
s
is
o
f
d
at
a
f
r
o
m
m
u
ltip
le
s
o
u
r
ce
s
,
in
cr
e
asin
g
th
e
r
eliab
ilit
y
an
d
v
alid
ity
o
f
t
h
e
r
esu
lts
o
b
tain
ed
.
Ma
tr
ix
n
o
r
m
aliza
tio
n
p
lay
s
a
cr
u
cial
r
o
le
in
p
r
o
d
u
c
in
g
m
o
r
e
p
r
ec
is
e
an
d
in
f
o
r
m
e
d
r
ec
o
m
m
en
d
atio
n
s
,
h
elp
in
g
d
ec
is
io
n
m
ak
er
s
to
m
a
k
e
m
o
r
e
o
b
jectiv
e
ch
o
ices
b
as
ed
o
n
b
alan
ce
d
d
ata
an
aly
s
is
.
2
.
5
.
Ca
lcula
t
ing
t
he
g
re
y
v
a
l
ue
Gr
ey
v
alu
e
is
a
co
n
ce
p
t
u
s
ed
in
g
r
ey
s
y
s
tem
an
aly
s
is
to
h
an
d
le
u
n
ce
r
tain
ty
an
d
in
co
m
p
le
te
d
ata
[
3
4
]
,
[
3
5
]
.
Gr
e
y
v
alu
es
r
ep
r
es
en
t
th
e
lev
el
o
f
ce
r
tain
ty
o
r
in
f
o
r
m
atio
n
av
ailab
le
ab
o
u
t
a
v
ar
iab
le
o
r
p
ar
am
eter
.
T
h
ese
g
r
ey
v
alu
es
ar
e
in
b
etwe
en
ex
ac
t
v
alu
es
(
b
lack
an
d
wh
ite)
,
r
ef
lectin
g
u
n
ce
r
tain
ty
o
r
lack
o
f
co
m
p
lete
in
f
o
r
m
atio
n
.
Gr
e
y
v
alu
es
ar
e
o
f
ten
u
s
ed
in
d
ec
is
io
n
-
m
ak
in
g
m
eth
o
d
s
an
d
d
ata
an
al
y
s
is
wh
er
e
a
v
ailab
le
d
ata
m
ay
b
e
lim
ited
o
r
n
o
t
c
o
m
p
l
etely
r
eliab
le.
B
y
in
c
o
r
p
o
r
atin
g
g
r
ey
v
alu
es,
g
r
e
y
s
y
s
tem
s
ca
n
p
r
o
v
id
e
m
o
r
e
f
lex
ib
le
an
d
r
ea
lis
tic
an
aly
s
i
s
,
allo
win
g
d
ec
is
io
n
m
ak
er
s
to
wo
r
k
with
im
p
er
f
ec
t
d
at
a
an
d
s
till
p
r
o
d
u
ce
in
f
o
r
m
ativ
e
a
n
d
r
elia
b
l
e
d
ec
is
io
n
s
.
I
n
(
4
)
is
th
e
f
o
r
m
o
f
g
r
ey
v
alu
e
in
th
e
G
2
M
w
eig
h
tin
g
m
eth
o
d
.
=
1
∑
=
1
(
4
)
T
h
e
m
ain
p
u
r
p
o
s
e
o
f
u
s
in
g
g
r
ey
v
alu
e
is
to
a
d
d
r
ess
an
d
m
a
n
ag
e
u
n
ce
r
tain
ty
an
d
in
co
m
p
l
ete
d
ata
in
th
e
d
ec
is
io
n
-
m
ak
in
g
an
d
an
al
y
s
is
p
r
o
ce
s
s
.
Gr
ey
v
alu
e
p
r
o
v
id
es
a
way
to
r
ep
r
esen
t
in
f
o
r
m
atio
n
th
at
is
p
ar
tial
o
r
n
o
t
f
u
lly
k
n
o
wn
,
allo
win
g
f
o
r
m
o
r
e
f
lex
ib
le
an
d
r
ea
lis
tic
an
aly
s
es
co
m
p
ar
ed
to
tr
ad
itio
n
al
ap
p
r
o
ac
h
es
th
at
r
eq
u
ir
e
co
m
p
lete
an
d
d
ef
i
n
itiv
e
d
ata
[
3
6
]
.
Usi
n
g
g
r
e
y
v
al
u
e,
d
ec
is
io
n
m
ak
er
s
ca
n
co
n
s
id
er
d
if
f
er
e
n
t
lev
els
o
f
ce
r
tain
ty
an
d
i
n
teg
r
ate
d
ata
o
f
v
ar
y
in
g
q
u
ality
in
to
th
e
an
aly
s
is
m
o
d
el.
T
h
is
p
r
o
ce
s
s
h
elp
s
in
g
e
n
er
atin
g
m
o
r
e
ac
cu
r
ate
an
d
ad
a
p
tiv
e
d
ec
is
io
n
s
to
co
m
p
lex
an
d
d
y
n
am
ic
s
itu
atio
n
s
,
wh
er
e
in
f
o
r
m
atio
n
is
o
f
ten
in
co
m
p
lete
o
r
u
n
d
er
c
o
n
d
itio
n
s
o
f
u
n
ce
r
tain
t
y
.
2
.
6
.
Ca
lcula
t
ing
f
ina
l w
eig
ht
o
f
cr
it
er
ia
Fin
al
weig
h
t
o
f
cr
iter
ia
is
th
e
f
in
al
r
esu
lt
o
f
t
h
e
cr
iter
ia
wei
g
h
tin
g
p
r
o
ce
s
s
r
ef
lectin
g
th
e
i
m
p
o
r
tan
ce
o
f
ea
ch
cr
iter
io
n
r
elativ
e
t
o
th
e
o
th
er
cr
iter
ia
[
3
7
]
,
[
3
8
]
.
T
h
ese
f
in
al
weig
h
ts
p
lay
a
cr
u
ci
al
r
o
le
in
d
ec
is
io
n
-
m
ak
in
g
,
as
th
ey
ar
e
u
s
ed
to
co
m
b
in
e
v
a
r
io
u
s
cr
iter
ia
in
to
o
n
e
co
m
p
r
eh
e
n
s
iv
e
s
ca
le
th
at
s
u
p
p
o
r
ts
th
e
ev
alu
atio
n
an
d
co
m
p
ar
is
o
n
o
f
d
ec
is
io
n
alter
n
ativ
es.
T
h
e
f
i
n
al
weig
h
t
o
f
cr
iter
ia
en
s
u
r
es
th
at
ea
ch
cr
iter
io
n
is
g
iv
en
th
e
ap
p
r
o
p
r
iate
p
r
o
p
o
r
ti
o
n
o
f
in
f
l
u
en
ce
ac
c
o
r
d
in
g
to
its
im
p
o
r
tan
ce
,
s
o
th
at
th
e
d
ec
i
s
io
n
tak
en
is
m
o
r
e
p
r
ec
is
e,
o
b
jectiv
e,
an
d
r
ef
lect
s
tr
u
e
p
r
io
r
ities
.
W
i
th
ac
cu
r
ate
an
d
f
air
f
in
al
weig
h
ts
,
DSS
ca
n
p
r
o
v
i
d
e
m
o
r
e
ef
f
ec
tiv
e
an
d
r
elev
a
n
t
r
ec
o
m
m
en
d
atio
n
s
f
o
r
v
ar
io
u
s
s
itu
atio
n
s
an
d
co
n
tex
ts
.
I
n
(
5
)
is
th
e
f
o
r
m
o
f
th
e
f
in
al
weig
h
t o
f
th
e
c
r
iter
ia
in
th
e
G
2
M
w
eig
h
tin
g
m
et
h
o
d
.
=
∑
=
1
(
5
)
T
h
e
m
ain
p
u
r
p
o
s
e
o
f
d
eter
m
i
n
in
g
t
h
e
f
i
n
al
weig
h
t
o
f
cr
iter
ia
is
to
e
n
s
u
r
e
th
at
ea
ch
cr
iter
io
n
in
th
e
d
ec
is
io
n
-
m
ak
in
g
p
r
o
ce
s
s
is
g
iv
en
an
a
p
p
r
o
p
r
iate
p
r
o
p
o
r
tio
n
o
f
in
f
lu
en
ce
ac
co
r
d
in
g
to
its
lev
el
o
f
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
8
,
No
.
1
,
Ap
r
il
20
2
5
:
4
0
3
-
4
1
6
408
im
p
o
r
tan
ce
[
3
9
]
,
[
4
0
]
.
T
h
e
f
i
n
al
weig
h
t
o
f
cr
iter
ia
h
elp
s
i
n
teg
r
ate
v
ar
i
o
u
s
cr
iter
ia
th
at
m
ay
h
av
e
d
if
f
e
r
en
t
u
n
its
o
r
s
ca
les,
in
to
o
n
e
co
m
p
r
eh
en
s
iv
e
s
ca
le
th
at
allo
ws
f
o
r
o
b
jectiv
e
an
d
f
air
ev
alu
atio
n
an
d
co
m
p
ar
is
o
n
o
f
d
ec
is
io
n
alter
n
a
tiv
es.
B
y
as
s
ig
n
in
g
ac
cu
r
ate
f
in
al
weig
h
ts
,
d
ec
is
io
n
m
a
k
er
s
ca
n
o
v
er
co
m
e
s
u
b
jectiv
e
p
er
ce
p
tio
n
s
,
e
n
s
u
r
in
g
t
h
at
all
im
p
o
r
tan
t
asp
ec
ts
ar
e
tak
e
n
i
n
to
ac
co
u
n
t
ac
co
r
d
in
g
to
th
ei
r
tr
u
e
p
r
io
r
ity
.
T
h
is
in
cr
ea
s
es
th
e
r
eliab
ilit
y
a
n
d
v
alid
ity
o
f
t
h
e
d
ec
is
io
n
r
e
s
u
lt
s
,
s
u
p
p
o
r
tin
g
d
ec
is
io
n
-
m
ak
in
g
t
h
at
is
m
o
r
e
in
f
o
r
m
ativ
e,
tr
a
n
s
p
ar
en
t,
a
n
d
r
esp
o
n
s
iv
e
to
v
ar
io
u
s
co
n
d
itio
n
s
an
d
n
ee
d
s
.
T
h
e
f
in
al
weig
h
t
o
f
cr
iter
ia
is
a
k
ey
elem
en
t
in
g
e
n
er
atin
g
m
o
r
e
p
r
ec
is
e
an
d
ef
f
ec
tiv
e
d
ec
is
io
n
r
ec
o
m
m
en
d
atio
n
s
in
v
ar
io
u
s
co
n
tex
ts
an
d
ap
p
licatio
n
s
.
2.
7
.
G
re
y
g
e
o
m
e
t
ric
m
ea
n w
eig
hting
G2
M
w
eig
h
tin
g
is
a
n
ew
ap
p
r
o
ac
h
as
an
in
n
o
v
ativ
e
m
eth
o
d
in
cr
iter
ia
weig
h
tin
g
th
at
c
o
m
b
in
es
g
r
ey
s
y
s
tem
an
aly
s
is
an
d
g
eo
m
et
r
ic
m
ea
n
ca
lcu
latio
n
to
p
r
o
d
u
c
e
m
o
r
e
ac
c
u
r
ate
an
d
p
r
o
p
o
r
ti
o
n
al
weig
h
ts
u
n
d
er
co
n
d
itio
n
s
o
f
u
n
ce
r
tain
ty
a
n
d
in
co
m
p
lete
d
ata.
T
h
e
m
eth
o
d
h
ar
n
ess
es
th
e
p
o
wer
o
f
g
r
ey
s
y
s
tem
s
to
m
an
ag
e
d
ata
u
n
ce
r
tain
ty
an
d
p
r
o
v
i
d
e
m
o
r
e
r
ea
lis
tic
est
im
ates,
w
h
ile
g
eo
m
etr
ic
av
er
ag
es
ar
e
u
s
ed
to
m
ain
tain
a
b
alan
ce
o
f
r
elativ
e
weig
h
ts
b
etwe
en
cr
iter
ia,
r
ed
u
ci
n
g
t
h
e
in
f
lu
en
ce
o
f
ex
tr
em
e
o
r
o
u
tlier
d
ata.
G2
M
w
eig
h
tin
g
is
d
esig
n
ed
to
in
cr
ea
s
e
o
b
jectiv
ity
an
d
co
n
s
is
ten
cy
in
th
e
cr
iter
ia
weig
h
tin
g
p
r
o
ce
s
s
,
r
esu
ltin
g
in
m
o
r
e
in
f
o
r
m
ed
an
d
p
r
ec
is
e
d
e
cisi
o
n
s
in
DSS.
G2
M
w
eig
h
ti
n
g
o
f
f
e
r
s
a
m
o
r
e
r
o
b
u
s
t
s
o
lu
tio
n
f
o
r
a
wid
e
r
a
n
g
e
o
f
ap
p
licatio
n
s
b
y
v
ir
tu
e
o
f
its
f
lex
ib
ilit
y
an
d
a
d
ap
tab
ilit
y
,
f
r
o
m
b
u
s
in
ess
an
d
m
an
ag
em
en
t
to
s
cien
tific
r
esear
ch
an
d
e
n
g
in
ee
r
in
g
in
s
u
p
p
o
r
t
o
f
m
o
r
e
r
esp
o
n
s
iv
e
an
d
r
eliab
le
d
ec
is
io
n
m
ak
in
g
.
W
ith
th
e
G2
M
w
eig
h
tin
g
ap
p
r
o
ac
h
,
t
h
e
p
r
o
ce
s
s
o
f
d
eter
m
in
in
g
cr
iter
ia
weig
h
ts
b
ec
o
m
es
m
o
r
e
tr
an
s
p
ar
en
t
a
n
d
r
eliab
le,
as
it
r
ed
u
ce
s
s
u
b
jectiv
e
p
r
ef
er
e
n
ce
s
th
at
o
f
ten
o
cc
u
r
in
tr
a
d
itio
n
al
m
eth
o
d
s
.
G2
M
w
eig
h
tin
g
’
s
ab
ilit
y
to
a
d
ap
t
to
in
c
o
m
p
lete
o
r
u
n
ce
r
tain
d
ata
m
ak
es
it
p
ar
ticu
la
r
ly
r
elev
an
t
in
co
m
p
le
x
an
d
d
y
n
am
ic
co
n
tex
ts
,
wh
er
e
av
ailab
le
in
f
o
r
m
atio
n
m
ay
n
o
t
alwa
y
s
b
e
p
er
f
ec
t.
T
h
e
en
d
r
esu
lt
is
an
im
p
r
o
v
em
e
n
t
in
th
e
q
u
ality
o
f
d
ec
is
io
n
s
ta
k
en
,
b
y
co
n
s
id
er
in
g
v
ar
io
u
s
f
ac
to
r
s
m
o
r
e
co
m
p
r
eh
en
s
iv
ely
an
d
o
b
jectiv
ely
.
G2
M
w
eig
h
tin
g
a
ls
o
f
ac
ilit
ates
in
teg
r
atio
n
in
v
a
r
io
u
s
DSS
s
y
s
tem
s
an
d
ap
p
licatio
n
s
,
m
ak
in
g
it
a
v
er
y
u
s
ef
u
l
to
o
l
f
o
r
p
r
o
f
ess
io
n
als
wh
o
r
ely
o
n
d
ata
to
m
a
k
e
s
tr
ateg
ic
d
ec
is
io
n
s
.
G2
M
w
eig
h
tin
g
n
o
t
o
n
ly
im
p
r
o
v
es
ef
f
icien
cy
an
d
ac
cu
r
ac
y
in
cr
iter
ia
weig
h
tin
g
,
b
u
t
also
co
n
tr
ib
u
tes
s
ig
n
if
ican
tly
t
o
th
e
d
e
v
elo
p
m
e
n
t
o
f
m
o
r
e
s
o
p
h
is
ticated
an
d
ad
a
p
tiv
e
d
ec
is
io
n
-
m
a
k
in
g
m
et
h
o
d
s.
T
h
e
m
ain
o
b
jectiv
e
o
f
G2
M
w
eig
h
tin
g
is
to
p
r
o
v
id
e
a
m
o
r
e
o
b
jectiv
e
an
d
ac
cu
r
ate
ap
p
r
o
ac
h
to
cr
iter
ia
weig
h
tin
g
in
DSS.
I
t
aim
s
to
o
v
er
co
m
e
th
e
lim
itatio
n
s
o
f
tr
ad
itio
n
al
m
eth
o
d
s
b
y
co
m
b
i
n
in
g
g
r
ey
s
y
s
tem
an
aly
s
is
an
d
g
eo
m
et
r
ic
m
ea
n
ca
lcu
latio
n
,
r
esu
lti
n
g
in
m
o
r
e
b
alan
ce
d
a
n
d
p
r
o
p
o
r
ti
o
n
al
cr
iter
ia
weig
h
ts
.
T
h
e
m
ai
n
ad
v
an
tag
e
o
f
G2
M
w
eig
h
tin
g
is
its
ab
i
lity
to
m
a
n
ag
e
u
n
ce
r
tain
ty
an
d
in
co
m
p
lete
d
ata,
wh
ich
o
f
ten
o
cc
u
r
in
co
m
p
lex
d
ec
is
io
n
en
v
ir
o
n
m
e
n
ts
.
G2
M
w
eig
h
tin
g
also
allo
ws
d
ec
i
s
i
o
n
m
ak
er
s
to
wo
r
k
with
p
a
r
tial
o
r
u
n
ce
r
tain
d
ata,
th
u
s
in
c
r
ea
s
in
g
f
le
x
ib
ilit
y
an
d
r
esp
o
n
s
iv
en
ess
in
d
ec
is
io
n
m
ak
in
g
.
T
h
e
r
esu
lts
o
f
G2
M
w
eig
h
tin
g
ca
n
h
elp
i
m
p
r
o
v
e
d
ec
is
io
n
q
u
ality
i
n
a
v
ar
iety
o
f
co
n
te
x
ts
,
f
r
o
m
b
u
s
in
ess
an
d
m
an
ag
em
en
t
to
s
cien
ce
a
n
d
tech
n
o
l
o
g
y
,
m
ak
in
g
it
a
p
r
o
m
is
i
n
g
ap
p
r
o
ac
h
in
th
e
d
ev
elo
p
m
en
t
o
f
m
o
r
e
s
o
p
h
is
ticated
an
d
r
eliab
le
DSS.
2.
8
.
M
ulti
-
o
bje
ct
iv
e
o
ptim
iz
a
t
io
n by
ra
t
io
a
na
ly
s
is
m
et
h
o
d
T
h
e
m
u
lti
-
o
b
jectiv
e
o
p
tim
izatio
n
b
y
r
atio
an
al
y
s
is
(
MO
OR
A
)
m
eth
o
d
is
o
n
e
o
f
th
e
d
ec
is
io
n
an
aly
s
is
m
eth
o
d
s
u
s
ed
to
s
elec
t
th
e
b
e
s
t
alter
n
ativ
e
f
r
o
m
a
s
et
o
f
av
ailab
le
alter
n
ativ
es.
T
h
is
m
et
h
o
d
allo
ws
d
ec
is
io
n
m
ak
er
s
to
e
v
alu
ate
alter
n
ativ
e
s
b
ased
o
n
s
ev
er
al
d
if
f
er
en
t
cr
iter
ia,
b
y
c
o
n
s
id
er
in
g
th
e
r
elativ
e
weig
h
t
o
f
ea
ch
o
f
th
ese
cr
iter
ia.
MO
OR
A
w
o
r
k
s
b
y
co
n
v
er
tin
g
th
e
ab
s
o
l
u
te
v
a
lu
e
o
f
ea
c
h
cr
iter
io
n
in
to
a
r
elativ
e
v
alu
e,
wh
ich
is
th
en
u
s
ed
to
ca
lcu
la
te
th
e
r
elativ
e
p
r
ef
er
en
ce
v
al
u
e
f
o
r
ea
ch
alter
n
ativ
e.
T
h
ese
r
elativ
e
p
r
ef
er
en
ce
v
alu
es
ar
e
th
en
u
s
ed
to
r
an
k
th
e
alter
n
ativ
es,
wh
er
e
th
e
alter
n
ativ
e
with
th
e
h
ig
h
est
r
elativ
e
p
r
ef
er
e
n
ce
v
alu
e
is
co
n
s
id
er
ed
th
e
m
o
s
t
d
esira
b
le
alter
n
ativ
e.
T
h
e
f
ir
s
t
MO
OR
A
s
tag
e
ca
lcu
lates
m
atr
ix
n
o
r
m
aliza
tio
n
u
s
in
g
in
(
6
)
.
∗
=
√
∑
2
=
1
(
6
)
T
h
e
last
s
tag
e
in
MO
OR
A
ca
lcu
lates
th
e
o
p
tim
izatio
n
v
alu
e
b
ased
o
n
th
e
r
esu
lt
o
f
m
atr
i
x
n
o
r
m
aliza
tio
n
m
u
ltip
lied
b
y
t
h
e
weig
h
t
o
f
th
e
cr
iter
ia.
T
h
e
m
ax
im
u
m
o
p
tim
izatio
n
v
al
u
e
will
b
e
r
ed
u
ce
d
b
y
th
e
m
in
im
u
m
o
p
tim
izatio
n
v
alu
e,
wh
er
e
th
e
m
a
x
im
u
m
o
p
tim
izatio
n
v
alu
e
is
f
o
r
cr
iter
i
a
th
at
ar
e
b
en
ef
its
an
d
th
e
m
in
im
u
m
o
p
tim
izati
o
n
v
alu
e
is
f
o
r
cr
iter
ia
th
at
ar
e
co
s
ts
.
T
h
e
o
p
tim
izatio
n
v
alu
e
is
ca
lcu
lated
u
s
in
g
in
(
7
)
.
∗
=
∑
∗
∗
=
1
−
∑
∗
∗
=
+
1
(
7
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
G2
M w
eig
h
tin
g
:
a
n
ew a
p
p
r
o
a
ch
b
a
s
ed
o
n
m
u
lti
-
o
b
jective
a
s
s
es
s
men
t d
a
ta
…
(
N
ir
w
a
n
a
Hen
d
r
a
s
tu
ty
)
409
T
h
e
f
in
al
r
esu
lt
o
f
th
e
MO
OR
A
o
p
tim
izatio
n
s
co
r
e
is
th
e
r
elativ
e
r
an
k
in
g
o
f
ea
ch
ev
alu
ated
alter
n
ativ
e.
E
ac
h
alter
n
ativ
e
will
h
av
e
a
r
elativ
e
p
r
ef
er
en
ce
v
alu
e
th
at
in
d
icate
s
its
r
elativ
e
d
e
g
r
ee
o
f
d
esira
b
ilit
y
o
r
s
u
itab
ilit
y
in
th
e
co
n
tex
t
o
f
th
e
cr
iter
ia
b
ein
g
ass
es
s
ed
.
T
h
e
alter
n
ativ
e
with
th
e
h
i
g
h
est
r
elativ
e
p
r
ef
er
en
ce
v
alu
e
is
co
n
s
id
er
ed
th
e
m
o
s
t d
esira
b
le
o
r
o
p
tim
al
alter
n
ativ
e
in
th
at
co
n
tex
t.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
e
im
p
lem
e
n
tatio
n
o
f
G2
M
w
eig
h
tin
g
in
cr
iter
ia
weig
h
tin
g
in
tr
o
d
u
ce
s
an
in
n
o
v
ativ
e
a
n
d
ef
f
ec
tiv
e
ap
p
r
o
ac
h
in
DSS.
T
h
is
m
et
h
o
d
u
tili
ze
s
a
co
m
b
in
atio
n
o
f
g
r
ey
s
y
s
tem
an
aly
s
is
an
d
g
eo
m
etr
ic
m
ea
n
ca
lcu
latio
n
to
o
v
e
r
co
m
e
d
ata
u
n
ce
r
tain
ty
a
n
d
p
r
o
d
u
ce
m
o
r
e
b
alan
ce
d
an
d
p
r
o
p
o
r
tio
n
al
cr
iter
ia
weig
h
ts
.
B
y
u
s
in
g
g
r
e
y
s
y
s
tem
an
aly
s
is
,
G2
M
w
eig
h
tin
g
ca
n
p
r
o
v
i
d
e
m
o
r
e
ac
cu
r
ate
a
n
d
r
ea
lis
tic
es
tim
ates,
wh
ile
g
eo
m
etr
ic
m
ea
n
ca
lcu
latio
n
h
elp
s
m
ain
tain
th
e
b
alan
ce
o
f
r
elativ
e
weig
h
ts
b
etwe
en
cr
iter
ia,
r
ed
u
cin
g
th
e
im
p
ac
t
o
f
e
x
tr
em
e
o
r
o
u
tlier
d
ata.
T
h
e
im
p
lem
e
n
tatio
n
o
f
G
2
M
w
eig
h
tin
g
i
n
d
eter
m
i
n
in
g
cr
iter
ia
weig
h
ts
ca
n
h
elp
im
p
r
o
v
e
th
e
q
u
ality
o
f
d
e
cisi
o
n
-
m
ak
in
g
b
y
p
r
o
v
i
d
in
g
a
m
o
r
e
s
o
lid
an
d
m
ea
s
u
r
ab
le
b
a
s
is
,
an
d
m
in
im
izin
g
p
er
ce
p
tio
n
s
th
at
m
a
y
ar
is
e
in
s
u
b
jectiv
e
ju
d
g
em
en
ts
.
G2
M
w
eig
h
tin
g
b
ec
o
m
es a
v
er
y
u
s
ef
u
l to
o
l in
im
p
r
o
v
in
g
th
e
ef
f
ec
tiv
en
ess
an
d
ac
c
u
r
ac
y
o
f
DSS in
v
ar
io
u
s
co
n
tex
ts
an
d
ap
p
licati
o
n
s
.
T
h
e
im
p
lem
e
n
tatio
n
o
f
G2
M
w
eig
h
tin
g
also
p
av
es
t
h
e
way
f
o
r
in
c
r
ea
s
ed
ef
f
icien
cy
an
d
ef
f
ec
tiv
en
ess
in
m
u
lti
-
cr
iter
ia
d
ec
is
io
n
m
ak
in
g
.
B
y
in
teg
r
ati
n
g
v
ar
io
u
s
asp
ec
ts
o
f
u
n
ce
r
tain
ty
an
d
co
m
p
lex
ity
in
d
ec
is
io
n
an
aly
s
is
,
th
is
m
eth
o
d
h
elp
s
d
ec
is
io
n
m
ak
e
r
s
t
o
u
n
d
er
s
tan
d
an
d
ev
al
u
ate
th
e
im
p
ac
t
o
f
ea
ch
cr
iter
io
n
m
o
r
e
h
o
lis
tically
.
I
n
ad
d
itio
n
,
th
e
im
p
lem
e
n
tatio
n
o
f
G2
M
w
eig
h
tin
g
ca
n
s
tr
en
g
th
en
th
e
b
asis
o
f
d
ec
is
io
n
an
aly
s
is
b
y
p
r
o
d
u
ci
n
g
m
o
r
e
m
e
asu
r
ab
le
a
n
d
o
b
jectiv
e
cr
iter
ia
weig
h
ts
,
wh
i
ch
ca
n
b
e
u
s
ed
as
g
u
id
e
lin
es
in
c
h
o
o
s
in
g
th
e
m
o
s
t
s
u
itab
le
alter
n
ativ
e.
T
h
e
i
m
p
lem
en
tatio
n
o
f
G
2
M
w
eig
h
tin
g
in
d
eter
m
in
i
n
g
cr
iter
ia
weig
h
ts
ca
n
b
r
in
g
s
ig
n
if
ican
t
b
en
ef
its
in
im
p
r
o
v
i
n
g
d
ec
is
io
n
q
u
ality
a
n
d
o
p
ti
m
izin
g
r
esu
lts
in
a
v
ar
iety
o
f
co
m
p
le
x
an
d
d
y
n
am
ic
d
ec
is
io
n
-
m
ak
in
g
s
it
u
atio
n
s
.
3
.
1
.
Da
t
a
c
o
llect
io
n (
ca
s
e
s
t
ud
y
det
er
m
ini
ng
s
up
pl
ier
per
f
o
rm
a
nce
e
v
a
lua
t
io
n us
ing
M
O
O
RA)
Data
co
llectio
n
in
d
eter
m
in
in
g
s
u
p
p
lier
p
er
f
o
r
m
a
n
ce
ev
alu
atio
n
is
an
im
p
o
r
tan
t
s
tep
in
m
ea
s
u
r
in
g
an
d
u
n
d
er
s
tan
d
i
n
g
th
e
ex
ten
t
to
wh
ich
s
u
p
p
lier
s
f
u
lf
il
th
e
s
et
r
eq
u
ir
em
e
n
ts
an
d
ex
p
ec
t
atio
n
s
.
T
h
is
p
r
o
ce
s
s
in
v
o
lv
es
co
llectin
g
in
f
o
r
m
atio
n
r
elate
d
to
v
ar
io
u
s
a
s
p
ec
ts
o
f
s
u
p
p
lier
p
er
f
o
r
m
an
ce
n
a
m
ely
av
er
ag
e
co
s
t
(
C
R
-
1
)
,
d
eliv
er
y
tim
e
(
C
R
-
2
)
,
p
r
o
d
u
ct
q
u
ality
(
C
R
-
3
)
,
f
lex
i
b
ilit
y
(
C
R
-
4
)
,
an
d
av
ailab
ilit
y
o
f
g
o
o
d
s
(
C
R
-
5
)
.
T
h
e
d
ata
is
o
b
tain
ed
f
r
o
m
th
e
co
m
p
an
y
’
s
ju
d
g
em
en
t
in
ev
al
u
atin
g
s
u
p
p
lier
p
er
f
o
r
m
an
ce
.
C
o
m
p
r
eh
en
s
iv
e
an
d
ac
cu
r
ate
d
ata
co
llectio
n
en
ab
les
co
m
p
an
ies
to
id
en
tify
s
u
p
p
lier
s
’
s
tr
en
g
th
s
an
d
wea
k
n
ess
es,
an
d
tak
e
n
ec
ess
ar
y
ac
tio
n
s
to
im
p
r
o
v
e
o
v
er
all
s
u
p
p
ly
ch
ain
p
e
r
f
o
r
m
an
ce
.
T
a
b
le
1
is
th
e
r
esu
lt
o
f
th
e
p
er
f
o
r
m
a
n
ce
ass
es
s
m
en
t o
f
ex
is
tin
g
s
u
p
p
lie
r
s
.
T
ab
le
1
.
Su
p
p
lier
p
er
f
o
r
m
a
n
ce
ass
es
s
m
en
t r
esu
lts
S
u
p
p
l
i
e
r
n
a
me
P
e
r
f
o
r
ma
n
c
e
r
a
t
i
n
g
o
f
e
a
c
h
c
r
i
t
e
r
i
o
n
CR
-
1
CR
-
2
CR
-
3
CR
-
4
CR
-
5
S
u
p
p
l
i
e
r
Y
A
8
0
0
3
5
4
4
S
u
p
p
l
i
e
r
F
T
7
5
0
5
4
5
4
S
u
p
p
l
i
e
r
H
E
8
6
0
2
5
3
3
S
u
p
p
l
i
e
r
TW
9
0
0
3
3
4
3
S
u
p
p
l
i
e
r
A
S
9
4
0
3
4
4
4
S
u
p
p
l
i
e
r
B
R
9
2
0
4
3
4
4
S
u
p
p
l
i
e
r
O
R
7
8
0
2
4
4
5
S
u
p
p
l
i
e
r
N
W
8
8
0
3
3
3
5
S
u
p
p
l
i
e
r
D
S
9
4
0
2
4
4
4
S
u
p
p
l
i
e
r
C
H
8
3
0
4
5
3
5
T
h
e
s
u
p
p
lier
ass
ess
m
en
t
d
ata
T
ab
le
1
o
b
tain
ed
f
r
o
m
th
e
c
o
m
p
an
y
is
v
er
y
v
alu
ab
le
i
n
f
o
r
m
atio
n
in
m
ea
s
u
r
in
g
s
u
p
p
lier
p
er
f
o
r
m
a
n
ce
an
d
co
n
tr
ib
u
tio
n
to
b
u
s
in
ess
o
p
er
atio
n
s
.
T
h
is
d
ata
i
n
clu
d
es
an
ev
alu
atio
n
o
f
v
ar
io
u
s
asp
ec
ts
o
f
s
u
p
p
lier
p
e
r
f
o
r
m
a
n
ce
,
n
am
ely
av
er
ag
e
co
s
t,
d
eliv
er
y
tim
e,
p
r
o
d
u
ct
q
u
al
ity
,
f
lex
ib
ilit
y
,
an
d
av
ailab
ilit
y
.
B
y
an
aly
zin
g
th
is
s
u
p
p
lier
ass
es
s
m
en
t
d
ata
,
th
e
co
m
p
an
y
ca
n
id
en
tify
s
u
p
p
lier
s
th
at
ad
d
th
e
m
o
s
t
v
alu
e,
as
well
as
id
en
tify
ar
ea
s
wh
er
e
s
u
p
p
lier
s
ca
n
im
p
r
o
v
e
th
eir
p
er
f
o
r
m
an
ce
.
T
h
is
en
ab
les
co
m
p
an
ies
to
tak
e
ap
p
r
o
p
r
iate
ac
tio
n
s
to
im
p
r
o
v
e
th
e
ef
f
icien
cy
,
q
u
ality
,
an
d
s
u
s
tain
ab
ilit
y
o
f
th
eir
s
u
p
p
ly
ch
ain
.
T
h
e
d
at
a
s
o
u
r
ce
in
t
h
e
ca
s
e
s
tu
d
y
was
o
b
tain
ed
f
r
o
m
th
e
r
esu
lts
o
f
an
in
ter
n
al
s
u
r
v
e
y
co
n
d
u
cte
d
o
n
th
e
c
o
m
p
an
y
’
s
p
r
o
cu
r
em
en
t
team
.
T
h
e
ass
es
s
m
en
t
was
co
n
d
u
cte
d
b
ased
o
n
wo
r
k
ex
p
er
ien
ce
with
1
0
s
u
p
p
lier
s
wh
o
wer
e
ass
es
s
ed
u
s
in
g
f
iv
e
m
ain
c
r
iter
ia:
p
r
o
d
u
ct
q
u
ality
,
p
r
ice,
d
eliv
er
y
ac
cu
r
ac
y
,
a
f
ter
-
s
ales
s
er
v
ice,
an
d
f
lex
i
b
ilit
y
.
T
h
e
d
ata
was
co
llected
f
r
o
m
th
e
co
m
p
an
y
’
s
h
is
to
r
ical
r
ec
o
r
d
o
f
s
u
p
p
lier
p
e
r
f
o
r
m
an
ce
o
v
er
th
e
p
ast
y
ea
r
,
as
well
as th
e
r
esu
lts
o
f
in
ter
v
iews with
p
r
o
cu
r
em
e
n
t m
an
a
g
er
s
an
d
lo
g
is
tics
team
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
8
,
No
.
1
,
Ap
r
il
20
2
5
:
4
0
3
-
4
1
6
410
3
.
2
.
I
m
ple
m
ent
a
t
io
n o
f
t
he
G
2
M
weig
hting
m
et
ho
d
T
h
e
im
p
lem
en
tatio
n
o
f
th
e
G2
M
w
eig
h
tin
g
m
eth
o
d
in
v
o
lv
es
a
s
y
s
tem
atic
s
er
ies
o
f
s
tep
s
to
d
eter
m
in
e
th
e
weig
h
ts
o
f
cr
it
er
ia
in
a
DSS.
T
h
e
im
p
lem
en
tatio
n
o
f
th
e
G2
M
w
eig
h
tin
g
m
eth
o
d
ca
n
h
elp
im
p
r
o
v
e
th
e
o
b
jectiv
ity
an
d
ac
cu
r
ac
y
o
f
d
eter
m
in
in
g
cr
iter
ia
weig
h
ts
,
th
er
eb
y
s
u
p
p
o
r
tin
g
b
etter
an
d
in
f
o
r
m
ed
d
ec
is
io
n
m
ak
in
g
.
T
h
e
im
p
le
m
en
tatio
n
o
f
G2
M
w
eig
h
tin
g
ca
n
b
e
a
v
er
y
ef
f
e
ctiv
e
to
o
l
in
o
v
er
c
o
m
in
g
u
n
ce
r
tain
ty
an
d
co
m
p
le
x
ity
in
d
ec
is
io
n
m
ak
in
g
,
as
well
as
im
p
r
o
v
in
g
th
e
q
u
ality
an
d
o
b
jectiv
ity
in
th
e
p
r
o
ce
s
s
o
f
d
eter
m
in
in
g
th
e
weig
h
t
o
f
cr
iter
ia
in
DSS.
T
h
e
f
ir
s
t
s
tag
e
in
G2
M
w
eig
h
tin
g
cr
ea
tes
a
d
ec
is
io
n
m
atr
ix
b
ased
o
n
th
e
ass
ess
m
e
n
t
d
ata
in
T
ab
le
1
.
T
h
e
d
ec
is
io
n
m
atr
ix
X
f
o
r
ea
ch
co
lu
m
n
is
th
e
c
r
iter
ia
an
d
f
o
r
th
e
r
o
ws
ar
e
th
e
alter
n
ativ
e
v
alu
es
f
o
r
ea
ch
cr
iter
io
n
.
Af
ter
th
e
d
ec
is
io
n
m
atr
i
x
is
m
ad
e,
th
en
ca
lcu
late
th
e
g
eo
m
etr
ic
m
ea
n
v
alu
e
f
o
r
ea
c
h
cr
iter
io
n
u
s
in
g
in
(
2
)
.
1
=
(
800
∗
750
∗
860
∗
900
∗
940
∗
920
∗
780
∗
880
∗
940
∗
930
)
1
10
⁄
=
857
.
5493
2
=
(
3
∗
5
∗
2
∗
3
∗
3
∗
4
∗
2
∗
3
∗
2
∗
4
)
1
1
0
⁄
=
2
.
9612
3
=
(
5
∗
4
∗
5
∗
3
∗
4
∗
3
∗
4
∗
3
∗
4
∗
5
)
1
10
⁄
=
3
.
9233
4
=
(
4
∗
5
∗
3
∗
4
∗
4
∗
4
∗
4
∗
3
∗
4
∗
3
)
1
10
⁄
=
3
.
7521
5
=
(
4
∗
4
∗
3
∗
3
∗
4
∗
4
∗
5
∗
5
∗
4
∗
5
)
1
10
⁄
=
4
.
0378
T
h
e
f
in
al
r
esu
lt
o
f
t
h
e
ca
lcu
lat
io
n
f
r
o
m
(
2
)
f
o
r
e
x
is
tin
g
cr
iter
ia
is
th
e
r
esu
lt
o
f
t
h
e
g
e
o
m
etr
i
c
m
ea
n
ca
lcu
latio
n
b
ased
o
n
th
e
v
alu
es o
f
all
ex
is
tin
g
alter
n
ativ
es.
T
h
e
n
ex
t stag
e
ca
lcu
lates th
e
n
o
r
m
aliza
tio
n
v
alu
e
u
s
in
g
in
(
3
)
.
11
=
11
1
=
800
857
.
5
4
93
=
0
.
9329
T
ab
le
2
is
th
e
o
v
er
all
r
esu
lt
o
f
m
atr
ix
n
o
r
m
aliza
tio
n
ca
l
cu
latio
n
s
f
o
r
ea
ch
alter
n
ativ
e
b
ased
o
n
ex
is
tin
g
cr
iter
ia.
T
ab
le
2
.
R
esu
lts
o
f
m
atr
ix
n
o
r
m
aliza
tio
n
S
u
p
p
l
i
e
r
n
a
me
N
o
r
mal
i
z
a
t
i
o
n
o
f
m
a
t
r
i
x
CR
-
1
CR
-
2
CR
-
3
CR
-
4
CR
-
5
S
u
p
p
l
i
e
r
Y
A
0
.
9
3
2
9
1
.
0
1
3
1
1
.
2
7
4
4
1
.
0
6
6
1
0
.
9
9
0
6
S
u
p
p
l
i
e
r
F
T
0
.
8
7
4
6
1
.
6
8
8
5
1
.
0
1
9
6
1
.
3
3
2
6
0
.
9
9
0
6
S
u
p
p
l
i
e
r
H
E
1
.
0
0
2
9
0
.
6
7
5
4
1
.
2
7
4
4
0
.
7
9
9
6
0
.
7
4
3
0
S
u
p
p
l
i
e
r
TW
1
.
0
4
9
5
1
.
0
1
3
1
0
.
7
6
4
7
1
.
0
6
6
1
0
.
7
4
3
0
S
u
p
p
l
i
e
r
A
S
1
.
0
9
6
1
1
.
0
1
3
1
1
.
0
1
9
6
1
.
0
6
6
1
0
.
9
9
0
6
S
u
p
p
l
i
e
r
B
R
1
.
0
7
2
8
1
.
3
5
0
8
0
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2
T
h
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f
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r
m
in
in
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cr
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ia
weig
h
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t
h
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G
2
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w
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g
m
eth
o
d
is
a
s
et
o
f
weig
h
ts
th
at
r
ef
lect
th
e
r
elativ
e
im
p
o
r
t
an
ce
o
f
ea
ch
cr
iter
io
n
.
T
h
ese
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h
ts
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e
g
en
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ated
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r
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h
a
co
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Ca
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ates
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o
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aliza
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in
(
6
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,
T
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3
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MO
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u
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in
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7
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,
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4
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T
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ased
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Fig
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2
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ased
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in
g
is
o
b
tain
ed
o
n
b
eh
alf
o
f
s
u
p
p
lier
C
H
with
an
o
p
tim
izatio
n
v
alu
e
o
f
0
.
1
0
0
7
.
T
h
is
r
esu
lt
en
s
u
r
es
th
at
th
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
8
,
No
.
1
,
Ap
r
il
20
2
5
:
4
0
3
-
4
1
6
412
s
elec
tio
n
o
f
alter
n
ativ
es
is
b
ased
o
n
a
co
m
p
r
eh
en
s
iv
e
a
n
d
o
b
jectiv
e
an
aly
s
is
,
th
u
s
h
elp
i
n
g
d
ec
is
io
n
m
ak
er
s
ch
o
o
s
e
th
e
b
est alter
n
ativ
e
b
ase
d
o
n
v
ar
io
u
s
im
p
o
r
tan
t c
r
iter
ia.
Fig
u
r
e
2
.
R
an
k
in
g
o
f
s
u
p
p
lier
p
er
f
o
r
m
an
ce
ev
al
u
atio
n
with
MO
OR
A
3.
4
.
Dis
cus
s
io
n
T
h
e
in
tr
o
d
u
ctio
n
o
f
th
e
G2
M
w
eig
h
tin
g
m
eth
o
d
r
ep
r
esen
ts
a
s
ig
n
if
ican
t
ad
v
an
ce
m
e
n
t
in
t
h
e
f
ield
o
f
m
u
lti
-
o
b
jectiv
e
d
ec
is
io
n
m
ak
i
n
g
b
y
o
v
er
co
m
i
n
g
s
o
m
e
o
f
th
e
m
ajo
r
lim
itatio
n
s
o
f
ex
is
tin
g
weig
h
tin
g
m
eth
o
d
s
.
T
r
ad
itio
n
al
m
et
h
o
d
s
s
u
c
h
as
AHP
h
av
e
b
ee
n
wid
el
y
u
s
ed
t
o
ev
alu
ate
cr
iter
ia
weig
h
ts
in
th
e
d
ec
is
io
n
-
m
a
k
in
g
p
r
o
ce
s
s
.
Ho
wev
er
,
th
ese
m
et
h
o
d
s
o
f
ten
s
tr
u
g
g
le
in
h
an
d
lin
g
in
co
m
p
lete
o
r
u
n
ce
r
tain
d
ata
an
d
m
ay
i
n
tr
o
d
u
ce
s
u
b
jectiv
e
p
r
ef
e
r
en
ce
s
d
u
r
in
g
th
e
weig
h
tin
g
p
r
o
ce
s
s
.
T
h
e
G2
M
w
eig
h
tin
g
m
eth
o
d
,
wh
ich
in
teg
r
ates
g
eo
m
etr
ic
m
ea
n
ca
lcu
latio
n
w
ith
g
r
ey
s
y
s
tem
th
eo
r
y
,
o
f
f
er
s
a
m
o
r
e
r
o
b
u
s
t
an
d
o
b
jectiv
e
ap
p
r
o
ac
h
.
B
y
in
co
r
p
o
r
atin
g
g
r
ey
s
y
s
tem
th
eo
r
y
,
G2
M
w
eig
h
tin
g
ca
n
ef
f
ec
tiv
ely
m
an
ag
e
u
n
ce
r
tain
t
y
in
d
ata,
m
ak
in
g
it
p
ar
ticu
lar
ly
s
u
itab
le
f
o
r
r
e
al
-
wo
r
ld
ap
p
licatio
n
s
wh
er
e
p
e
r
f
ec
t
in
f
o
r
m
atio
n
is
r
a
r
ely
a
v
ail
ab
le.
T
h
e
g
eo
m
etr
ic
m
ea
n
co
m
p
o
n
e
n
t
en
s
u
r
es
th
a
t
th
e
in
f
lu
e
n
ce
o
f
ex
t
r
em
e
v
alu
es
is
m
in
im
ized
,
p
r
o
v
i
d
in
g
a
m
o
r
e
b
alan
ce
d
r
ep
r
esen
tatio
n
o
f
cr
iter
ia
im
p
o
r
tan
ce
.
I
n
o
u
r
ca
s
e
s
tu
d
y
o
n
s
u
p
p
lier
p
er
f
o
r
m
a
n
ce
ev
al
u
atio
n
,
G2
M
w
eig
h
tin
g
d
em
o
n
s
tr
ated
its
ef
f
ec
tiv
e
n
ess
b
y
p
r
o
d
u
ci
n
g
c
o
n
s
is
ten
t
an
d
r
eliab
le
r
atin
g
s
ev
e
n
with
in
co
m
p
lete
d
ata
.
T
h
e
m
eth
o
d
’
s
ab
ilit
y
to
p
r
o
d
u
ce
s
tab
le
weig
h
ts
am
id
s
t
d
ata
g
ap
s
h
ig
h
lig
h
ts
its
s
tr
e
n
g
th
s
co
m
p
ar
ed
to
co
n
v
en
tio
n
al
m
eth
o
d
s
.
T
o
test
th
e
G2
M
w
eig
h
tin
g
m
eth
o
d
we
p
er
f
o
r
m
ed
wi
th
s
ev
er
al
o
th
er
DSS
m
eth
o
d
s
in
clu
d
in
g
GR
A,
s
im
p
le
ad
d
itiv
e
weig
h
tin
g
(
SAW
)
,
m
u
lti
-
attr
ib
u
tiv
e
id
ea
l
-
r
ea
l
c
o
m
p
ar
ativ
e
an
aly
s
is
(
MA
I
R
C
A)
,
weig
h
ted
p
r
o
d
u
ct
(
W
P),
co
m
b
i
n
ed
c
o
m
p
r
o
m
is
e
s
o
lu
tio
n
(
C
OC
OS
O)
,
vlsekri
teri
ju
mska
o
p
timiz
a
cija
i
ko
mp
r
o
mis
n
o
r
esen
je
(
VI
KOR),
an
d
a
n
ew
ad
d
itiv
e
r
atio
ass
ess
m
en
t
(
AR
AS)
.
T
ab
le
5
s
h
o
ws
th
e
r
an
k
in
g
r
esu
lts
o
f
th
e
test
ed
m
eth
o
d
s
b
y
a
p
p
ly
in
g
G2
M
w
eig
h
tin
g
in
th
e
ca
s
e
o
f
s
u
p
p
lier
p
er
f
o
r
m
an
ce
ev
alu
atio
n
.
T
ab
le
5
.
R
an
k
in
g
r
esu
lts
o
f
s
e
v
er
al
DSS
m
eth
o
d
s
S
u
p
p
l
i
e
r
n
a
m
e
M
O
O
R
A
r
a
n
k
i
n
g
s
G
R
A
r
a
n
k
i
n
g
s
S
A
W
r
a
n
k
i
n
g
s
M
A
I
R
C
A
r
a
n
k
i
n
g
s
WP
r
a
n
k
i
n
g
s
C
O
C
O
S
O
r
a
n
k
i
n
g
s
V
I
K
O
R
r
a
n
k
i
n
g
s
M
O
O
R
A
r
a
n
k
i
n
g
s
S
u
p
p
l
i
e
r
C
H
1
3
2
3
3
3
4
3
S
u
p
p
l
i
e
r
Y
A
2
4
3
4
5
2
2
1
S
u
p
p
l
i
e
r
F
T
3
2
1
2
2
1
3
2
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u
p
p
l
i
e
r
D
S
4
1
5
1
1
5
1
4
S
u
p
p
l
i
e
r
B
R
5
6
8
6
6
8
6
8
S
u
p
p
l
i
e
r
A
S
6
5
6
5
4
6
5
5
S
u
p
p
l
i
e
r
O
R
7
7
4
7
8
4
7
6
S
u
p
p
l
i
e
r
TW
8
10
10
10
9
10
10
10
S
u
p
p
l
i
e
r
N
W
9
8
7
8
7
7
8
7
S
u
p
p
l
i
e
r
H
E
10
9
9
9
10
9
9
9
Evaluation Warning : The document was created with Spire.PDF for Python.