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imp
a
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rti
ficia
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telli
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c
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(AI)
in
su
p
p
l
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c
h
a
in
m
a
n
a
g
e
m
e
n
t
(S
CM
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.
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o
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win
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fiv
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-
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t
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se
s.
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e
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re
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n
t
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rti
c
les
fo
r
a
n
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ly
sis,
fo
c
u
sin
g
o
n
AI
tec
h
n
iq
u
e
s.
Th
e
a
n
a
ly
sis
e
x
p
l
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ir
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a
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e
s,
a
n
d
b
a
rriers
to
a
d
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p
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S
CM
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e
stu
d
y
a
lso
d
isc
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ss
e
d
k
e
y
c
h
a
ll
e
n
g
e
s,
in
c
lu
d
in
g
fin
a
n
c
ial,
o
rg
a
n
iza
ti
o
n
a
l,
stra
teg
ic,
tec
h
n
o
lo
g
ica
l,
a
n
d
leg
a
l
b
a
rri
e
rs.
Th
e
fi
n
d
i
n
g
s
s
u
g
g
e
st
t
h
a
t
wh
il
e
AI
tec
h
n
iq
u
e
s
o
ffe
r
s
ig
n
ifi
c
a
n
t
p
o
ten
ti
a
l
fo
r
imp
r
o
v
in
g
S
CM
,
se
v
e
ra
l
o
b
sta
c
les
h
in
d
e
r
t
h
e
i
r
b
ro
a
d
e
r
imp
lem
e
n
tatio
n
.
Ad
d
re
ss
in
g
t
h
e
se
o
b
sta
c
les
re
q
u
ires
in
v
e
stm
e
n
ts
in
in
fra
stru
c
tu
re
,
s
k
il
ls
d
e
v
e
lo
p
m
e
n
t
,
a
n
d
e
ffe
c
ti
v
e
c
h
a
n
g
e
m
a
n
a
g
e
m
e
n
t.
K
ey
w
o
r
d
s
:
Ar
tific
ial
in
tellig
en
ce
lo
g
is
tics
Pro
d
u
ctio
n
SC
M
ap
p
licatio
n
s
Su
p
p
ly
ch
ai
n
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
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ing
A
uth
o
r
:
O
u
ah
b
i
Yo
u
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E
q
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ip
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I
n
tellig
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Secu
r
ity
o
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lty
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Scien
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d
V
Un
iv
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ity
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ab
at,
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o
cc
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E
m
ail: Y
o
u
n
ess
e_
o
u
ah
b
i@
u
m
5
.
ac
.
m
a
1.
I
NT
RO
D
UCT
I
O
N
I
n
to
d
ay
’
s
d
y
n
am
ic
g
lo
b
al
ec
o
n
o
m
y
,
th
e
f
ield
o
f
s
u
p
p
ly
ch
ain
m
an
a
g
em
en
t
(
S
C
M)
f
ac
es
u
n
p
r
ec
e
d
en
ted
ch
allen
g
es
an
d
o
p
p
o
r
t
u
n
ities
d
r
iv
en
b
y
tech
n
o
lo
g
ical
a
d
v
an
ce
m
en
t
s
,
n
o
tab
ly
ar
tific
ial
in
tellig
en
ce
(
AI
)
[
1
]
.
T
h
is
p
a
p
er
d
elv
es
in
to
th
e
tr
an
s
f
o
r
m
ativ
e
p
o
ten
tial
o
f
AI
with
in
SC
M
co
n
tex
ts
,
aim
in
g
to
elu
cid
ate
its
p
r
o
f
o
u
n
d
im
p
ac
t
o
n
o
p
er
atio
n
al
e
f
f
icien
c
y
,
co
s
t
m
an
a
g
em
en
t,
an
d
r
is
k
m
itig
atio
n
ac
r
o
s
s
in
tr
icate
s
u
p
p
ly
n
etwo
r
k
s
.
As
in
d
u
s
tr
ies
n
av
ig
ate
co
m
p
lex
ities
ex
ac
er
b
ated
b
y
g
lo
b
aliza
tio
n
,
m
ar
k
et
v
o
latilit
y
,
an
d
s
u
s
tain
ab
ilit
y
im
p
er
ativ
es,
th
e
in
te
g
r
atio
n
o
f
AI
em
er
g
es
as
a
cr
itical
e
n
ab
ler
of
a
d
a
p
tiv
e
a
n
d
r
esil
ien
t
s
u
p
p
ly
ch
ain
s
tr
ateg
ies
[
2
]
.
T
r
ad
itio
n
al
SC
M
f
r
am
ewo
r
k
s
co
n
f
r
o
n
t
escalatin
g
co
m
p
le
x
ities
am
id
a
lan
d
s
ca
p
e
ch
ar
ac
ter
ized
b
y
r
ap
i
d
tech
n
o
lo
g
ical
e
v
o
lu
tio
n
an
d
h
eig
h
t
en
ed
c
u
s
to
m
er
e
x
p
ec
tatio
n
s
.
C
h
allen
g
es
s
u
ch
as
d
em
an
d
v
ar
iab
ilit
y
,
s
u
p
p
ly
ch
ain
d
is
r
u
p
tio
n
s
,
an
d
th
e
i
m
p
er
ativ
e
f
o
r
s
u
s
tain
ab
le
p
r
ac
tices
n
ec
ess
ita
te
in
n
o
v
ativ
e
s
o
lu
tio
n
s
ca
p
ab
le
o
f
en
h
a
n
cin
g
ag
ilit
y
an
d
r
e
s
p
o
n
s
iv
en
ess
.
AI
tech
n
o
lo
g
ies
o
f
f
er
p
r
o
m
is
in
g
av
en
u
es
to
ad
d
r
ess
th
ese
ch
allen
g
es
b
y
h
ar
n
ess
in
g
d
ata
-
d
r
iv
en
in
s
ig
h
ts
an
d
au
to
m
atio
n
ca
p
ab
ilit
ies
to
o
p
tim
ize
d
ec
is
io
n
-
m
a
k
in
g
p
r
o
ce
s
s
es a
n
d
o
p
er
atio
n
al
ef
f
icien
cies
[
3
]
.
L
iter
atu
r
e
r
ef
lects
a
g
r
o
win
g
co
n
s
en
s
u
s
o
n
AI
’
s
tr
an
s
f
o
r
m
ativ
e
p
o
te
n
tial
ac
r
o
s
s
d
iv
er
s
e
SC
M
d
o
m
ain
s
.
Fo
r
in
s
tan
ce
,
Kh
ash
ei
an
d
C
h
ah
k
o
u
ta
h
i
[
4
]
u
n
d
er
s
co
r
e
AI
’
s
r
o
le
in
p
r
e
d
ictiv
e
an
aly
tics
,
f
ac
ilit
atin
g
ac
cu
r
ate
d
em
a
n
d
f
o
r
ec
asti
n
g
a
n
d
in
v
en
to
r
y
o
p
tim
izatio
n
.
Si
m
ilar
ly
,
Sar
i
[
5
]
d
is
cu
s
s
es
AI
-
d
r
iv
en
a
u
to
n
o
m
o
u
s
s
y
s
tem
s
th
at
en
h
an
ce
o
p
er
a
tio
n
al
ef
f
icien
cy
a
n
d
r
ed
u
c
e
lead
tim
es
with
in
war
eh
o
u
s
e
m
a
n
ag
em
e
n
t
.
Fu
r
th
er
m
o
r
e
,
r
ec
e
n
t
s
tu
d
ies
b
y
L
ăz
ă
r
o
iu
et
a
l.
[
6
]
h
ig
h
li
g
h
t
AI
’
s
ap
p
licatio
n
in
en
h
a
n
cin
g
s
u
p
p
ly
ch
ai
n
v
is
ib
ilit
y
th
r
o
u
g
h
r
ea
l
-
tim
e
d
ata
an
aly
tics
,
en
ab
lin
g
p
r
o
ac
t
iv
e
r
is
k
m
an
ag
em
en
t
an
d
ag
il
e
d
ec
is
io
n
-
m
ak
in
g
.
T
h
ese
in
s
ig
h
ts
u
n
d
er
s
co
r
e
AI
’
s
ca
p
ac
ity
to
m
itig
ate
d
is
r
u
p
tio
n
s
an
d
en
h
an
ce
r
esil
ien
ce
am
id
s
t
d
y
n
am
ic
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
:
32
1
-
3
3
2
322
m
ar
k
et
c
o
n
d
itio
n
s
.
T
h
is
p
ap
e
r
ad
v
o
ca
tes
f
o
r
a
c
o
m
p
r
e
h
en
s
iv
e
f
r
am
ewo
r
k
th
at
in
te
g
r
ates
AI
m
eth
o
d
o
lo
g
ies
with
estab
lis
h
ed
SC
M
p
r
in
cip
les
to
f
o
s
ter
ad
ap
tiv
e
an
d
in
tellig
en
t
s
u
p
p
ly
ch
ain
ec
o
s
y
s
tem
s
.
By
lev
er
ag
in
g
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
an
d
ad
v
an
ce
d
an
aly
tics
,
o
r
g
a
n
izatio
n
s
ca
n
au
g
m
en
t
d
ec
is
io
n
s
u
p
p
o
r
t
s
y
s
tem
s
,
m
itig
ate
o
p
er
atio
n
al
r
is
k
s
,
an
d
o
p
tim
ize
r
eso
u
r
ce
allo
ca
tio
n
.
T
h
e
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
em
p
h
asizes
th
e
s
tr
ateg
ic
ali
g
n
m
en
t
o
f
AI
in
itiativ
es
with
o
r
g
an
izatio
n
al
g
o
als,
ad
v
o
ca
tin
g
f
o
r
a
p
h
ased
in
teg
r
at
io
n
th
at
p
r
i
o
r
itizes
s
ca
lab
ilit
y
,
s
u
s
tain
ab
ilit
y
,
an
d
o
p
er
atio
n
al
r
esil
ien
ce
.
T
h
is
s
tu
d
y
co
n
tr
ib
u
tes
to
th
e
s
ch
o
lar
ly
d
is
co
u
r
s
e
b
y
elu
ci
d
atin
g
p
r
ac
tical
s
tr
ateg
ies
an
d
em
p
i
r
ical
ev
id
en
ce
s
u
p
p
o
r
tin
g
t
h
e
ad
o
p
t
io
n
o
f
A
I
in
SC
M
co
n
tex
ts
.
B
y
ex
am
in
in
g
ca
s
e
s
tu
d
ies
an
d
em
p
ir
ical
an
aly
s
es,
th
is
r
esear
ch
u
n
d
er
s
co
r
es
AI
’
s
tr
an
s
f
o
r
m
ativ
e
p
o
ten
tial
in
en
h
an
cin
g
s
u
p
p
ly
ch
ain
e
f
f
icien
cy
,
r
ed
u
cin
g
c
o
s
ts
,
an
d
f
o
s
ter
in
g
in
n
o
v
atio
n
.
T
h
e
s
y
n
th
esis
o
f
th
eo
r
etica
l
in
s
ig
h
ts
with
p
r
ac
tical
ap
p
licatio
n
s
aim
s
to
em
p
o
we
r
s
tak
eh
o
ld
er
s
with
ac
tio
n
ab
le
r
ec
o
m
m
en
d
atio
n
s
f
o
r
n
av
ig
atin
g
th
e
ev
o
lv
in
g
lan
d
s
ca
p
e
of
g
l
o
b
al
s
u
p
p
ly
ch
ain
s
.
2.
M
E
T
H
O
D
W
e
ap
p
lied
a
s
tr
u
ctu
r
ed
ap
p
r
o
ac
h
to
o
u
r
an
aly
s
is
,
in
s
p
ir
ed
b
y
5
s
tep
s
p
r
o
ce
s
s
d
escr
ib
ed
Dav
id
an
d
Dav
id
[
7
]
.
T
h
is
ap
p
r
o
ac
h
b
eg
an
with
a
p
r
elim
in
a
r
y
ex
p
lo
r
atio
n
o
f
th
e
liter
atu
r
e
to
g
ain
in
s
ig
h
t
in
t
o
th
e
cu
r
r
en
t
r
esear
ch
lan
d
s
ca
p
e.
I
t
also
h
elp
ed
estab
lis
h
th
e
p
ar
am
eter
s
f
o
r
s
elec
tin
g
r
el
ev
an
t
p
u
b
licatio
n
s
,
allo
win
g
u
s
to
r
ef
in
e
th
e
r
esear
ch
an
d
o
u
tlin
e
th
e
s
u
b
s
eq
u
en
t
p
h
ases
.
Ou
r
r
ev
iew
in
v
o
lv
ed
f
iv
e
s
ep
ar
ate
s
tag
es,
wh
ich
ar
e
p
r
esen
ted
in
Fig
u
r
e
1.
Fig
u
r
e
1.
Stu
d
y
d
esig
n
2
.
1
.
Sea
rc
h
m
et
ho
do
lo
g
y
Fo
llo
win
g
m
eth
o
d
o
l
o
g
ical
r
ig
o
r
,
a
p
r
elim
in
ar
y
s
ea
r
ch
was
u
n
d
er
tak
en
d
u
r
in
g
th
e
in
itial
p
h
ase
to
en
h
an
ce
co
m
p
r
eh
en
s
io
n
o
f
th
e
s
tu
d
ied
d
o
m
ain
an
d
ex
tan
t
l
iter
atu
r
e.
T
h
e
id
en
tific
atio
n
o
f
liter
atu
r
e
s
o
u
r
ce
s
was
en
s
u
r
ed
th
r
o
u
g
h
s
y
s
tem
atic
s
cr
u
tin
y
of
d
esig
n
ated
s
ea
r
ch
p
ar
am
eter
s
ac
r
o
s
s
d
iv
er
s
e
el
ec
tr
o
n
ic
d
atab
ases
of
r
ep
u
tab
le
p
u
b
lis
h
er
s
,
in
clu
d
in
g
Sco
p
u
s
,
Scien
ce
Dir
ec
t,
Sp
r
in
g
er
L
in
k
,
W
eb
o
f
Sci
en
ce
,
an
d
Go
o
g
l
e
Sch
o
lar
.
Ar
ticles
wer
e
s
ea
r
c
h
ed
b
ased
o
n
th
eir
titl
e,
a
b
s
tr
ac
t,
k
ey
wo
r
d
s
,
a
n
d
ca
p
tio
n
f
ield
s
,
en
co
m
p
ass
in
g
all
r
elev
an
t f
ield
s
.
Key
wo
r
d
s
s
u
ch
as
“a
r
tific
ial
in
tellig
en
ce
,
”
“
AI
,
”
“su
p
p
ly
ch
ain
,
”
“su
p
p
ly
ch
ain
m
an
ag
em
e
n
t,
”
“
SC
M,
”
“a
r
tific
ial
in
tellig
en
ce
ap
p
licatio
n
s
,
”
“p
r
o
d
u
ctio
n
,
”
an
d
“lo
g
is
tics
”
wer
e
u
tili
ze
d
to
r
ef
in
e
t
h
e
s
ea
r
ch
s
co
p
e.
T
h
e
s
ea
r
ch
was
lim
it
ed
to
a
r
ticles
p
u
b
lis
h
ed
b
et
wee
n
2
0
0
0
an
d
2
0
2
4
to
f
o
c
u
s
on
co
n
tem
p
o
r
a
r
y
liter
atu
r
e
p
er
tin
en
t
to
th
e
s
tu
d
y
.
2
.
2
.
Resea
rc
h
qu
estio
ns
T
h
e
f
o
r
m
u
latio
n
o
f
a
well
-
d
ef
in
ed
an
d
ac
tio
n
ab
le
r
esea
r
ch
q
u
esti
o
n
g
u
id
in
g
th
e
i
n
q
u
ir
y
is
a
s
ig
n
if
ican
t
s
tep
[
8
]
.
C
r
af
tin
g
s
u
ch
a
q
u
esti
o
n
co
n
s
titu
tes
a
p
iv
o
tal,
alb
eit
c
h
allen
g
in
g
,
f
ac
et
o
f
r
esear
ch
d
esig
n
,
as
it
n
o
t
o
n
ly
s
teer
s
th
e
s
elec
tio
n
o
f
r
esear
ch
m
eth
o
d
o
lo
g
ies
a
n
d
s
tr
ateg
ies
b
u
t
also
u
n
d
er
p
in
s
th
e
en
tire
in
v
esti
g
ativ
e
en
d
ea
v
o
r
[
9
]
.
T
h
is
r
esear
ch
’
s
p
r
im
a
r
y
q
u
esti
o
n
,
‘
Ho
w
d
o
es
AI
in
f
l
u
en
ce
s
tu
d
ies
in
th
e
f
ield
o
f
SC
M?
’
,
was
r
e
f
in
ed
t
h
r
o
u
g
h
iter
ativ
e
p
ilo
t
s
ea
r
c
h
e
s
.
To
p
r
o
v
i
d
e
a
clea
r
a
n
s
wer
,
th
e
m
ain
q
u
esti
o
n
was
b
r
o
k
e
n
d
o
wn
in
t
o
th
r
ee
s
u
b
s
id
iar
y
r
esear
c
h
q
u
esti
o
n
s
:
−
W
h
at
AI
tech
n
iq
u
es a
r
e
c
o
m
m
o
n
ly
u
s
ed
in
SC
M
s
tu
d
ies?
−
W
h
at
AI
tech
n
iq
u
es
co
u
ld
be
ap
p
lied
to
SC
M
r
esear
ch
?
−
W
h
at
o
b
s
tacle
s
h
in
d
er
th
e
im
p
lem
en
tatio
n
o
f
A
I
in
SC
M?
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
A
d
va
n
cin
g
s
u
p
p
ly
c
h
a
in
m
a
n
a
g
eme
n
t th
r
o
u
g
h
a
r
tifi
cia
l
…
(
Ou
a
h
b
i Yo
u
n
ess
e
)
323
2
.
3
.
L
o
ca
t
ing
t
he
s
t
ud
ies
I
n
th
e
p
u
r
s
u
it
o
f
p
e
r
tin
en
t
s
t
u
d
ies,
m
eticu
lo
u
s
s
elec
tio
n
o
f
s
ea
r
ch
en
g
in
es
an
d
s
ea
r
ch
s
tr
in
g
s
was
im
p
er
ativ
e.
T
o
en
s
u
r
e
a
co
m
p
r
eh
en
s
iv
e
ex
a
m
in
atio
n
o
f
p
ee
r
-
r
ev
iewe
d
liter
atu
r
e
with
in
a
s
p
ec
if
ied
tim
ef
r
am
e,
we
s
elec
ted
f
iv
e
p
r
o
m
in
en
t
d
a
tab
ases
k
n
o
wn
f
o
r
t
h
eir
b
r
o
ad
co
v
er
ag
e:
Sco
p
u
s
,
Scien
ce
Di
r
ec
t,
Sp
r
in
g
er
L
i
n
k
,
Go
o
g
le
Sch
o
lar
,
an
d
W
eb
o
f
Scien
ce
.
W
e
cr
af
ted
s
ea
r
ch
q
u
er
ies
d
esig
n
ed
to
ex
tr
ac
t
th
e
m
o
s
t
r
elev
an
t
ar
ticles,
f
o
llo
win
g
th
e
g
u
id
el
in
es
s
et
o
u
t
b
y
[
1
0
]
.
T
h
ese
q
u
er
ies
wer
e
ca
r
ef
u
lly
a
p
p
l
ied
ac
r
o
s
s
all
f
iv
e
d
atab
ases
as
s
h
o
wn
in
Fig
u
r
e
2
.
T
h
is
ap
p
r
o
ac
h
h
elp
e
d
u
s
g
ath
er
co
n
tr
ib
u
tio
n
s
th
at
d
i
r
ec
tly
r
elate
to
our
r
esear
ch
q
u
esti
o
n
.
T
h
e
s
ea
r
ch
q
u
er
ies
in
clu
d
ed
c
o
m
b
in
atio
n
s
lik
e
“
a
r
tific
ial
in
t
ellig
en
ce
”
alo
n
g
with
s
p
ec
if
ic
k
ey
wo
r
d
s
s
u
ch
as
“
s
u
p
p
ly
c
h
ain
,
”
“
p
r
o
d
u
ctio
n
,
”
an
d
“
lo
g
is
tics
”
.
T
h
es
e
k
ey
wo
r
d
s
wer
e
d
e
r
iv
ed
f
r
o
m
Sto
ck
an
d
B
o
y
er
[
1
1
]
d
etailed
d
escr
ip
tio
n
o
f
S
C
M.
Alth
o
u
g
h
th
e
p
r
im
ar
y
s
ea
r
ch
p
r
o
to
co
ls
wer
e
co
n
s
is
ten
t
ac
r
o
s
s
all
d
atab
ases
,
s
lig
h
t
ad
ju
s
tm
en
ts
wer
e
m
ad
e
to
ac
co
u
n
t
f
o
r
t
h
e
u
n
i
q
u
e
s
ea
r
ch
alg
o
r
ith
m
s
u
s
ed
by
ea
c
h
p
l
atf
o
r
m
.
2
.
4
.
P
a
pers
s
elec
t
io
n
T
h
e
p
ilo
t
s
ea
r
ch
y
ield
ed
2
,
9
4
0
ar
ticles.
T
wo
p
r
im
a
r
y
cr
iter
ia
wer
e
ap
p
lied
f
o
r
s
elec
tio
n
:
tem
p
o
r
a
l
r
elev
an
ce
(
lim
ited
to
liter
atu
r
e
p
u
b
lis
h
ed
b
etwe
en
2
0
0
0
an
d
2
0
2
4
)
in
Fig
u
r
e
2
an
d
ad
h
er
en
ce
to
s
tan
d
ar
d
s
o
f
r
elev
an
ce
a
n
d
q
u
ality
,
th
er
e
b
y
c
o
n
f
i
n
in
g
th
e
r
ev
iew
to
p
ee
r
-
r
ev
iewe
d
jo
u
r
n
al
an
d
co
n
f
er
en
ce
p
a
p
er
s
.
Ad
d
itio
n
ally
,
a
b
esp
o
k
e
ar
tic
le
in
clu
s
io
n
p
r
o
to
co
l
was
d
ev
is
ed
,
m
an
d
atin
g
ad
h
er
en
ce
to
th
r
ee
ad
d
itio
n
al
cr
iter
ia:
(
1
)
E
n
g
lis
h
lan
g
u
a
g
e
p
r
o
f
icien
cy
,
(
2
)
ce
n
tr
al
u
tili
za
tio
n
o
f
AI
,
an
d
(
3
)
co
n
t
r
ib
u
tio
n
to
th
e
SC
M
d
o
m
ain
.
A
m
eticu
lo
u
s
s
cr
ee
n
in
g
p
r
o
ce
s
s
in
v
o
lv
in
g
in
ter
-
r
a
ter
r
eliab
ilit
y
ch
ec
k
s
y
ield
ed
a
f
in
al
s
elec
tio
n
o
f
4
2
6
ar
ticles f
o
r
an
aly
s
is
an
d
s
y
n
th
esis
.
Fig
u
r
e
2.
Data
b
ase
s
elec
tio
n
2
.
5
.
F
ind
ing
s
re
po
rt
ing
T
h
e
s
elec
ted
ar
ticles
wer
e
d
ec
o
n
s
tr
u
cted
b
ased
o
n
s
p
ec
if
ic
attr
ib
u
tes
o
f
th
e
r
esear
ch
q
u
esti
o
n
,
in
clu
d
in
g
th
e
f
ield
of
SC
M
u
n
d
er
s
tu
d
y
,
em
p
l
o
y
ed
AI
tec
h
n
iq
u
es,
an
d
tar
g
et
in
d
u
s
tr
ies
f
o
r
im
p
r
o
v
e
m
en
t.
Sy
n
th
esis
ef
f
o
r
ts
aim
ed
at
elu
cid
atin
g
in
ter
r
elatio
n
s
h
ip
s
am
o
n
g
th
ese
attr
ib
u
tes.
I
n
ac
co
r
d
an
ce
with
s
tan
d
ar
d
ac
ad
em
ic
m
eth
o
d
s
,
th
is
s
tu
d
y
’
s
o
u
tco
m
es
ar
e
co
m
m
u
n
ic
ated
th
r
o
u
g
h
a
co
m
b
in
atio
n
of
tab
les,
s
tati
s
tical
ev
alu
atio
n
s
,
an
d
co
m
p
r
e
h
en
s
iv
e
d
is
cu
s
s
io
n
s
.
Fo
llo
win
g
th
e
ap
p
r
o
ac
h
d
escr
ib
e
d
Dav
id
an
d
Dav
id
[
7
]
,
th
e
r
esu
lts
an
d
d
is
cu
s
s
io
n
s
s
ec
tio
n
s
u
m
m
ar
izes
th
e
r
ev
iewe
d
liter
atu
r
e,
p
o
in
tin
g
o
u
t
b
o
th
t
h
e
well
-
estab
lis
h
ed
in
s
ig
h
ts
an
d
th
e
r
em
ai
n
in
g
g
ap
s
in
u
n
d
er
s
tan
d
i
n
g
r
elate
d
to
t
h
e
r
esear
ch
f
o
cu
s.
3.
RE
SU
L
T
S
Ou
t
o
f
th
e
4
2
6
ar
ticles
s
elec
ted
f
o
r
th
is
r
e
v
iew,
7
4
f
o
cu
s
ed
o
n
lo
g
is
tics
,
1
6
5
o
n
p
r
o
d
u
ctio
n
,
an
d
1
8
6
o
n
th
e
b
r
o
ad
er
f
ield
o
f
s
u
p
p
l
y
ch
ain
.
T
h
is
r
ev
iew
co
v
e
r
s
th
e
p
er
io
d
f
r
o
m
2
0
0
0
to
2
0
2
4
,
6
8
%
wer
e
jo
u
r
n
al
p
ap
er
s
wh
ile
3
2
%
ca
m
e
f
r
o
m
co
n
f
er
en
ce
p
r
o
ce
ed
in
g
s
.
T
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r
a
n
s
p
o
r
t
a
t
i
o
n
t
i
m
e
a
n
d
c
o
s
t
s
[
2
6
]
-
[
2
8
]
S
h
i
p
p
i
n
g
,
p
r
o
c
e
ss
i
n
q
u
i
r
y
,
a
n
d
l
o
g
i
s
t
i
c
s
o
p
t
i
m
i
z
a
t
i
o
n
A
g
r
i
c
u
l
t
u
r
a
l
l
o
g
i
s
t
i
c
s
o
p
t
i
m
i
z
a
t
i
o
n
w
i
t
h
GAs
I
n
c
r
e
a
se
in
c
u
st
o
m
e
r
s
a
t
i
sf
a
c
t
i
o
n
[
2
9
]
-
[
3
1
]
R
e
t
u
r
n
s
N
e
t
w
o
r
k
d
e
si
g
n
R
e
ma
n
u
f
a
c
t
u
r
i
n
g
l
o
g
i
st
i
c
s
o
p
t
i
m
i
z
a
t
i
o
n
w
i
t
h
a
n
t
c
o
l
o
n
y
o
p
t
i
m
i
z
a
t
i
o
n
(
M
A
C
O
)
A
u
t
o
ma
t
i
o
n
of
r
e
t
u
r
n
s
o
r
t
i
n
g
a
n
d
i
mp
r
o
v
e
me
n
t
in
c
u
s
t
o
mer s
e
r
v
i
c
e
[
3
2
]
,
[
3
3
]
C
ol
l
e
c
t
i
on
a
c
t
i
vi
t
y
o
p
t
i
m
i
z
a
t
i
o
n
F
u
z
z
y
l
o
g
i
c
a
n
d
d
e
c
i
s
i
o
n
s
u
p
p
o
r
t
s
y
st
e
m
s
f
o
r
r
e
v
e
r
se
l
o
g
i
s
t
i
c
s
in
a
c
i
r
c
u
l
a
r
e
c
o
n
o
my
R
e
d
u
c
t
i
o
n
i
n
i
n
v
e
n
t
o
r
y
c
a
r
r
y
i
n
g
a
n
d
h
o
l
d
i
n
g
c
o
st
s
[
3
4
]
,
[
3
5
]
R
e
v
e
r
se
l
o
g
i
st
i
c
s
o
p
t
i
m
i
z
a
t
i
o
n
GA
s
f
o
r
r
e
v
e
r
se
l
o
g
i
s
t
i
c
s,
r
o
u
t
i
n
g
,
a
n
d
n
e
t
w
o
r
k
d
e
s
i
g
n
I
n
c
r
e
a
s
e
d
e
n
v
i
r
o
n
me
n
t
a
l
su
st
a
i
n
a
b
i
l
i
t
y
[
3
6
]
-
[
3
8
]
4.
DIS
CU
SS
I
O
N
AI
tech
n
iq
u
es
en
c
o
m
p
ass
es
th
e
r
an
g
e
of
s
p
ec
if
ic
alg
o
r
ith
m
s
,
s
y
s
tem
ar
ch
itectu
r
es,
d
ata
s
tr
u
ctu
r
es,
k
n
o
wled
g
e
r
ep
r
esen
tatio
n
s
,
a
n
d
m
eth
o
d
o
l
o
g
ical
ap
p
r
o
ac
h
e
s
th
at
ca
n
b
e
clea
r
ly
d
ef
in
e
d
(
as
d
is
cu
s
s
ed
by
B
u
n
d
y
[
3
9
]
)
.
Ou
r
an
aly
s
is
b
eg
an
b
y
id
en
tif
y
in
g
ac
ad
em
i
c
r
eso
u
r
ce
s
th
at
lis
t
AI
tech
n
iq
u
es
f
r
o
m
b
o
th
a
p
r
ac
tical
an
d
th
e
o
r
etica
l
p
er
s
p
ec
tiv
e.
Key
r
e
f
er
en
ce
s
in
th
is
co
n
tex
t
in
clu
d
e
s
tu
d
ies
b
y
C
h
en
et
a
l.
[
4
0
]
,
w
h
ich
in
v
esti
g
ated
a
v
a
r
iety
o
f
AI
te
ch
n
iq
u
es
a
n
d
th
e
ir
a
p
p
licatio
n
s
,
an
d
th
e
wo
r
k
b
y
[
4
1
]
.
B
u
n
d
y
[
3
9
]
wo
r
k
is
also
a
k
ey
s
o
u
r
ce
,
o
f
f
e
r
in
g
an
ex
t
en
s
iv
e
ca
talo
g
o
f
AI
tech
n
iq
u
es
th
at
ad
d
r
ess
a
b
r
o
ad
s
p
ec
tr
u
m
o
f
n
ee
d
s
.
T
h
is
s
ec
tio
n
in
clu
d
es
a
d
d
itio
n
al
s
o
u
r
ce
s
f
r
o
m
v
a
r
ied
o
r
ig
i
n
s
,
em
p
h
asizin
g
th
e
co
m
p
r
eh
e
n
s
iv
e
s
co
p
e
of
AI
-
r
elate
d
m
eth
o
d
o
l
o
g
ies.
4
.
1
.
AI
t
ec
hn
iqu
es
AI
tech
n
iq
u
es
h
av
e
r
ev
o
lu
tio
n
ized
th
e
way
co
m
p
lex
p
r
o
b
l
em
-
s
o
lv
in
g
task
s
ar
e
ap
p
r
o
ac
h
ed
ac
r
o
s
s
v
ar
io
u
s
d
o
m
ain
s
.
Am
o
n
g
th
e
p
r
o
m
in
e
n
t
AI
m
eth
o
d
o
lo
g
ies
ar
e
ar
tific
ial
n
eu
r
al
n
etwo
r
k
s
(
ANN)
,
m
ac
h
in
e
lear
n
in
g
,
e
x
p
er
t
s
y
s
tem
s
,
GA
s
,
an
d
ag
en
t
-
b
ased
s
y
s
tem
s
.
T
h
ese
tech
n
iq
u
es
ar
e
p
ar
ticu
la
r
ly
tr
an
s
f
o
r
m
ativ
e
i
n
f
ield
s
lik
e
SC
M
,
wh
er
e
th
ey
e
n
h
an
ce
ef
f
icien
cy
,
ac
cu
r
ac
y
,
a
n
d
ad
ap
tab
ilit
y
.
ANNs
r
ep
licate
th
e
b
r
ain
’
s
p
r
o
ce
s
s
es
to
tack
le
co
m
p
lex
p
r
o
b
lem
-
s
o
lv
in
g
t
ask
s
th
at
ar
e
ty
p
ically
d
if
f
icu
lt
f
o
r
h
u
m
an
s
to
ad
d
r
e
s
s
.
T
h
ey
co
n
s
is
t
o
f
in
ter
co
n
n
ec
ted
n
o
d
es
th
at
p
r
o
ce
s
s
in
p
u
t
d
ata
to
g
en
er
ate
o
u
tp
u
ts
b
ased
on
weig
h
ts
co
n
s
tan
tly
im
p
r
o
v
in
g
th
r
o
u
g
h
s
elf
-
lear
n
in
g
m
ec
h
an
is
m
s
.
In
th
e
c
o
n
tex
t
of
d
ec
is
io
n
-
m
ak
in
g
,
at
tactica
l
an
d
s
tr
at
eg
ic
lev
els
o
p
er
atio
n
al
r
esea
r
ch
h
as
lo
n
g
b
ee
n
a
p
p
lied
s
in
ce
th
e
1
9
8
0
s
f
o
r
lo
g
is
tics
,
tr
an
s
p
o
r
tatio
n
,
an
d
o
p
er
atio
n
s
p
lan
n
in
g
.
T
o
d
a
y
w
ith
in
cr
ea
s
in
g
co
m
p
lex
ity
an
d
d
ata
av
ailab
ilit
y
in
s
u
p
p
ly
ch
ain
p
r
o
ce
s
s
es
ANN
s
o
f
f
er
an
ap
p
r
o
ac
h
co
m
p
a
r
e
d
to
tr
ad
itio
n
al
o
p
er
atio
n
al
r
esear
ch
tech
n
iq
u
es
tailo
r
ed
f
o
r
in
d
iv
i
d
u
al
s
u
b
-
p
r
o
b
lem
s
,
with
in
s
u
p
p
l
y
c
h
ain
p
la
n
n
in
g
.
Sin
ce
2
0
1
0
,
o
n
e
o
f
th
e
n
o
tab
le
ap
p
licatio
n
s
o
f
ANNs
h
as
b
ee
n
i
n
s
u
p
p
ly
ch
a
in
p
lan
n
i
n
g
.
T
h
is
ap
p
licatio
n
en
c
o
m
p
ass
es
f
u
n
ctio
n
s
s
u
ch
as
co
m
p
u
tin
g
s
etu
p
tim
es,
id
en
tify
in
g
id
ea
l
lo
t
s
izes
f
o
r
s
u
p
p
l
y
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
:
32
1
-
3
3
2
326
ch
ain
p
r
o
ce
s
s
es,
an
d
s
ettin
g
s
u
itab
le
in
v
en
to
r
y
lev
els
f
o
r
b
o
th
d
em
an
d
f
o
r
ec
asti
n
g
an
d
p
r
o
d
u
ctio
n
p
lan
n
i
n
g
.
B
r
u
zz
o
n
e
an
d
Or
s
o
n
i
[
4
2
]
co
n
d
u
cte
d
a
r
is
k
ass
ess
m
en
t
wh
er
ein
ANNs
wer
e
em
p
lo
y
ed
to
ascer
tain
pr
o
d
u
ctio
n
c
o
s
t
lo
s
s
an
d
ass
o
ciate
d
r
is
k
s
,
in
co
m
p
ar
is
o
n
to
an
alter
n
ativ
e
s
im
u
latio
n
-
b
ased
m
eth
o
d
o
lo
g
y
.
T
h
e
f
in
d
in
g
s
s
u
g
g
ested
th
at
th
e
ch
o
s
en
m
o
d
u
lar
s
tr
u
ctu
r
e
f
ac
ilit
ated
more
ef
f
icien
t
co
s
t
esti
m
atio
n
an
d
b
u
d
g
etin
g
by
en
a
b
lin
g
an
ANN
-
b
ased
m
eth
o
d
o
l
o
g
y
.
Pre
cise
s
ce
n
ar
io
s
r
eg
ar
d
in
g
p
r
o
d
u
ctio
n
tim
e,
q
u
an
titi
es,
an
d
ca
p
ac
ities
wer
e
in
p
u
tted
to
th
e
ANNs,
alo
n
g
s
id
e
co
r
r
esp
o
n
d
in
g
co
s
t
esti
m
ates/o
u
tp
u
ts
.
Dr
aw
in
g
in
s
ig
h
ts
f
r
o
m
th
ese
lear
n
in
g
d
at
asets
,
ANN
s
co
u
ld
estab
lis
h
r
elatio
n
s
h
ip
s
b
etwe
en
in
p
u
ts
an
d
o
u
tp
u
ts
,
th
er
eb
y
en
ab
lin
g
c
o
s
t
esti
m
atio
n
s
ac
r
o
s
s
v
ar
io
u
s
s
ce
n
ar
io
s
[
4
2
]
.
Z
h
a
o
an
d
Y
u
[
3
]
a
p
p
lie
d
ANNs
to
ad
d
r
ess
th
e
ch
allen
g
es
f
ac
ed
b
y
th
e
s
u
p
p
lier
’
s
C
B
R
s
y
s
tem
.
AN
Ns
h
av
e
a
s
tr
o
n
g
s
elf
-
ad
ap
tiv
e
ca
p
ab
ilit
y
,
wh
ich
im
p
r
o
v
es
th
e
a
cc
u
r
ac
y
o
f
u
p
d
ate
pha
s
es
an
d
en
h
an
ce
s
d
ec
is
io
n
-
m
ak
in
g
in
en
te
r
p
r
is
e
s
u
p
p
lier
s
elec
tio
n
.
J
ian
g
an
d
Sh
en
g
[
4
3
]
in
tr
o
d
u
ce
d
an
alg
o
r
ith
m
d
e
s
ig
n
ed
to
e
n
h
an
ce
th
e
s
elec
tio
n
o
f
s
u
p
p
lier
s
.
Si
m
ilar
ly
,
C
h
en
et
a
l.
[
4
4
]
in
v
e
s
tig
ated
B
ay
e
s
ian
lear
n
in
g
tec
h
n
iq
u
es
to
ex
am
in
e
d
s
u
p
p
lier
r
eliab
ilit
y
,
but
th
eir
m
o
d
el
’
s
ab
ilit
y
to
be
g
en
er
al
ized
to
ad
d
itio
n
al
v
ar
iab
les
i
s
r
estricte
d
d
u
e
to
s
o
m
e
ex
clu
d
e
d
p
ar
am
eter
s
.
G
ar
v
ey
et
a
l.
[
4
5
]
d
e
v
elo
p
e
d
r
i
s
k
d
ep
en
d
en
cy
g
r
ap
h
s
,
wh
ich
ar
e
ad
a
p
tab
le
f
o
r
u
n
co
v
e
r
in
g
n
ew
in
s
ig
h
ts
an
d
f
ac
ilit
atin
g
im
p
r
o
v
ed
r
is
k
p
r
o
p
ag
atio
n
m
o
d
elin
g
[
4
6
]
.
T
h
is
m
ea
n
s
th
at
th
is
is
a
m
ath
em
atica
l
to
o
l
u
s
ed
to
h
an
d
le
d
at
a
t
h
at
ar
e
in
d
e
f
in
ite,
u
n
s
tead
y
,
im
p
r
ec
is
e,
an
d
n
o
is
y
.
I
t
atte
m
p
ts
to
ex
p
lain
th
e
co
n
ce
p
t
o
f
u
n
ce
r
tain
ty
b
y
d
ef
in
in
g
lo
wer
an
d
u
p
p
er
ap
p
r
o
x
im
atio
n
s
o
f
o
r
ig
in
al
s
et
s
.
Mo
r
eo
v
er
,
f
o
r
it
to
b
e
a
d
ec
is
iv
e
b
asis
o
n
wh
ic
h
d
ec
is
io
n
s
ca
n
b
e
m
a
d
e;
th
is
d
y
n
am
ic
s
u
p
p
ly
ch
ain
p
er
f
o
r
m
an
ce
m
ea
s
u
r
em
en
t
h
as
to
be
r
o
b
u
s
t
a
n
d
ag
ile.
T
h
is
p
r
o
b
lem
is
ad
d
r
ess
ed
by
r
o
u
g
h
s
et
th
eo
r
y
.
Su
p
p
ly
c
h
ain
m
ea
s
u
r
em
e
n
ts
with
in
a
m
u
lticr
iter
ia
d
ec
is
io
n
f
r
am
e
wer
e
o
u
tlin
ed
in
Z
h
en
g
a
n
d
L
ai
[
4
7
]
.
In
m
o
n
ito
r
in
g
u
p
s
tr
ea
m
s
u
p
p
ly
ch
ain
p
er
f
o
r
m
an
ce
[
4
8
]
it
was
u
s
ed
.
Fo
r
ex
am
p
le
,
B
ai
an
d
Sar
k
is
[
4
9
]
p
o
i
n
ted
o
u
t
th
at
r
o
u
g
h
s
et
th
eo
r
y
was
em
p
lo
y
ed
in
d
eter
m
in
in
g
k
ey
p
er
f
o
r
m
an
c
e
in
d
icato
r
s
(
KPI
s
)
f
o
r
s
u
p
p
lier
s
u
s
tain
ab
ilit
y
ev
alu
atio
n
s
.
An
o
th
er
m
o
d
el
h
as
b
ee
n
d
ev
e
lo
p
ed
b
y
Z
h
an
[
5
0
]
r
eg
a
r
d
in
g
co
m
b
in
at
o
r
ial
ju
d
g
m
en
t
m
atr
ices
b
ased
on
o
b
jectiv
e
an
d
s
u
b
jectiv
e
ju
d
g
m
en
t
m
atr
ices.
T
h
e
y
d
ef
in
e
d
th
eir
m
in
in
g
alg
o
r
ith
m
with
cr
iter
io
n
weig
h
tin
g
as
Gen
g
an
d
L
iu
[
5
1
]
.
R
o
u
g
h
s
et
th
eo
r
y
was
em
p
lo
y
e
d
b
y
th
ese
wr
iter
s
ab
o
v
e
f
o
r
ch
o
o
s
in
g
th
e
m
o
s
t
ap
p
r
o
p
r
iate
s
u
p
p
lier
am
o
n
g
s
ev
er
al
q
u
alif
ied
o
n
es u
s
in
g
m
u
l
tip
le
y
et
co
n
f
lictin
g
s
u
p
p
lier
s
’
s
elec
tio
n
cr
iter
ia.
Ma
ch
in
e
l
ea
r
n
in
g
in
Fig
u
r
e
5
is
a
m
eth
o
d
wh
er
eb
y
a
m
ac
h
in
e
lear
n
s
th
r
o
u
g
h
th
e
ac
cu
m
u
latio
n
of
ex
p
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ce
r
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er
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e
x
p
lic
it
p
r
o
g
r
am
m
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to
p
er
f
o
r
m
t
h
e
task
b
etter
b
y
r
ef
er
r
i
n
g
b
ac
k
to
v
ar
io
u
s
d
ata
s
o
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r
ce
s
o
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tain
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f
r
o
m
elec
tr
o
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ic
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ac
k
i
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g
.
Ma
c
h
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e
lear
n
i
n
g
class
i
f
icatio
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can
be
b
r
o
k
en
d
o
wn
in
to
th
r
ee
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te
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ies;
s
u
p
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v
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ed
lear
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in
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,
u
n
s
u
p
e
r
v
is
ed
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n
in
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,
an
d
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ein
f
o
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e
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t
lear
n
in
g
.
Pre
d
ictin
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b
eh
av
io
r
is
a
cr
u
cial
asp
ec
t
of
m
ac
h
in
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lear
n
in
g
,
p
ar
ticu
lar
ly
in
its
ap
p
licatio
n
to
s
o
lv
in
g
v
ar
io
u
s
ch
allen
g
es
with
in
s
u
p
p
ly
ch
ain
s
.
T
h
is
p
r
ed
ictiv
e
ca
p
a
b
i
lity
is
u
tili
ze
d
in
b
o
th
u
p
s
tr
ea
m
an
d
d
o
wn
s
tr
ea
m
m
an
ag
e
m
e
n
t
[
5
2
]
.
Fig
u
r
e
5.
ML
a
p
p
licatio
n
in
s
u
p
p
ly
ch
ain
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5
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4
7
52
A
d
va
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cin
g
s
u
p
p
ly
c
h
a
in
m
a
n
a
g
eme
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t th
r
o
u
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h
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r
tifi
cia
l
…
(
Ou
a
h
b
i Yo
u
n
ess
e
)
327
E
x
p
er
t
s
y
s
tem
s
h
av
e
p
r
o
v
en
to
b
e
ef
f
ec
tiv
e
ac
r
o
s
s
a
r
an
g
e
o
f
d
o
m
ai
n
s
,
s
u
ch
as
m
an
ag
i
n
g
air
lin
e
r
ev
en
u
e
a
n
d
m
ain
tain
in
g
v
e
h
icles.
T
h
ey
a
r
e
also
e
m
p
lo
y
e
d
in
air
tr
a
f
f
ic
c
o
n
tr
o
l
[
5
3
]
,
d
em
o
n
s
tr
atin
g
th
eir
v
er
s
atility
an
d
p
o
ten
tial
ap
p
licatio
n
s
in
th
e
s
u
p
p
l
y
ch
ai
n
d
o
m
ain
.
No
tab
le
u
s
es
o
f
s
u
p
p
l
y
ch
ain
ex
p
er
t
s
y
s
tem
s
en
co
m
p
ass
s
u
p
p
lier
ass
e
s
s
m
e
n
t; th
ir
d
-
p
ar
ty
lo
g
is
tics
p
r
o
v
id
er
s
(
3
PL)
s
elec
tio
n
; lo
g
is
tic
s
s
tr
ateg
y
f
o
r
m
u
latio
n
;
p
r
o
d
u
ctio
n
s
ch
ed
u
lin
g
an
d
m
a
n
ag
em
en
t;
d
em
a
n
d
f
o
r
ec
asti
n
g
;
ev
alu
atio
n
o
f
s
u
p
p
lier
s
an
d
s
u
b
co
n
tr
ac
to
r
s
;
an
d
th
e
d
ev
elo
p
m
en
t o
f
a
s
y
s
tem
f
o
r
p
ar
tn
er
s
elec
tio
n
[5
]
,
[
41
]
,
[
5
4
]
.
GAs
in
Fig
u
r
e
6
ar
e
s
ea
r
ch
tech
n
iq
u
es
th
at
m
im
ic
th
e
m
ec
h
an
is
m
s
o
f
ev
o
lu
tio
n
an
d
n
atu
r
al
s
elec
tio
n
,
wh
er
e
s
o
lu
tio
n
s
to
co
m
p
lex
p
r
o
b
lem
s
ar
e
ev
o
l
v
ed
th
r
o
u
g
h
a
p
r
o
ce
s
s
o
f
r
a
n
d
o
m
v
ar
iatio
n
an
d
s
elec
tio
n
[
5
5
]
.
T
h
ese
alg
o
r
it
h
m
s
h
av
e
p
r
o
v
en
ef
f
ec
tiv
e
i
n
ad
d
r
ess
in
g
v
ar
io
u
s
ch
allen
g
in
g
s
u
p
p
ly
c
h
ain
p
r
o
b
lem
s
[
4
1
]
.
GAs
wer
e
u
s
ed
to
ad
d
r
ess
tr
ad
itio
n
al
lo
g
i
s
tics
ch
allen
g
es,
s
u
ch
as
f
ac
ilit
y
lay
o
u
t
p
la
n
n
in
g
[
5
6
]
.
T
h
ey
h
av
e
b
ee
n
u
tili
ze
d
to
o
p
tim
ize
in
v
en
to
r
y
m
a
n
ag
em
en
t
[
5
7
]
.
GAs
h
av
e
h
elp
ed
im
p
r
o
v
e
th
e
r
eliab
ilit
y
o
f
d
eliv
er
y
s
er
v
ice
s
.
T
h
ey
h
av
e
b
ee
n
u
s
ed
t
o
d
ev
elo
p
ef
f
icien
t
f
r
eig
h
t
co
n
s
o
lid
atio
n
m
eth
o
d
s
.
T
h
ese
alg
o
r
ith
m
s
h
av
e
s
u
p
p
o
r
ted
r
esear
ch
i
n
ex
p
r
ess
co
u
r
ier
s
er
v
ice
o
p
tim
izatio
n
.
G
As
h
av
e
also
b
ee
n
ap
p
lied
to
aid
in
s
u
p
p
lier
s
e
lectio
n
d
u
r
i
n
g
p
u
r
c
h
asin
g
[
5
8
]
.
J
au
h
ar
an
d
Pan
t
[
5
9
]
r
ev
i
ewe
d
2
2
0
r
esear
ch
in
v
esti
g
at
in
g
th
e
GAs
ap
p
licatio
n
in
s
u
p
p
l
y
c
h
ain
s
.
T
h
eir
f
in
d
in
g
s
i
n
d
icate
d
th
at
th
e
s
e
alg
o
r
ith
m
s
a
r
e
p
r
im
ar
ily
u
s
ed
to
m
an
a
g
e
p
r
o
d
u
ctio
n
f
lo
w
an
d
im
p
r
o
v
e
o
r
d
er
f
u
lf
illme
n
t
p
r
o
ce
s
s
es.
Fig
u
r
e
6.
GAs
ap
p
licatio
n
in
s
u
p
p
ly
ch
ai
n
T
h
o
s
e
s
y
s
tem
s
co
n
s
is
t
o
f
au
to
n
o
m
o
u
s
en
titi
es,
wh
ich
ca
n
b
e
p
r
o
ce
s
s
es,
r
o
b
o
ts
,
o
r
h
u
m
an
s
with
in
a
s
p
ec
if
ic
en
v
ir
o
n
m
en
t.
T
h
ese
ag
en
ts
in
ter
ac
t
b
ased
o
n
d
ef
i
n
ed
r
u
les
an
d
ex
h
ib
it
a
d
eg
r
e
e
o
f
in
d
ep
e
n
d
en
ce
.
T
h
is
ch
ar
ac
ter
is
tic
m
ak
es
th
e
m
u
s
ef
u
l
in
s
u
p
p
ly
ch
ain
p
er
f
o
r
m
an
ce
m
o
n
ito
r
in
g
an
d
o
p
t
im
izatio
n
.
Var
io
u
s
s
tu
d
ies
h
av
e
h
i
g
h
lig
h
te
d
th
e
p
o
ten
tial
o
f
ag
en
t
-
b
ased
s
y
s
tem
s
in
ad
d
r
ess
in
g
s
u
p
p
l
y
ch
ai
n
i
s
s
u
es.
Fo
r
in
s
tan
ce
,
Du
et
a
l.
[
6
0
]
ex
p
lo
r
e
d
th
eir
u
s
e
in
d
ea
lin
g
with
d
em
an
d
f
lu
ctu
atio
n
s
,
wh
ile
L
im
a
et
a
l.
[
6
1
]
f
o
cu
s
ed
o
n
jo
i
n
t
p
r
o
d
u
ctio
n
p
la
n
n
in
g
.
C
h
en
an
d
W
ei
[
6
2
]
ap
p
lied
th
ese
s
y
s
tem
s
to
o
r
d
er
m
o
n
ito
r
i
n
g
an
d
m
an
ag
i
n
g
o
u
ts
o
u
r
cin
g
r
elatio
n
s
h
ip
s
.
Ag
en
t
-
b
ased
s
y
s
tem
s
s
er
v
e
as
ef
f
ec
tiv
e
s
im
u
latio
n
to
o
ls
in
in
v
en
to
r
y
m
an
a
g
em
en
t,
allo
win
g
f
o
r
th
e
m
o
d
elin
g
o
f
co
m
p
lex
in
ter
a
ctio
n
s
b
etwe
en
v
ar
io
u
s
en
titi
es.
T
h
ey
ca
n
h
e
lp
r
ed
u
ce
c
o
s
ts
an
d
im
p
r
o
v
e
in
v
en
t
o
r
y
f
ill
r
ates,
a
s
d
em
o
n
s
tr
ated
b
y
C
h
an
an
d
C
h
an
[
6
3
]
.
Ad
d
itio
n
ally
,
th
ese
s
y
s
tem
s
ca
n
m
o
d
el
in
ter
ac
tio
n
s
b
etwe
en
d
if
f
er
en
t
in
v
en
to
r
y
ap
p
r
o
ac
h
es,
as seen
in
Po
n
te
et
a
l.
[
6
4
]
.
Gh
iass
i
an
d
Sp
er
a
[
6
5
]
i
n
clu
d
ed
co
n
tr
ib
u
tio
n
s
d
ea
lin
g
with
f
ac
to
r
s
th
at
af
f
ec
t
th
e
r
es
ilien
ce
o
f
s
u
p
p
ly
c
h
ain
e
v
en
in
d
ir
e
ctly
,
r
eg
ar
d
in
g
s
u
p
p
ly
ch
ain
r
elat
io
n
s
h
ip
m
a
n
ag
em
en
t,
wh
ile
Gian
n
ak
is
et
L
o
u
is
b
ased
o
n
au
t
o
n
o
m
o
u
s
co
r
r
e
ctiv
e
ac
tio
n
s
to
war
d
s
im
p
r
o
v
in
g
th
e
ag
ilit
y
o
f
s
u
p
p
ly
ch
ain
.
Su
p
p
ly
c
h
ain
co
o
r
d
in
atio
n
is
a
cr
itical
ele
m
en
t,
wh
er
e
in
s
ig
n
if
ican
t
f
lu
ctu
atio
n
s
ca
u
s
e
s
ig
n
if
ican
t
v
ar
iatio
n
s
in
o
r
d
er
s
u
p
s
tr
ea
m
in
th
e
s
u
p
p
ly
ch
ain
.
T
h
e
is
s
u
e
h
as
b
ee
n
ex
ten
s
iv
el
y
r
esear
ch
e
d
,
with
Alzo
u
b
i
an
d
Yan
a
m
an
d
r
a
[
6
6
]
o
f
f
er
in
g
v
ar
io
u
s
in
s
ig
h
ts
in
to
s
u
p
p
ly
ch
ai
n
p
er
f
o
r
m
a
n
ce
im
p
r
o
v
em
e
n
t,
lead
tim
e
r
e
d
u
ctio
n
,
an
d
s
tr
ateg
ies
to
m
in
im
ize
th
e
b
u
llwh
ip
ef
f
ec
t.
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d
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et
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l.
[
6
7
]
ex
am
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d
way
s
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m
itig
ate
th
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b
u
llwh
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p
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im
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d
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m
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Ad
d
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ally
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K
wo
n
et
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l.
[
6
8
]
d
elv
ed
in
to
co
llab
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r
a
tio
n
is
s
u
es
th
at
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p
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llab
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ch
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t
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te
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tr
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ag
a
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s
t
o
th
er
co
m
p
etito
r
s
.
Kwo
n
et
a
l.
[
6
8
]
h
av
e
n
o
ta
b
ly
e
x
p
lo
r
ed
c
o
llab
o
r
ativ
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ty
with
in
th
e
s
u
p
p
l
y
ch
ain
.
Pan
an
d
C
h
o
i
[
6
9
]
h
a
v
e
f
o
c
u
s
ed
o
n
n
eg
o
tiatin
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p
r
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ates
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p
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ch
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p
a
r
tn
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s
,
wh
i
le
Sin
g
h
an
d
C
h
alla
[
1
6
]
h
a
v
e
co
n
tr
ib
u
ted
to
o
u
r
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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52
In
d
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J
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C
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Vo
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3
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20
2
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328
u
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Du
b
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[
7
0
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4
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2
.
B
a
rr
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f
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f
icia
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ellig
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n su
p
ply
cha
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T
h
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im
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lem
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tatio
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AI
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ican
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4
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[
7
1
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[
7
4
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.
A
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[
7
5
]
,
[
7
6
]
.
Ad
d
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4
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p
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jects
[
7
4
]
.
Or
g
an
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ar
r
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s
ar
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m
ajo
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ch
allen
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Or
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a
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s
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f
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s
tr
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ce
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b
r
ac
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d
ig
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ltu
r
e
[
7
3
]
-
[
7
6
].
Ma
n
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s
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o
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ce
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ch
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g
e
[
7
1
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-
[
73
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,
[
75
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,
[
7
6
]
.
I
n
ef
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icien
t
k
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wled
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m
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n
t
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d
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ch
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ca
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h
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n
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Sev
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h
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in
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ated
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ch
allen
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g
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way
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to
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v
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.
R
aj
et
a
l.
[
7
1
]
h
ig
h
li
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h
ts
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im
p
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tan
ce
o
f
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b
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I
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d
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4
.
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tatio
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ac
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,
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7
1
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[
74
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[
7
6
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.
An
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[
7
2
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[
7
3
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,
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ate
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r
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elate
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r
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ies
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r
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s
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cles
to
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f
icien
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,
lead
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to
a
s
tag
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atio
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in
p
r
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d
u
cti
v
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d
r
ed
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ce
d
co
m
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To
b
r
id
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is
g
a
p
,
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r
g
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izatio
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s
n
ee
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lead
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wh
o
p
r
i
o
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itize
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d
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ctio
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n
d
f
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ter
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ltu
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e
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tin
u
o
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s
i
m
p
r
o
v
e
m
en
t.
Ku
m
ar
et
a
l.
[
7
2
]
an
d
Nim
a
wat
an
d
Gid
wan
i
[7
6
]
b
o
th
n
o
ted
t
h
at
s
tak
eh
o
ld
er
in
v
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lv
em
en
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ag
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t
ar
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s
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f
f
icien
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in
m
an
y
in
s
tan
ce
s
.
T
h
is
s
h
o
r
tc
o
m
in
g
in
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g
em
en
t
ca
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l
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in
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f
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I
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d
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s
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r
y
4
.
0
,
a
s
K
u
m
a
r
e
t
a
l
.
[
7
2
]
m
e
n
t
i
o
n
e
d
.
F
u
r
t
h
e
r
m
o
r
e
,
S
t
e
n
t
o
f
t
e
t
a
l
.
[
7
3
]
h
ig
h
lig
h
ted
th
at
lim
ited
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o
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n
b
etwe
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ac
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c
in
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s
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s
tr
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e
r
co
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c
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ld
b
e
b
e
n
ef
icial.
An
ad
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b
ar
r
ier
is
an
o
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em
p
h
asis
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asp
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ts
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e
ex
p
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d
d
ev
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m
en
t,
wh
ich
can
im
p
ed
e
I
n
d
u
s
tr
y
4
.
0
ad
o
p
tio
n
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T
h
e
f
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s
on
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tr
en
d
h
ig
h
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th
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ea
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n
d
f
o
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alan
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d
ap
p
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ac
h
[
7
3
]
,
[
74
]
,
[
7
6
]
.
I
n
d
u
s
tr
y
4
.
0
b
r
in
g
s
s
ig
n
i
f
ican
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ical
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d
in
f
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elate
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A
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en
a
b
lin
g
d
ata
-
ce
n
tr
ic
s
er
v
ices.
T
h
is
is
co
m
p
o
u
n
d
e
d
b
y
in
a
d
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q
u
at
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ad
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is
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u
p
ted
d
ata
ex
ch
a
n
g
es.
Ku
m
ar
et
a
l.
[
7
2
]
,
a
n
d
Ma
ju
m
d
ar
et
a
l.
[
7
4
]
h
av
e
d
em
o
n
s
tr
ated
how
th
ese
is
s
u
es
h
in
d
er
th
e
f
lo
w
of
in
f
o
r
m
atio
n
.
An
o
th
er
ch
allen
g
e
r
e
v
o
lv
e
s
ar
o
u
n
d
in
teg
r
atio
n
,
in
clu
d
in
g
s
ca
lab
ilit
y
,
co
m
p
atib
ilit
y
,
an
d
in
ter
o
p
er
a
b
ilit
y
.
T
h
e
ca
ll
f
o
r
s
tan
d
ar
d
ized
s
o
lu
tio
n
s
to
ad
d
r
ess
th
ese
in
teg
r
atio
n
is
s
u
es
h
as
b
ee
n
ec
h
o
ed
b
y
[
7
4
]
-
[
7
6
]
.
Ad
d
itio
n
ally
,
th
e
s
lo
w
p
ac
e
o
f
tech
n
o
lo
g
ical
m
at
u
r
ity
h
as
af
f
ec
ted
th
e
b
r
o
ad
er
im
p
lem
en
tatio
n
o
f
I
n
d
u
s
tr
y
4
.
0
.
R
aj
et
a
l.
[
7
1
]
,
Nim
awa
t
an
d
Gid
wa
n
i
[7
6
]
in
d
icate
th
e
r
eq
u
i
r
ed
tec
h
n
o
l
o
g
i
es
f
o
r
I
n
d
u
s
tr
y
4
.
0
th
at
ar
e
s
till
in
th
eir
ea
r
ly
s
ta
g
es,
lim
itin
g
th
ier
wid
esp
r
ea
d
in
teg
r
atio
n
.
Secu
r
ity
a
n
d
p
r
iv
ac
y
co
n
ce
r
n
s
als
o
co
n
tr
ib
u
te
to
th
e
co
m
p
lex
ity
o
f
I
n
d
u
s
tr
y
4
.
0
in
teg
r
atio
n
.
R
es
is
tan
ce
to
d
ata
s
h
ar
in
g
d
u
e
to
in
ad
eq
u
ate
s
ec
u
r
ity
m
ea
s
u
r
es
cr
ea
tes
ad
d
itio
n
al
o
b
s
tacle
s
.
T
h
e
n
ee
d
f
o
r
r
o
b
u
s
t
d
ata
s
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r
ity
is
em
p
h
asized
b
y
[
7
2
]
,
[
74
]
-
[
7
6
]
.
Ad
d
r
ess
in
g
th
ese
is
s
u
es
i
s
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i
tal
f
o
r
p
r
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m
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d
ata
ex
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f
ac
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I
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d
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4
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0
im
p
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en
tatio
n
.
A
cr
itical
i
s
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e
i
s
th
e
in
ad
eq
u
ac
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o
f
leg
al
m
ea
s
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r
es
to
p
r
ev
en
t
cy
b
er
cr
im
e
an
d
d
ata
th
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t,
wh
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lead
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ln
er
a
b
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ies
in
p
r
o
tectin
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d
ata
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in
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p
r
o
p
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T
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is
d
ef
icien
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in
l
eg
al
s
tr
u
ctu
r
es
h
as
b
ee
n
h
ig
h
lig
h
te
d
b
y
[
7
2
]
,
[
74
]
-
[
7
6
]
.
Fu
r
th
er
m
o
r
e,
th
er
e
’
s
a
n
o
tab
le
ab
s
en
ce
o
f
clea
r
s
tan
d
ar
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an
d
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m
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t
r
eg
u
latio
n
s
in
t
h
e
r
ea
lm
o
f
I
n
d
u
s
tr
y
4
.
0
,
esp
ec
ially
co
n
c
er
n
in
g
la
b
o
r
laws,
em
p
lo
y
m
en
t
p
r
ac
tices,
d
ata
o
wn
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ip
,
a
n
d
co
p
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ig
h
t
is
s
u
es.
T
h
is
ab
s
en
ce
o
f
f
o
r
m
al
g
u
id
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es
r
esu
lts
in
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n
ce
r
tain
ty
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d
co
n
f
u
s
io
n
f
o
r
b
u
s
in
ess
es
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d
em
p
lo
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s
alik
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T
h
e
n
ee
d
f
o
r
co
m
p
r
eh
en
s
iv
e
leg
al
f
r
am
ewo
r
k
s
th
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d
r
ess
th
ese
is
s
u
es
h
as
b
ee
n
e
m
p
h
asized
by
[
7
5
]
,
[
7
6
].
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
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N:
2
5
0
2
-
4
7
52
A
d
va
n
cin
g
s
u
p
p
ly
c
h
a
in
m
a
n
a
g
eme
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t th
r
o
u
g
h
a
r
tifi
cia
l
…
(
Ou
a
h
b
i Yo
u
n
ess
e
)
329
5.
CO
NCLU
SI
O
N
ANN
en
ab
le
g
o
o
d
SC
M,
b
u
t
th
ey
d
o
n
o
t
o
f
f
e
r
a
h
ig
h
lev
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p
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ec
is
io
n
,
in
tellig
en
ce
,
in
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co
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f
ig
u
r
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p
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o
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s
s
tr
ain
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.
D
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to
f
in
a
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cial
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s
tr
a
in
ts
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d
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g
an
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b
s
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ased
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tain
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u
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t
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ca
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e
d
o
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in
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elativ
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m
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way
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n
in
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to
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cr
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cial
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ac
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SC
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I
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AI
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tr
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f
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s
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d
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u
lato
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p
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ce
ar
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all
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ar
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.
B
u
t
th
e
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to
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d
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p
tim
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f
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s
eq
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en
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s
o
f
AI
in
teg
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n
in
SC
M
is
em
p
lo
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tr
ain
in
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an
d
c
h
an
g
e
m
an
ag
em
en
t.
Ag
e
n
t
tech
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lo
g
y
is
m
o
r
e
t
h
an
ju
s
t
m
ath
em
atica
lly
o
p
tim
ized
f
o
r
th
e
p
r
ac
tical
im
p
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e
n
tatio
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f
th
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ab
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y
to
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tu
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th
e
p
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asin
g
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f
n
eg
o
ti
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n
an
d
c
o
o
r
d
in
atio
n
.
RE
F
E
R
E
NC
E
S
[
1
]
P
.
K
u
m
a
r
a
n
d
A
.
S
.
H
a
t
i
,
“
R
e
v
i
e
w
o
n
mac
h
i
n
e
l
e
a
r
n
i
n
g
a
l
g
o
r
i
t
h
m
b
a
se
d
f
a
u
l
t
d
e
t
e
c
t
i
o
n
i
n
i
n
d
u
c
t
i
o
n
m
o
t
o
r
s,”
Arc
h
i
v
e
s
o
f
C
o
m
p
u
t
a
t
i
o
n
a
l
M
e
t
h
o
d
s
i
n
En
g
i
n
e
e
r
i
n
g
,
v
o
l
.
2
8
,
n
o
.
3
,
p
p
.
1
9
2
9
–
1
9
4
0
,
M
a
y
2
0
2
1
,
d
o
i
:
1
0
.
1
0
0
7
/
s
1
1
8
3
1
-
020
-
0
9
4
4
6
-
w.
[
2
]
D
.
S
c
h
u
t
z
e
r
,
“
B
u
s
i
n
e
ss
e
x
p
e
r
t
s
y
s
t
e
ms:
t
h
e
c
o
mp
e
t
i
t
i
v
e
e
d
g
e
,
”
Ex
p
e
r
t
S
y
st
e
m
s
W
i
t
h
A
p
p
l
i
c
a
t
i
o
n
s
,
v
o
l
.
1
,
n
o
.
1
,
p
p
.
1
7
–
2
1
,
Ja
n
.
1
9
9
0
,
d
o
i
:
1
0
.
1
0
1
6
/
0
9
5
7
-
4
1
7
4
(
9
0
)
9
0
0
6
5
-
3.
[
3
]
K
.
Z
h
a
o
a
n
d
X
.
Y
u
,
“
A
c
a
se
b
a
se
d
r
e
a
s
o
n
i
n
g
a
p
p
r
o
a
c
h
o
n
su
p
p
l
i
e
r
s
e
l
e
c
t
i
o
n
i
n
p
e
t
r
o
l
e
u
m
e
n
t
e
r
p
r
i
ses
,
”
Ex
p
e
r
t
S
y
s
t
e
m
s
w
i
t
h
Ap
p
l
i
c
a
t
i
o
n
s
,
v
o
l
.
3
8
,
n
o
.
6
,
p
p
.
6
8
3
9
–
6
8
4
7
,
J
u
n
.
2
0
1
1
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
e
sw
a
.
2
0
1
0
.
1
2
.
0
5
5
.
[
4]
M
.
K
h
a
s
h
e
i
a
n
d
F
.
C
h
a
h
k
o
u
t
a
h
i
,
“
El
e
c
t
r
i
c
i
t
y
d
e
ma
n
d
f
o
r
e
c
a
s
t
i
n
g
u
si
n
g
f
u
z
z
y
h
y
b
r
i
d
i
n
t
e
l
l
i
g
e
n
c
e
-
b
a
s
e
d
s
e
a
s
o
n
a
l
m
o
d
e
l
s,”
J
o
u
rn
a
l
o
f
M
o
d
e
l
l
i
n
g
i
n
Ma
n
a
g
e
m
e
n
t
,
v
o
l
.
1
7
,
n
o
.
1
,
p
p
.
1
5
4
–
1
7
6
,
F
e
b
.
2
0
2
2
,
d
o
i
:
1
0
.
1
1
0
8
/
J
M
2
-
06
-
2
0
2
0
-
0
1
5
9
.
[
5
]
K
.
S
a
r
i
,
“
M
o
d
e
l
i
n
g
o
f
a
f
u
z
z
y
e
x
p
e
r
t
s
y
st
e
m
f
o
r
c
h
o
o
s
i
n
g
a
n
a
p
p
r
o
p
r
i
a
t
e
s
u
p
p
l
y
c
h
a
i
n
c
o
l
l
a
b
o
r
a
t
i
o
n
st
r
a
t
e
g
y
,
”
I
n
t
e
l
l
i
g
e
n
t
Au
t
o
m
a
t
i
o
n
a
n
d
S
o
f
t
C
o
m
p
u
t
i
n
g
,
v
o
l
.
2
4
,
n
o
.
2
,
p
p
.
4
0
5
–
4
1
2
,
Ju
n
.
2
0
1
8
,
d
o
i
:
1
0
.
1
0
8
0
/
1
0
7
9
8
5
8
7
.
2
0
1
7
.
1
3
5
2
2
5
8
.
[
6
]
G
.
Lă
z
ă
r
o
i
u
,
T.
K
l
i
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9
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