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Vo
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12
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1
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Octo
b
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2
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1
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17
–
29
18
T
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s
te
m
s
,
h
a
s
a
s
ig
n
i
f
ica
n
t
i
n
f
l
u
e
n
ce
o
n
t
h
e
s
ec
u
r
e
an
d
ec
o
n
o
m
ic
o
p
er
atio
n
o
f
p
o
w
er
s
y
s
te
m
s
[
4
]
.
Op
ti
m
al
co
m
m
it
m
e
n
t
s
ch
ed
u
lin
g
ca
n
n
o
t
o
n
l
y
s
av
e
m
illi
o
n
s
o
f
d
o
llar
s
f
o
r
p
o
w
er
co
m
p
a
n
ies,
i
t
also
en
s
u
r
e
s
s
y
s
te
m
r
eliab
ilit
y
b
y
m
ai
n
tai
n
in
g
th
e
p
r
o
p
er
s
p
in
n
in
g
r
eser
v
e.
T
h
e
U
C
p
r
o
b
lem
i
s
m
at
h
e
m
atica
ll
y
f
o
r
m
u
la
ted
as
a
n
o
n
li
n
ea
r
,
lar
g
escale
a
n
d
m
i
x
ed
i
n
teg
er
co
m
b
in
ato
r
ial
o
p
ti
-
m
izat
io
n
p
r
o
b
lem
[
5
]
,
[
6
]
.
T
h
e
n
u
m
b
e
r
o
f
co
m
b
in
at
io
n
s
o
f
0
-
1
v
ar
iab
les g
r
o
w
s
e
x
p
o
n
e
n
tiall
y
f
o
r
a
lar
g
e
-
s
ca
le
UC
p
r
o
b
lem
.
T
h
er
ef
o
r
e,
th
e
U
C
i
s
o
n
e
o
f
t
h
e
m
o
s
t
d
if
f
ic
u
lt
p
r
o
b
le
m
s
i
n
th
e
ar
ea
o
f
p
o
w
er
s
y
s
te
m
o
p
ti
m
izat
io
n
.
T
h
e
UC
P
is
a
NP
-
Har
d
p
r
o
b
l
e
m
[
7
]
w
h
ic
h
ca
n
n
o
t
b
e
s
o
lv
e
d
ex
ac
tl
y
i
n
r
ea
s
o
n
ab
l
e
co
m
p
u
ti
n
g
ti
m
e
f
o
r
lar
g
e
s
ca
le
p
r
o
b
le
m
s
.
R
esear
c
h
e
f
f
o
r
ts
,
th
er
e
f
o
r
e,
h
a
v
e
co
n
ce
n
tr
at
ed
o
n
ef
f
ic
ien
t
an
d
n
ea
r
-
o
p
tim
al
UC
a
lg
o
r
it
h
m
s
w
h
ic
h
ca
n
b
e
ap
p
lied
to
r
ea
lis
tic
p
o
w
er
s
y
s
te
m
s
a
n
d
h
av
e
r
ea
s
o
n
ab
le
s
to
r
ag
e
a
n
d
co
m
p
u
tatio
n
ti
m
e
r
eq
u
ir
e
m
en
ts
.
T
h
e
o
p
ti
m
iz
atio
n
m
e
th
o
d
s
f
o
r
UC
p
r
o
b
le
m
s
c
an
b
e
d
iv
id
ed
in
to
t
w
o
clas
s
es
th
r
o
u
g
h
a
s
u
r
v
e
y
o
f
liter
atu
r
e
as
f
o
llo
w
s
:
T
h
e
f
ir
s
t
ar
e
n
u
m
er
ical
o
p
ti
m
izatio
n
tech
n
iq
u
e
s
s
u
ch
a
s
p
r
io
r
ity
lis
t
m
et
h
o
d
s
[
4
]
,
d
y
n
a
m
ic
p
r
o
g
r
a
m
m
i
n
g
[
8
]
,
[
9
]
,
L
ag
r
an
g
ia
n
r
elax
atio
n
m
e
th
o
d
s
[
10
]
,
b
r
an
ch
-
a
n
d
-
b
o
u
n
d
m
eth
o
d
s
[
11
]
,
an
d
m
i
x
ed
in
te
g
er
p
r
o
g
r
am
m
i
n
g
[
12
]
.
T
h
e
o
th
er
ar
e
s
to
ch
asti
c
s
ea
r
ch
m
eth
o
d
s
s
u
c
h
as
g
en
etic
al
g
o
r
ith
m
s
(
GA
)
[
1
3
]
,
ev
o
l
u
tio
n
ar
y
p
r
o
g
r
a
m
m
i
n
g
(
E
P
)
[
1
4
]
,
s
i
m
u
la
ted
an
n
ea
lin
g
(
S
A
)
[
1
5
]
,
an
d
p
ar
ticle
s
w
ar
m
o
p
tim
izatio
n
(
P
SO)
[
1
6
].
T
o
s
o
lv
e
u
n
i
t
co
m
m
it
m
e
n
t
p
r
o
b
lem
(
UC
P
)
,
it
h
a
s
b
ec
o
m
e
ev
id
en
t
th
a
t
t
h
e
r
esear
ch
er
s
c
o
n
ce
n
tr
ated
o
n
u
s
i
n
g
s
i
n
g
le
m
eta
h
eu
r
i
s
tic
s
.
Ho
w
e
v
er
,
th
er
e
ar
e
s
o
m
e
li
m
itat
io
n
s
to
th
is
.
T
o
o
v
er
co
m
e
th
i
s
p
r
o
b
lem
,
a
w
id
e
v
ar
iet
y
o
f
h
y
b
r
id
ap
p
r
o
ac
h
es
ar
e
p
r
o
p
o
s
ed
in
t
h
e
li
ter
a
tu
r
e.
T
h
e
co
r
e
id
ea
o
f
a
h
y
b
r
id
w
ith
t
w
o
o
r
m
o
r
e
m
eta
h
eu
r
i
s
tics
w
a
s
in
s
p
ir
ed
b
y
th
e
p
o
s
s
ib
ili
t
y
t
h
at
th
e
n
e
w
h
y
b
r
id
ized
alg
o
r
ith
m
co
m
b
i
n
es
th
e
s
tr
e
n
g
th
s
o
f
ea
ch
o
f
th
e
s
e
al
g
o
r
ith
m
s
to
p
r
o
v
id
e
th
e
f
o
llo
w
i
n
g
ad
v
a
n
ta
g
es:
(
i)
T
o
p
r
o
d
u
ce
b
etter
s
o
lu
t
io
n
s
,
(
ii)
to
p
r
o
v
id
e
s
o
lu
tio
n
s
in
le
s
s
ti
m
e.
Am
o
n
g
th
e
ex
is
ti
n
g
m
eta
-
h
eu
r
i
s
tic
alg
o
r
ith
m
s
,
ea
g
le
s
tr
ateg
y
(
E
S)
is
a
t
w
o
-
s
ta
g
e
m
et
h
o
d
r
ec
en
tl
y
p
r
o
p
o
s
ed
b
y
[
1
7
]
to
s
o
lv
e
o
p
tim
iza
tio
n
p
r
o
b
lem
s
.
I
n
t
h
i
s
t
w
o
-
s
ta
g
e
s
tr
a
teg
y
,
th
e
f
ir
s
t
s
ta
g
e
ex
p
lo
r
es
th
e
s
ea
r
c
h
s
p
ac
e
g
lo
b
all
y
b
y
u
s
in
g
t
h
e
s
o
-
ca
lled
lev
y
f
li
g
h
t;
i
f
it
f
i
n
d
s
a
p
r
o
m
is
i
n
g
s
o
lu
tio
n
,
th
e
n
an
in
te
n
s
i
v
e
lo
ca
l
s
ea
r
ch
i
s
e
m
p
lo
y
ed
u
s
in
g
a
m
o
r
e
ef
f
icie
n
t
lo
ca
l
o
p
ti
m
izer
,
s
u
ch
as
h
ill
-
cli
m
b
in
g
a
n
d
t
h
e
d
o
w
n
h
ill
s
i
m
p
lex
m
et
h
o
d
.
T
h
en
,
th
e
t
w
o
-
s
tag
e
p
r
o
ce
s
s
s
tar
ts
ag
ai
n
w
it
h
n
e
w
g
lo
b
al
ex
p
lo
r
atio
n
,
f
o
llo
w
ed
b
y
a
lo
ca
l
s
ea
r
ch
in
a
n
e
w
r
e
g
io
n
.
On
e
o
f
t
h
e
r
e
m
ar
k
ab
le
ad
v
a
n
tag
e
s
o
f
s
u
ch
a
co
m
b
i
n
atio
n
is
to
u
s
e
a
b
ala
n
ce
d
tr
ad
eo
f
f
b
et
w
ee
n
g
lo
b
al
s
ea
r
c
h
(
w
h
ich
is
g
e
n
er
all
y
s
lo
w
)
a
n
d
a
r
ap
id
lo
ca
l
s
ea
r
c
h
.
An
o
th
er
ad
v
an
ta
g
e
i
s
t
h
at
th
is
is
a
m
et
h
o
d
o
lo
g
y
o
r
s
tr
ate
g
y
,
n
o
t
an
al
g
o
r
ith
m
.
I
n
f
ac
t,
w
e
ca
n
u
s
e
d
if
f
er
e
n
t
al
g
o
r
ith
m
s
at
d
if
f
er
en
t
s
ta
g
es
an
d
at
d
if
f
er
en
t
ti
m
es
d
u
r
in
g
iter
atio
n
s
.
Up
to
n
o
w
,
m
o
s
t
p
u
b
lis
h
ed
w
o
r
k
s
o
n
E
S
co
m
b
in
ed
w
it
h
e
f
f
ic
ien
t
lo
ca
l
s
ea
r
ch
,
s
u
c
h
a
s
t
h
e
f
o
llo
w
er
p
o
lli
n
atio
n
alg
o
r
it
h
m
(
P
FA
)
[
1
8
]
,
an
d
d
if
f
er
e
n
tial
,
1
)
e
v
o
lu
tio
n
(
DE
)
[
1
9
]
m
ai
n
l
y
f
o
c
u
s
ed
o
n
s
o
lv
i
n
g
th
e
co
n
tin
u
o
u
s
o
p
ti
m
iza
tio
n
p
r
o
b
le
m
;
t
h
er
e
is
n
o
p
r
ev
io
u
s
wo
r
k
th
at
atte
m
p
t
s
to
u
s
e
E
S
in
co
n
j
u
n
ctio
n
w
i
th
a
lo
ca
l
o
p
tim
izer
,
s
u
ch
as
cr
o
w
s
ea
r
c
h
alg
o
r
ith
m
[
20
]
to
s
o
lv
e
th
e
u
n
it
co
m
m
i
t
m
e
n
t
p
r
o
b
lem
.
2
)
T
h
e
r
est
o
f
th
is
p
ap
er
is
o
r
g
a
n
ized
as
f
o
llo
w
s
.
Sectio
n
2
co
n
tain
s
t
h
e
m
at
h
e
m
atica
l
f
o
r
m
u
latio
n
o
f
th
e
UC
P
.
Secti
o
n
3
in
tr
o
d
u
ce
s
t
h
e
r
ep
air
in
g
m
ec
h
a
n
i
s
m
s
ap
p
lied
to
t
h
e
U
C
P
.
Sectio
n
4
b
r
ief
l
y
p
r
esen
ts
th
e
b
asi
cs
o
f
E
S
an
d
C
S
A
.
Sectio
n
5
p
r
o
p
o
s
es
t
h
e
b
in
ar
y
ea
g
le
s
tr
ate
g
y
b
ased
cr
o
w
s
ea
r
ch
alg
o
r
it
h
m
to
s
o
lv
e
UC
P
.
Sectio
n
6
p
r
o
v
id
es th
e
co
m
p
u
tatio
n
al
r
es
u
lt
s
.
Fin
all
y
,
Sectio
n
7
o
u
tli
n
es t
h
e
co
n
clu
s
io
n
s
.
2
.
F
O
R
M
UL
AT
I
O
N
O
F
UCP
2
.
1
.
O
bje
c
t
iv
e
f
un
ct
io
n
T
h
e
p
u
r
p
o
s
e
o
f
UC
P
is
p
r
in
c
ip
all
y
f
i
n
d
in
g
t
h
e
m
in
i
m
u
m
c
o
s
t
to
a
g
r
o
u
p
o
f
g
en
er
ato
r
s
b
y
t
u
r
n
i
n
g
ev
er
y
o
n
e
eit
h
er
o
n
o
r
o
f
f
o
v
er
a
s
p
ec
if
ic
ti
m
e
to
s
atis
f
y
t
h
e
lo
ad
s
an
d
m
ee
t
a
d
if
f
er
e
n
t
o
p
er
atio
n
al
co
n
s
tr
ai
n
ts
.
T
h
e
to
tal
co
s
t
F
o
v
er
t
h
e
w
h
o
l
e
s
c
h
ed
u
li
n
g
p
er
io
d
s
is
t
h
e
s
u
m
o
f
t
h
e
o
p
er
ati
n
g
co
s
t
a
n
d
s
t
ar
t
-
u
p
co
s
t
f
o
r
all
o
f
th
e
u
n
it.
T
h
er
ef
o
r
e,
th
e
o
b
j
ec
tiv
e
f
u
n
ctio
n
o
f
t
h
e
U
C
p
r
o
b
le
m
is
:
∑
∑
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esia
n
J
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&
C
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p
Sci
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N:
2502
-
4752
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a
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tr
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y
B
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(
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b
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ch
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19
W
h
er
e
H
is
to
tal
s
ch
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s
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n
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1
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d
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f
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=
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)
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er
all
y
,
f
i(
P
ih
)
d
en
o
tes th
e
f
u
e
l c
o
s
t p
er
u
n
it
w
h
ic
h
is
m
at
h
e
m
a
ticall
y
a
q
u
a
d
r
atic
f
u
n
ctio
n
:
(
)
(
2
)
W
h
er
e
ai,
b
i a
n
d
ci
r
ep
r
esen
t th
e
u
n
it c
o
s
t c
o
ef
f
ic
ien
t
s
.
T
h
e
s
tar
tu
p
co
s
t o
f
th
e
it
h
u
n
i
t
is
g
i
v
en
a
s
:
{
W
h
er
e
an
d
ar
e
th
e
h
o
t
s
tar
t
u
p
co
s
t
an
d
co
ld
s
tar
t
u
p
co
s
t
o
f
t
h
e
it
h
u
n
it;
is
t
h
e
co
n
ti
n
u
o
u
s
o
f
f
ti
m
e
d
u
r
atio
n
o
f
th
e
it
h
u
n
it;
is
th
e
m
i
n
i
m
u
m
d
o
w
n
ti
m
e
o
f
th
e
ith
u
n
it
;
is
th
e
co
ld
s
tar
t
h
o
u
r
s
o
f
th
e
it
h
u
n
it
.
2
.
2
.
C
o
ns
t
ra
ints
2
.
2
.
1
.
Sy
s
t
em
po
w
er
ba
la
nce
∑
W
h
er
e
is
s
y
s
te
m
lo
ad
d
em
a
n
d
at
ti
m
e
t.
2
.
2
.
2
.
Sy
s
t
e
m
s
pin
n
ing
re
s
er
v
e
re
qu
ire
m
ent
∑
W
h
er
e
is
s
p
in
n
i
n
g
r
eser
v
e
at
t
i
m
e
t.
2
.
2
.
3
.
G
ener
a
t
io
n po
w
er
li
m
it
s
W
h
er
e
an
d
ar
e
m
i
n
i
m
u
m
a
n
d
m
ax
i
m
u
m
g
e
n
er
atio
n
li
m
i
t o
f
u
n
i
t i,
r
esp
ec
tiv
el
y
2
.
2
.
4
.
Unit
m
i
ni
m
u
m
u
p t
i
m
e
On
ce
a
u
n
it
i
s
s
tar
ted
u
p
,
it
s
h
o
u
ld
n
o
t
b
e
s
h
u
t
-
d
o
w
n
b
e
f
o
r
e
a
m
i
n
i
m
u
m
u
p
-
ti
m
e
p
er
io
d
is
m
et
a
n
d
it
m
at
h
-
e
m
at
icall
y
ex
p
r
es
s
ed
f
o
r
ith
g
e
n
er
ati
n
g
u
n
it
as
f
o
llo
w
s
:
A
u
n
it
m
u
s
t
b
e
o
n
f
o
r
a
ce
r
ta
in
n
u
m
b
er
o
f
h
o
u
r
s
b
ef
o
r
e
it c
an
b
e
s
h
u
t d
o
w
n
.
W
h
er
e
is
co
n
tin
u
o
u
s
l
y
o
n
ti
m
e
o
f
u
n
it i
u
p
to
ti
m
e
h
,
is
u
n
i
t
i
m
in
i
m
u
m
u
p
ti
m
e.
2
.
2
.
5
.
Unit
m
i
ni
m
u
m
do
w
n t
im
e
On
ce
a
u
n
i
t
is
s
tar
ted
d
o
w
n
,
it
s
h
o
u
ld
n
o
t
b
e
s
h
u
t
-
u
p
b
ef
o
r
e
a
m
i
n
i
m
u
m
d
o
w
n
-
ti
m
e
p
er
io
d
is
m
et
an
d
it
m
at
h
e
m
atica
ll
y
e
x
p
r
ess
ed
f
o
r
ith
g
e
n
er
ati
n
g
u
n
it
as
f
o
llo
w
s
:
A
u
n
it
m
u
s
t
b
e
o
f
f
f
o
r
a
ce
r
tain
n
u
m
b
er
o
f
h
o
u
r
s
b
ef
o
r
e
it c
an
b
e
b
r
o
u
g
h
t
o
n
lin
e
W
h
er
e
is
co
n
tin
u
o
u
s
l
y
o
f
f
ti
m
e
o
f
u
n
it i
u
p
to
ti
m
e
h
,
is
u
n
i
t
i
m
in
i
m
u
m
d
o
w
n
ti
m
e.
2
.
2
.
6
.
Unit
ini
t
ia
l st
a
t
us
T
h
e
in
itial st
at
u
s
at
th
e
s
tar
t o
f
th
e
s
c
h
ed
u
li
n
g
p
er
io
d
m
u
s
t b
e
tak
en
i
n
to
ac
co
u
n
t.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4752
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l.
12
,
No
.
1
,
Octo
b
er
2
0
1
8
:
17
–
29
20
3.
T
H
E
RE
P
AIRI
NG
M
E
CH
ANIS
M
S F
O
R
T
H
E
UC
P
Sin
ce
t
h
e
s
ize
o
f
t
h
e
d
is
cr
ete
s
ea
r
ch
s
p
ac
e
o
f
th
e
UC
P
in
cr
ea
s
es
e
x
p
o
n
en
t
iall
y
w
it
h
t
h
e
i
n
cr
ea
s
i
n
g
n
u
m
b
er
o
f
u
n
it
s
to
b
e
s
ch
ed
u
l
ed
,
it
b
ec
o
m
es
a
m
at
h
e
m
atica
l
l
y
co
m
p
le
x
co
m
-
b
in
ato
r
ial
o
p
ti
m
izatio
n
p
r
o
b
lem
.
I
t
is
a
to
u
g
h
j
o
b
f
o
r
an
y
a
lg
o
r
ith
m
to
r
eg
ai
n
t
h
e
f
ea
s
ib
il
it
y
o
f
i
n
f
ea
s
ib
le
s
o
l
u
tio
n
s
o
f
s
u
c
h
p
r
o
b
le
m
s
,
w
h
ic
h
s
u
g
g
e
s
ts
t
h
e
u
s
e
o
f
s
o
m
e
m
ec
h
an
i
s
m
s
f
o
r
f
o
r
cib
l
y
s
ati
s
f
y
i
n
g
th
e
co
n
s
tr
ai
n
ts
o
f
a
p
r
o
b
lem
.
I
n
s
u
c
h
an
atte
m
p
t,
th
e
f
o
llo
w
in
g
f
o
u
r
r
ep
air
in
g
m
ec
h
a
n
i
s
m
s
,
t
h
e
f
ir
s
t
th
r
ee
o
f
w
h
ich
ar
e
u
s
ed
b
y
m
an
y
r
esear
ch
er
s
f
o
r
th
e
UC
P
[
2
1
–
2
6
]
,
ar
e
in
co
r
p
o
r
ated
in
th
e
p
r
o
p
o
s
ed
b
in
ar
y
-
ES
-
C
S
A
ad
d
r
ess
ed
in
Sectio
n
5
:
3
.
1
.
P
ri
o
rit
y
lis
t
f
o
r
un
it
-
s
ch
eduli
ng
P
r
io
r
ity
lis
t
is
m
ad
e
ac
co
r
d
in
g
to
ev
er
y
u
n
it
an
d
its
p
ar
a
m
e
ter
,
an
d
as
w
e
o
b
s
er
v
e
th
e
d
i
f
f
er
en
ce
in
th
e
o
u
tp
u
t
p
o
w
er
s
,
w
e
ca
n
s
ee
th
at
co
s
t
p
er
p
r
o
d
u
ce
d
o
f
a
u
n
it
at
it
s
m
ax
i
m
u
m
o
u
tp
u
t
p
o
w
er
i
s
le
s
s
th
a
n
th
at
o
th
er
o
u
tp
u
t
p
o
w
er
.
I
n
th
is
r
e
s
ea
r
ch
,
p
r
io
r
it
y
lis
t
is
b
ased
o
n
f
u
el
co
s
t
o
b
tain
ed
g
ai
n
ed
f
r
o
m
t
h
e
a
v
er
ag
e
f
u
el
co
s
t
o
f
ea
ch
u
n
it
o
p
er
atin
g
at
its
m
a
x
i
m
u
m
o
u
tp
u
t
p
o
w
er
.
T
h
e
a
v
er
ag
e
f
u
ll
-
lo
ad
co
s
t
a
o
f
a
u
n
it
is
d
e
f
i
n
ed
as
th
e
co
s
t
p
er
u
n
it
o
f
p
o
w
er
(
$
/
MW
)
w
h
e
n
th
e
u
n
it
is
at
its
f
u
ll
ca
p
ac
it
y
.
W
h
en
th
e
f
u
el
co
s
t
o
f
u
n
it
i
s
g
iv
e
n
b
y
Eq
u
atio
n
(
2
)
,
ca
n
b
e
ex
p
r
ess
ed
as:
T
h
e
u
n
its
ar
e
r
an
k
ed
b
y
th
eir
in
ascen
d
i
n
g
o
r
d
er
.
T
h
u
s
,
th
e
p
r
io
r
ity
lis
t
o
f
u
n
i
ts
w
ill
b
e
f
o
r
m
u
lated
b
ased
o
n
th
e
o
r
d
er
o
f
i,
in
w
h
i
ch
a
u
n
it
w
it
h
t
h
e
lo
w
e
s
t i
w
il
l
h
av
e
t
h
e
h
i
g
h
est p
r
io
r
it
y
to
b
e
d
is
p
atch
ed
.
3
.
2
.
Spinning
r
e
ser
v
e
c
o
nst
r
a
int
s
r
e
pa
ir
ing
T
h
e
s
p
in
n
i
n
g
r
eser
v
e
m
a
y
n
o
t
m
ee
t
th
e
s
tan
d
ar
d
s
o
f
t
h
e
o
b
tain
ed
p
r
im
ar
y
u
n
it
s
c
h
ed
u
li
n
g
u
s
i
n
g
B
in
ar
y
E
S
-
C
S
A
.
H
en
ce
,
t
h
e
h
eu
r
i
s
tic
s
ea
r
c
h
s
tr
ate
g
y
r
ep
ai
r
s
th
e
s
p
i
n
n
in
g
r
eser
v
e
co
n
s
tr
ain
ts
(
5
)
.
T
o
ex
p
lain
th
at,
i
n
o
r
d
er
to
k
ee
p
th
e
r
an
d
o
m
n
e
s
s
n
atu
r
e
o
f
E
S
-
C
S
A
w
h
ich
is
k
n
o
w
n
a
s
a
s
to
ch
a
s
tic
s
ea
r
ch
in
g
al
g
o
r
ith
m
,
a
co
n
s
tan
t
p
r
is
d
ef
i
n
ed
as
a
u
tili
za
t
io
n
r
atio
o
f
th
e
p
r
io
r
ity
lis
t.
T
h
e
p
r
o
ce
d
u
r
es
f
o
r
r
ep
air
in
g
t
h
e
s
p
i
n
n
i
n
g
r
eser
v
e
v
io
latio
n
s
i
n
p
r
i
m
ar
y
u
n
it sc
h
ed
u
l
in
g
ar
e
s
h
o
w
n
as
f
o
llo
w
s
[
2
6
]
:
Step
1
: Set
h
=
1
Step
2
: Fo
r
all
u
n
co
m
m
itted
u
n
its
a
t h
o
u
r
t,
ca
lcu
la
te
th
e
a
v
e
r
ag
e
f
u
ll
-
lo
ad
co
s
t
u
s
i
n
g
f
o
r
m
u
la
(
9
)
.
So
r
t th
e
m
in
asce
n
d
in
g
o
r
d
er
o
f
to
o
b
tain
a
lis
t S
S (
)
Step
3
: T
h
e
am
o
u
n
t o
f
e
x
ce
s
s
i
v
e
s
p
in
n
i
n
g
r
eser
v
e
at
ea
ch
h
o
u
r
is
ca
lcu
lated
b
y
:
∑
Step
4
: I
f
R
h
0
,
g
o
to
Step
6
;
Step
5
:
Gen
er
ate
a
r
an
d
o
m
n
u
m
b
er
[
0
;
1
]
.
I
f
<
p
r
,
co
m
m
it
an
u
n
co
m
m
itted
u
n
it
in
S
S
(
)
w
it
h
th
e
lo
w
es
t
an
d
r
etu
r
n
to
s
tep
3
;
Oth
er
w
is
e,
r
a
n
d
o
m
l
y
co
m
m
i
t
an
u
n
co
m
m
i
tted
u
n
it
i
n
SS
(
)
an
d
r
etu
r
n
to
s
tep
3
.
Step
6
: I
f
h
<
H,
h
=
h
+
1
an
d
r
etu
r
n
to
Step
2
.
Oth
er
w
is
e,
s
t
o
p
.
3
.
4
.
M
ini
m
u
m
u
p a
nd
do
w
n t
i
m
e
co
ns
t
ra
int
s
re
pa
iring
Sin
ce
t
h
e
o
b
tain
ed
u
n
i
t
s
c
h
e
d
u
le
m
a
y
n
o
t
m
ee
t
th
e
d
e
m
a
n
d
s
o
f
t
h
e
m
i
n
i
m
u
m
u
p
an
d
d
o
w
n
t
i
m
e
co
n
s
tr
ain
ts
/l
i
m
itatio
n
s
,
b
ec
au
s
e
th
e
y
ar
e
f
a
iled
in
t
h
e
p
r
ev
io
u
s
p
r
o
ce
s
s
.
T
h
u
s
,
t
h
e
y
o
u
g
h
t
t
o
b
e
ex
am
in
ed
an
d
f
i
x
ed
if
t
h
e
v
io
latio
n
s
ex
is
t.
T
h
e
m
i
n
i
m
u
m
d
o
w
n
t
i
m
e
i
s
u
s
u
all
y
v
io
lated
at
h
ig
h
lo
ad
lev
el
s
at
w
h
ic
h
th
e
p
ea
k
lo
ad
h
o
u
r
s
ar
e
s
h
o
r
ter
t
h
an
t
h
e
m
i
n
i
m
u
m
u
p
ti
m
e,
f
o
r
t
h
is
ca
u
s
e
th
e
h
ills
e
x
is
t.
I
n
al
m
o
s
t
th
e
s
a
m
e
w
a
y
,
t
h
e
m
i
n
i
m
u
m
d
o
w
n
ti
m
e
o
f
th
e
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n
its
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t
h
u
s
v
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y
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e
x
is
t.
He
u
r
is
tic
s
ea
r
ch
alg
o
r
it
h
m
w
il
l
b
e
u
s
ed
to
b
an
k
h
ill
s
an
d
f
ill
v
alle
y
s
.
T
o
ch
ec
k
f
o
r
v
io
latio
n
s
,
o
n
an
d
o
f
f
ti
m
e
s
o
f
u
n
its
ar
e
d
eter
m
i
n
ed
in
ad
v
a
n
ce
.
T
h
e
co
n
tin
u
o
u
s
l
y
o
n
/o
f
f
ti
m
es o
f
t
h
e
u
n
it i
u
p
to
h
o
u
r
t is ca
lc
u
lated
as
f
o
llo
w
s
:
{
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esia
n
J
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lec
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n
g
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C
o
m
p
Sci
I
SS
N:
2502
-
4752
E
a
g
le
S
tr
a
teg
y
B
a
s
ed
C
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w
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o
r
ith
m
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r
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o
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g
Un
it C
o
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(
R
a
ch
id
Ha
b
a
ch
i
)
21
{
T
h
e
d
etails
p
r
o
ce
d
u
r
es
to
r
ep
air
v
io
latio
n
s
o
f
th
e
m
in
i
m
u
m
u
p
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d
d
o
w
n
ti
m
es
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n
s
tr
ain
ts
ar
e
as
f
o
llo
w
s
[
2
6
]
:
Step
1
: Calcu
late
t
h
e
d
u
r
atio
n
o
n
an
d
o
f
f
ti
m
e
s
o
f
all
u
n
its
f
o
r
th
e
w
h
o
le
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ed
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le
ti
m
e
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o
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iz
o
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u
s
i
n
g
f
o
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m
u
las (
10
)
an
d
(
11
)
Step
2
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h
=1
Step
3
: Set
i=1
Step
4
:
an
d
an
d
th
en
s
e
t
.
Step
5
:
an
d
an
d
an
d
th
en
s
e
t
.
Step
6
:
an
d
an
d
an
d
∑
th
en
s
e
t
.
Step
7
: U
p
d
ate
th
e
d
u
r
atio
n
o
n
/o
f
f
ti
m
e
s
f
o
r
th
e
u
n
it i
u
s
i
n
g
u
s
in
g
f
o
r
m
u
las (
10
)
an
d
(
11
).
Step
8
:
an
d
.
Step
9
:
an
d
.
o
th
er
w
i
s
e,
s
to
p
.
3
.
5
.
Dec
o
m
m
it
m
ent
o
f
ex
ce
s
s
un
it
s
R
ep
air
in
g
th
e
m
in
i
m
u
m
u
p
/d
o
w
n
ti
m
e
co
n
s
tr
ai
n
ts
o
f
a
u
n
it
m
a
y
lead
to
ex
tr
e
m
e
s
p
i
n
n
i
n
g
r
eser
v
es
at
s
o
m
e
ti
m
e
in
s
ta
n
ts
t
h
at
ar
e
n
o
t
d
esira
b
le
f
r
o
m
t
h
e
p
o
in
t
o
f
o
p
er
atin
g
co
s
t.
Hen
ce
a
h
eu
r
i
s
ti
c
alg
o
r
ith
m
is
u
s
ed
to
d
ec
o
m
m
it
s
o
m
e
u
n
it
s
o
n
e
b
y
o
n
e,
i
n
d
esce
n
d
i
n
g
o
r
d
er
o
f
th
e
ir
av
er
a
g
e
f
u
l
l
lo
ad
co
s
ts
as
g
i
v
e
n
b
y
Eq
u
atio
n
(
3
.
1
)
,
u
n
til
th
e
s
p
in
n
in
g
r
eser
v
e
co
n
s
tr
ai
n
t
g
iv
e
n
b
y
E
q
.
(
2
.
5
)
is
j
u
s
t
s
ati
s
f
ied
at
an
y
ti
m
e
i
n
s
ta
n
t.
Ho
w
e
v
er
,
s
u
c
h
d
ec
o
m
m
it
m
e
n
t
is
m
ad
e
s
u
b
j
ec
t
to
th
e
s
ati
s
f
ac
tio
n
o
f
t
h
e
u
p
/d
o
w
n
ti
m
e
c
o
n
s
tr
ain
ts
o
f
a
u
n
i
t,
i.e
.
,
a
u
n
it
w
i
ll
b
e
d
ec
o
m
m
i
tted
o
n
l
y
i
f
n
o
u
p
/d
o
w
n
t
i
m
e
co
n
s
tr
ai
n
t
o
f
t
h
e
u
n
i
t
is
v
io
lated
f
r
o
m
s
u
c
h
d
ec
o
m
m
it
m
e
n
t.
4
.
O
VE
RVIE
W
O
F
E
A
G
L
E
S
T
RA
T
E
G
Y
AND
CRO
W
SE
AR
CH
AL
G
O
RI
T
H
M
4
.
1
.
E
g
a
le
Str
a
t
eg
y
E
ag
le
s
tr
ate
g
y
i
s
a
t
w
o
-
s
tag
e
o
p
tim
iza
tio
n
s
tr
ateg
y
w
as
p
r
esen
ted
b
y
[
1
7
]
.
T
h
is
alg
o
r
it
h
m
m
i
m
ic
s
b
eh
av
io
r
o
f
ea
g
les
in
n
at
u
r
e.
I
n
f
ac
t,
ea
g
les
u
s
e
t
w
o
d
i
f
f
er
e
n
t
co
m
p
o
n
en
ts
to
s
ea
r
ch
f
o
r
t
h
eir
p
r
e
y
.
T
h
e
f
ir
s
t
o
n
e
is
a
r
an
d
o
m
s
ea
r
ch
p
er
f
o
r
m
ed
b
y
f
l
y
i
n
g
f
r
ee
l
y
a
n
d
th
e
s
ec
o
n
d
o
n
e
is
a
n
i
n
te
n
s
i
v
e
s
ea
r
ch
to
ca
tch
p
r
e
y
w
h
e
n
th
e
y
s
ee
th
e
m
.
I
n
t
h
i
s
t
w
o
-
s
ta
g
e
s
tr
ate
g
y
,
th
e
f
ir
s
t
s
ta
g
e
ex
p
lo
r
es
t
h
e
s
ea
r
c
h
s
p
ac
e
g
lo
b
all
y
b
y
u
s
in
g
a
L
e
v
y
f
lig
h
t:
i
f
it
f
i
n
d
s
a
p
r
o
m
is
in
g
s
o
l
u
tio
n
,
th
e
n
an
i
n
ten
s
i
v
e
lo
ca
l
s
ea
r
ch
is
e
m
p
lo
y
ed
u
s
in
g
m
o
r
e
ef
f
icie
n
t
lo
ca
l
o
p
tim
izer
,
s
u
c
h
as
h
ill
-
cli
m
b
in
g
a
n
d
th
e
d
o
w
n
-
h
ill
s
i
m
p
le
x
m
e
th
o
d
.
T
h
en
,
th
e
t
w
o
-
s
ta
g
e
p
r
o
ce
s
s
co
m
m
e
n
ce
s
an
o
t
h
er
ti
m
e
w
i
t
h
n
e
w
g
lo
b
al
ex
p
lo
r
atio
n
,
f
o
l
lo
w
ed
b
y
lo
ca
l
s
ea
r
ch
i
n
a
n
e
w
ar
ea
.
O
n
e
o
f
t
h
e
r
e
m
ar
k
ab
le
ad
v
an
ta
g
es
o
f
s
u
c
h
a
co
m
b
in
a
tio
n
is
to
u
s
e
a
p
ar
allel
b
a
lan
ce
b
et
w
ee
n
g
lo
b
a
l
s
ea
r
ch
(
w
h
ich
is
g
en
er
all
y
s
lo
w
)
a
n
d
a
r
ap
id
lo
ca
l
s
ea
r
ch
.
T
h
er
e
is
a
n
o
t
h
er
a
d
v
an
ta
g
e
t
h
at
is
ca
lled
a
m
et
h
o
d
o
lo
g
y
o
r
s
tr
ate
g
y
,
n
o
t
an
al
g
o
r
ith
m
.
I
n
f
ac
t,
t
h
er
e
ar
e
d
if
f
er
en
t
alg
o
r
it
h
m
s
t
h
at
ca
n
b
e
u
s
ed
at
d
i
f
f
er
e
n
t
ti
m
e
s
an
d
s
ta
g
es
d
u
r
in
g
iter
atio
n
s
.
T
h
e
m
ai
n
s
tep
s
o
f
t
h
e
E
S a
r
e
o
u
tli
n
ed
in
A
l
g
o
r
ith
m
1
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4752
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l.
12
,
No
.
1
,
Octo
b
er
2
0
1
8
:
17
–
29
22
4
.
2
.
Cro
w
s
ea
rc
h
a
lg
o
rit
h
m
T
h
e
cr
o
w
s
ea
r
c
h
al
g
o
r
ith
m
(
C
S
A
)
i
s
a
n
e
w
p
o
p
u
la
tio
n
-
b
ased
s
to
ch
a
s
tic
s
ea
r
ch
a
lg
o
r
it
h
m
r
ec
e
n
tl
y
p
r
o
p
o
s
ed
b
y
[
20
]
.
T
h
e
C
S
A
is
a
n
e
w
l
y
d
ev
elo
p
ed
o
p
ti
m
izatio
n
tec
h
n
iq
u
e
to
s
o
lv
e
co
m
p
lex
e
n
g
i
n
ee
r
in
g
o
p
tim
izatio
n
p
r
o
b
le
m
s
[
2
7
]
,
[
2
8
]
.
I
t
is
in
s
p
ir
ed
b
y
th
e
in
tel
li
g
en
t
b
eh
a
v
io
r
o
f
cr
o
w
s
.
T
h
e
p
r
in
cip
les
o
f
C
S
A
ar
e
lis
ted
as f
o
llo
w
s
[
20
]:
a.
C
r
o
w
s
liv
e
i
n
t
h
e
f
o
r
m
o
f
th
e
f
lo
ck
.
b.
C
r
o
w
s
m
e
m
o
r
ize
th
e
p
o
s
itio
n
o
f
th
eir
h
id
in
g
p
lace
s
.
c.
C
r
o
w
s
f
o
llo
w
ea
c
h
o
th
er
to
co
m
m
it t
h
iev
er
y
.
d.
C
r
o
w
s
p
r
o
tect
th
eir
ca
ch
es
f
r
o
m
b
ein
g
p
ilf
er
ed
th
r
o
u
g
h
p
r
o
b
ab
ilit
y
.
Fo
llo
w
i
n
g
th
e
ab
o
v
e
as
s
u
m
p
t
io
n
s
,
th
e
co
r
e
m
ec
h
an
is
m
o
f
th
e
C
S
A
co
n
s
i
s
ts
o
f
th
r
ee
b
asic
p
h
ase
s
,
n
a
m
e
l
y
i
n
it
ializatio
n
,
g
e
n
er
at
e
a
n
e
w
p
o
s
itio
n
,
a
n
d
u
p
d
at
in
g
th
e
m
e
m
o
r
y
o
f
cr
o
w
s
.
A
t
f
ir
s
t,
th
e
i
n
it
ial
p
o
p
u
latio
n
o
f
cr
o
w
s
r
ep
r
esen
t
ed
b
y
n
d
i
m
e
n
s
io
n
is
r
a
ndo
ml
y
g
e
n
e
ra
t
e
d.
At
iter
atio
n
t,
t
h
e
p
o
s
itio
n
o
f
cr
o
w
is
s
p
ec
if
ied
b
y
[
]
an
d
it
is
ass
u
m
ed
th
at
th
i
s
cr
o
w
h
as
m
e
m
o
r
ized
its
b
est
ex
p
er
ien
ce
t
h
u
s
f
ar
i
n
it
s
m
e
m
o
r
y
[
]
T
o
g
en
er
ate
a
n
e
w
p
o
s
itio
n
,
cr
o
w
i
s
elec
t
r
an
d
o
m
l
y
a
cr
o
w
j
,
f
o
r
ex
a
m
p
le,
f
r
o
m
t
h
e
p
o
p
u
latio
n
a
n
d
atte
m
p
ts
to
f
o
llo
w
i
t
to
f
i
n
d
th
e
p
o
s
itio
n
o
f
i
ts
h
id
in
g
p
lace
(
m
j
)
.
I
n
th
i
s
ca
s
e,
ac
co
r
d
in
g
to
a
p
ar
a
m
eter
n
a
m
ed
a
w
ar
en
e
s
s
p
r
o
b
ab
ilit
y
(
A
P
)
,
t
w
o
s
tate
s
m
a
y
h
ap
p
en
:
a.
State
1
:
C
r
o
w
j
d
o
es
n
o
t
k
n
o
w
t
h
at
cr
o
w
i
is
f
o
llo
w
in
g
it.
As
a
r
esu
lt,
t
h
e
cr
o
w
i
w
ill
d
eter
m
i
n
e
th
e
h
id
in
g
p
lace
o
f
cr
o
w
j
.
b.
State
2
:
C
r
o
w
j
k
n
o
w
s
t
h
at
cr
o
w
j
is
f
o
llo
w
in
g
it.
A
s
a
r
esu
lt,
to
p
r
o
tect
its
ca
ch
e
f
r
o
m
b
e
in
g
p
il
f
er
ed
,
th
e
cr
o
w
j
w
i
ll f
o
o
l c
r
o
w
i b
y
g
o
in
g
to
an
o
th
er
p
o
s
itio
n
w
h
i
t
in
th
e
s
ea
r
ch
s
p
ac
e.
A
cc
o
r
d
in
g
to
States
1
an
d
2
,
th
e
p
o
s
itio
n
o
f
t
h
e
cr
o
w
s
is
u
p
d
ated
as f
o
llo
w
s
:
{
W
h
er
e
r
j
is
a
u
n
if
o
r
m
l
y
d
i
s
tr
ib
u
ted
f
u
zz
y
n
u
m
b
er
f
r
o
m
[
0
; 1
]
an
d
d
en
o
tes th
e
a
w
ar
e
n
es
s
p
r
o
b
ab
ilit
y
o
f
cr
o
w
j
at
iter
atio
n
iter
.
Fin
all
y
,
th
e
cr
o
w
s
u
p
d
ate
th
eir
m
e
m
o
r
y
as
f
o
llo
w
s
:
{
W
h
er
e
f
(
-
)
d
en
o
tes
th
e
o
b
j
ec
tiv
e
f
u
n
cti
o
n
v
alu
e.
I
t
is
s
ee
n
t
h
at
if
t
h
e
f
itn
e
s
s
f
u
n
ctio
n
v
alu
e
o
f
t
h
e
n
e
w
p
o
s
itio
n
o
f
a
cr
o
w
i
s
b
etter
t
h
an
t
h
e
f
it
n
es
s
f
u
n
ctio
n
v
al
u
e
o
f
th
e
m
e
m
o
r
ized
p
o
s
itio
n
,
t
h
e
cr
o
w
u
p
d
ates
its
m
e
m
o
r
y
b
y
t
h
e
n
e
w
p
o
s
itio
n
.
T
h
e
ab
o
v
e
p
r
o
ce
s
s
is
r
ep
ea
te
d
u
n
til
a
g
i
v
en
ter
m
in
at
io
n
cr
iter
io
n
(
iter
m
ax
)
is
m
et.
Fi
n
all
y
,
th
e
b
es
t
s
o
lu
tio
n
o
f
t
h
e
m
e
m
o
r
ies
i
s
r
etu
r
n
e
d
as
th
e
o
p
ti
m
al
s
o
lu
tio
n
f
o
u
n
d
b
y
th
e
C
S
A
.
T
h
e
m
ai
n
s
tep
s
o
f
t
h
e
C
S
A
ar
e
o
u
tl
in
ed
in
A
l
g
o
r
ith
m
2
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4752
E
a
g
le
S
tr
a
teg
y
B
a
s
ed
C
r
o
w
S
ea
r
ch
A
lg
o
r
ith
m
fo
r
S
o
lvin
g
Un
it C
o
mmitmen
t
…
(
R
a
ch
id
Ha
b
a
ch
i
)
23
5
.
B
I
NARY
E
A
G
L
E
S
T
R
A
T
E
G
Y
B
AS
E
D
CRO
W
SE
ARCH
AL
G
O
RI
T
H
M
F
O
R
UCP
T
h
e
b
in
ar
y
E
S
-
C
S
A
is
u
s
ed
to
o
p
tim
i
s
e
t
h
e
u
n
it
-
s
c
h
ed
u
lin
g
p
r
o
b
lem
i
n
t
h
e
f
ir
s
t
s
te
p
,
an
d
th
e
L
a
m
b
d
a
-
iter
atio
n
m
et
h
o
d
[
4
]
is
u
s
ed
to
s
o
lv
e
th
e
ec
o
n
o
m
ic
lo
ad
d
is
p
atch
p
r
o
b
lem
in
t
h
e
s
ec
o
n
d
s
tep
.
T
h
ese
t
w
o
s
tep
s
r
u
n
iter
at
iv
el
y
u
n
til
th
e
al
g
o
r
ith
m
m
ee
ts
th
e
s
to
p
p
in
g
cr
i
ter
io
n
.
Op
ti
m
is
i
n
g
t
h
e
f
i
r
s
t
s
u
b
p
r
o
b
lem
o
f
u
n
i
t
-
s
ch
ed
u
li
n
g
is
m
o
r
e
d
if
f
ic
u
lt
t
h
a
n
t
h
e
o
th
er
s
u
b
-
p
r
o
b
lem
o
f
E
L
D.
So
t
h
is
p
ap
er
m
ai
n
l
y
d
is
c
u
s
s
e
s
h
o
w
to
m
o
d
el
B
E
S
C
S
A
f
o
r
th
e
f
ir
s
t
s
u
b
-
p
r
o
b
le
m
,
an
d
t
h
e
s
ec
o
n
d
s
u
b
-
p
r
o
b
le
m
is
s
o
lv
ed
b
y
th
e
t
r
ad
itio
n
al
L
a
m
b
d
a
-
iter
atio
n
m
et
h
o
d
.
T
h
ese
t
w
o
s
u
b
-
p
r
o
b
lem
s
ar
e
o
p
ti
m
is
ed
i
ter
ativ
el
y
u
n
til
th
e
al
g
o
r
ith
m
m
ee
ts
t
h
e
s
to
p
p
in
g
cr
iter
io
n
.
T
h
e
E
q
u
atio
n
s
(
9
)
an
d
(
14
)
ar
e
tr
an
s
f
er
f
r
o
m
co
n
ti
n
u
e
s
to
b
in
ar
y
s
p
ac
e
u
s
i
n
g
t
h
e
f
o
llo
w
i
n
g
eq
u
atio
n
s
:
{
W
h
er
e
=
,
y
=
1
+
an
d
r
an
d
(
)
is
a
r
an
d
o
m
n
u
m
b
er
f
r
o
m
u
n
i
f
o
r
m
d
is
tr
ib
u
tio
n
[
0
;
1
]
a
n
d
is
th
e
u
p
d
ated
b
in
ar
y
p
o
s
it
io
n
at
iter
iter
atio
n
.
5
.
1
.
So
lutio
n r
epre
s
ent
a
t
io
n
a
nd
I
nitia
liza
t
io
n
B
ef
o
r
e
u
s
i
n
g
t
h
e
p
r
o
p
o
s
ed
b
i
n
ar
y
E
S
-
C
S
A
to
s
o
l
v
e
U
C
P
,
th
e
r
ep
r
esen
tatio
n
o
f
a
cr
o
w
m
u
s
t
b
e
d
ef
in
ed
.
A
cr
o
w
is
al
s
o
ca
lled
an
i
n
d
i
v
id
u
al.
Hen
ce
,
w
e
d
ef
i
n
ed
ea
ch
u
n
it
o
n
/o
f
f
(
o
r
1
/0
)
s
tatu
s
as
a
g
e
n
e,
all
av
ailab
le
u
n
it
s
tat
u
s
at
ea
c
h
h
o
u
r
m
ak
e
u
p
a
s
u
b
-
c
h
r
o
m
o
s
o
m
e,
a
n
d
th
er
e
ar
e
H
s
u
b
-
c
h
r
o
m
o
s
o
m
e
s
o
v
er
th
e
ti
m
e
h
o
r
izo
n
H
co
m
p
r
is
i
n
g
a
n
i
n
d
iv
id
u
al.
An
i
n
d
iv
id
u
al
wo
u
ld
d
is
p
la
y
th
e
u
n
it
co
m
m
it
m
en
t
s
c
h
ed
u
le
o
v
er
th
e
ti
m
e
h
o
r
izo
n
H.
T
h
e
o
n
/
o
f
f
s
c
h
ed
u
le
o
f
t
h
e
u
n
it
s
is
s
to
r
e
d
as
an
i
n
te
g
er
-
m
atr
i
x
U
w
it
h
d
i
m
en
s
io
n
N
G
H.
A
m
atr
i
x
r
ep
r
esen
tatio
n
o
f
an
in
d
iv
id
u
al
i
n
th
e
p
o
p
u
latio
n
is
s
h
o
w
n
a
s
f
o
llo
w
s
:
W
h
er
e
u
h
i i
s
u
n
it o
n
/o
f
f
s
tat
u
s
o
f
u
n
it i
at
ti
m
e
h
(
u
h
i =
1
=0
f
o
r
o
n
/o
f
f
)
.
I
n
th
e
i
n
it
ializatio
n
p
r
o
ce
s
s
,
a
s
et
o
f
i
n
d
iv
id
u
als
i
s
cr
ea
ted
at
r
an
d
o
m
.
Fo
r
th
e
co
m
p
lete
N
P
p
o
p
u
latio
n
,
th
e
ca
n
d
id
ate
s
o
lu
tio
n
o
f
ea
c
h
in
d
iv
id
u
al
Uj
;
(
j
=
1
;
2
;
:::;
N
P
)
is
r
an
d
o
m
l
y
i
n
it
ialized
.
T
h
e
p
o
s
it
io
n
u
h
i o
f
ea
c
h
cr
o
w
Uj
is
g
e
n
er
ated
u
s
i
n
g
a
u
n
i
f
o
r
m
d
i
s
tr
ib
u
ted
r
an
d
o
m
f
u
n
ct
io
n
,
w
h
ich
g
e
n
er
ates e
ith
er
0
o
r
1
an
d
th
e
y
ar
e
eq
u
all
y
lik
el
y
.
5
.
2
.
G
ener
a
t
e
new
s
o
lutio
ns
As
m
e
n
tio
n
ed
ab
o
v
e,
th
e
E
S
i
s
a
t
w
o
-
s
tag
e
s
tr
ateg
y
,
an
d
w
e
ca
n
u
s
e
d
if
f
er
en
t
al
g
o
r
ith
m
s
a
t
d
if
f
er
e
n
t
s
tag
e
s
.
I
n
t
h
e
f
ir
s
t
s
ta
g
e,
E
S
u
s
es
t
h
e
s
o
-
ca
lled
L
ev
y
f
lig
h
ts
,
w
h
ic
h
r
ep
r
esen
t
a
k
i
n
d
o
f
n
o
n
-
Ga
u
s
s
ian
s
to
ch
ast
ic
p
r
o
ce
s
s
w
h
o
s
e
s
te
p
s
izes
ar
e
d
is
tr
ib
u
ted
b
ased
o
n
a
L
e
v
y
s
tab
le
d
is
tr
ib
u
tio
n
to
g
en
er
ate
n
e
w
s
o
lu
tio
n
s
.
W
h
e
n
a
n
e
w
s
o
l
u
tio
n
is
p
r
o
d
u
ce
d
,
th
e
f
o
llo
w
i
n
g
L
ev
y
f
li
g
h
t is ap
p
lied
:
Her
e,
is
th
e
s
tep
s
ize
th
at
is
r
elev
an
t
to
th
e
s
ca
les
o
f
t
h
e
p
r
o
b
lem
.
T
h
e
p
r
o
d
u
ct
m
ea
n
s
en
tr
y
-
w
i
s
e
m
u
ltip
licatio
n
s
.
L
ev
y
f
lig
h
t
s
ess
e
n
tiall
y
p
r
o
v
id
e
a
r
an
d
o
m
w
al
k
w
h
ile
t
h
eir
r
an
d
o
m
s
tep
s
ar
e
d
r
aw
n
f
r
o
m
a
L
e
v
y
d
is
tr
ib
u
tio
n
f
o
r
lar
g
e
s
te
p
s
:
I
n
th
is
p
ap
er
,
w
e
w
i
ll
u
s
e
t
h
e
Ma
n
te
g
n
a
al
g
o
r
ith
m
[
2
8
]
,
w
h
i
ch
is
o
n
e
o
f
th
e
m
o
s
t
ef
f
icie
n
t
alg
o
r
ith
m
s
u
s
ed
to
i
m
p
le
m
en
t
L
e
v
y
f
l
ig
h
ts
.
W
e
ass
u
m
e
th
at
L
e
v
y
(
)
=
s
,
s
o
th
e
f
o
r
m
u
la
ca
n
al
s
o
b
e
d
escr
ib
e
d
a
s
f
o
llo
w
s
:
B
y
u
s
i
n
g
Ma
n
te
g
n
a
’
s
al
g
o
r
ith
m
[
2
6
]
,
th
e
s
tep
len
g
th
s
ca
n
b
e
ca
lcu
lated
as f
o
llo
w
s
:
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4752
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l.
12
,
No
.
1
,
Octo
b
er
2
0
1
8
:
17
–
29
24
B
y
u
s
i
n
g
Ma
n
te
g
n
a
’
s
al
g
o
r
ith
m
[
2
6
]
,
th
e
s
tep
len
g
th
s
ca
n
b
e
ca
l
cu
lated
as f
o
llo
w
s
:
|
|
⁄
W
h
er
e
d
r
aw
f
r
o
m
t
h
e
n
o
r
m
al
d
itru
tio
n
s
r
esp
ec
ti
v
el
y
.
th
a
t
is
:
an
d
ar
e
ca
lcu
lated
as
f
o
llo
w
s
(
)
,
Her
e
0
an
d
(
:)
is
th
e
Ga
m
m
a
f
u
n
ctio
n
.
Fo
r
th
e
s
ec
o
n
d
s
ta
g
e,
w
e
ca
n
u
s
e
t
h
e
cr
o
w
s
ea
r
c
h
al
g
o
r
ith
m
(
C
S
A
)
f
o
r
th
e
i
n
te
n
s
i
v
e
lo
ca
l
s
ea
r
ch
.
W
e
k
n
o
w
t
h
e
C
S
A
is
a
g
lo
b
al
s
ea
r
ch
alg
o
r
ith
m
,
b
u
t
it
ca
n
ea
s
il
y
b
e
tu
n
ed
to
d
o
an
ef
f
icie
n
t
lo
ca
l
s
ea
r
c
h
b
y
li
m
iti
n
g
n
e
w
s
o
lu
tio
n
s
lo
ca
ll
y
ar
o
u
n
d
t
h
e
m
o
s
t
p
r
o
m
i
s
i
n
g
r
eg
io
n
.
A
s
m
en
tio
n
ed
ab
o
v
e,
in
th
e
C
S
A
,
t
h
er
e
ar
e
t
w
o
s
p
ec
i
f
ic
p
ar
am
eter
s
:
a
w
ar
e
n
es
s
p
r
o
b
ab
ilit
y
(
A
P
)
an
d
f
li
g
h
t
le
n
g
t
h
(
f
l)
.
S
m
all
v
al
u
es
o
f
A
P
in
ten
s
i
f
y
t
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ith
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it to
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ith
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ica
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al
la
m
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d
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m
et
h
o
d
[
4
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u
s
ed
to
s
o
lv
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th
e
E
L
D
p
r
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b
lem
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th
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ap
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lv
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m
3
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5
.
4
.
So
lutio
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th
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s
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tio
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th
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n
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v
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le
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ased
cr
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d
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en
th
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m
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s
ed
to
s
o
lv
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th
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s
u
b
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b
le
m
E
DP
.
T
h
e
m
ain
s
tep
s
ar
e
lis
ted
as f
o
llo
w
:
Step
1
:
R
an
d
o
m
l
y
i
n
itialize
t
h
e
p
ar
a
m
eter
s
(
R
;
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P
;
f
l;
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p
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,
f
ea
s
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le
v
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d
i
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lize
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m
e
m
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ea
ch
cr
o
w
at
t =
0
as in
Sec
tio
n
4
.
4
.
1
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2
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late
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r
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r
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s
t
o
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s
in
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3
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1
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if
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t’
s
s
tatu
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o
f
i
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d
iv
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als
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w
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ati
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y
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s
p
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n
n
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r
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Sectio
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3
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3
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2
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air
ea
ch
cr
o
w
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t
h
e
s
w
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m
f
o
r
m
i
n
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m
u
m
u
p
/d
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wn
ti
m
e
v
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latio
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s
as i
n
Sect
io
n
3
.
3
.
3
.
Step
5
:
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o
m
m
i
t
u
n
its
o
f
ea
ch
cr
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w
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n
th
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s
w
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m
to
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ce
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x
ce
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Sectio
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3
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3
.
4
Step
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lv
e
E
L
D
p
r
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b
le
m
u
s
in
g
eq
u
al
la
m
b
d
a
-
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m
e
th
o
d
as in
Sect
io
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5
.
3
Step
7
:
Fi
t
n
es
s
e
v
al
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o
f
all
cr
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w
s
.
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a
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la
te
t
h
e
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it
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f
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in
g
t
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b
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1
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h
cr
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w
i
n
t
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p
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p
u
latio
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.
Step
8
: G
e
n
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ate
a
s
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cr
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ws X
f
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ale
ex
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g
t
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t
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t
o
Sectio
n
5
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5
.
1
an
d
r
u
n
s
tep
s
3
-
7
Step
9
: U
p
d
ate
th
e
m
e
m
o
r
y
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f
cr
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w
s
ac
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g
to
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q
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(
14
)
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1
0
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ith
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3
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1
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m
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f
cr
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14
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Step
1
3
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f
(
a
b
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)
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d
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RE
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3
2
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,
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1
4
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,
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2
5
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.
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a
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