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it
y
f
u
n
ctio
n
s
s
u
c
h
as
o
r
ig
i
n
al
C
o
b
b
-
Do
u
g
la
s
,
q
u
asi
li
n
ea
r
,
p
er
f
ec
t
s
u
b
s
tit
u
te
u
t
ilit
y
f
u
n
ctio
n
s
t
h
at
ar
e
u
s
ed
in
th
r
ee
t
y
p
e
s
o
f
p
r
ici
n
g
s
c
h
e
m
e
s
f
o
r
in
f
o
r
m
at
io
n
s
er
v
ice
s
t
h
at
ar
e
f
lat
f
ee
,
u
s
a
g
e
b
ased
a
n
d
t
w
o
-
p
ar
t
tar
i
f
f
b
o
th
an
al
y
ticall
y
[
1
1
]
,
an
d
n
u
m
er
ic
all
y
w
it
h
t
h
e
h
e
lp
o
f
L
I
NGO
1
1
.
0
s
o
f
t
w
ar
e
ap
p
licatio
n
s
[
1
2
]
,
[
1
3
]
.
B
ased
o
n
th
ese
r
es
u
lts
,
a
n
e
w
m
et
h
o
d
o
f
s
ea
r
c
h
i
n
g
in
f
o
r
m
a
tio
n
s
er
v
ices
b
y
co
n
s
id
er
i
n
g
t
h
e
f
u
n
ctio
n
o
f
t
h
e
p
r
ec
is
e
u
tili
t
y
f
u
n
ct
io
n
h
av
e
p
r
o
v
e
n
to
g
en
er
ate
h
u
g
e
p
r
o
f
it
s
f
o
r
I
SP
s
to
ad
o
p
t
th
is
t
y
p
e
o
f
p
r
icin
g
s
c
h
e
m
es,
ar
e
av
ailab
le,
b
u
t
t
h
e
s
tu
d
y
o
n
l
y
o
n
t
h
e
s
elec
tio
n
o
f
u
til
it
y
f
u
n
ctio
n
s
th
at
ca
n
m
ax
i
m
iz
in
g
p
r
o
f
its
f
o
r
I
SP
s
a
n
d
ig
n
o
r
e
th
e
m
ar
g
i
n
al
co
s
t
s
an
d
m
o
n
ito
r
in
g
co
s
ts
.
Ot
h
er
r
esear
ch
also
co
n
s
id
er
p
r
icin
g
s
c
h
e
m
e
o
f
in
f
o
r
m
a
tio
n
s
er
v
ices
w
it
h
r
eg
ar
d
to
p
er
f
ec
t
s
u
b
s
t
itu
te
[
1
4
]
an
d
C
o
b
b
-
Do
u
g
la
s
u
tili
t
y
f
u
n
ctio
n
s
[
1
5
]
w
h
er
e
b
y
u
s
i
n
g
t
h
r
ee
p
r
icin
g
s
tr
ateg
ies,
a
n
d
co
n
s
id
e
r
in
g
m
ar
g
i
n
al
a
n
d
m
o
n
ito
r
in
g
co
s
ts
,
t
h
e
o
p
ti
m
al
ca
s
e
f
o
r
ea
c
h
co
n
s
u
m
er
ca
n
b
e
o
b
tain
ed
.
B
ased
o
n
th
at,
th
e
a
u
th
o
r
s
atte
m
p
t
to
p
r
o
ce
ed
w
it
h
o
th
er
u
tili
t
y
f
u
n
ctio
n
to
b
e
an
al
y
ze
d
to
f
lat
f
ee
,
u
s
a
g
e
b
ased
p
r
icin
g
s
tr
ateg
ie
s
.
I
n
g
en
er
al,
th
e
m
ar
g
i
n
al
co
s
ts
ar
e
d
ef
i
n
ed
as
t
h
e
co
s
t
s
ad
j
u
s
ted
to
t
h
e
le
v
el
o
f
p
r
o
d
u
ctio
n
o
f
g
o
o
d
s
w
h
ic
h
is
r
es
u
lti
n
g
d
if
f
er
en
ce
s
in
f
ix
ed
co
s
t
s
d
u
e
to
th
e
ad
d
itio
n
o
f
t
h
e
n
u
m
b
er
o
f
u
n
it
s
p
r
o
d
u
ce
d
,
w
h
ile
t
h
e
co
s
t
o
f
m
o
n
ito
r
in
g
i
s
th
e
co
s
t
i
n
cu
r
r
ed
b
y
t
h
e
co
m
p
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y
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m
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ito
r
an
d
co
n
tr
o
l
th
e
ac
ti
v
it
ie
s
ca
r
r
ied
o
u
t
b
y
th
e
ag
en
c
y
in
m
a
n
a
g
i
n
g
co
m
p
an
y
.
I
n
f
ac
t,
th
e
m
ar
g
i
n
al
co
s
t
a
n
d
th
e
co
s
t
o
f
m
o
n
i
to
r
in
g
i
s
also
an
i
m
p
o
r
tan
t
is
s
u
e
in
th
e
d
ev
elo
p
m
e
n
t
o
f
i
n
f
o
r
m
atio
n
s
er
v
ices
p
r
i
m
ar
i
l
y
a
f
f
ec
ts
th
e
m
a
x
i
m
u
m
o
b
j
ec
tiv
e
f
u
n
ctio
n
f
o
r
th
r
ee
p
r
icin
g
s
c
h
e
m
e
s
ar
e
f
lat
f
ee
,
u
s
a
g
e
-
b
ased
an
d
t
w
o
-
p
ar
t
tar
i
f
f
.
T
o
th
at
e
n
d
,
it
is
n
ec
e
s
s
ar
y
s
tu
d
y
o
n
m
ar
g
i
n
al
co
s
ts
an
d
t
h
e
co
s
t
o
f
m
o
n
ito
r
i
n
g
th
e
p
r
ici
n
g
s
c
h
e
m
e
s
i
n
v
o
l
v
in
g
in
f
o
r
m
a
tio
n
s
er
v
ice
s
u
t
ilit
y
f
u
n
ctio
n
s
t
h
at
ar
e
o
f
ten
u
s
ed
,
w
h
ic
h
is
p
er
f
ec
t
s
u
b
s
tit
u
te
u
ti
lit
y
f
u
n
ct
io
n
,
q
u
asi
lin
ea
r
u
t
ilit
y
f
u
n
c
tio
n
,
an
d
C
o
b
b
-
Do
u
g
la
s
f
u
n
ctio
n
.
T
h
en
th
e
m
ai
n
co
n
tr
o
b
u
t
io
n
o
f
th
is
p
ap
er
is
b
asicall
y
to
ex
te
n
d
th
e
ap
p
licatio
n
o
f
t
h
e
p
r
ici
n
g
s
c
h
e
m
e
o
f
p
r
icin
g
s
tr
ateg
ies o
f
i
n
f
o
r
m
atio
n
s
er
v
ice
w
it
h
r
eg
ar
d
to
m
ar
g
in
al
a
n
d
m
o
n
ito
r
in
g
co
s
ts
t
o
en
ab
le
p
r
o
v
id
er
to
h
av
e
o
th
er
i
n
s
i
g
h
t
o
n
th
e
ad
v
a
n
tag
e
o
f
ap
p
l
y
i
n
g
m
ar
g
in
al
a
n
d
m
o
n
ito
r
in
g
co
s
ts
to
in
f
o
r
m
at
io
n
s
er
v
ice
p
r
icin
g
s
ch
e
m
e
an
d
w
it
h
t
h
e
u
s
ag
e
o
f
q
u
asi li
n
ea
r
u
til
it
y
f
u
n
ct
io
n
.
2.
RE
S
E
ARCH
M
E
T
H
O
D
Step
s
co
n
d
u
cted
i
n
th
i
s
r
esear
ch
ar
e
as f
o
llo
w
s
.
1.
Dete
r
m
i
n
e
t
h
e
i
n
f
o
r
m
atio
n
s
er
v
ice
p
r
icin
g
s
ch
e
m
e
m
o
d
els
a
cc
o
r
d
in
g
to
q
u
a
s
i
li
n
ea
r
,
u
tili
t
y
f
u
n
ctio
n
s
w
it
h
f
lat
f
ee
,
u
s
a
g
e
-
b
ased
,
d
an
t
w
o
-
p
ar
t ta
r
if
f
p
r
icin
g
s
c
h
e
m
e
f
o
r
h
o
m
o
g
en
eo
u
s
an
d
h
eter
o
g
en
e
o
u
s
co
n
s
u
m
er
s
.
a.
Fo
r
f
lat
f
ee
p
r
icin
g
s
c
h
e
m
e
,
,
an
d
P
ad
alah
is
p
o
s
itiv
e.
b.
Fo
r
u
s
ag
e
-
b
ased
s
c
h
e
m
e
,
an
d
ar
e
p
o
s
itiv
e
an
d
P
=
0
.
c.
Fo
r
t
w
o
-
p
ar
t ta
r
i
f
f
s
ch
e
m
e
,
P,
an
d
ar
e
p
o
s
itiv
e.
2.
Fo
r
m
u
la
te
q
u
a
s
i
li
n
ea
r
u
tili
t
y
f
u
n
c
tio
n
ac
co
r
d
in
g
to
f
lat
f
ee
,
u
s
a
g
e
-
b
ased
,
d
an
t
w
o
-
p
ar
t
tar
if
f
p
r
ici
n
g
s
ch
e
m
es
f
o
r
h
o
m
o
g
e
n
eo
u
s
an
d
h
eter
o
g
e
n
eo
u
s
co
n
s
u
m
e
r
s
w
ith
p
a
y
i
n
g
a
tten
tio
n
to
m
ar
g
i
n
al
a
n
d
m
o
n
ito
r
i
n
g
co
ts
.
5
.
P
r
o
ce
s
s
m
ai
l f
r
o
m
lo
c
al
s
er
v
er
.
6.
A
p
p
l
y
t
h
e
o
p
ti
m
al
p
r
ici
n
g
s
ch
e
m
e
o
f
lo
ca
l d
ata
s
er
v
er
o
f
m
a
il tr
af
f
ic
d
ata.
7.
C
o
m
p
ar
e
th
e
p
r
icin
g
s
ch
e
m
e
m
o
d
el
s
to
ea
ch
u
tili
t
y
f
u
n
ctio
n
p
r
ev
io
u
s
l
y
d
escr
ib
ed
in
p
r
e
v
io
u
s
r
esear
ch
p
r
o
p
o
s
ed
b
y
[
1
4
]
,
[
15]
.
8
.
C
o
n
cl
u
d
e
an
d
o
b
tain
th
e
b
est
s
o
lu
tio
n
o
f
i
n
f
o
r
m
atio
n
s
er
v
ice
p
r
icin
g
s
c
h
e
m
e.
3.
RE
SU
L
T
S
A
ND
AN
AL
Y
SI
S
T
h
is
s
ec
tio
n
d
i
s
cu
s
s
e
s
o
th
er
u
tili
t
y
f
u
n
ct
io
n
t
h
at
i
s
also
w
ell
k
w
o
w
n
n
a
m
el
y
q
u
asi
li
n
ea
r
u
t
ilit
y
f
u
n
ctio
n
.
Or
i
g
i
n
al
C
o
b
b
-
Do
u
g
la
s
[
1
5
]
,
p
er
f
ec
t
s
u
b
s
t
itu
te
[
1
6
]
an
d
m
o
d
i
f
ied
C
o
b
b
-
Do
u
g
las
u
t
ilit
y
f
u
n
ctio
n
s
[
9
]
,
[
1
7
]
ar
e
al
r
ea
d
y
d
i
s
cu
s
s
ed
in
p
r
ev
io
u
s
r
ese
ar
ch
,
b
u
t
th
e
co
m
p
ar
is
o
n
f
r
o
m
all
th
e
s
e
u
ti
lit
y
f
u
n
ctio
n
s
ar
e
s
h
o
w
ed
to
ex
p
lain
th
e
b
est
u
til
it
y
f
u
n
ct
io
n
t
h
at
ca
n
m
a
x
i
m
ize
t
h
e
p
r
o
f
it o
f
I
S
P
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
E
C
E
I
SS
N:
2
0
8
8
-
8708
Utilit
y
F
u
n
ctio
n
-
B
a
s
ed
P
r
icin
g
S
tr
a
teg
ies in
Ma
ximizi
n
g
th
e
I
n
fo
r
m
a
tio
n
S
ervice
.
.
.
.
(
R
o
b
i
n
s
o
n
S
itep
u
)
879
3
.
1
.
M
o
del o
f
P
ricing
Sche
m
e
B
a
s
ed
Q
ua
s
i li
nea
r
Ut
ility
F
un
ct
io
n
T
h
e
g
en
er
al
f
o
r
m
o
f
u
ti
lit
y
f
u
n
ctio
n
b
ased
o
n
t
h
e
q
u
asi
li
n
e
ar
U
(
X
,
Y
)
=
a
X
+
f
(
Y
)
,
w
h
er
e
f
(
Y
)
=
Y
b
Her
e,
q
u
asi
lin
ea
r
u
tili
t
y
f
u
n
c
tio
n
an
al
y
ze
d
f
o
r
h
o
m
o
g
e
n
eo
u
s
an
d
h
eter
o
g
e
n
eo
u
s
co
n
s
u
m
er
s
(
h
i
g
h
-
e
n
d
an
d
lo
w
-
en
d
)
as
w
ell
a
s
h
e
ter
o
g
en
eo
u
s
(
h
i
g
h
-
d
e
m
a
n
d
an
d
lo
w
-
d
e
m
an
d
)
co
n
s
u
m
er
s
ar
e
b
ased
o
n
th
r
ee
s
tr
ateg
ie
s
o
f
p
r
icin
g
s
c
h
e
m
e
s
t
h
at
p
r
icin
g
s
ch
e
m
e
s
f
lat
-
f
ee
,
p
r
icin
g
s
ch
e
m
es
u
s
a
g
e
-
b
ased
,
t
w
o
-
p
ar
t
p
r
icin
g
s
c
h
e
m
e
tar
if
f
.
3
.
2
.
H
o
m
o
g
eneo
us
Co
n
s
u
m
er
C
o
n
s
u
m
er
Op
ti
m
izatio
n
P
r
o
b
le
m
s
w
i
ll b
e
as f
o
llo
w
s
.
(
)
(
)
(
1
)
Su
b
j
ec
t to
̅
(
2
)
̅
(
3
)
(
)
(
)
(
4
)
(
5
)
Op
ti
m
izatio
n
P
r
o
b
lem
s
o
f
th
e
p
r
o
v
id
er
s
w
ill b
e
as
f
o
llo
w
s
.
∑
(
)
(
6
)
w
it
h
(
X
*
,
Y*
,
Z*
)
=
ar
g
m
ax
(
)
–
P
x
X
–
P
y
Y
–
PZ
(
)
Su
b
j
ec
t to
̅
̅
(
)
–
–
–
(
)
Fo
r
u
s
ag
e
-
b
ased
p
r
icin
g
s
c
h
e
m
e
an
d
a
t
w
o
-
p
ar
t ta
r
i
f
f
:
C
o
n
s
u
m
er
Op
ti
m
izatio
n
P
r
o
b
le
m
s
:
(
)
(
)
(
)
(
7
)
w
it
h
co
n
s
tr
ain
ts
:
̅
(
8
)
̅
(9
)
(
)
(
)
(
)
(
1
0
)
(
1
1
)
Op
ti
m
izatio
n
P
r
o
b
lem
s
o
f
P
r
o
v
id
er
s
:
∑
(
)
(
1
2
)
w
it
h
(
X
*
,
Y*
,
Z*
)
=
ar
g
m
ax
(
)
–
P
x
X
–
P
y
Y
–
PZ
(
)
(
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
J
E
C
E
Vo
l.
7
,
No
.
2
,
A
p
r
il 2
0
1
7
:
8
7
7
–
8
8
7
880
w
it
h
co
n
s
tr
ain
ts
:
̅
̅
(
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–
–
–
(
)
(
)
Ca
s
e
1
.
I
f
th
e
I
SP
is
u
s
i
n
g
f
lat
-
f
ee
p
r
icin
g
s
c
h
e
m
e
b
y
s
ettin
g
,
an
d
.
Op
ti
m
izatio
n
p
r
o
b
lem
s
co
n
s
u
m
er
s
f
o
r
f
lat
-
f
ee
p
r
icin
g
s
c
h
e
m
e
b
e
:
(
)
(
)
(
)
(
)
(
)
(
)
(
)
B
y
u
s
i
n
g
C
o
n
s
tr
ai
n
t (
4
)
,
th
en
(
)
(
)
(
)
(
)
(
)
(
)
(
)
(
)
(
)
Op
ti
m
izatio
n
o
f
p
r
o
v
id
er
b
ec
am
e:
∑
(
)
∑
(
(
)
(
)
(
)
)
∑
(
(
)
(
)
(
)
(
)
)
∑
(
)
(
)
T
h
is
m
ea
n
s
th
at
i
f
t
h
e
I
SP
p
r
o
v
id
es
th
is
p
r
ice,
t
h
e
le
v
el
o
f
co
n
s
u
m
er
s
p
e
n
d
in
g
i
n
to
̅
an
d
̅
w
it
h
m
a
x
i
m
u
m
u
ti
lit
y
,
co
n
s
u
m
er
s
ca
n
g
et
̅
(
̅
)
(
̅
̅
)
.
I
SP
o
p
tim
al
p
r
ice
u
s
ed
i
s
̅
(
̅
)
(
̅
̅
)
,
th
e
m
a
x
i
m
u
m
b
en
e
f
it i
s
∑
,
̅
(
̅
)
(
̅
̅
)
-
.
B
ased
o
n
th
is
ca
s
e
L
e
m
m
a
1
ca
n
b
e
s
tated
as f
o
llo
w
s
.
L
e
mm
a
1
:
I
f
t
h
e
I
SP
is
u
s
in
g
f
lat
-
f
ee
p
r
ici
n
g
s
c
h
e
m
e,
t
h
e
o
p
ti
m
al
p
r
ice
is
̅
(
̅
)
(
̅
̅
)
an
d
th
e
m
ax
i
m
u
m
p
r
o
f
it to
b
e
∑
,
̅
(
̅
)
(
̅
̅
)
-
Ca
s
e
2
.
I
f
I
SP
s
u
s
e
u
s
ag
e
-
b
ased
p
r
icin
g
s
ch
e
m
e
b
y
s
e
ttin
g
,
an
d
.
Op
ti
m
izatio
n
p
r
o
b
lem
s
co
n
s
u
m
er
s
o
n
u
s
ag
e
-
b
ased
p
r
icin
g
s
ch
e
m
e
b
e:
(
)
(
)
(
)
(
1
3
)
T
o
m
a
x
i
m
ize
E
q
.
(
1
3
)
,
d
o
d
if
f
er
en
tiatio
n
o
f
th
e
X
an
d
Y
:
(
(
)
(
)
(
)
)
th
en
(
)
̅
(
1
4
)
an
d
(
(
)
(
)
(
)
)
̅
an
d
(
̅
)
(
)
̅
(
1
5
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
E
C
E
I
SS
N:
2
0
8
8
-
8708
Utilit
y
F
u
n
ctio
n
-
B
a
s
ed
P
r
icin
g
S
tr
a
teg
ies in
Ma
ximizi
n
g
th
e
I
n
fo
r
m
a
tio
n
S
ervice
.
.
.
.
(
R
o
b
i
n
s
o
n
S
itep
u
)
881
Op
ti
m
izatio
n
o
f
p
r
o
d
u
ctio
n
p
r
o
b
lem
s
b
ec
a
m
e:
∑
(
)
∑
,
(
̅
)
(
̅
)
-
∑
[
(
(
)
)
̅
(
(
̅
)
(
)
)
̅
]
∑
,
̅
̅
(
̅
)
(
)
̅
(
)
̅
-
T
o
m
a
x
i
m
i
ze
th
e
f
u
n
ctio
n
o
p
ti
m
izatio
n
p
r
o
b
lem
p
r
o
v
id
er
s
,
I
SP
s
s
h
o
u
ld
m
i
n
i
m
ize
an
d
.
I
f
k
n
o
w
n
an
d
d
ec
lin
e,
th
e
n
X*
an
d
Y*
in
cr
ea
s
e,
if
X
an
d
Y
ar
e
r
estricte
d
,
th
en
X
*
=
̅
an
d
Y*
=
̅
.
an
d
y
an
g
o
p
ti
m
a
l
in
to
(
)
an
d
(
̅
)
(
)
w
i
t
h
m
ax
i
m
u
m
p
r
o
f
it
∑
,
̅
̅
(
̅
)
(
)
̅
(
)
̅
-
.
T
h
en
p
r
o
ce
ed
t
o
n
ex
t le
m
m
a
a
s
f
o
llo
w
s
.
L
e
mm
a
2
:
I
f
I
SP
s
u
s
e
u
s
a
g
e
-
b
ased
p
r
icin
g
s
c
h
e
m
e,
th
e
o
p
t
i
m
al
p
r
ice
is
(
)
an
d
(
̅
)
(
)
,
th
e
m
a
x
i
m
u
m
b
en
e
f
it i
s
:
∑
,
̅
̅
(
̅
)
(
)
̅
(
)
̅
-
.
Ca
s
e
3
.
I
f
th
e
I
SP
u
s
es
a
t
w
o
-
p
ar
t
p
r
icin
g
s
ch
e
m
e
tar
if
f
b
y
s
ettin
g
,
an
d
.
B
y
u
s
i
n
g
E
q
.
(
8
)
-
(
9
)
.
I
f
th
ese
eq
u
atio
n
ar
e
s
u
b
s
tit
u
te
d
in
to
E
q
.
(
1
0
)
an
d
to
m
ax
i
m
ize
th
e
o
b
j
ec
tiv
e
f
u
n
c
ti
o
n
(
7
)
,
th
en
:
Fo
r
t
w
o
-
p
ar
t ta
r
i
f
f
w
i
ll b
e
(
)
–
(
)
(
)
(
)
(
(
)
)
̅
(
(
̅
)
(
)
)
̅
(
)
(
)
̅
(
)
̅
(
̅
)
(
)
̅
(
)
̅
(
)
(
)
B
y
s
u
b
s
t
itu
tin
g
t
h
e
v
al
u
e
to
th
e
o
b
j
ec
tiv
e
f
u
n
ctio
n
(
7
)
)
,
o
p
ti
m
izatio
n
p
r
o
b
lem
s
o
f
p
r
o
v
id
er
s
b
ein
g
:
∑
(
)
∑
,
(
̅
)
(
̅
)
-
∑
,
(
(
)
)
̅
(
(
̅
)
(
)
)
̅
(
̅
(
̅
)
̅
(
̅
)
(
)
̅
(
)
̅
(
)
(
)
)
-
∑
,
̅
(
̅
)
(
)
̅
(
)
̅
-
T
o
m
a
x
i
m
ize
th
e
f
u
n
ctio
n
o
p
ti
m
izatio
n
p
r
o
b
lem
p
r
o
v
id
er
s
,
I
SP
s
s
h
o
u
ld
m
i
n
i
m
ize
an
d
.
if
k
n
o
w
n
an
d
d
ec
lin
e,
th
e
n
X*
an
d
Y*
i
n
cr
ea
s
e,
i
f
X
a
n
d
Y
ar
e
b
o
u
n
d
ed
,
th
e
n
X
*
≤
̅
an
d
Y*
≤
̅
.
I
n
o
th
er
w
o
r
d
s
,
an
d
o
p
tim
al
w
ill
b
e
(
)
an
d
(
̅
)
(
)
T
h
e
m
a
x
i
m
u
m
g
ai
n
is
ac
h
iev
ed
∑
,
̅
(
̅
)
(
)
̅
(
)
̅
-
B
ased
o
n
th
is
ca
s
e,
n
ex
t le
m
m
a
w
as o
b
tai
n
ed
.
L
e
mm
a
3
:
I
f
th
e
I
SP
u
s
es
a
t
w
o
-
p
ar
t
tar
if
f
r
ates,
th
e
n
b
est
an
d
b
e
an
d
(
̅
)
.
Ma
x
i
m
u
m
p
r
o
f
it,
∑
,
̅
(
̅
)
(
)
̅
(
)
̅
-
I
f
it
is
ass
u
m
ed
̅
(
̅
)
(
̅
)
;
̅
an
d
f
u
n
ctio
n
(
̅
)
=
is
a
n
o
n
lin
ea
r
f
u
n
ct
io
n
,
th
e
n
̅
̅
(
̅
)
(
)
̅
(
)
̅
∑
,
̅
(
̅
)
(
̅
̅
)
-
̅
(
̅
)
(
)
̅
(
)
̅
,
u
s
a
g
e
-
b
ased
p
r
icin
g
s
ch
e
m
es
g
en
er
ate
g
r
ea
ter
p
r
o
f
its
t
h
an
t
h
e
f
la
t
-
f
ee
a
n
d
t
w
o
-
p
ar
t
tar
if
f
p
r
icin
g
s
c
h
e
m
es
f
o
r
h
o
m
o
g
en
eo
u
s
co
n
s
u
m
er
.
3
.
3
.
H
et
er
o
g
eneo
us
Co
ns
u
m
er
Su
p
p
o
s
e
th
at
t
h
er
e
ar
e
m
h
ig
h
-
e
n
d
co
n
s
u
m
er
(
th
e
u
p
p
er
class
)
(
i
=
1
)
an
d
n
lo
w
-
en
d
co
n
s
u
m
er
(
lo
w
er
clas
s
)
(
i
=
2
)
.
T
o
f
i
n
d
h
eter
o
g
e
n
eo
u
s
co
n
s
u
m
e
r
s
'
w
illi
n
g
n
e
s
s
to
p
a
y
a
g
i
v
en
p
r
ice
s
ch
e
m
e
af
f
ec
t
881
881
881
s
er
v
ice
p
r
o
v
id
er
s
,
it is
a
s
s
u
m
ed
e
v
er
y
co
n
s
u
m
er
i
n
b
o
th
s
e
g
m
en
t
s
h
av
e
a
n
u
p
p
er
li
m
i
t o
n
th
e
s
a
m
e
X
an
d
Y
at
p
ea
k
h
o
u
r
s
,
an
d
Fo
r
f
lat
-
f
ee
p
r
icin
g
s
c
h
e
m
e
C
o
n
s
u
m
er
Op
ti
m
izatio
n
P
r
o
b
le
m
s
:
(
)
(
)
(
1
6
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
J
E
C
E
Vo
l.
7
,
No
.
2
,
A
p
r
il 2
0
1
7
:
8
7
7
–
8
8
7
882
Su
b
j
ec
t to
̅
(
1
7
)
̅
(
1
8
)
(
)
(
)
(
1
9
)
o
r
1
(
2
0
)
Op
ti
m
izatio
n
P
r
o
b
lem
s
o
f
P
r
o
v
id
er
s
:
(
)
(
)
(
2
1
)
w
it
h
(
)
(
)
s
u
b
j
ec
t to
̅
̅
(
)
Fo
r
u
s
ag
e
-
b
ased
p
r
icin
g
s
c
h
e
m
es a
n
d
t
w
o
-
p
ar
t ta
r
if
f
C
o
n
s
u
m
er
Op
t
i
m
izatio
n
P
r
o
b
le
m
s
:
(
)
(
)
(
)
(
2
2
)
Su
b
j
ec
t to
̅
(
2
3
)
̅
(
2
4
)
(
)
(
)
(
2
5
)
o
r
1
(
2
6
)
Op
ti
m
izatio
n
P
r
o
b
lem
s
o
f
P
r
o
v
id
er
s
:
(
)
(
)
(
2
7
)
w
it
h
(
)
(
)
s
u
b
j
ec
t to
̅
̅
(
)
Step
s
to
g
et
t
h
e
m
a
x
i
m
u
m
p
r
o
f
it o
n
a
n
y
p
r
ici
n
g
s
ch
e
m
e
u
s
ed
b
y
I
SP
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
E
C
E
I
SS
N:
2
0
8
8
-
8708
Utilit
y
F
u
n
ctio
n
-
B
a
s
ed
P
r
icin
g
S
tr
a
teg
ies in
Ma
ximizi
n
g
th
e
I
n
fo
r
m
a
tio
n
S
ervice
.
.
.
.
(
R
o
b
i
n
s
o
n
S
itep
u
)
883
Ca
s
e
4
.
I
f
th
e
I
SP
u
s
i
n
g
f
la
t
-
f
ee
p
r
icin
g
s
ch
e
m
e
b
y
s
etti
n
g
,
an
d
,
w
h
er
e
t
h
e
p
r
ice
u
s
ed
b
y
th
e
I
SP
h
as
n
o
e
f
f
ec
t
o
n
th
e
ti
m
e
o
f
p
ea
k
h
o
u
r
s
u
s
e
o
r
o
f
f
-
p
ea
k
h
o
u
r
s
,
th
e
n
co
n
s
u
m
er
s
c
h
o
o
s
e
t
h
e
m
ax
i
m
u
m
co
n
s
u
m
p
t
io
n
̅
,
̅
,
̅
an
d
̅
.
T
h
u
s
,
a
n
y
h
ig
h
-
en
d
co
n
s
u
m
er
co
s
t
n
o
m
o
r
e
th
an
̅
(
̅
)
(
̅
̅
)
an
d
lo
w
-
e
n
d
co
n
s
u
m
er
is
n
o
t
o
v
er
̅
(
̅
)
(
̅
̅
)
.
C
ase
4
is
a
f
lat
-
f
ee
p
r
icin
g
s
c
h
e
m
e
s
o
t
h
at
P
is
eq
u
i
v
ale
n
t
f
o
r
b
o
th
t
y
p
es
o
f
h
eter
o
g
e
n
eo
u
s
co
n
s
u
m
er
s
.
I
f
it
i
s
estab
li
s
h
ed
th
en
f
o
r
th
e
p
r
o
v
is
io
n
o
f
h
i
g
h
-
e
n
d
co
n
s
u
m
er
co
s
ts
w
ill
f
o
llo
w
th
e
p
r
ice
f
o
r
th
e
co
s
t
o
f
lo
w
-
en
d
co
n
s
u
m
er
s
o
(
)
(
)
(
)
.
T
h
is
m
ea
n
s
t
h
at
if
th
e
co
n
s
u
m
er
is
ch
ar
g
ed
at
̅
(
̅
)
(
̅
̅
)
,
t
h
en
o
n
ly
t
h
e
h
i
g
h
-
e
n
d
co
n
s
u
m
er
w
h
o
ca
n
f
o
llo
w
t
h
is
s
er
v
ice.
I
f
co
n
s
u
m
er
s
ar
e
ch
a
r
g
ed
a
f
ee
o
f
̅
(
̅
)
(
̅
̅
)
,
th
e
n
b
o
t
h
t
y
p
es
o
f
co
n
s
u
m
er
s
ca
n
f
o
llo
w
t
h
i
s
s
er
v
ice,
n
a
m
el
y
th
e
co
n
s
u
m
er
s
o
f
h
i
g
h
-
e
n
d
an
d
lo
w
-
en
d
co
n
s
u
m
er
.
T
o
m
ax
i
m
ize
b
en
e
f
i
ts
,
I
SP
ch
ar
g
e
̅
(
̅
)
(
̅
̅
)
I
n
th
is
ca
s
e
f
o
r
Op
ti
m
izatio
n
P
r
o
b
lem
s
o
f
P
r
o
v
id
er
s
:
(
)
(
)
*
̅
(
̅
)
(
̅
̅
)
+
*
̅
(
̅
)
(
̅
̅
)
+
=
(
)
,
̅
(
̅
)
(
̅
̅
)
-
Ma
x
i
m
u
m
p
r
o
f
it
y
an
g
o
b
tain
ab
le
p
r
o
d
u
s
en
is
(
)
,
̅
(
̅
)
(
̅
̅
)
-
.
B
ased
o
n
th
is
,
th
e
le
m
m
a
w
a
s
o
b
tain
ed
.
L
e
mm
a
4
:
I
f
I
SP
s
u
s
e
p
r
ici
n
g
s
c
h
e
m
e
s
fla
t
-
fee
,
th
e
n
h
ar
g
a
y
an
g
d
ik
e
n
a
k
an
ad
alah
̅
(
̅
)
(
̅
̅
)
w
it
h
m
ax
i
m
u
m
p
r
o
f
it o
b
tain
ed
a
m
o
u
n
ted
(
)
,
̅
(
̅
)
(
̅
̅
)
-
Ca
s
e
5
.
I
f
I
SP
s
u
s
e
p
r
icin
g
s
c
h
e
m
e
s
u
s
a
g
e
-
b
a
s
ed
b
y
s
etti
n
g
,
an
d
th
en
:
Op
ti
m
izatio
n
p
r
o
b
lem
s
f
o
r
h
i
g
h
-
e
n
d
h
eter
o
g
e
n
eo
u
s
co
n
s
u
m
e
r
s
:
(
)
–
(
)
(
)
T
o
m
ax
i
m
ize
f
u
n
c
tio
n
alit
y
o
n
C
o
n
s
u
m
er
Op
ti
m
izat
io
n
P
r
o
b
lem
s
f
o
r
h
eter
o
g
e
n
eo
u
s
h
i
g
h
-
e
n
d
co
n
s
u
m
er
,
d
o
d
if
f
er
e
n
tiatio
n
ag
ain
s
an
d
:
(
)
̅
(
2
8
)
an
d
(
)
(
)
̅
(
2
9
)
Op
ti
m
izatio
n
p
r
o
b
lem
s
f
o
r
h
et
er
o
g
en
eo
u
s
lo
w
-
e
n
d
co
n
s
u
m
er
:
Fu
n
ctio
n
s
in
C
o
n
s
u
m
er
Op
ti
m
izatio
n
P
r
o
b
lem
s
:
(
)
–
(
)
(
)
T
o
m
a
x
i
m
ize
f
u
n
ctio
n
ali
t
y
o
n
C
o
n
s
u
m
er
Op
ti
m
izat
io
n
P
r
o
b
lem
s
f
o
r
h
eter
o
g
en
eo
u
s
lo
w
-
en
d
co
n
s
u
m
er
s
,
d
o
d
if
f
er
en
tiat
io
n
ag
ain
s
t
an
d
:
(
(
)
–
(
)
(
)
)
,
th
en
(
)
̅
(
3
0
)
an
d
(
)
(
)
̅
(
3
1
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
J
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Vo
l.
7
,
No
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2
,
A
p
r
il 2
0
1
7
:
8
7
7
–
8
8
7
884
Op
ti
m
izatio
n
P
r
o
b
lem
s
o
f
P
r
o
v
id
er
s
is
as
f
o
llo
w
s
.
(
)
(
)
,
(
̅
)
(
̅
)
-
,
(
̅
)
(
̅
)
-
,
̅
̅
(
̅
)
(
)
̅
(
)
̅
-
,
̅
̅
(
̅
)
(
)
̅
(
)
̅
-
I
f
ap
p
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th
e
p
r
o
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lem
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p
ea
k
h
o
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r
s
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to
m
a
x
i
m
ize
th
e
f
u
n
ct
io
n
,
t
h
e
I
SP
m
u
s
t
m
i
n
i
m
i
ze
an
d
h
en
ce
b
est
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r
ice
ca
n
n
o
t
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e
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r
ea
ter
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an
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n
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ter
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(
)
.
On
th
e
o
th
er
h
an
d
,
if
th
e
I
SP
s
et
p
r
ices
b
elo
w
(
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r
o
f
it
is
n
o
t
o
p
tim
al.
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f
ap
p
lied
to
p
r
o
b
le
m
s
i
n
o
f
f
-
p
ea
k
h
o
u
r
s
,
t
h
e
b
est
p
r
ices
̅
(
)
(
)
On
th
e
o
th
er
h
an
d
,
if
th
e
I
SP
s
et
p
r
ices
b
el
ow
̅
(
)
(
)
,
th
en
p
r
o
f
it
is
n
o
t
o
p
tim
a
l
w
h
e
n
*
≤
̅
an
d
*
≤
̅
.
B
e
ca
u
s
e
b
y
t
h
e,
p
r
ice
is
b
est
(
)
(
)
(
)
(
)
.
I
f
th
e
p
r
ice
is
th
is
i
n
ter
v
al,
t
h
e
d
e
m
a
n
d
f
r
o
m
h
ig
h
-
e
n
d
co
n
s
u
m
er
r
e
m
ai
n
s
o
n
̅
an
d
̅
,
T
h
u
s
t
h
e
o
p
ti
m
a
l
p
r
ice
is
g
i
v
en
f
o
r
th
e
r
u
s
h
h
o
u
r
is
(
)
an
d
o
p
ti
m
al
p
r
ices
i
n
o
f
f
-
p
ea
k
h
o
u
r
s
i
s
(
̅
)
(
)
m
ax
i
m
u
m
p
r
o
f
it is
(
̅
̅
(
̅
)
(
)
̅
(
)
̅
)
.
B
ased
o
n
th
is
ca
s
e
t
h
e
le
m
m
a
w
a
s
o
b
tain
ed
.
L
e
mm
a
5
:
I
f
I
SP
s
u
s
e
u
s
ag
e
-
b
ased
p
r
ice,
th
en
th
e
o
p
ti
m
al
p
r
ice
is
g
iv
en
f
o
r
th
e
r
u
s
h
h
o
u
r
is
(
)
an
d
o
p
tim
a
l p
r
ices in
o
f
f
-
p
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k
h
o
u
r
s
is
(
̅
)
(
)
w
it
h
m
ax
i
m
u
m
p
r
o
f
it is
(
)
(
̅
̅
(
̅
)
(
)
̅
(
)
̅
)
.
Ca
s
e
6
.
I
f
I
SP
s
u
s
e
p
r
icin
g
s
ch
e
m
e
s
tw
o
-
p
a
r
t
ta
r
iff
,
th
en
,
,
an
d
w
h
er
e
th
er
e
is
a
co
s
t
in
cu
r
r
ed
i
f
t
h
e
co
n
s
u
m
er
ch
o
o
s
es
to
j
o
in
t
h
e
s
er
v
ice
a
n
d
t
h
e
p
r
ices
c
h
ar
g
ed
d
u
r
in
g
p
ea
k
h
o
u
r
s
a
n
d
o
f
f
-
p
ea
k
h
o
u
r
s
,
t
h
e
f
ir
s
t
o
r
d
er
co
n
d
itio
n
f
o
r
eq
u
alit
y
C
o
n
s
u
m
er
O
p
ti
m
izatio
n
P
r
o
b
lem
s
o
f
h
ig
h
-
e
n
d
an
d
lo
w
-
e
n
d
co
n
s
u
m
er
.
I
f
it
is
estab
li
s
h
ed
th
en
it c
a
n
b
e
a
s
s
u
m
ed
t
h
at
(
)
(
)
(
)
.
T
h
is
m
ea
n
s
t
h
at
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f
t
h
e
co
n
s
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m
er
is
ch
ar
g
ed
at
(
)
an
d
(
)
(
)
an
d
̅
(
)
̅
(
̅
)
(
)
̅
(
)
̅
(
)
(
)
th
en
o
n
l
y
t
h
e
h
i
g
h
-
en
d
co
n
s
u
m
er
w
h
o
ca
n
f
o
llo
w
t
h
is
s
er
v
ic
e.
I
f
co
n
s
u
m
er
s
ar
e
ch
ar
g
ed
a
f
ee
o
f
(
)
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d
(
̅
)
(
)
th
e
n
b
o
th
t
y
p
es
o
f
co
n
s
u
m
er
s
ca
n
f
o
llo
w
t
h
e
s
er
v
ice,
n
a
m
el
y
th
e
co
n
s
u
m
er
s
o
f
h
i
g
h
-
e
n
d
an
d
lo
w
-
en
d
co
n
s
u
m
er
.
I
SP
s
m
a
y
c
h
o
o
s
e
to
d
ec
lin
e
k
a
n
d
ib
ec
au
s
e
k
a
n
co
s
t
m
an
y
co
n
s
u
m
er
s
i
n
to
k
a
n
s
u
b
s
cr
ip
tio
n
f
ee
s
a
s
a
b
ar
r
ier
s
o
th
at
it
ca
n
at
tr
ac
t
m
o
r
e
co
n
s
u
m
er
s
,
I
SP
s
ca
n
p
r
o
v
id
e
p
r
ices
(
)
(
)
(
)
an
d
m
i
n
i
m
ize
th
e
co
s
t o
f
s
u
b
s
cr
ip
t
io
n
.
Op
ti
m
izatio
n
P
r
o
b
lem
s
P
r
o
v
id
er
s
in
to
:
(
)
(
)
(
)
,
̅
(
̅
)
(
)
̅
(
)
̅
)
-
T
h
u
s
th
e
m
a
x
i
m
u
m
p
r
o
f
it i
s
ac
h
iev
ed
(
)
(
̅
(
̅
)
(
)
̅
(
)
̅
)
B
ased
o
n
th
is
ca
s
e
w
a
s
o
b
tain
ed
.
L
e
mm
a
6
:
I
f
th
e
I
SP
u
s
e
s
a
t
w
o
-
p
ar
t
tar
if
f
r
ates,
o
p
ti
m
al
r
esp
ec
tiv
el
y
ar
e
(
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d
(
̅
)
(
)
,
an
d
w
it
h
m
ax
i
m
u
m
p
r
o
f
it o
b
tain
ab
le
is
(
)
,
̅
(
̅
)
(
)
̅
(
)
̅
-
.
I
f
it
is
a
s
s
u
m
ed
t
h
at
̅
(
̅
)
(
̅
)
,
w
i
th
̅
an
d
(
̅
)
is
n
o
n
l
in
ea
r
f
u
n
ct
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n
,
(
̅
)
=
,
,
th
en
(
)
(
̅
̅
(
̅
)
)
(
)
(
̅
(
̅
)
)
B
ec
au
s
e
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y
t
h
en
,
u
s
a
g
e
-
b
ased
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r
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g
s
ch
e
m
e
i
s
b
etter
th
a
n
th
e
f
lat
-
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Evaluation Warning : The document was created with Spire.PDF for Python.
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
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8
8
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8708
I
J
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Vo
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ased
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b
b
-
Do
u
g
las
f
u
n
ctio
n
.
T
h
is
is
ag
ai
n
,
d
u
e
to
th
e
f
ac
t
th
at
ex
p
o
n
e
n
tial
f
o
r
m
o
f
f
u
n
ctio
n
h
a
s
m
o
r
e
p
ea
k
s
o
n
ce
r
t
ain
r
an
g
e
o
f
lo
ca
l
o
p
tim
a
l
s
o
l
u
tio
n
s
.
Ot
h
er
asp
e
ct
th
at
ca
n
b
e
s
h
o
w
ed
t
h
at,
a
ls
o
in
p
r
e
v
io
u
s
r
esear
ch
[
9
]
,
[
1
7
]
m
ar
g
i
n
al
a
n
d
m
o
n
ito
r
i
n
g
co
s
t
f
o
r
h
eter
o
g
en
eo
u
s
co
n
s
u
m
er
s
,
ar
e
n
o
t
r
ea
ll
y
d
is
cu
s
s
ed
,
th
e
r
esear
ch
m
o
s
tl
y
f
o
c
u
s
ed
o
n
p
r
icin
g
s
tr
ate
g
ie
s
f
o
r
in
f
o
r
m
at
i
o
n
s
er
v
ice
w
it
h
o
u
t
m
ar
g
i
n
al
a
n
d
m
o
n
i
to
r
in
g
co
s
t.
4.
CO
NCLU
SI
O
N
Fro
m
t
h
e
r
es
u
lt
s
,
it
ca
n
b
e
co
n
clu
d
ed
th
at
b
y
ap
p
l
y
in
g
C
o
b
b
-
Do
u
g
la
s
u
tili
t
y
f
u
n
ctio
n
,
w
ill
i
m
p
ac
t
o
n
h
ig
h
er
r
e
v
en
u
e
f
o
r
I
SP
o
n
u
s
a
g
e
b
ased
p
r
icin
g
s
tr
ateg
ie
s
f
o
r
h
o
m
o
g
en
eo
u
s
a
n
d
h
eter
o
g
en
e
o
u
s
co
n
s
u
m
er
s
f
o
r
h
ig
h
e
n
d
an
d
lo
w
en
d
u
s
er
s
.
T
w
o
p
ar
t
tar
if
f
p
r
icin
g
s
tr
ate
g
y
is
b
est
s
tr
ate
g
y
to
b
e
ap
p
lied
in
h
eter
o
g
en
eo
u
s
ca
s
e
o
f
h
i
g
h
an
d
lo
w
d
e
m
a
n
d
u
s
er
s
.
ACK
NO
WL
E
D
G
E
M
E
NT
S
T
h
e
r
esear
ch
lead
in
g
to
t
h
is
s
t
u
d
y
w
a
s
f
i
n
an
cia
ll
y
s
u
p
p
o
r
ted
b
y
Dir
ec
to
r
ate
o
f
Hi
g
h
er
E
d
u
ca
tio
n
I
n
d
o
n
e
s
ia
(
DI
KT
I
)
f
o
r
s
u
p
p
o
r
t th
r
o
u
g
h
“
Hib
a
h
Fu
n
d
a
m
e
n
tal
T
ah
u
n
I
”,
2
0
1
6
.
RE
F
E
R
E
NC
E
S
[1
]
W
.
F
a
n
a
n
d
S
.
Ya
n
g
,
“
M
u
lt
i
-
So
u
rc
e
In
f
o
rm
a
ti
o
n
S
e
r
v
ice
(M
S
I
S
)
P
ro
c
e
ss
M
a
n
a
g
e
m
e
n
t
i
n
Cl
o
u
d
C
o
m
p
u
ti
n
g
En
v
iro
n
m
e
n
t
,”
In
t
e
rn
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
Clo
u
d
Co
m
p
u
t
in
g
a
n
d
S
e
rv
ice
s
S
c
ien
c
e
(
IJ
-
CL
OS
ER
)
,
v
o
l/
issu
e
:
1
(1
)
,
p
p
.
2
0
1
2
,
2
0
1
2
.
[2
]
H.
Yu
a
n
d
W
.
Zh
a
n
g
,
“
Re
se
a
rc
h
o
n
Re
a
l
-
ti
m
e
a
n
d
Dy
n
a
m
i
c
Urb
a
n
T
ra
ff
ic
In
f
o
r
m
a
ti
o
n
S
e
rv
ice
S
y
ste
m
,”
T
EL
KOM
NIKA
,
v
o
l/
issu
e
:
1
0
(
4
)
,
p
p
.
8
0
6
-
811
,
2
0
1
2
.
[3
]
C.
Yu
,
e
t
a
l.
,
“
De
v
e
lo
p
m
e
n
t
a
n
d
A
p
p
li
c
a
ti
o
n
o
f
Un
iv
e
rsity
In
fo
rm
a
ti
o
n
S
e
rv
ice
,”
T
EL
KOM
NIKA
In
d
o
n
e
si
a
n
J
o
u
rn
a
l
o
f
El
e
c
trica
l
En
g
in
e
e
rin
g
,
v
o
l/
issu
e
:
1
2
(
5
)
,
p
p
.
3
2
8
9
-
3
2
9
6
,
201
4
.
[4
]
D.
Ba
rth
,
e
t
a
l
.
,
“
P
ricin
g
,
Q
o
S
a
n
d
Util
it
y
m
o
d
e
ls
f
o
r
th
e
In
tern
e
t
,”
2
0
0
4
.
[5
]
X
.
W
a
n
g
a
n
d
H.
S
c
h
u
lzrin
n
e
,
“
P
ricin
g
Ne
t
w
o
rk
R
e
so
u
rc
e
s
f
o
r
A
d
a
p
ti
v
e
A
p
p
li
c
a
ti
o
n
s
in
a
Diff
e
re
n
ti
a
ted
S
e
rv
ice
s
Ne
tw
o
rk
,”
2001.
[6
]
C.
Cu
re
sc
u
,
“
Util
it
y
-
b
a
se
d
Op
ti
m
is
a
ti
o
n
o
f
Re
so
u
rc
e
A
ll
o
c
a
ti
o
n
f
o
r
W
ir
e
les
s
N
e
t
w
o
rk
s
,
”
in
De
p
a
rtme
n
t
o
f
Co
mp
u
ter
a
n
d
In
fo
rm
a
t
io
n
S
c
ien
c
e
,
Li
n
k
ö
p
in
g
s u
n
iv
e
rsitet:
L
in
k
ö
p
in
g
,
p
p
.
1
7
8
,
2
0
0
5
.
[7
]
F
.
M
.
P
u
sp
it
a
,
e
t
a
l
.
,
“
A
Co
m
p
a
ri
so
n
o
f
Op
ti
m
iza
ti
o
n
o
f
Ch
a
rg
in
g
S
c
h
e
m
e
in
M
u
lt
ip
le
Q
o
S
Ne
tw
o
rk
s
,”
Pro
c
e
e
d
in
g
o
f
1
st
AK
EP
T
Y
o
u
n
g
Res
e
a
rc
h
e
rs
Co
n
fer
e
n
c
e
a
n
d
Exh
ib
it
io
n
(
AY
RC
X
3
2
0
1
1
)
Bey
o
n
d
2
0
2
0
:
T
o
d
a
y
'
s
Y
o
u
n
g
Res
e
a
rc
h
e
r T
o
mo
rr
o
w
'
s L
e
a
d
e
r 1
9
-
2
0
De
c
e
mb
e
r 2
0
1
1
,
2
0
1
1
.
[8
]
F
.
M
.
P
u
sp
it
a
,
e
t
a
l.
,
“
Im
p
ro
v
e
d
M
o
d
e
ls
o
f
In
tern
e
t
Ch
a
rg
in
g
S
c
h
e
m
e
o
f
S
in
g
le
Bo
tt
len
e
c
k
L
in
k
in
M
u
l
ti
Qo
S
Ne
t
w
o
rk
s
,”
J
o
u
rn
a
l
o
f
Ap
p
li
e
d
S
c
ien
c
e
s
,
v
o
l/
issu
e
:
1
3
(4
)
,
p
p
.
5
7
2
-
5
7
9
,
2
0
1
3
.
[9
]
S
.
Y.
Wu
a
n
d
R.
D.
B
a
n
k
e
r,
“
Be
st
P
ricin
g
S
trate
g
y
f
o
r
In
f
o
rm
a
ti
o
n
S
e
rv
ice
s
,”
J
o
u
rn
a
l
o
f
th
e
Asso
c
ia
ti
o
n
fo
r
In
fo
rm
a
t
io
n
S
y
ste
ms
,
v
o
l/
iss
u
e
:
1
1
(6
)
,
p
p
.
3
3
9
-
3
6
6
,
2
0
1
0
.
[1
0
]
V
.
K.
S
i
n
g
h
,
e
t
a
l.
,
“
A
p
p
ro
x
ima
ti
o
n
s
o
f
F
u
z
z
y
S
y
ste
m
s
,”
In
d
o
n
e
sia
n
J
o
u
rn
a
l
o
f
El
e
c
trica
l
En
g
in
e
e
rin
g
a
n
d
In
fo
rm
a
t
ics
(
IJ
EE
I)
,
v
o
l/
issu
e
:
1
(
1
)
,
p
p
.
1
4
-
20
,
2
0
1
3
.
[1
1
]
In
d
ra
w
a
ti
,
e
t
a
l
.,
“
Co
b
b
-
D
o
u
g
las
s
Util
it
y
F
u
n
c
ti
o
n
i
n
Op
ti
m
izin
g
th
e
In
tern
e
t
P
r
icin
g
S
c
h
e
m
e
M
o
d
e
l
,”
T
EL
KOM
NIKA
,
v
o
l/
issu
e
:
1
2
(
1
)
,
2
0
1
4
.
[1
2
]
In
d
ra
w
a
ti
,
e
t
a
l
.
,
“
In
tern
e
t
p
ricin
g
o
n
b
a
n
d
w
id
th
f
u
n
c
ti
o
n
d
im
in
is
h
e
d
w
it
h
in
c
re
a
sin
g
b
a
n
d
w
id
th
u
ti
li
ty
f
u
n
c
ti
o
n
,”
T
EL
KOM
NIKA
,
v
o
l/
issu
e
:
1
3
(
1
)
,
p
p
.
2
9
9
-
304
,
2
0
1
5
.
[1
3
]
In
d
ra
w
a
ti
,
e
t
a
l.
,
“
Nu
m
e
ric
a
l
S
o
lu
ti
o
n
o
f
In
tern
e
t
P
ricin
g
S
c
h
e
m
e
B
a
se
d
o
n
P
Erf
e
c
t
S
u
b
stit
u
te
Util
i
ty
F
u
n
c
ti
o
n
,”
i
n
1
st
In
ter
n
a
ti
o
n
a
l
C
o
n
fer
e
n
c
e
o
n
Co
mp
u
ter
S
c
ien
c
e
a
n
d
En
g
in
e
e
rin
g
,
P
a
lem
b
a
n
g
,
S
o
u
th
S
u
m
a
tera
In
d
o
n
e
sia
:
Ju
ru
sa
n
S
istem
Ko
m
p
u
ter Un
iv
e
rsitas
S
riw
ij
a
y
a
,
2
0
1
4
.
[1
4
]
R.
S
it
e
p
u
,
e
t
a
l
.
,
“
Im
p
ro
v
e
d
M
o
d
e
l
P
a
d
a
S
k
e
m
a
P
e
m
b
i
a
y
a
a
n
L
a
y
a
n
a
n
In
f
o
rm
a
si
De
n
g
a
n
Biay
a
P
e
n
g
a
w
a
s
a
n
(M
o
n
i
to
ri
n
g
Co
st)
Da
n
Biay
a
M
a
rji
n
a
l
(M
a
rg
in
a
l
Co
st)
Un
t
u
k
F
u
n
g
si
Util
it
a
s
P
e
rf
e
c
t
S
u
b
stit
u
te
,”
in
S
e
min
a
r
d
a
n
Ra
p
a
t
T
a
h
u
n
a
n
BKS
Bi
d
a
n
g
M
IP
A
,
Un
iv
e
rsitas
S
riw
ij
a
y
a
,
2016
.
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