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Per
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p
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[
1
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,
[
2
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.
As
co
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[
3
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-
[
6
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.
Desp
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[
7
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-
[
9
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.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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I
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J
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&
C
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Sci
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Vo
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3
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No
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1
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Ju
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20
25
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425
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[
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Similar
ly
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B
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Dav
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li
[
1
5
]
c
r
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tr
a
f
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latio
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m
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Fo
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Sm
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[
1
6
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d
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[
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W
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f
ten
c
o
m
e
with
h
ig
h
in
s
tallatio
n
an
d
m
ai
n
ten
a
n
ce
co
s
ts
an
d
s
till
r
eq
u
ir
e
c
o
n
s
id
er
ab
le
h
u
m
an
m
an
ag
em
en
t.
T
h
e
em
er
g
e
n
ce
o
f
th
e
in
ter
n
et
o
f
th
in
g
s
(
I
o
T
)
an
d
a
d
v
an
ce
m
en
ts
in
elec
tr
o
n
ics
o
f
f
er
p
r
o
m
is
in
g
av
en
u
es
f
o
r
en
h
a
n
cin
g
p
er
s
o
n
n
el
t
r
an
s
p
o
r
t
s
y
s
tem
s
.
I
n
t
ellig
en
t
tr
an
s
p
o
r
t
m
a
n
ag
em
e
n
t
s
y
s
tem
s
(
T
MS)
lev
er
ag
in
g
th
ese
tech
n
o
lo
g
ies
ca
n
p
r
o
v
id
e
r
ea
l
-
tim
e
tr
ac
k
in
g
,
au
to
m
ated
d
ec
is
io
n
-
m
a
k
i
n
g
,
an
d
im
p
r
o
v
ed
ef
f
icien
cy
.
Sm
ar
t c
ities
lik
e
N
ew
Yo
r
k
an
d
Du
b
ai
h
a
v
e
b
eg
u
n
in
teg
r
atin
g
s
u
ch
s
y
s
tem
s
,
u
tili
zin
g
a
n
etwo
r
k
o
f
s
en
s
o
r
s
an
d
r
ea
l
-
tim
e
d
ata
p
r
o
ce
s
s
in
g
to
o
p
tim
ize
u
r
b
an
m
o
b
ilit
y
an
d
o
p
e
r
atio
n
al
e
f
f
icien
cy
[
1
8
]
-
[
2
3
]
.
T
h
ese
s
y
s
tem
s
r
ep
r
esen
t
a
s
ig
n
if
ica
n
t
leap
f
o
r
war
d
b
u
t
ar
e
s
till
c
o
n
f
in
ed
to
a
lim
ited
n
u
m
b
e
r
o
f
s
m
ar
t
cities.
T
h
e
m
ajo
r
ity
o
f
u
r
b
an
a
n
d
i
n
d
u
s
tr
ial
ar
ea
s
lack
s
u
ch
c
o
m
p
r
e
h
e
n
s
iv
e
in
f
r
astru
ctu
r
e,
h
i
g
h
lig
h
t
in
g
a
cr
itical
g
a
p
in
cu
r
r
en
t tr
a
n
s
p
o
r
t m
a
n
ag
em
en
t
p
r
ac
tices.
I
n
lig
h
t
o
f
th
ese
ch
allen
g
e
s
,
th
is
p
ap
er
p
r
o
p
o
s
es
a
n
o
v
el
ap
p
r
o
ac
h
to
p
e
r
s
o
n
n
el
tr
an
s
p
o
r
t
o
p
tim
izatio
n
t
h
r
o
u
g
h
th
e
ap
p
l
icatio
n
o
f
m
ac
h
in
e
lear
n
in
g
te
ch
n
iq
u
es,
s
p
ec
if
ically
u
n
s
u
p
er
v
is
ed
lear
n
in
g
a
n
d
clu
s
ter
in
g
alg
o
r
ith
m
s
.
Un
lik
e
tr
ad
itio
n
al
m
eth
o
d
s
,
th
e
p
r
o
p
o
s
ed
s
y
s
tem
aim
s
to
lev
er
a
g
e
ad
v
a
n
ce
d
d
ata
an
aly
s
is
to
en
h
a
n
ce
d
ec
is
io
n
-
m
ak
in
g
p
r
o
ce
s
s
es,
r
ed
u
ce
o
p
e
r
atio
n
al
co
s
ts
,
an
d
im
p
r
o
v
e
o
v
er
all
ef
f
icien
cy
.
B
y
in
teg
r
atin
g
th
ese
m
ac
h
in
e
lea
r
n
in
g
tech
n
iq
u
es,
th
e
s
y
s
tem
o
f
f
er
s
a
s
ca
lab
le
s
o
lu
tio
n
a
d
ap
tab
le
to
v
ar
io
u
s
tech
n
o
lo
g
ical
co
n
tex
ts
,
a
d
d
r
e
s
s
in
g
th
e
lim
itatio
n
s
o
f
ex
is
tin
g
s
y
s
tem
s
an
d
p
r
o
v
id
in
g
a
p
ath
t
o
war
d
m
o
r
e
ef
f
icien
t
an
d
in
tellig
en
t
tr
a
n
s
p
o
r
t
m
an
a
g
em
en
t.
T
h
is
r
esear
ch
co
n
tr
i
b
u
tes
to
th
e
f
ield
b
y
p
r
esen
tin
g
a
n
ew
p
ar
ad
ig
m
f
o
r
p
er
s
o
n
n
el
tr
an
s
p
o
r
t
m
an
a
g
em
en
t
t
h
at
alig
n
s
with
co
n
tem
p
o
r
ar
y
tec
h
n
o
lo
g
ical
ad
v
an
ce
m
en
ts
an
d
ad
d
r
ess
es
th
e
s
h
o
r
tco
m
in
g
s
o
f
tr
ad
itio
n
al
s
y
s
tem
s
.
T
h
e
p
ap
er
d
etails
th
e
d
ev
elo
p
m
en
t
an
d
ap
p
licatio
n
o
f
th
is
ap
p
r
o
ac
h
,
h
i
g
h
lig
h
tin
g
its
p
o
ten
tial
to
tr
an
s
f
o
r
m
p
er
s
o
n
n
el
tr
an
s
p
o
r
t
p
r
ac
tices
in
b
o
th
ex
is
tin
g
an
d
em
er
g
in
g
i
n
d
u
s
tr
ial
s
ettin
g
s
.
2.
M
E
T
H
O
D
2
.
1
.
P
r
o
po
s
ed
a
pp
ro
a
ch
T
h
e
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
in
tr
o
d
u
ce
s
OPT
-
T
MS,
an
in
n
o
v
at
iv
e
tr
an
s
p
o
r
tatio
n
m
an
ag
em
e
n
t
s
y
s
tem
(
T
MS)
d
esig
n
ed
to
tr
a
n
s
f
o
r
m
p
er
s
o
n
n
el
tr
an
s
p
o
r
t
o
p
e
r
atio
n
s
th
r
o
u
g
h
ad
v
a
n
ce
d
o
p
tim
izatio
n
an
d
m
ac
h
in
e
lear
n
in
g
tech
n
iq
u
es.
OPT
-
T
MS
aim
s
to
im
p
r
o
v
e
o
p
e
r
atio
n
al
ef
f
icien
c
y
,
r
eso
u
r
ce
u
t
ilizatio
n
,
an
d
c
o
s
t
-
ef
f
ec
tiv
en
ess
in
m
an
a
g
in
g
em
p
lo
y
ee
tr
a
n
s
p
o
r
tatio
n
b
y
lev
er
ag
in
g
s
o
p
h
is
ticated
alg
o
r
ith
m
s
an
d
r
ea
l
-
tim
e
d
ata
an
aly
s
is
.
OPT
-
T
MS
o
p
er
a
tes
th
r
o
u
g
h
a
s
y
s
tem
atic
wo
r
k
f
lo
w,
as
illu
s
tr
ated
in
Fig
u
r
e
1
,
wh
ich
in
clu
d
es
s
ev
er
al
k
ey
p
h
ases
:
Step
1
:
d
ata
co
llectio
n
I
n
th
e
in
itial
p
h
ase,
OPT
-
T
MS
g
ath
er
s
ess
en
tial
d
ata
r
eq
u
ir
ed
f
o
r
o
p
tim
izin
g
tr
an
s
p
o
r
tat
io
n
lo
g
is
tics
.
T
h
is
d
ata
in
clu
d
es:
-
E
m
p
lo
y
ee
co
o
r
d
in
ates:
p
r
ec
i
s
e
g
eo
g
r
ap
h
ic
co
o
r
d
i
n
ates
(
latitu
d
e
an
d
lo
n
g
itu
d
e)
o
f
ea
ch
em
p
lo
y
ee
’
s
lo
ca
tio
n
.
-
T
im
e
o
f
wo
r
k
en
tr
y
(
T
W
)
:
t
h
e
s
ch
ed
u
led
tim
e
at
wh
ich
e
m
p
l
o
y
ee
s
ar
e
ex
p
ec
ted
to
s
tar
t w
o
r
k
.
-
Ma
x
im
u
m
tim
e
f
o
r
b
u
s
tu
r
n
(
MT
T
)
:
t
h
e
m
ax
im
u
m
allo
wab
le
tim
e
f
o
r
b
u
s
es
to
co
m
p
lete
th
eir
r
o
u
tes
an
d
tu
r
n
ar
o
u
n
d
.
-
Ma
x
im
u
m
walk
in
g
d
is
tan
ce
(
MWD)
:
t
h
e
f
u
r
th
est
d
is
tan
ce
em
p
lo
y
ee
s
ar
e
ex
p
ec
te
d
to
walk
f
r
o
m
th
eir
p
ick
u
p
o
r
d
r
o
p
-
o
f
f
p
o
in
ts
.
-
B
u
s
c
ap
ac
ity
(
B
C
)
:
t
h
e
m
ax
im
u
m
n
u
m
b
er
o
f
em
p
lo
y
ee
s
th
at
ea
ch
b
u
s
ca
n
ac
c
o
m
m
o
d
at
e.
-
Nu
m
b
er
o
f
b
u
s
es (
NB
)
:
t
h
e
to
t
al
n
u
m
b
er
o
f
b
u
s
es a
v
ailab
le
f
o
r
tr
an
s
p
o
r
tatio
n
.
T
h
is
d
ata
f
o
r
m
s
th
e
f
o
u
n
d
atio
n
f
o
r
s
u
b
s
eq
u
en
t
an
aly
s
is
,
en
s
u
r
in
g
t
h
at
all
cr
itical
p
ar
am
ete
r
s
ar
e
co
n
s
id
er
e
d
i
n
th
e
o
p
tim
izatio
n
p
r
o
ce
s
s
.
Step
2
:
d
ata
p
r
e
p
ar
atio
n
a
n
d
a
d
ap
tatio
n
Af
ter
d
ata
co
llectio
n
,
t
h
e
d
ata
u
n
d
er
g
o
es m
eticu
lo
u
s
p
r
ep
ar
at
io
n
to
en
s
u
r
e
its
q
u
ality
an
d
r
e
lev
an
ce
:
-
Han
d
lin
g
m
is
s
in
g
v
alu
es:
m
is
s
in
g
o
r
in
co
m
p
lete
d
ata
en
tr
ie
s
ar
e
ad
d
r
ess
ed
th
r
o
u
g
h
r
em
o
v
al
o
r
im
p
u
tatio
n
tech
n
iq
u
es,
d
e
p
en
d
in
g
o
n
th
e
ex
ten
t a
n
d
n
atu
r
e
o
f
th
e
m
is
s
i
n
g
d
ata.
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:
2502
-
4
7
5
2
OP
T
-
TM
S
:
a
tr
a
n
s
p
o
r
t m
a
n
a
g
eme
n
t sys
tem
b
a
s
ed
o
n
…
(
S
o
u
fia
n
e
R
e
g
u
ema
li
)
427
-
Data
co
n
s
is
ten
cy
:
th
e
co
o
r
d
in
ate
s
y
s
tem
an
d
u
n
its
u
s
ed
ac
r
o
s
s
th
e
d
ataset
ar
e
s
tan
d
ar
d
ized
to
en
s
u
r
e
co
n
s
is
ten
cy
.
T
h
is
s
tep
in
v
o
lv
es
v
er
if
y
in
g
an
d
co
r
r
ec
tin
g
an
y
d
is
cr
ep
a
n
cies
to
m
ain
tai
n
ac
cu
r
ate
d
ata
th
r
o
u
g
h
o
u
t t
h
e
p
r
o
ce
s
s
.
-
D
ata
in
teg
r
atio
n
:
c
o
m
b
in
i
n
g
d
ata
f
r
o
m
v
ar
io
u
s
s
o
u
r
ce
s
(
e.
g
.
,
em
p
lo
y
ee
lo
ca
tio
n
s
,
s
ch
ed
u
le
s
)
in
to
a
u
n
if
ied
f
o
r
m
at
th
at
f
ac
ilit
ates f
u
r
th
e
r
a
n
aly
s
is
.
-
Pro
p
er
d
ata
p
r
ep
a
r
atio
n
is
cr
u
cial
f
o
r
th
e
in
teg
r
ity
o
f
th
e
o
p
t
im
izatio
n
r
esu
lts
an
d
th
e
o
v
e
r
all
ef
f
ec
tiv
en
e
s
s
o
f
th
e
s
y
s
tem
.
Step
3
:
g
r
o
u
p
co
n
s
tr
u
ctio
n
u
s
in
g
K
-
m
ea
n
s
with
c
o
n
s
tr
ain
ts
T
h
e
co
r
e
f
u
n
ctio
n
ality
o
f
OP
T
-
T
MS
in
v
o
lv
es
clu
s
ter
in
g
e
m
p
lo
y
ee
s
in
to
o
p
tim
al
g
r
o
u
p
s
u
s
in
g
th
e
K
-
m
ea
n
s
alg
o
r
ith
m
[
2
4
]
,
en
h
a
n
ce
d
with
p
ar
am
eter
o
p
tim
izatio
n
:
-
I
n
itial
p
ar
am
eter
s
etu
p
:
th
e
n
u
m
b
er
o
f
clu
s
ter
s
(
Nb
_
clu
s
ter
s
)
is
in
itially
esti
m
ated
b
ased
o
n
th
e
r
atio
o
f
th
e
em
p
lo
y
ee
lis
t
s
ize
to
b
u
s
ca
p
ac
ity
(
s
ize
(
lis
t
(
X,
Y)
)
/
B
C
)
.
T
h
is
esti
m
atio
n
p
r
o
v
id
es
a
s
t
ar
tin
g
p
o
in
t
f
o
r
clu
s
ter
in
g
.
-
K
-
m
ea
n
s
clu
s
ter
in
g
:
th
e
K
-
m
e
an
s
alg
o
r
ith
m
p
ar
titi
o
n
s
th
e
e
m
p
lo
y
ee
d
ata
in
to
clu
s
ter
s
b
a
s
ed
o
n
d
is
tan
ce
.
I
t
ass
ig
n
s
ea
ch
em
p
lo
y
ee
to
t
h
e
n
ea
r
est
clu
s
ter
ce
n
tr
o
id
an
d
iter
ates
to
u
p
d
ate
th
e
ce
n
tr
o
id
s
b
ased
o
n
th
e
m
ea
n
p
o
s
itio
n
o
f
th
e
ass
ig
n
ed
em
p
lo
y
ee
s
.
T
h
e
p
r
o
ce
s
s
co
n
tin
u
es
u
n
til
th
e
alg
o
r
ith
m
co
n
v
e
r
g
es
o
r
r
ea
ch
es
th
e
m
ax
im
u
m
n
u
m
b
er
o
f
iter
at
io
n
s
.
-
C
o
n
s
tr
ain
t
ch
ec
k
in
g
:
p
o
s
t
-
cl
u
s
ter
in
g
,
th
e
s
y
s
tem
ev
alu
a
tes
wh
eth
er
th
e
clu
s
ter
s
m
ee
t
p
r
ed
ef
in
e
d
co
n
s
tr
ain
ts
,
in
clu
d
in
g
MT
T
an
d
MWD.
I
f
th
e
co
n
s
tr
ain
t
s
ar
e
n
o
t
s
atis
f
ied
,
th
e
alg
o
r
ith
m
ad
ju
s
ts
th
e
n
u
m
b
er
o
f
cl
u
s
te
r
s
(
Nb
_
clu
s
ter
s
)
an
d
r
e
p
ea
ts
th
e
clu
s
ter
in
g
p
r
o
ce
s
s
.
-
Ad
ap
tiv
e
o
p
tim
izatio
n
:
th
e
al
g
o
r
ith
m
d
y
n
am
ically
a
d
ju
s
ts
th
e
n
u
m
b
e
r
o
f
cl
u
s
ter
s
an
d
r
e
-
ev
alu
ates
th
e
co
n
s
tr
ain
ts
to
en
s
u
r
e
o
p
tim
al
g
r
o
u
p
in
g
.
T
h
is
iter
ativ
e
a
p
p
r
o
ac
h
b
alan
ce
s
m
in
im
izin
g
th
e
n
u
m
b
er
o
f
clu
s
ter
s
with
m
ee
tin
g
all
co
n
s
tr
ain
ts
,
th
u
s
im
p
r
o
v
in
g
t
h
e
s
y
s
tem
’
s
ef
f
icien
cy
.
Fin
ally
,
OPT
-
T
MS
o
f
f
er
s
a
s
ca
lab
le
an
d
a
d
ap
tiv
e
s
o
lu
tio
n
th
at
ev
o
lv
es
with
th
e
ch
an
g
in
g
n
ee
d
s
o
f
tr
an
s
p
o
r
tatio
n
m
an
a
g
em
en
t,
u
ltima
tely
ac
h
iev
in
g
g
r
ea
ter
e
f
f
icien
cy
an
d
c
o
s
t
s
av
in
g
s
.
T
h
r
o
u
g
h
th
ese
d
etailed
s
tep
s
,
OPT
-
T
MS
p
r
o
v
id
es
a
co
m
p
r
eh
e
n
s
iv
e
s
o
lu
tio
n
f
o
r
o
p
tim
izin
g
p
er
s
o
n
n
el
tr
a
n
s
p
o
r
t
,
lev
er
ag
in
g
d
ata
-
d
r
iv
en
in
s
ig
h
ts
an
d
ad
v
an
ce
d
alg
o
r
ith
m
s
to
en
h
an
ce
o
v
er
all
o
p
er
atio
n
al
p
er
f
o
r
m
an
ce
.
Fig
u
r
e
1.
W
o
r
k
f
lo
w
o
f
th
e
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
9
,
No
.
1
,
Ju
ly
20
25
:
425
-
4
3
5
428
2
.
2
.
Da
t
a
c
o
llect
io
n
T
o
d
ev
el
o
p
a
n
d
v
alid
ate
th
e
OPT
-
T
MS,
we
co
llected
a
co
m
p
r
eh
en
s
iv
e
d
ataset
f
r
o
m
an
an
o
n
y
m
o
u
s
lo
g
is
tics
an
d
tr
an
s
p
o
r
tatio
n
m
an
ag
em
en
t
c
o
m
p
a
n
y
b
ased
in
C
asab
lan
ca
,
Mo
r
o
cc
o
.
T
h
is
d
ataset
is
cr
u
cial
f
o
r
co
n
s
tr
u
ctin
g
a
n
d
ev
al
u
ati
n
g
th
e
s
y
s
tem
’
s
o
p
tim
izatio
n
ca
p
ab
ilit
ies.
T
h
e
co
llected
d
ata
in
cl
u
d
es:
-
E
m
p
lo
y
ee
lo
ca
tio
n
s
:
latitu
d
e
an
d
lo
n
g
itu
d
e
co
o
r
d
in
ates
r
ep
r
e
s
en
tin
g
th
e
lo
ca
tio
n
s
o
f
n
u
m
e
r
o
u
s
em
p
lo
y
ee
s
in
C
asab
lan
ca
.
T
h
is
s
p
atial
d
at
a
is
ess
en
tia
l f
o
r
u
n
d
e
r
s
tan
d
in
g
th
e
d
is
tr
ib
u
tio
n
o
f
em
p
lo
y
ee
s
ac
r
o
s
s
th
e
city
an
d
f
o
r
m
s
th
e
b
asis
f
o
r
cl
u
s
ter
in
g
an
d
r
o
u
te
o
p
tim
izatio
n
.
-
T
W
:
t
h
e
s
ch
ed
u
led
tim
e
at
wh
ich
ea
ch
em
p
lo
y
ee
is
ex
p
ec
ted
to
b
eg
in
th
eir
wo
r
k
d
a
y
.
T
h
is
tem
p
o
r
al
in
f
o
r
m
atio
n
is
v
ital f
o
r
p
lan
n
i
n
g
tr
an
s
p
o
r
tatio
n
s
ch
ed
u
les an
d
en
s
u
r
in
g
th
at
b
u
s
es a
r
r
iv
e
o
n
tim
e.
-
MT
T
:
t
h
e
m
ax
im
u
m
allo
wab
l
e
tim
e
f
o
r
b
u
s
es
to
co
m
p
lete
th
eir
r
o
u
tes
an
d
t
u
r
n
a
r
o
u
n
d
f
o
r
th
e
n
ex
t
tr
i
p
.
T
h
is
co
n
s
tr
ain
t h
elp
s
in
p
la
n
n
i
n
g
ef
f
icien
t
b
u
s
r
o
u
tes an
d
m
a
n
ag
in
g
t
u
r
n
ar
o
u
n
d
tim
es.
-
MWD:
t
h
e
m
ax
im
u
m
d
is
tan
ce
em
p
lo
y
ee
s
ar
e
e
x
p
ec
ted
to
walk
f
r
o
m
th
ei
r
p
ic
k
u
p
o
r
d
r
o
p
-
o
f
f
p
o
in
ts
.
T
h
is
d
ata
is
u
s
ed
to
en
s
u
r
e
t
h
at
walk
in
g
d
is
tan
ce
s
ar
e
m
in
im
ize
d
,
im
p
r
o
v
in
g
em
p
l
o
y
ee
co
n
v
en
ie
n
ce
.
-
B
C
:
t
h
e
m
ax
im
u
m
n
u
m
b
er
o
f
em
p
lo
y
ee
s
th
at
ea
ch
b
u
s
ca
n
ac
co
m
m
o
d
ate.
T
h
is
in
f
o
r
m
ati
o
n
is
cr
u
cial
f
o
r
d
eter
m
in
in
g
h
o
w
m
a
n
y
b
u
s
es a
r
e
n
ee
d
ed
an
d
f
o
r
e
f
f
ec
tiv
e
g
r
o
u
p
in
g
o
f
e
m
p
lo
y
ee
s
.
-
NB
:
t
h
e
to
tal
n
u
m
b
er
o
f
b
u
s
es
av
ailab
le
f
o
r
tr
an
s
p
o
r
tatio
n
.
T
h
is
d
ata
h
elp
s
in
p
lan
n
in
g
th
e
d
is
tr
ib
u
tio
n
o
f
em
p
lo
y
ee
s
ac
r
o
s
s
av
ailab
le
b
u
s
es a
n
d
o
p
tim
izin
g
b
u
s
r
o
u
tes.
Fig
u
r
e
2
p
r
o
v
id
es
a
m
ap
v
ie
w
s
h
o
wca
s
in
g
th
e
s
p
atial
d
is
tr
ib
u
tio
n
o
f
em
p
lo
y
ee
co
o
r
d
in
ates
ac
r
o
s
s
C
asab
lan
ca
.
T
h
is
v
is
u
al
r
ep
r
e
s
en
tatio
n
allo
ws
f
o
r
a
clea
r
u
n
d
er
s
tan
d
i
n
g
o
f
th
e
em
p
lo
y
ee
lo
ca
tio
n
s
an
d
is
in
s
tr
u
m
en
tal
in
id
en
tif
y
in
g
cl
u
s
ter
s
an
d
o
p
tim
izin
g
b
u
s
r
o
u
t
es.
B
y
an
aly
zin
g
th
is
s
p
atial
d
at
a,
OPT
-
T
MS
ca
n
:
-
I
d
en
tify
clu
s
ter
s
:
d
eter
m
in
e
a
r
ea
s
with
h
ig
h
c
o
n
ce
n
t
r
atio
n
s
o
f
em
p
lo
y
ee
s
to
s
tr
ea
m
lin
e
b
u
s
r
o
u
tes
a
n
d
im
p
r
o
v
e
c
o
llectio
n
ef
f
icien
cy
.
-
Op
tim
ize
r
o
u
tes:
u
s
e
th
e
s
p
atial
d
is
tr
ib
u
tio
n
d
ata
to
p
lan
o
p
t
im
al
b
u
s
r
o
u
tes
th
at
m
in
im
ize
tr
av
el
d
is
tan
ce
s
an
d
tim
e,
wh
ile
co
n
s
id
er
in
g
t
h
e
m
ax
im
u
m
walk
i
n
g
d
is
tan
ce
f
o
r
em
p
lo
y
ee
s
.
-
Vis
u
alize
co
n
s
tr
ain
ts
:
u
n
d
er
s
tan
d
th
e
g
eo
g
r
a
p
h
ical
co
n
s
tr
ain
ts
an
d
th
e
s
p
atial
r
elatio
n
s
h
ip
s
b
etwe
en
em
p
lo
y
ee
lo
ca
tio
n
s
,
wh
ich
ar
e
cr
u
cial
f
o
r
ef
f
ec
t
iv
e
tr
a
n
s
p
o
r
t
atio
n
m
an
ag
e
m
en
t.
I
n
ad
d
itio
n
to
s
p
atial
d
ata,
th
e
co
llected
in
f
o
r
m
atio
n
o
n
b
u
s
ca
p
ac
ity
an
d
n
u
m
b
er
o
f
b
u
s
es e
n
ab
les
OPT
-
T
MS
to
o
p
tim
ize
tr
an
s
p
o
r
tatio
n
lo
g
is
tics
f
u
r
th
er
:
-
R
o
u
te
o
p
tim
izatio
n
:
d
eter
m
in
e
th
e
m
o
s
t
ef
f
icien
t
r
o
u
tes
f
o
r
ea
ch
b
u
s
,
co
n
s
id
er
i
n
g
f
ac
to
r
s
lik
e
d
is
tan
ce
tr
av
eled
a
n
d
b
u
s
t
u
r
n
ar
o
u
n
d
t
im
es.
T
h
is
en
s
u
r
es
th
at
b
u
s
es
ar
e
u
tili
ze
d
e
f
f
ec
tiv
ely
,
r
ed
u
cin
g
o
p
er
atio
n
al
co
s
ts
.
-
R
eso
u
r
ce
allo
ca
tio
n
:
o
p
tim
ize
th
e
allo
c
atio
n
o
f
b
u
s
es
b
ased
o
n
th
e
n
u
m
b
e
r
o
f
em
p
l
o
y
ee
s
an
d
th
eir
lo
ca
tio
n
s
,
en
s
u
r
in
g
th
at
ea
c
h
b
u
s
is
u
s
ed
to
its
f
u
ll
ca
p
ac
ity
wh
ile
ad
h
e
r
in
g
t
o
co
n
s
tr
ain
ts
lik
e
MWD
an
d
MTT.
-
E
f
f
icien
cy
m
ax
im
izatio
n
:
e
n
h
an
ce
o
v
e
r
all
tr
an
s
p
o
r
tatio
n
e
f
f
icien
cy
b
y
m
in
im
izin
g
walk
in
g
d
is
tan
ce
s
f
o
r
em
p
lo
y
ee
s
an
d
o
p
tim
izin
g
b
u
s
s
ch
ed
u
les to
m
ee
t w
o
r
k
en
tr
y
tim
es.
Fig
u
r
e
2
.
E
m
p
lo
y
ee
lo
ca
tio
n
s
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:
2502
-
4
7
5
2
OP
T
-
TM
S
:
a
tr
a
n
s
p
o
r
t m
a
n
a
g
eme
n
t sys
tem
b
a
s
ed
o
n
…
(
S
o
u
fia
n
e
R
e
g
u
ema
li
)
429
T
h
e
in
teg
r
atio
n
o
f
th
is
co
m
p
r
eh
en
s
iv
e
d
ataset
allo
ws
OP
T
-
T
MS
to
ap
p
ly
ad
v
an
ce
d
o
p
tim
izatio
n
tech
n
iq
u
es,
s
u
c
h
as
th
e
K
-
m
e
an
s
alg
o
r
ith
m
,
to
g
r
o
u
p
em
p
lo
y
ee
s
ef
f
ec
tiv
ely
an
d
p
la
n
tr
a
n
s
p
o
r
tatio
n
lo
g
is
tics
.
T
h
e
s
y
s
tem
’
s
ab
ilit
y
to
an
aly
ze
an
d
ad
ap
t
to
r
ea
l
-
w
o
r
ld
d
ata
en
s
u
r
es
th
at
th
e
p
r
o
p
o
s
ed
s
o
lu
tio
n
s
ar
e
p
r
ac
tical
an
d
ap
p
licab
le
to
d
y
n
am
ic
tr
a
n
s
p
o
r
tatio
n
s
ce
n
ar
i
o
s
as sh
o
wn
in
Fig
u
r
e
3
.
Fig
u
r
e
3
.
I
n
p
u
t a
n
d
o
u
tp
u
t
o
f
o
u
r
s
y
s
tem
2
.
3
.
O
P
T
-
T
M
S
dev
elo
pm
ent
T
h
e
OPT
-
T
MS
is
d
esig
n
ed
to
o
p
tim
ize
th
e
clu
s
ter
in
g
o
f
em
p
lo
y
ee
s
b
ased
o
n
p
r
o
x
i
m
ity
wh
ile
ad
d
r
ess
in
g
k
ey
co
n
s
tr
ain
ts
co
m
m
o
n
ly
f
ac
ed
in
p
er
s
o
n
n
el
m
an
ag
em
en
t,
s
u
ch
as
co
llecti
o
n
tim
e.
Giv
en
th
e
cr
itical
im
p
o
r
tan
ce
o
f
p
u
n
ct
u
a
lity
to
em
p
lo
y
ee
s
atis
f
ac
tio
n
,
m
in
im
izin
g
d
ela
y
s
in
th
e
co
ll
ec
tio
n
p
r
o
ce
s
s
is
a
p
r
im
ar
y
g
o
al
o
f
th
e
s
y
s
tem
.
B
y
f
o
cu
s
in
g
o
n
cl
u
s
ter
in
g
em
p
lo
y
ee
s
b
ased
o
n
th
eir
s
p
atial
d
is
tr
ib
u
tio
n
,
OPT
-
T
MS
en
s
u
r
es
th
at
b
u
s
es
co
v
er
s
m
aller
a
r
ea
s
,
wh
ich
f
ac
ilit
ates
q
u
ick
er
an
d
m
o
r
e
ef
f
icien
t
em
p
lo
y
ee
co
llectio
n
.
T
h
e
d
ev
elo
p
m
en
t
o
f
OPT
-
T
M
S
in
v
o
lv
es
s
ev
er
al
k
ey
s
tep
s
.
I
n
itially
,
em
p
lo
y
ee
s
ar
e
clu
s
ter
ed
u
s
in
g
th
e
K
-
m
ea
n
s
alg
o
r
ith
m
,
wh
ic
h
g
r
o
u
p
s
in
d
iv
id
u
als
b
ased
o
n
th
eir
p
r
o
x
im
ity
to
ea
c
h
o
t
h
er
.
T
h
is
ap
p
r
o
ac
h
r
ed
u
c
es
th
e
ar
ea
th
at
b
u
s
es
n
e
ed
to
co
v
e
r
,
allo
win
g
f
o
r
f
aster
co
llectio
n
tim
es.
T
o
f
u
r
th
er
r
ef
in
e
th
e
clu
s
ter
in
g
p
r
o
ce
s
s
,
th
e
s
y
s
tem
id
en
tifie
s
o
p
tim
al
co
llectio
n
p
o
in
ts
,
o
r
ce
n
tr
o
id
s
,
th
at
ar
e
s
tr
ateg
ically
p
o
s
itio
n
ed
to
m
in
im
ize
walk
in
g
d
is
tan
ce
s
f
o
r
em
p
lo
y
e
es.
T
h
is
o
p
tim
izatio
n
is
p
ar
ticu
lar
ly
cr
itical
as
it
en
s
u
r
es
th
at
b
u
s
es
ar
e
d
ir
ec
ted
t
o
th
e
m
o
s
t
ef
f
e
ctiv
e
co
llectio
n
p
o
in
ts
,
r
ed
u
c
in
g
th
e
tim
e
em
p
lo
y
ee
s
s
p
en
d
walk
in
g
to
th
ese
p
o
in
ts
.
I
n
teg
r
al
to
th
e
OPT
-
T
MS
is
it
s
in
teg
r
atio
n
with
s
atellite
in
f
o
r
m
atio
n
t
h
r
o
u
g
h
th
e
o
p
en
r
o
u
te
s
er
v
ices
(
OR
S)
API
.
T
h
is
in
teg
r
atio
n
p
r
o
v
id
es
r
ea
l
-
tim
e
r
o
u
te
o
p
ti
m
izatio
n
,
allo
win
g
th
e
s
y
s
tem
to
d
eliv
er
th
e
m
o
s
t
ef
f
icien
t
itin
er
ar
ies
to
b
u
s
d
r
i
v
er
s
.
T
h
e
OR
S
API
h
elp
s
d
et
er
m
in
e
th
e
b
est
r
o
u
tes
to
ea
c
h
co
llectio
n
p
o
in
t,
tak
in
g
in
t
o
ac
c
o
u
n
t
cu
r
r
en
t
t
r
af
f
ic
c
o
n
d
itio
n
s
an
d
o
th
e
r
v
ar
iab
les.
B
y
i
n
co
r
p
o
r
atin
g
t
h
is
r
ea
l
-
tim
e
d
ata,
OPT
-
T
MS
en
s
u
r
es
th
at
b
u
s
es
f
o
llo
w
o
p
tim
ized
p
ath
s
,
r
ed
u
cin
g
b
o
t
h
tr
av
el
tim
e
a
n
d
d
is
tan
ce
.
T
h
e
s
y
s
tem
also
u
s
es
in
f
o
r
m
atio
n
f
r
o
m
t
h
e
OR
S
API
to
c
o
m
m
u
n
icate
p
r
e
cise
co
llectio
n
tim
es
to
e
m
p
lo
y
ee
s
.
T
h
is
f
ea
tu
r
e
allo
ws
em
p
lo
y
ee
s
to
b
e
at
th
eir
d
esig
n
ated
co
llectio
n
p
o
i
n
ts
at
th
e
r
ig
h
t
tim
e,
f
u
r
th
er
en
h
an
cin
g
ef
f
icien
cy
an
d
s
atis
f
ac
tio
n
.
T
o
tailo
r
th
e
K
-
m
ea
n
s
alg
o
r
i
th
m
to
th
e
s
p
ec
if
ic
n
ee
d
s
o
f
o
u
r
s
y
s
tem
,
we
h
av
e
c
u
s
to
m
i
ze
d
it
b
y
ex
p
er
im
en
tin
g
with
v
ar
io
u
s
p
ar
am
eter
v
alu
es.
W
e
f
o
cu
s
o
n
d
eter
m
in
in
g
b
o
th
th
e
m
in
i
m
u
m
an
d
m
ax
im
u
m
n
u
m
b
er
o
f
clu
s
ter
s
,
co
n
s
id
er
i
n
g
th
e
n
u
m
b
er
o
f
av
ailab
le
b
u
s
es
as
a
lim
itin
g
f
ac
to
r
.
T
h
e
ca
p
ac
ity
o
f
ea
ch
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
9
,
No
.
1
,
Ju
ly
20
25
:
425
-
4
3
5
430
clu
s
ter
,
wh
ich
co
r
r
esp
o
n
d
s
to
b
u
s
ca
p
a
city
,
is
also
tak
en
in
to
ac
co
u
n
t.
T
h
is
cu
s
to
m
ized
ap
p
r
o
ac
h
e
n
s
u
r
es
th
at
th
e
K
-
m
ea
n
s
alg
o
r
ith
m
ca
n
a
cc
u
r
ately
p
r
e
d
ict
clu
s
ter
s
th
at
ad
h
er
e
to
o
u
r
co
n
s
tr
ain
ts
,
o
p
tim
izin
g
b
u
s
r
o
u
tes
an
d
im
p
r
o
v
i
n
g
o
v
er
all
tr
an
s
p
o
r
tatio
n
m
an
ag
em
e
n
t.
Ov
e
r
all,
th
e
OPT
-
T
MS
d
ev
elo
p
m
en
t
p
r
o
ce
s
s
co
m
b
in
es
ad
v
an
ce
d
clu
s
ter
in
g
tech
n
iq
u
e
s
with
r
ea
l
-
tim
e
r
o
u
te
o
p
tim
izatio
n
to
cr
ea
te
a
h
ig
h
ly
ef
f
ici
en
t
an
d
r
esp
o
n
s
iv
e
TMS
.
T
h
is
ap
p
r
o
ac
h
n
o
t
o
n
l
y
en
h
an
ce
s
o
p
e
r
atio
n
al
ef
f
ici
en
cy
b
u
t
also
s
ig
n
if
ican
tly
i
m
p
r
o
v
es
em
p
l
o
y
ee
s
atis
f
ac
tio
n
b
y
m
in
im
iz
in
g
wa
it tim
es a
n
d
o
p
tim
izin
g
t
h
e
tr
a
n
s
p
o
r
tatio
n
e
x
p
er
ien
ce
.
T
ec
h
n
ically
,
th
e
d
ev
elo
p
ed
K
-
m
ea
n
s
c
lu
s
ter
in
g
alg
o
r
ith
m
a
s
s
h
o
wn
in
Alg
o
r
ith
m
1
aim
s
to
g
r
o
u
p
d
ata
p
o
in
ts
r
ep
r
esen
tin
g
th
e
l
o
ca
tio
n
s
o
f
em
p
lo
y
ee
s
i
n
to
cl
u
s
ter
s
,
co
n
s
id
er
in
g
a
s
p
ec
if
ied
n
u
m
b
er
o
f
clu
s
ter
s
an
d
a
m
ax
im
u
m
s
ize
co
n
s
tr
ai
n
t.
T
h
e
alg
o
r
ith
m
b
eg
in
s
b
y
i
n
itializin
g
th
e
clu
s
ter
s
.
I
t
th
en
iter
ativ
ely
ass
ig
n
s
d
ata
p
o
in
ts
to
th
e
n
e
ar
est
clu
s
ter
ce
n
tr
o
id
wh
ile
r
esp
ec
tin
g
t
h
e
s
ize
co
n
s
tr
ain
t,
r
ep
ea
tin
g
t
h
is
p
r
o
ce
s
s
f
o
r
th
e
d
esire
d
n
u
m
b
er
o
f
clu
s
ter
s
.
N
ex
t,
it
ass
ig
n
s
clu
s
ter
in
d
ices
to
ea
ch
d
ata
p
o
in
t.
T
h
e
al
g
o
r
i
th
m
d
eter
m
i
n
es
th
e
m
o
s
t
co
m
m
o
n
cl
u
s
ter
ass
ig
n
m
en
t
am
o
n
g
th
e
d
ata
p
o
in
ts
a
n
d
ass
ig
n
s
an
y
u
n
ass
ig
n
ed
d
at
a
p
o
in
ts
to
th
is
m
o
s
t
co
m
m
o
n
clu
s
ter
.
Fin
ally
,
it o
u
tp
u
ts
th
e
clu
s
ter
ass
ig
n
m
en
ts
,
in
d
icatin
g
wh
ich
clu
s
ter
ea
ch
p
er
s
o
n
b
elo
n
g
s
to
.
Alg
o
r
ith
m
1.
K
-
m
ea
n
s
co
n
s
tr
ain
ed
clu
s
ter
in
g
Input:
-
Data points :
X = {(x_1, y_1), (x_2, y_2),...,(x_n, y_n)}
-
Max number of clusters
-
Maximum size of each cluster
utput:
-
Cluster assignments: C = {c_1, c_2, ..., c_n}
Step 1: Initialization
Set clusters = [ ]
Step 2: Cluster Assignment
Repeat Nbr_bus times:
Se
t current_cluster = [ ].
For each data point (x_i, y_i) in X:
Assign (x_i, y_i) to the cluster with the nearest centroid that satisfies the
constraint of not exceeding size_max.
Append current_cluster to clusters.
Step 3: Cluster Index
Assignment
For each data point (x_i, y_i) in X:
Assign the cluster index to each data point.
Step 4: Determine the Most Common Cluster Assignment
Count the occurrences of each cluster index in C.
Find the cluster index j with the highest count.
Let most_common = j.
Create a list unassigned = [ ].
For each data point (x_i, y_i) in X without an assigned
cluster index:
Assign most_common to c_i.
Add (x_i, y_i) to unassigned.
Step 5: Assign Unassigned Data Points
3.
RE
SU
L
T
S AN
D
VA
L
I
DAT
I
O
N
T
h
e
OPT
-
T
MS
is
an
ad
v
an
ce
d
s
y
s
tem
th
at
lev
er
ag
es
cu
ttin
g
-
ed
g
e
a
r
tific
ial
in
tellig
en
ce
tech
n
iq
u
es,
p
ar
ticu
lar
ly
u
n
s
u
p
er
v
is
ed
lea
r
n
in
g
,
to
o
p
tim
ize
tr
an
s
p
o
r
ta
tio
n
m
an
a
g
em
en
t.
T
h
e
p
r
o
je
ct
b
eg
an
with
an
ex
ten
s
iv
e
d
ata
c
o
llectio
n
p
r
o
ce
s
s
f
o
cu
s
in
g
o
n
em
p
lo
y
ee
l
o
ca
tio
n
s
with
in
C
asab
lan
ca
.
T
h
is
co
m
p
r
e
h
en
s
iv
e
d
ataset
in
clu
d
ed
p
r
ec
is
e
latitu
d
e
an
d
lo
n
g
itu
d
e
co
o
r
d
i
n
ates
o
f
em
p
lo
y
ee
s
,
tim
e
o
f
wo
r
k
e
n
tr
y
,
b
u
s
ca
p
ac
ity
,
an
d
o
th
e
r
cr
itical
p
ar
am
eter
s
.
T
o
en
s
u
r
e
th
e
ac
c
u
r
ac
y
an
d
r
eliab
ilit
y
o
f
th
e
d
ataset,
a
r
ig
o
r
o
u
s
v
alid
atio
n
p
r
o
ce
s
s
was
em
p
lo
y
ed
.
T
h
is
v
alid
atio
n
was
co
n
d
u
cted
b
y
a
team
o
f
lo
g
is
tics
ex
p
er
ts
f
am
iliar
with
C
asab
lan
ca
’
s
u
n
iq
u
e
lan
d
s
ca
p
e,
en
s
u
r
in
g
th
at
th
e
d
ataset
was
m
eticu
lo
u
s
ly
r
e
v
iewe
d
.
Go
o
g
l
e
Ma
p
s
was
also
u
s
ed
as
a
s
u
p
p
lem
en
tar
y
to
o
l
f
o
r
d
ata
v
er
if
icati
o
n
,
e
n
h
an
ci
n
g
th
e
r
eliab
ilit
y
o
f
o
u
r
d
ataset.
T
h
is
d
u
al
-
v
alid
atio
n
ap
p
r
o
ac
h
en
s
u
r
e
d
t
h
at
th
e
f
o
u
n
d
atio
n
o
f
th
e
OPT
-
T
MS
was
b
o
th
ac
cu
r
ate
an
d
r
o
b
u
s
t.
Fo
llo
win
g
th
e
s
u
cc
ess
f
u
l
v
ali
d
atio
n
o
f
d
ata
f
r
o
m
C
asab
lan
ca
,
th
e
s
y
s
tem
was
ex
p
an
d
e
d
to
in
clu
d
e
d
ata
f
r
o
m
o
th
e
r
Mo
r
o
cc
an
cities
s
u
c
h
as
Ken
itra
an
d
Ma
r
r
ak
ec
h
.
T
h
e
v
er
if
icatio
n
p
r
o
ce
s
s
was
r
ep
ea
ted
f
o
r
th
ese
ex
ten
d
e
d
d
atasets
,
m
ain
tain
i
n
g
co
n
s
is
ten
cy
an
d
ac
cu
r
ac
y
ac
r
o
s
s
d
if
f
er
en
t
lo
ca
les.
T
h
e
d
ata
f
r
o
m
th
ese
cities
was
in
teg
r
ated
in
to
th
e
OPT
-
T
MS,
en
ab
lin
g
th
e
s
y
s
tem
to
o
p
er
ate
ef
f
ec
tiv
ely
a
cr
o
s
s
a
b
r
o
ad
e
r
g
eo
g
r
ap
h
ic
ar
e
a.
T
h
e
d
ep
lo
y
m
en
t
o
f
OPT
-
T
MS
o
n
th
is
ex
ten
d
ed
d
ataset
y
ield
ed
two
s
ig
n
if
ican
t
o
u
tp
u
ts
:
th
e
f
o
r
m
atio
n
o
f
em
p
lo
y
ee
g
r
o
u
p
s
an
d
co
r
r
esp
o
n
d
in
g
m
ap
s
f
o
r
ea
ch
g
r
o
u
p
.
T
o
v
alid
ate
th
ese
o
u
tp
u
ts
,
a
p
h
y
s
ical
v
alid
atio
n
p
r
o
ce
s
s
was
co
n
d
u
cted
,
wh
ich
in
v
o
l
v
ed
t
r
av
er
s
i
n
g
th
e
p
r
o
p
o
s
ed
r
o
u
tes
in
a
v
eh
icle
to
c
h
ec
k
t
h
e
tem
p
o
r
al
p
r
ec
is
io
n
o
f
ea
ch
tu
r
n
an
d
th
e
ac
c
u
r
ac
y
o
f
ass
em
b
l
y
p
o
in
ts
.
T
h
e
r
esu
lts
f
r
o
m
th
is
v
alid
atio
n
clo
s
ely
alig
n
ed
with
th
e
s
y
s
tem
’
s
p
r
ed
ictio
n
s
,
with
o
n
l
y
m
in
o
r
d
is
cr
ep
an
cies
o
f
o
n
e
o
r
two
m
in
u
tes
o
b
s
er
v
e
d
.
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:
2502
-
4
7
5
2
OP
T
-
TM
S
:
a
tr
a
n
s
p
o
r
t m
a
n
a
g
eme
n
t sys
tem
b
a
s
ed
o
n
…
(
S
o
u
fia
n
e
R
e
g
u
ema
li
)
431
Su
ch
m
in
im
al
d
ev
iatio
n
s
a
r
e
c
o
n
s
id
er
ed
e
x
ce
p
tio
n
al
with
in
t
h
e
r
ea
lm
o
f
TMS
an
d
ar
e
well
with
in
ac
ce
p
ta
b
le
lim
its
.
T
h
e
s
u
cc
ess
f
u
l
v
alid
atio
n
o
f
OPT
-
T
MS
in
C
asab
lan
ca
,
Ken
itra
,
an
d
Ma
r
r
a
k
ec
h
u
n
d
e
r
s
co
r
es
its
r
o
b
u
s
tn
ess
an
d
a
d
ap
tab
ilit
y
.
T
h
is
s
u
cc
ess
h
as
attr
ac
ted
s
ig
n
if
ican
t
in
ter
est
f
r
o
m
v
a
r
io
u
s
en
ter
p
r
is
es
in
Mo
r
o
cc
o
,
h
ig
h
lig
h
tin
g
th
e
s
y
s
tem
’
s
p
o
ten
tial
as
an
in
tellig
en
t
s
o
lu
tio
n
f
o
r
em
p
lo
y
ee
tr
an
s
p
o
r
tatio
n
m
an
ag
em
en
t.
I
n
s
u
m
m
a
r
y
,
t
h
e
ex
ten
s
iv
e
v
alid
atio
n
ef
f
o
r
ts
,
wh
ich
i
n
clu
d
ed
b
o
th
d
a
ta
v
er
if
icatio
n
an
d
p
h
y
s
ical
v
alid
atio
n
,
a
f
f
ir
m
th
e
r
eliab
ilit
y
an
d
ac
c
u
r
ac
y
o
f
th
e
OPT
-
T
MS
o
u
t
p
u
ts
.
T
h
e
s
y
s
tem
’
s
s
u
cc
ess
f
u
l
ap
p
licatio
n
ac
r
o
s
s
m
u
ltip
le
Mo
r
o
cc
an
cities
estab
lis
h
es
it
as
a
v
iab
le
an
d
ef
f
ec
tiv
e
s
o
lu
t
io
n
in
th
e
f
ield
o
f
in
tellig
en
t tr
an
s
p
o
r
tatio
n
m
an
a
g
em
en
t.
3
.
1
.
O
P
T
-
T
M
S
we
b a
pp
T
o
en
h
a
n
ce
th
e
ac
ce
s
s
ib
ilit
y
an
d
u
s
ab
ilit
y
o
f
OPT
-
T
MS,
we
h
av
e
d
e
v
elo
p
e
d
a
u
s
er
-
f
r
i
en
d
ly
web
ap
p
licatio
n
as
s
h
o
wn
in
Fig
u
r
e
4
.
T
h
e
a
p
p
licatio
n
was
cr
ea
ted
u
s
in
g
FLASK,
a
Py
th
o
n
web
f
r
am
ewo
r
k
,
to
b
u
ild
an
in
ter
ac
tiv
e
in
ter
f
ac
e
[
2
5
]
.
T
o
f
ac
ilit
ate
s
ea
m
les
s
c
o
m
m
u
n
icatio
n
b
etwe
en
t
h
e
b
ac
k
en
d
p
r
o
ce
s
s
es
an
d
th
e
u
s
er
in
ter
f
ac
e,
Djan
g
o
was
in
teg
r
ated
.
T
h
is
co
m
b
in
atio
n
en
s
u
r
es
ef
f
icien
t
in
ter
a
ctio
n
b
etwe
en
th
e
s
y
s
tem
’
s
co
r
e
f
u
n
ctio
n
alities
an
d
th
e
g
r
ap
h
ical
in
te
r
f
ac
e.
Fig
u
r
e
4.
OPT
-
T
MS
w
eb
a
p
p
l
icatio
n
T
h
e
web
a
p
p
h
o
s
ts
a
d
ed
icate
d
f
o
ld
e
r
f
o
r
th
e
d
atab
ase,
wh
ic
h
s
to
r
es
r
esu
lts
,
m
ap
s
,
an
d
o
t
h
er
r
elev
an
t
in
f
o
r
m
atio
n
.
T
h
e
s
y
s
tem
p
r
o
m
p
ts
u
s
er
s
to
u
p
lo
ad
a
C
SV
f
ile
co
n
tain
i
n
g
e
m
p
lo
y
ee
lo
ca
tio
n
s
,
I
Ds,
an
d
ad
d
r
ess
es.
T
h
is
d
ata
is
u
s
ed
t
o
ex
tr
ac
t
c
o
o
r
d
in
ates
(
latitu
d
e
an
d
lo
n
g
itu
d
e)
f
o
r
p
r
o
ce
s
s
in
g
.
T
h
e
s
y
s
tem
also
r
eq
u
ir
es
in
p
u
ts
s
u
ch
as
th
e
c
o
o
r
d
in
ates
o
f
th
e
f
ac
to
r
y
(
th
e
b
u
s
d
ep
ar
tu
r
e
p
o
in
t)
,
b
u
s
ca
p
ac
ity
,
m
ax
im
u
m
tu
r
n
tim
e,
an
d
th
e
m
a
x
im
u
m
walk
in
g
d
is
tan
ce
f
r
o
m
h
o
m
e
to
th
e
ass
em
b
ly
p
o
in
t.
User
s
c
an
ch
o
o
s
e
f
r
o
m
th
r
e
e
ex
ec
u
tio
n
m
o
d
es:
“
r
ap
id
tim
e
,
”
wh
ich
p
r
io
r
itizes m
in
im
izin
g
tr
av
el
tim
e;
“
m
in
im
u
m
d
is
tan
ce
,
”
wh
ich
f
o
cu
s
es
o
n
m
in
im
izin
g
th
e
d
is
tan
ce
tr
av
eled
;
an
d
a
h
y
b
r
i
d
o
p
tio
n
t
h
at
b
alan
ce
s
b
o
th
tim
e
an
d
d
i
s
tan
ce
as
s
h
o
wn
in
Fig
u
r
e
5
.
B
ased
o
n
th
ese
in
p
u
ts
,
th
e
s
y
s
tem
in
itiates
th
e
o
p
tim
izatio
n
p
r
o
ce
s
s
,
g
en
e
r
atin
g
m
u
ltip
le
g
r
o
u
p
s
th
at
ad
h
er
e
to
th
e
s
p
ec
if
ied
c
o
n
s
tr
ain
ts
.
T
h
e
r
esu
lts
ar
e
d
is
p
lay
ed
as
click
ab
le
b
u
tto
n
s
,
allo
win
g
u
s
er
s
to
v
iew
d
etailed
m
ap
s
s
h
o
win
g
b
u
s
r
o
u
tes
an
d
ass
em
b
ly
p
o
i
n
ts
.
Ad
d
itio
n
ally
,
an
E
x
ce
l
f
ile
is
g
en
er
ated
,
p
r
o
v
id
i
n
g
co
m
p
r
eh
e
n
s
iv
e
in
f
o
r
m
atio
n
a
b
o
u
t
ea
c
h
g
r
o
u
p
,
in
cl
u
d
in
g
a
s
s
em
b
ly
p
o
in
t
tim
es
a
n
d
b
u
s
I
Ds
as
s
h
o
wn
i
n
Fig
u
r
e
6
.
T
h
is
f
ile
s
er
v
es
as
a
co
m
m
u
n
icatio
n
to
o
l
f
o
r
in
f
o
r
m
in
g
em
p
lo
y
ee
s
ab
o
u
t
th
eir
a
s
s
ig
n
ed
g
r
o
u
p
,
b
u
s
I
D,
an
d
c
o
llectio
n
tim
e.
T
h
e
OPT
-
T
MS
web
ap
p
a
d
d
r
e
s
s
es
s
ev
er
al
lo
g
is
tical
ch
allen
g
es
b
y
o
p
tim
izin
g
b
u
s
s
ch
ed
u
l
es,
r
o
u
tes,
an
d
m
in
im
izin
g
em
p
lo
y
ee
w
ait
tim
es.
F
ig
u
r
e
7
illu
s
tr
ates
an
ex
am
p
le
o
f
th
e
tr
ajec
to
r
y
p
r
o
v
i
d
ed
b
y
th
e
s
y
s
tem
.
T
h
e
ap
p
licatio
n
’
s
ad
ap
tab
ilit
y
is
a
k
ey
f
ea
tu
r
e,
allo
win
g
it
to
b
e
lau
n
ch
ed
an
d
u
p
d
ated
in
r
ea
l
-
tim
e
to
ac
co
m
m
o
d
ate
c
h
an
g
es
in
em
p
lo
y
ee
av
ailab
ilit
y
,
wo
r
k
in
g
h
o
u
r
s
,
an
d
o
th
er
v
ar
iab
les.
T
h
is
d
y
n
am
ic
ca
p
ab
ilit
y
em
p
o
wer
s
d
e
cisi
o
n
-
m
a
k
er
s
t
o
g
en
er
ate
n
ew
r
esu
lts
an
d
ad
ju
s
t
lo
g
is
tics
ef
f
icien
tly
,
m
ak
in
g
OPT
-
T
MS
a
v
er
s
atile
to
o
l f
o
r
a
d
v
an
ce
d
tr
a
n
s
p
o
r
tatio
n
m
a
n
ag
em
e
n
t.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
9
,
No
.
1
,
Ju
ly
20
25
:
425
-
4
3
5
432
Fig
u
r
e
5
.
OPT
-
T
MS
in
p
u
ts
Fig
u
r
e
6
.
E
m
p
lo
y
ee
g
r
o
u
p
s
r
e
s
u
lts
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:
2502
-
4
7
5
2
OP
T
-
TM
S
:
a
tr
a
n
s
p
o
r
t m
a
n
a
g
eme
n
t sys
tem
b
a
s
ed
o
n
…
(
S
o
u
fia
n
e
R
e
g
u
ema
li
)
433
Fig
u
r
e
7
.
E
x
am
p
le
o
f
a
tr
ajec
t
o
r
y
4.
CO
NCLU
SI
O
N
OPT
-
T
MS
r
ep
r
esen
ts
a
s
ig
n
i
f
ican
t
ad
v
an
ce
m
e
n
t
in
tr
an
s
p
o
r
tatio
n
m
a
n
ag
em
en
t,
o
f
f
er
i
n
g
a
n
ew
p
er
s
p
ec
tiv
e
o
n
o
p
tim
izin
g
d
ec
is
io
n
-
m
ak
in
g
p
r
o
ce
s
s
es
with
u
n
p
r
ec
ed
e
n
ted
e
f
f
icien
cy
.
T
h
is
in
n
o
v
ativ
e
s
y
s
tem
is
b
u
ilt
o
n
a
co
m
p
r
eh
en
s
iv
e
d
ataset
th
at
in
clu
d
es
ess
en
tial
d
etails
s
u
ch
as
em
p
l
o
y
ee
tim
in
g
s
,
d
is
tan
ce
s
,
a
n
d
g
eo
g
r
a
p
h
ic
lo
ca
tio
n
s
.
B
y
in
teg
r
atin
g
u
n
s
u
p
er
v
is
ed
m
ac
h
i
n
e
lear
n
in
g
tech
n
iq
u
es
with
th
is
d
ata,
OPT
-
T
MS
ef
f
ec
tiv
ely
cr
ea
tes
o
p
tim
al
e
m
p
lo
y
ee
cl
u
s
ter
s
an
d
r
ef
in
es
b
u
s
r
o
u
tes,
lead
in
g
to
s
u
b
s
tan
tial
im
p
r
o
v
em
e
n
ts
in
tr
an
s
p
o
r
tatio
n
lo
g
is
tics
.
T
h
e
s
tr
en
g
th
o
f
OP
T
-
T
MS
lies
i
n
its
ab
ilit
y
to
lev
er
ag
e
d
ata
-
d
r
iv
en
in
s
ig
h
ts
to
p
r
o
d
u
ce
p
r
ec
is
e
o
u
tp
u
ts
,
in
cl
u
d
in
g
ex
ac
t
p
ick
u
p
tim
es
an
d
d
esig
n
ated
ass
em
b
ly
p
o
in
ts
f
o
r
em
p
lo
y
ee
s
.
T
h
is
lev
el
o
f
d
etail
e
n
s
u
r
es
th
at
tr
an
s
p
o
r
tatio
n
is
n
o
t
o
n
l
y
p
u
n
ctu
al
b
u
t
also
h
ig
h
ly
o
r
g
an
iz
e
d
,
p
r
o
v
i
d
in
g
ea
ch
em
p
lo
y
ee
with
a
s
p
ec
if
ic
s
p
o
t
o
n
th
e
b
u
s
.
T
h
e
s
y
s
tem
ex
em
p
lifie
s
h
o
w
tech
n
o
lo
g
y
an
d
in
tellig
en
ce
ca
n
wo
r
k
in
tan
d
em
to
ad
d
r
ess
th
e
co
m
p
lex
ities
o
f
tr
an
s
p
o
r
tatio
n
m
a
n
ag
em
en
t,
o
f
f
er
in
g
a
p
o
we
r
f
u
l
to
o
l
f
o
r
d
ec
is
io
n
-
m
ak
er
s
to
n
av
ig
ate
an
d
o
p
t
im
ize
lo
g
is
tics
.
T
h
e
f
lex
ib
ilit
y
o
f
OPT
-
T
MS
allo
ws
f
o
r
r
ea
l
-
tim
e
ad
ju
s
tm
en
ts
,
en
ab
lin
g
th
e
g
en
e
r
atio
n
o
f
u
p
d
ated
g
r
o
u
p
s
an
d
m
ap
s
in
r
esp
o
n
s
e
to
ch
a
n
g
in
g
em
p
l
o
y
ee
s
ch
ed
u
les
an
d
o
p
er
atio
n
al
n
ee
d
s
.
T
h
is
ad
ap
tab
ilit
y
en
s
u
r
es
th
at
tr
an
s
p
o
r
tatio
n
s
o
lu
tio
n
s
r
em
ai
n
r
elev
an
t
an
d
ef
f
ec
tiv
e,
em
p
o
wer
in
g
d
e
cisi
o
n
-
m
ak
er
s
to
ad
d
r
ess
em
er
g
in
g
ch
allen
g
es
p
r
o
m
p
tly
.
B
y
o
p
tim
izin
g
tr
an
s
p
o
r
tatio
n
lo
g
is
tics
,
OPT
-
T
MS
en
h
an
ce
s
o
v
er
all
ef
f
icien
c
y
an
d
b
o
o
s
ts
em
p
lo
y
ee
s
atis
f
ac
tio
n
.
Fu
r
th
er
m
o
r
e
,
th
e
s
y
s
tem
’
s
in
teg
r
atio
n
with
th
e
tr
an
s
p
o
r
tatio
n
wo
r
k
f
o
r
ce
is
cr
u
cial.
B
y
f
ac
ilit
atin
g
ef
f
icien
t
co
llectio
n
o
f
em
p
lo
y
ee
s
an
d
r
e
d
u
cin
g
th
e
n
ee
d
f
o
r
m
an
u
al
in
te
r
v
en
tio
n
,
O
PT
-
T
MS
n
o
t
o
n
ly
im
p
r
o
v
es
r
eso
u
r
ce
allo
ca
tio
n
b
u
t
also
elev
ates
th
e
ex
p
er
i
en
ce
f
o
r
b
o
th
em
p
lo
y
ee
s
an
d
d
ec
is
io
n
-
m
ak
e
r
s
.
T
h
e
ali
g
n
m
en
t o
f
s
y
s
tem
p
r
ed
i
ctio
n
s
with
r
ea
l
-
wo
r
ld
ex
ec
u
tio
n
is
r
ein
f
o
r
ce
d
b
y
s
h
ar
in
g
o
u
tp
u
t
m
ap
s
with
b
u
s
co
n
d
u
ct
o
r
s
,
en
s
u
r
in
g
s
ea
m
les
s
o
p
er
atio
n
an
d
e
n
h
an
ci
n
g
th
e
o
v
er
all
lo
g
is
tics
ex
p
er
ien
ce
.
I
n
s
u
m
m
ar
y
,
OPT
-
T
MS
s
et
s
a
n
ew
b
en
ch
m
ar
k
f
o
r
in
tellig
en
t
TMS
.
I
ts
i
n
n
o
v
ativ
e
u
s
e
o
f
d
ata
an
d
ad
v
an
ce
d
m
eth
o
d
o
lo
g
ies
tr
an
s
lates
in
to
ac
tio
n
ab
le
i
n
s
ig
h
ts
,
en
ab
lin
g
p
r
ec
is
e
n
av
ig
atio
n
o
f
tr
an
s
p
o
r
tatio
n
lo
g
is
tics
.
T
h
is
ap
p
r
o
ac
h
s
ig
n
if
ican
tly
en
h
an
ce
s
b
o
th
o
p
er
atio
n
al
ef
f
icien
cy
a
n
d
em
p
lo
y
ee
s
atis
f
ac
tio
n
,
m
ar
k
in
g
a
s
ig
n
if
ican
t
ad
v
an
ce
m
e
n
t in
th
e
f
iel
d
o
f
t
r
a
n
s
p
o
r
tatio
n
m
a
n
ag
em
e
n
t.
F
UNDING
I
NF
O
R
M
A
T
I
O
N
T
h
e
au
th
o
r
d
ec
lar
es
th
at
n
o
f
u
n
d
in
g
was
r
ec
ei
v
ed
f
o
r
th
i
s
r
esear
ch
.
T
h
e
wo
r
k
was
en
tire
ly
s
elf
-
f
u
n
d
e
d
.
Evaluation Warning : The document was created with Spire.PDF for Python.
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Mo
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C
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eq
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RE
F
E
R
E
NC
E
S
[
1
]
I
.
A
.
V
e
r
sh
i
n
i
n
a
,
A
.
R
.
K
u
r
b
a
n
o
v
,
a
n
d
A
.
V
.
L
i
a
d
o
v
a
,
“
I
n
d
u
st
r
i
a
l
z
o
n
e
s
i
n
mo
d
e
r
n
c
i
t
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e
s:
a
s
o
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r
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e
o
f
so
c
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o
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c
o
l
o
g
i
c
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e
q
u
a
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p
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t
u
n
i
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y
f
o
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p
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s
p
e
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,
”
Ec
o
l
o
g
y
a
n
d
I
n
d
u
s
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ry
o
f
R
u
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a
,
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l
.
2
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o
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p
p
.
6
5
–
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A
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g
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2
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o
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:
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0
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1
2
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1
6
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3
9
5
-
2
0
1
8
-
8
-
65
-
7
1
.
[
2
]
A
.
S
e
l
e
z
n
e
v
a
n
d
M
.
R
u
d
a
k
o
v
,
“
S
o
me
g
e
o
c
h
e
m
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c
a
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c
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c
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me
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t
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f
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v
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e
o
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c
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e
o
g
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c
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n
d
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d
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a
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s,
”
C
a
rp
a
t
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a
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o
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r
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2
.
[
3
]
K
.
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,
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.
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m
a
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e
me
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sy
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m
,
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2
.
[
4
]
V
.
C
h
a
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a
n
,
M
.
P
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t
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l
,
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.
Ta
n
w
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,
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.
Ty
a
g
i
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a
n
d
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.
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u
mar
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o
T
e
n
a
b
l
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d
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e
a
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m
a
n
a
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e
m
e
n
t
s
y
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m,”
C
o
m
p
u
t
e
rs
& E
l
e
c
t
r
i
c
a
l
E
n
g
i
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e
e
r
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,
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.
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0
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g
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0
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1
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6
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4
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.
[
5
]
M
.
S
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sa
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a
a
n
d
A
.
E.
Ez
u
g
w
u
,
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I
r
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u
s
:
a
r
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p
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a
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s
t
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m
,
”
i
n
2
0
2
0
C
o
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f
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re
n
c
e
o
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I
n
f
o
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C
o
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m
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n
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c
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s T
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c
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S
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(
I
C
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M
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3
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.
[
6
]
M
.
E
.
N
w
a
f
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a
n
d
O
.
V
.
O
n
y
a
,
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R
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a
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a
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v
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i
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N
i
g
e
r
i
a
:
p
r
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b
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ms
a
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d
p
r
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s
p
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c
t
s
,
”
A
d
v
a
n
c
e
J
o
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r
n
a
l
o
f
Ec
o
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o
m
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c
s
A
n
d
M
a
rk
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t
i
n
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Re
se
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c
h
,
v
o
l
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