I
nte
rna
t
io
na
l J
o
urna
l o
f
E
lect
rica
l a
nd
Co
m
p
ute
r
E
ng
in
ee
ring
(
I
J
E
CE
)
Vo
l.
7
,
No
.
5
,
Octo
b
e
r
2
0
1
7
,
p
p
.
2
7
9
1
~
2
797
I
SS
N:
2088
-
8708
,
DOI
: 1
0
.
1
1
5
9
1
/
i
j
ec
e
.
v
7
i
5
.
pp
2
7
9
1
-
2
797
2791
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ia
e
s
jo
u
r
n
a
l.c
o
m/o
n
lin
e/in
d
ex
.
p
h
p
/I
JE
C
E
Co
m
pa
riso
n of
E
m
ergen
cy
Medica
l Services
Deliv
er
y
Perf
o
r
m
a
nce
usin
g
M
a
x
im
a
l
Co
v
ering
Lo
ca
tion a
nd
G
ra
dua
l
Co
v
er Lo
ca
tion P
ro
ble
m
s
M
o
hd
H
a
f
iz
Aziz
a
n
1
,
T
ing
L
o
o
ng
G
o
2
,
W.
A.
L
utf
i W.
M
.
H
a
t
t
a
3
,
Cheng
Sio
ng
L
i
m
4
*
,
So
o
Sia
ng
T
eo
h
5
1,
2,
3,
4
F
a
c
u
l
ty
o
f
El
e
c
tri
c
a
l
En
g
in
e
e
rin
g
,
Un
iv
e
rsiti
T
e
k
n
o
lo
g
i
M
a
lay
sia
,
8
1
3
1
0
UT
M
S
k
u
d
a
i,
Jo
h
o
r
,
M
a
la
y
sia
5
S
c
h
o
o
l
o
f
El
e
c
tri
c
a
l
a
n
d
El
e
c
tro
n
ic E
n
g
in
e
e
rin
g
,
Un
iv
e
rsiti
S
a
in
s
M
a
la
y
sia
,
1
4
3
0
0
Ni
b
o
n
g
T
e
b
a
l,
M
a
la
y
sia
Art
icle
I
nfo
AB
ST
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
Feb
14
,
2
0
1
7
R
ev
i
s
ed
Ma
y
30
,
2
0
1
7
A
cc
ep
ted
A
u
g
11
,
2
0
1
7
Am
b
u
lan
c
e
lo
c
a
ti
o
n
is
o
n
e
o
f
th
e
c
rit
ica
l
f
a
c
to
rs
th
a
t
d
e
term
in
e
th
e
e
ff
ici
e
n
c
y
o
f
e
m
e
rg
e
n
c
y
m
e
d
ica
l
se
r
v
ice
s
d
e
li
v
e
r
y
.
M
a
x
i
m
a
l
Co
v
e
rin
g
L
o
c
a
ti
o
n
P
r
o
b
lem
is
o
n
e
o
f
th
e
w
id
e
l
y
u
se
d
a
m
b
u
lan
c
e
lo
c
a
ti
o
n
m
o
d
e
ls.
Ho
w
e
v
e
r,
it
s
c
o
v
e
ra
g
e
f
u
n
c
ti
o
n
is
c
o
n
si
d
e
re
d
u
n
re
a
li
stic
b
e
c
a
u
se
o
f
it
s
a
b
il
it
y
t
o
a
b
ru
p
tl
y
c
h
a
n
g
e
f
ro
m
f
u
ll
y
c
o
v
e
re
d
to
u
n
c
o
v
e
re
d
.
On
t
h
e
c
o
n
trary
,
G
ra
d
u
a
l
Co
v
e
r
L
o
c
a
ti
o
n
P
r
o
b
lem
c
o
v
e
r
a
g
e
is
c
o
n
sid
e
re
d
m
o
re
r
e
a
li
stic
c
o
m
p
a
re
d
to
M
a
x
i
m
a
l
Co
v
e
r
L
o
c
a
ti
o
n
P
r
o
b
l
e
m
b
e
c
a
u
se
th
e
c
o
v
e
r
a
g
e
d
e
c
re
a
se
s
o
v
e
r
d
istan
c
e
.
T
h
is
p
a
p
e
r
e
x
a
m
in
e
s
th
e
d
e
li
v
e
r
y
o
f
E
m
e
rg
e
n
c
y
M
e
d
ica
l
S
e
rv
ice
s
u
n
d
e
r
t
h
e
m
o
d
e
ls
o
f
M
a
x
i
m
a
l
Co
v
e
rin
g
L
o
c
a
ti
o
n
P
r
o
b
lem
a
n
d
G
ra
d
u
a
l
Co
v
e
r
L
o
c
a
ti
o
n
P
ro
b
lem
.
T
h
e
re
su
lt
s
sh
o
w
th
a
t
th
e
latter
m
o
d
e
l
is
su
p
e
rio
r,
e
sp
e
c
iall
y
w
h
e
n
th
e
M
a
x
im
a
l
Co
v
e
rin
g
L
o
c
a
ti
o
n
P
r
o
b
lem
h
a
s
b
e
e
n
d
e
e
m
e
d
f
u
ll
y
c
o
v
e
re
d
.
K
ey
w
o
r
d
s
:
Am
b
u
lan
ce
lo
ca
tio
n
m
o
d
el
Gr
ad
u
al
co
v
er
lo
ca
tio
n
p
r
o
b
le
m
Ma
x
i
m
al
co
v
er
ag
e
lo
ca
tio
n
p
r
o
b
lem
P
ar
ticle
s
w
ar
m
o
p
ti
m
izatio
n
Co
p
y
rig
h
t
©
2
0
1
7
In
stit
u
te o
f
A
d
v
a
n
c
e
d
E
n
g
i
n
e
e
rin
g
a
n
d
S
c
ien
c
e
.
Al
l
rig
h
ts
re
se
rv
e
d
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
C
h
e
n
g
Sio
n
g
L
i
m
,
Facu
lt
y
o
f
E
lectr
ical
E
n
g
in
ee
r
in
g
,
Un
i
v
er
s
iti T
ek
n
o
lo
g
i M
ala
y
s
ia
,
8
1
3
1
0
U
T
M
Sk
u
d
ai,
J
o
h
o
r
,
M
ala
y
s
ia
.
E
m
ail:
lc
s
io
n
g
@
u
t
m
.
m
y
1.
I
NT
RO
D
UCT
I
O
N
T
h
e
ef
f
icie
n
c
y
o
f
E
m
er
g
e
n
c
y
Me
d
ical
Ser
v
ice
s
(
E
MS)
is
v
er
y
i
m
p
o
r
tan
t
to
e
n
s
u
r
e
p
atien
ts
s
u
r
v
iv
ab
ili
t
y
[
1
–
3
]
.
On
e
o
f
t
h
e
E
MS
ef
f
icie
n
c
y
m
ea
s
u
r
e
m
e
n
ts
i
s
a
m
b
u
la
n
ce
r
esp
o
n
s
e
ti
m
e
(
AR
T
)
.
Gen
er
all
y
,
an
a
m
b
u
la
n
ce
s
r
esp
o
n
s
e
ti
m
e
ca
n
b
e
d
ef
i
n
ed
as,
th
e
in
ter
v
al
f
r
o
m
t
h
e
ti
m
e
t
h
e
ca
ll
w
as
r
e
ce
iv
ed
b
y
t
h
e
E
MS
p
r
o
v
id
er
,
to
th
e
ar
r
iv
al
o
f
t
h
e
a
m
b
u
lan
ce
to
th
e
e
m
er
g
en
c
y
s
c
e
n
e
[4
–
7
]
.
A
d
e
m
an
d
p
o
in
t
is
co
n
s
id
er
ed
co
v
er
ed
if
i
t
ca
n
b
e
s
er
v
ed
w
it
h
in
a
d
ef
in
ed
t
i
m
e
o
r
d
is
tan
ce
to
an
y
o
f
th
e
a
v
ailab
le
f
ac
i
liti
e
s
;
w
h
ile
a
d
e
m
a
n
d
p
o
in
t
f
ar
t
h
er
th
a
n
t
h
e
d
ef
i
n
ed
t
i
m
e
o
r
d
is
ta
n
ce
is
co
n
s
id
er
ed
as
n
o
t
co
v
er
ed
.
A
n
a
m
b
u
lan
ce
s
lo
ca
tio
n
i
s
o
n
e
o
f
th
e
f
ac
to
r
s
t
h
at
d
ir
ec
tl
y
af
f
ec
t
s
th
e
A
R
T
[
8
]
.
A
s
a
r
esu
l
t,
am
b
u
la
n
ce
lo
ca
tio
n
m
o
d
el
is
o
n
e
o
f
th
e
e
f
f
ec
t
iv
e
w
a
y
s
to
i
m
p
r
o
v
e
A
R
T
.
Am
b
u
lan
ce
lo
ca
tio
n
m
o
d
el
s
ca
n
b
e
ca
teg
o
r
is
ed
in
to
d
eter
m
i
n
i
s
t
ic,
p
r
o
b
ab
ilis
tic
an
d
d
y
n
a
m
ic
m
o
d
el
s
[
9
]
.
On
e
o
f
th
e
ea
r
l
iest
d
eter
m
in
i
s
tic
m
o
d
els
is
Ma
x
i
m
al
C
o
v
er
i
n
g
L
o
ca
tio
n
P
r
o
b
lem
(
MCL
P
)
w
h
ich
h
a
s
b
ee
n
in
tr
o
d
u
ce
d
b
y
C
h
u
r
ch
a
n
d
R
ev
el
le
[
1
0
]
.
Giv
en
a
f
i
x
ed
n
u
m
b
er
o
f
f
ac
ilit
ies,
M
C
L
P
is
u
s
ed
to
m
a
x
i
m
is
e
t
h
e
to
tal
co
v
er
ag
e
w
ith
l
i
m
ited
r
eso
u
r
ce
s
.
MC
L
P
an
d
its
v
ar
ia
n
ts
ar
e
t
h
e
m
o
s
t
w
id
el
y
u
s
ed
lo
ca
tio
n
m
o
d
el
s
.
I
n
1
9
8
4
,
th
e
r
eo
r
g
an
is
atio
n
o
f
E
MS
in
A
u
s
ti
n
,
T
ex
as,
u
s
i
n
g
MC
L
P
,
s
av
ed
$
3
.
4
m
illi
o
n
o
f
co
n
s
tr
u
ctio
n
co
s
t
a
n
d
$
1
.
2
m
illi
o
n
o
f
o
p
er
atin
g
co
s
ts
a
n
n
u
all
y
[
1
1
]
.
MC
L
P
h
as
b
ee
n
u
s
ed
f
o
r
r
ea
l
lif
e
p
r
o
b
lem
s
to
s
o
l
v
e
h
ier
ar
ch
ical
ly
d
esi
g
n
ed
h
ea
lt
h
s
y
s
te
m
s
[
1
2
-
1
3
]
,
co
n
g
ested
s
er
v
ice
s
y
s
te
m
s
[
1
4
]
an
d
b
u
s
s
to
p
allo
ca
tio
n
s
[
1
5
]
.
MCL
P
is
a
NP
-
Har
d
p
r
o
b
lem
.
Var
io
u
s
ap
p
r
o
ac
h
es,
s
u
ch
as
ex
ac
t
m
eth
o
d
,
h
eu
r
i
s
tic
a
n
d
m
eta
-
h
e
u
r
is
tic,
ca
n
b
e
u
s
ed
to
s
o
l
v
e
a
MC
L
P
p
r
o
b
lem
.
An
ex
ac
t
ex
ec
u
tio
n
o
f
th
e
m
et
h
o
d
ca
n
g
u
ar
an
tee
t
h
e
m
o
s
t
o
p
ti
m
a
l
s
o
lu
t
io
n
,
b
u
t
m
a
y
h
av
e
a
lo
n
g
er
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
.
5
,
Octo
b
er
201
7
:
2
7
9
1
–
2
7
9
7
2792
p
r
o
ce
s
s
in
g
ti
m
e,
esp
ec
iall
y
f
o
r
a
lar
g
er
p
r
o
b
lem
.
O
n
t
h
e
o
th
er
h
a
n
d
,
m
e
ta
-
h
e
u
r
is
tic
m
et
h
o
d
d
o
es
n
o
t
g
u
ar
a
n
tee
t
h
e
m
o
s
t
o
p
ti
m
al
s
o
lu
tio
n
,
th
o
u
g
h
a
n
ea
r
o
p
ti
m
al
s
o
lu
tio
n
f
o
r
a
lar
g
e
p
r
o
b
le
m
c
an
b
e
ac
h
iev
ed
w
i
t
h
a
r
ea
s
o
n
ab
le
a
m
o
u
n
t
o
f
ti
m
e.
T
h
e
ev
alu
ated
ap
p
r
o
ac
h
es
in
s
o
lv
in
g
M
C
L
P
o
r
its
ex
ten
s
io
n
s
ar
e
li
n
ea
r
p
r
o
g
r
am
m
i
n
g
an
d
th
e
h
e
u
r
is
t
ic
m
et
h
o
d
Gr
ee
d
y
A
d
d
in
g
(
GA
d
)
alg
o
r
it
h
m
[
1
0
]
,
[
1
6
]
,
t
ab
u
s
ea
r
ch
[
1
7
-
1
8
]
,
L
a
g
r
an
g
ea
n
a
n
d
S
u
r
r
o
g
ate
R
e
lax
atio
n
s
[
1
9
]
,
M
y
o
p
ic
o
r
Gr
ee
d
y
h
e
u
r
is
tic
[
2
0
]
,
Heu
r
is
tic
C
o
n
ce
n
tr
atio
n
[
2
1
]
,
Gen
etic
A
l
g
o
r
ith
m
(
G
A
)
[
2
2
]
,
an
d
P
ar
ticle
S
w
ar
m
Op
ti
m
iza
tio
n
(
P
SO)
[
2
3
]
.
C
o
v
er
ag
e
o
f
M
C
L
P
is
co
n
s
i
d
er
ed
u
n
r
ea
lis
tic
b
ec
au
s
e
t
h
e
co
v
er
ag
e
ca
n
ab
r
u
p
tl
y
c
h
a
n
g
e
f
r
o
m
co
v
er
ed
to
u
n
co
v
er
ed
af
ter
ex
ce
ed
in
g
a
d
ef
i
n
ed
d
is
tan
ce
.
O
n
e
o
f
th
e
p
r
o
p
o
s
ed
s
o
lu
tio
n
s
is
ap
p
ly
i
n
g
a
v
ar
iab
le
co
v
er
ag
e.
B
er
m
a
n
et
al.
[
2
4
]
r
ev
ie
w
ed
th
e
id
ea
s
o
f
u
s
i
n
g
d
if
f
er
en
t
co
v
er
a
g
e
t
y
p
es.
T
h
e
m
ain
p
u
r
p
o
s
e
o
f
th
is
p
ap
er
is
to
co
m
p
ar
e
t
h
e
r
e
s
u
lts
o
f
M
C
L
P
to
g
r
ad
u
a
l
co
v
er
ag
e
lo
ca
tio
n
m
o
d
el
(
G
C
L
P
)
in
ter
m
s
o
f
E
M
S
d
eliv
er
y
.
Fro
m
t
h
e
s
i
m
u
latio
n
s
,
A
R
T
an
d
to
tal
tr
av
el
d
is
ta
n
ce
ar
e
r
ep
o
r
ted
.
I
n
th
e
n
e
x
t
s
ec
tio
n
,
w
e
r
ev
ie
w
p
r
ev
io
u
s
r
elate
d
w
o
r
k
to
GC
L
P
an
d
E
MS
d
y
n
a
m
ic
s
i
m
u
latio
n
.
I
n
Sectio
n
3
,
w
e
e
x
p
lain
b
o
th
M
C
L
P
a
n
d
GC
L
P
i
n
d
e
tail.
T
h
e
s
i
m
u
la
tio
n
s
etu
p
i
s
g
i
v
en
i
n
Sectio
n
4
.
R
es
u
lt
s
an
d
d
is
cu
s
s
io
n
o
f
th
e
f
i
n
d
in
g
s
ar
e
p
r
esen
ted
in
Sectio
n
5
.
L
a
s
tl
y
,
Sectio
n
6
co
n
clu
d
es t
h
e
f
i
n
d
in
g
s
.
2.
P
RE
VIOU
S RE
L
A
T
E
D
WO
RK
S
C
h
u
r
ch
a
n
d
R
o
b
er
ts
[
2
5
]
ar
e
th
e
p
io
n
ee
r
s
w
h
o
p
r
o
p
o
s
ed
s
tep
f
u
n
ct
io
n
to
r
ep
lace
c
o
v
er
ed
an
d
u
n
co
v
er
ed
d
is
j
u
n
ctio
n
i
n
a
t
y
p
ical
co
v
er
ag
e
m
o
d
el.
B
er
m
a
n
a
n
d
Kr
ass
[
2
6
]
in
tr
o
d
u
ce
G
en
er
alize
d
Ma
x
i
m
a
l
C
o
v
er
in
g
L
o
ca
tio
n
p
r
o
b
le
m
wh
ich
also
u
s
es
th
e
d
ec
r
ea
s
in
g
s
tep
f
u
n
ctio
n
to
allo
w
p
ar
tia
l
co
v
er
ag
e.
T
h
e
y
also
s
h
o
w
t
h
at
th
e
ir
p
r
o
b
lem
is
id
e
n
tical
to
th
e
u
n
ca
p
ac
itated
f
ac
i
lit
y
lo
ca
tio
n
p
r
o
b
le
m
(
UF
L
P
)
.
B
er
m
a
n
et
al.
[
2
7
]
u
s
e
t
h
e
co
v
er
ag
e
d
ec
a
y
f
u
n
ctio
n
to
r
ep
lace
th
e
s
tep
f
u
n
ctio
n
.
T
h
e
co
v
er
ag
e
d
ec
a
y
f
u
n
ctio
n
h
as
t
w
o
r
ad
ii
R
m
in
an
d
R
m
a
x
w
h
er
e
R
m
i
n
≤
R
m
a
x
.
Fo
r
d
e
m
a
n
d
s
w
it
h
in
r
ad
iu
s
R
m
i
n
,
i
t
is
co
n
s
id
er
ed
as
f
u
ll
y
co
v
er
ed
.
T
h
e
co
v
er
ag
e
v
alu
e
b
et
w
ee
n
r
ad
ii
R
m
in
a
n
d
R
m
a
x
is
d
eter
m
in
ed
b
y
t
h
e
co
v
er
ag
e
d
ec
a
y
f
u
n
ctio
n
,
f
(
r
ij).
L
ast
l
y
f
o
r
d
e
m
an
d
s
w
it
h
d
is
tan
ce
m
o
r
e
t
h
an
r
ad
iu
s
R
m
a
x
,
it
i
s
co
n
s
id
er
ed
a
s
u
n
co
v
er
ed
.
On
e
o
f
t
h
e
f
i
n
d
i
n
g
s
s
h
o
w
s
t
h
at,
t
h
e
p
r
o
p
o
s
ed
m
o
d
el
ca
n
g
e
n
er
ali
s
e
t
h
e
m
-
m
ed
ian
an
d
M
C
L
P
p
r
o
b
lem
s
.
T
h
e
lo
ca
tio
n
p
r
o
b
l
e
m
s
o
lv
ed
ca
n
also
b
e
f
o
r
m
u
l
ated
as
UFL
P
.
Kar
asak
al
an
d
Kar
asak
al
[
2
8
]
u
s
e
s
i
m
ilar
f
u
n
ctio
n
s
to
B
er
m
a
n
’
s
to
in
v
es
tig
a
te
g
r
ad
u
a
l
co
v
e
r
ag
e
an
d
s
o
l
v
e
it
u
s
in
g
L
a
g
r
an
g
ea
n
R
ela
x
a
tio
n
.
Dr
ez
n
er
et
al.
[
2
9
]
ex
p
lo
r
e
g
r
ad
u
al
co
v
er
a
g
e
u
s
in
g
b
r
an
ch
an
d
b
o
u
n
d
alg
o
r
ith
m
.
M
o
s
t
o
f
th
e
r
e
v
ie
w
ed
r
esear
ch
es
u
s
e
M
C
L
P
f
o
r
g
r
ad
u
al
co
v
er
m
o
d
el,
b
u
t
E
is
e
lt
a
n
d
Ma
r
ian
o
v
[
3
0
]
lo
o
k
in
to
g
r
ad
u
al
co
v
er
m
o
d
el
b
y
u
s
i
n
g
L
o
ca
tio
n
Set
C
o
v
er
in
g
Mo
d
el
(
L
S
C
M)
.
Si
m
u
latio
n
is
u
s
ed
f
o
r
d
if
f
e
r
en
t
r
ea
s
o
n
s
in
t
h
e
s
tu
d
y
o
f
E
MS
[
3
1
]
.
S
w
o
v
ela
n
d
et
al.
[
3
2
]
u
s
e
s
i
m
u
lat
io
n
to
f
i
n
d
s
y
s
te
m
c
h
ar
ac
ter
is
tic
s
b
et
w
ee
n
d
i
f
f
er
en
t
E
MS
r
u
le
s
o
r
p
o
licies.
T
h
e
o
u
tp
u
t
o
f
th
e
s
i
m
u
lat
io
n
is
th
e
n
u
s
ed
to
f
in
d
th
e
m
o
s
t
o
p
ti
m
al
lo
ca
tio
n
m
o
d
el
.
Si
m
u
lat
io
n
is
also
u
s
ed
to
test
o
r
an
aly
ze
t
h
e
ef
f
ec
tiv
e
n
e
s
s
o
f
n
e
w
s
y
s
te
m
s
o
r
l
o
ca
tio
n
m
o
d
els
[
3
3
-
3
4
]
.
Mo
s
t
o
f
th
e
r
esear
ch
in
v
o
l
v
i
n
g
lo
ca
tio
n
m
o
d
els
f
o
cu
s
es
o
n
eit
h
er
t
h
e
co
v
er
ag
e
o
f
a
m
o
d
el,
o
r
th
e
ef
f
icie
n
c
y
o
f
t
h
e
alg
o
r
it
h
m
in
s
o
lv
i
n
g
a
lo
ca
tio
n
m
o
d
el.
Ho
w
e
v
er
,
th
er
e
is
li
m
ited
n
u
m
b
er
o
f
r
esear
c
h
th
at
v
alid
at
es
th
e
co
v
er
ag
e
o
f
a
n
a
m
b
u
la
n
ce
lo
ca
tio
n
m
o
d
el,
w
it
h
it
s
ac
tu
a
l c
o
v
er
ag
e
d
ata
d
u
r
in
g
E
MS
d
eli
v
er
y
.
T
h
er
e
h
as
b
ee
n
li
m
ited
w
o
r
k
o
n
d
y
n
a
m
ic
s
i
m
u
latio
n
o
f
E
M
S
d
eliv
er
y
.
Ma
x
w
e
ll
et
al.
[
3
5
]
s
i
m
u
late
E
MS
o
p
er
atio
n
s
to
ev
alu
a
te
th
e
p
er
f
o
r
m
a
n
ce
o
f
h
ig
h
q
u
alit
y
r
ed
ep
lo
y
m
e
n
t
p
o
licies
u
s
i
n
g
ap
p
r
o
x
i
m
ate
d
y
n
a
m
ic
p
r
o
g
r
a
m
m
in
g
(
A
DP
)
.
T
h
e
s
i
m
u
latio
n
is
b
ased
o
n
d
is
cr
ete
-
e
v
en
t
s
th
at
co
v
er
th
e
en
tire
cy
c
le
o
f
an
e
m
er
g
e
n
c
y
ca
ll.
I
n
t
h
e
p
ap
er
,
ex
p
en
s
iv
e
co
m
p
u
tat
io
n
h
as
to
b
e
ca
lcu
lated
in
ad
v
an
ce
i
n
o
r
d
er
to
lig
h
ten
th
e
co
m
p
u
tatio
n
al
lo
ad
d
u
r
in
g
r
ea
l
ti
m
e
o
p
er
atio
n
.
I
n
t
h
e
ir
E
MS
d
eli
v
er
y
s
i
m
u
latio
n
,
t
h
e
a
m
b
u
lan
ce
r
ed
ep
lo
y
m
e
n
t
s
u
g
g
ested
b
y
ADP
ca
n
b
e
ig
n
o
r
ed
b
y
d
i
s
p
atch
er
s
i
f
th
e
y
c
h
o
o
s
e
to
.
L
i
m
et
al.
[
3
6
]
p
er
f
o
r
m
ed
a
n
E
MS
s
i
m
u
latio
n
to
co
m
p
ar
e
b
et
w
ee
n
d
i
f
f
er
en
t
a
m
b
u
la
n
ce
d
is
p
atc
h
p
o
licies.
L
i
m
et
al.
p
r
o
p
o
s
ed
f
r
ee
-
a
m
b
u
la
n
ce
-
ex
p
lo
it
d
is
p
atc
h
w
h
ic
h
r
ea
s
s
i
g
n
s
a
m
b
u
la
n
ce
s
th
at
h
a
v
e
j
u
s
t
s
er
v
ed
an
e
m
er
g
e
n
c
y
ca
ll.
T
h
e
s
i
m
u
lat
io
n
s
h
o
w
s
t
h
at
r
er
o
u
te
-
en
ab
led
d
is
p
atch
en
h
a
n
ce
s
r
es
p
o
n
s
e
ti
m
e
f
o
r
u
r
g
e
n
t c
all
s
.
Hen
d
er
s
o
n
an
d
Ma
s
o
n
[
3
7
]
d
ev
elo
p
ed
B
A
R
T
SIM
w
h
ich
is
u
s
ed
as
a
d
ec
is
io
n
s
u
p
p
o
r
t
to
o
l
f
o
r
a
m
b
u
la
n
ce
m
a
n
ag
er
s
at
St.
J
o
h
n
Am
b
u
lan
ce
Ser
v
ice
in
th
e
Au
c
k
lan
d
R
e
g
io
n
o
f
Ne
w
Z
ea
la
n
d
.
B
AR
T
SIM
h
as
a
n
u
m
b
er
o
f
i
m
p
o
r
tan
t
f
e
atu
r
es
s
u
c
h
a
s
t
h
e
ti
m
e
tr
a
v
el
m
o
d
el,
d
ir
ec
t
r
eu
s
e
o
f
r
ec
o
r
d
ed
d
ata
an
d
s
p
atial
v
is
u
ali
s
atio
n
o
f
h
i
s
to
r
ical
d
ata
an
d
r
es
u
lt
s
i
m
u
latio
n
.
B
AR
T
SIM
p
r
e
-
co
m
p
u
tes
th
e
s
h
o
r
tes
t
p
ath
to
r
ed
u
ce
t
h
e
co
m
p
u
tatio
n
lo
ad
d
u
r
in
g
s
i
m
u
latio
n
.
T
h
e
au
t
h
o
r
s
al
s
o
d
is
cu
s
s
co
n
ce
r
n
s
t
h
at
s
h
o
u
l
d
b
e
co
n
s
id
er
ed
in
a
d
is
cr
ete
ev
en
t
s
i
m
u
lato
r
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
E
C
E
I
SS
N:
2088
-
8708
C
o
mp
a
r
is
o
n
o
f E
merg
en
cy
Med
ica
l S
ervices D
elive
r
y
P
erfo
r
ma
n
ce
u
s
in
g
…
(
Mo
h
d
Ha
fiz
A
z
iz
a
n
)
2793
3.
L
O
CAT
I
O
N
P
RO
B
L
E
M
F
O
RM
UL
AT
I
O
N
3
.
1
.
M
CL
P
MC
L
P
m
o
d
el
is
d
ef
i
n
ed
as
g
r
ap
h
G
=
(
V
U
W
,
E
)
,
w
h
er
e
V
an
d
W
r
ep
r
esen
t
d
e
m
an
d
p
o
in
ts
an
d
p
o
ten
tial
a
m
b
u
la
n
ce
s
ite
s
r
es
p
ec
tiv
el
y
.
E
s
ta
n
d
s
f
o
r
d
is
tan
ce
b
et
w
ee
n
V
an
d
W
.
M
C
L
P
m
o
d
el
is
w
r
itte
n
as
f
o
llo
w
s
:
ii
iV
M
a
x
i
m
i
z
e
d
y
(
1
)
i
ii
jW
x
y
i
V
(
2
)
i
j
jW
xp
(
3
)
0
,
1
j
x
j
W
(
4
)
0
,
1
i
y
i
V
(
5
)
d
i
r
ep
r
esen
ts
th
e
d
e
m
an
d
at
p
o
in
t
i
.
y
i
is
a
b
i
n
ar
y
v
ar
iab
le
th
at
w
i
ll
b
e
1
if
an
d
o
n
l
y
i
f
p
o
in
t
i
is
co
v
er
ed
b
y
at
least
o
n
e
a
m
b
u
la
n
ce
.
x
j
is
b
in
ar
y
v
ar
iab
le
th
at
w
i
ll
b
e
1
if
an
a
m
b
u
lan
ce
i
s
allo
ca
ted
at
s
it
e
j
.
p
is
th
e
n
u
m
b
er
o
f
a
m
b
u
lan
ce
s
to
b
e
lo
ca
te
d
.
3
.
2
.
G
CL
P
Fo
r
ea
ch
d
e
m
a
n
d
p
o
in
t
iV
,
th
er
e
ex
i
s
ts
t
w
o
r
ad
ii
R
max
a
n
d
R
min
w
h
er
e
0
<
R
min
<
R
max
.
r
ij
is
th
e
s
h
o
r
test
tr
a
v
e
l
d
is
ta
n
ce
f
r
o
m
a
m
b
u
la
n
ce
s
ite
j
to
d
e
m
a
n
d
p
o
in
t
i
.
A
d
e
m
a
n
d
p
o
in
t
is
co
n
s
id
er
ed
as
f
u
ll
y
co
v
er
ed
if
it
s
d
is
ta
n
ce
to
t
h
e
clo
s
est
a
m
b
u
la
n
ce
s
ite
i
s
,
r
ij
<
R
mi
n
.
Oth
er
w
i
s
e,
it
i
s
eit
h
er
d
ee
m
ed
as
p
ar
tiall
y
co
v
er
ed
if
R
min
<
r
ij
<
R
max
,
o
r
u
n
co
v
er
ed
i
f
r
ij
>
R
max
.
I
n
t
h
is
p
ap
er
,
th
e
f
o
llo
w
in
g
d
ec
a
y
f
u
n
ctio
n
i
s
u
s
ed
.
I
f
a
n
ar
ea
is
co
v
er
ed
b
y
m
u
ltip
le
a
m
b
u
la
n
ce
s
,
t
h
e
a
m
b
u
la
n
ce
w
it
h
th
e
h
i
g
h
est
co
v
er
a
g
e
v
a
lu
e
i
s
ch
o
s
e
n
.
No
te
th
a
t
if
R
min
is
eq
u
al
to
R
max
,
GC
L
P
b
ec
o
m
es
id
en
tical
to
MC
L
P
m
o
d
el.
T
h
e
co
v
er
ag
e
d
ec
a
y
f
u
n
ct
io
n
f
(
r
ij
)
is
d
ef
in
ed
as:
m
i
n
m
a
x
m
i
n
m
a
x
m
a
x
m
i
n
m
a
x
1
()
()
0
ij
ij
i
j
i
j
ij
i
f
r
R
Rr
f
r
i
f
R
r
R
RR
i
f
r
R
(
6
)
Fin
all
y
,
t
h
e
g
r
ad
u
al
co
v
er
m
o
d
el
is
d
ef
in
ed
as
f
o
llo
w
s
:
()
i
i
j
iV
M
a
x
i
m
i
z
e
d
f
r
j
W
(
7
)
()
i
j
i
j
jW
x
f
r
i
V
(
8
)
j
jW
xp
(
9
)
0
,
1
j
x
j
W
(
1
0
)
w
h
er
e
all
p
ar
a
m
eter
s
ar
e
d
ef
i
n
ed
s
i
m
ilar
to
th
e
M
C
L
P
alg
o
r
ith
m
.
4.
M
E
T
H
O
DO
L
O
G
Y
T
h
er
e
ar
e
a
n
u
m
b
er
o
f
m
et
h
o
d
s
th
at
h
a
v
e
b
ee
n
p
r
o
p
o
s
ed
to
s
o
lv
e
an
a
m
b
u
la
n
ce
lo
ca
tio
n
m
o
d
el
s
u
ch
as
ex
ac
t
m
et
h
o
d
,
m
ath
e
m
at
ical
p
r
o
g
r
am
m
i
n
g
h
eu
r
i
s
tic
an
d
m
eta
-
h
e
u
r
is
tic
m
et
h
o
d
s
.
T
h
e
e
x
ac
t
m
et
h
o
d
ca
n
b
e
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
.
5
,
Octo
b
er
201
7
:
2
7
9
1
–
2
7
9
7
2794
u
s
ed
to
f
i
n
d
all
th
e
p
o
s
s
ib
le
co
m
b
i
n
atio
n
s
o
f
f
ac
ilit
ie
s
an
d
id
en
ti
f
y
t
h
e
co
m
b
in
at
io
n
w
it
h
th
e
b
est
o
b
j
ec
tiv
e
f
u
n
ctio
n
.
Ho
w
ev
er
,
s
o
l
v
i
n
g
a
n
a
m
b
u
la
n
ce
lo
ca
tio
n
m
o
d
el
b
y
u
s
in
g
t
h
e
ex
ac
t
m
e
th
o
d
ca
n
tak
e
a
v
er
y
lo
n
g
ti
m
e
i
f
t
h
e
v
al
u
e
o
f
W
a
n
d
V
ar
e
lar
g
e.
T
h
e
s
ec
o
n
d
m
et
h
o
d
u
s
es
m
at
h
e
m
atica
l
p
r
o
g
r
a
m
m
in
g
.
Ho
w
e
v
er
,
co
m
m
er
cial
m
at
h
e
m
atica
l
p
r
o
g
r
a
m
m
i
n
g
s
o
f
t
w
ar
e
m
a
y
al
s
o
f
ail
to
s
o
lv
e
lar
g
er
p
r
o
b
l
e
m
s
[
2
8
]
.
B
y
u
s
in
g
h
eu
r
i
s
tic
o
r
m
eta
-
h
e
u
r
is
tic
m
e
th
o
d
,
n
ea
r
o
p
tim
al
r
esu
lts
ca
n
b
e
p
r
o
d
u
ce
d
,
b
u
t
at
a
s
h
o
r
ter
ti
m
e
co
m
p
ar
ed
to
th
e
ex
ac
t
an
d
m
at
h
e
m
atica
l
p
r
o
g
r
a
m
m
i
n
g
m
eth
o
d
s
.
W
e
h
av
e
ch
o
s
en
to
u
s
e
P
SO
to
f
in
d
s
tr
ateg
ic
a
m
b
u
lan
c
e
lo
ca
tio
n
s
b
ec
au
s
e
i
t h
a
s
b
ee
n
s
u
cc
es
s
f
u
ll
y
p
r
o
v
en
to
s
o
l
v
e
n
u
m
er
o
u
s
co
m
b
i
n
ato
r
ial
p
r
o
b
lem
s
[
3
8
]
.
Du
e
to
a
lack
o
f
r
ea
l
d
ata
an
d
E
MS
r
esear
ch
[
3
9
]
,
E
MS
d
e
m
a
n
d
is
g
e
n
er
ated
b
ased
o
n
th
e
p
o
p
u
latio
n
o
f
th
e
J
o
h
o
r
B
ah
r
u
r
eg
io
n
.
T
h
e
r
eg
io
n
is
s
p
lit
in
t
o
d
em
an
d
zo
n
es
w
i
th
t
h
e
s
ize
o
f
4
2
k
m
x
3
0
k
m
an
d
d
iv
id
ed
b
y
r
ec
ta
n
g
u
lar
g
r
i
d
s
.
T
h
e
p
o
p
u
latio
n
d
ata
is
m
a
p
p
ed
as
d
em
an
d
s
i
n
t
h
e
r
eg
io
n
s
o
as
to
r
ef
lect
th
e
r
ea
l
J
o
h
o
r
B
ah
r
u
p
o
p
u
latio
n
.
A
to
tal
o
f
1
2
1
2
an
n
u
al
d
e
m
an
d
s
,
w
h
ic
h
ar
e
p
r
o
p
o
r
tio
n
al
to
t
h
e
p
o
p
u
latio
n
d
en
s
it
y
,
ar
e
p
lo
tted
.
I
n
th
e
r
eg
io
n
,
th
er
e
ar
e
4
h
o
s
p
itals
,
an
d
5
5
p
o
ten
tial
a
m
b
u
lan
ce
s
ites
b
ased
at
th
e
p
etr
o
l
s
tatio
n
s
.
Fo
r
s
i
m
p
licit
y
,
o
n
l
y
o
n
e
t
y
p
e
o
f
a
m
b
u
lan
ce
s
is
u
s
ed
.
T
h
er
e
is
n
o
t
u
r
n
o
u
t
ti
m
e
f
o
r
th
e
a
m
b
u
l
an
ce
s
.
Oth
er
attr
ib
u
tes
t
h
at
m
a
y
a
f
f
e
ct
th
e
v
elo
cit
y
o
f
a
m
b
u
lan
ce
s
s
u
c
h
as
th
e
t
y
p
e
o
f
r
o
ad
s
,
tr
af
f
ic
co
n
d
itio
n
s
a
n
d
tr
af
f
ic
lig
h
ts
ar
e
ig
n
o
r
ed
.
E
u
clid
ian
d
is
ta
n
ce
is
u
s
ed
f
o
r
d
is
tan
ce
m
ea
s
u
r
e
m
e
n
t
an
d
th
e
ca
lcu
latio
n
o
f
a
m
b
u
la
n
ce
tr
av
el
p
at
h
s
.
T
h
e
s
i
m
u
lati
o
n
i
s
b
ased
o
n
a
c
o
m
p
lete
c
y
c
le
o
f
a
n
e
m
er
g
en
c
y
ca
ll,
w
h
ic
h
co
n
s
i
s
ts
o
f
;
(
1
)
a
cc
ep
tan
ce
o
f
an
e
m
er
g
en
c
y
ca
ll,
(
2
)
a
m
b
u
la
n
ce
d
is
p
atc
h
to
t
h
e
e
m
er
g
en
c
y
s
ce
n
e,
(
3
)
p
ar
am
ed
i
cs
tr
ea
t
m
e
n
t
o
n
t
h
e
v
icti
m
,
(
4
)
tr
an
s
p
o
r
tatio
n
o
f
t
h
e
v
icti
m
to
t
h
e
h
o
s
p
ital,
(
5
)
t
r
an
s
f
er
o
f
v
icti
m
in
th
e
h
o
s
p
it
al,
an
d
(
6
)
r
etu
r
n
o
f
th
e
a
m
b
u
la
n
ce
to
its
b
ase.
C
all
s
et
is
r
an
d
o
m
l
y
g
e
n
er
ated
b
ased
o
n
t
h
e
p
o
p
u
latio
n
d
at
a.
W
e
g
e
n
er
ate
4
0
in
co
m
i
n
g
ca
ll
s
b
ased
o
n
t
h
e
J
o
h
o
r
B
ah
r
u
p
o
p
u
latio
n
f
o
r
an
8
h
o
u
r
s
i
m
u
latio
n
ti
m
e.
Ma
n
y
f
ac
to
r
s
ca
n
co
n
tr
ib
u
te
to
th
e
v
ar
ian
ce
o
f
c
all
p
r
io
r
ity
as
d
i
s
cu
s
s
ed
i
n
[
4
0
-
4
1
]
.
W
e
d
iv
id
ed
th
e
ca
lls
i
n
to
lo
w
p
r
io
r
it
y
an
d
h
ig
h
p
r
io
r
it
y
w
i
th
1
:1
r
atio
.
T
h
e
clo
s
e
s
t
a
m
b
u
lan
ce
is
al
w
a
y
s
d
is
p
atc
h
ed
to
t
h
e
ca
l
l,
an
d
a
n
a
m
b
u
la
n
ce
o
n
t
h
e
w
a
y
to
a
lo
w
p
r
io
r
it
y
ca
ll
ca
n
b
e
r
er
o
u
ted
to
a
h
ig
h
p
r
io
r
ity
ca
ll.
Z
o
n
es
w
it
h
h
ig
h
er
p
o
p
u
latio
n
s
h
a
v
e
a
h
i
g
h
er
ch
an
ce
o
f
g
en
er
ati
n
g
e
m
er
g
e
n
c
y
ca
lls
.
E
ac
h
a
m
b
u
la
n
ce
h
a
s
a
co
n
s
ta
n
t
s
p
ee
d
o
f
6
0
k
m
/
h
.
Fo
r
ea
ch
ca
ll,
a
n
a
m
b
u
la
n
ce
is
n
ee
d
ed
an
d
all
v
icti
m
s
ar
e
r
eq
u
ir
ed
to
b
e
tr
an
s
p
o
r
ted
to
th
e
h
o
s
p
i
tal.
E
ac
h
a
m
b
u
la
n
ce
n
ee
d
s
a
f
i
x
ed
ti
m
e
o
f
1
0
m
i
n
u
tes
to
s
er
v
e
a
v
icti
m
at
an
e
m
er
g
e
n
c
y
s
ce
n
e,
an
d
an
o
th
er
1
0
m
i
n
u
tes
f
o
r
th
e
v
icti
m
to
b
e
tr
an
s
f
er
r
ed
to
th
e
h
o
s
p
ital.
Fo
r
ea
ch
m
o
d
el
o
f
MC
L
P
an
d
GC
L
P
,
th
e
s
i
m
u
latio
n
is
p
er
f
o
r
m
ed
f
o
r
n
i
n
e
f
leet
s
izes
in
t
h
e
r
an
g
e
o
f
f
o
u
r
to
1
2
am
b
u
lan
ce
s
.
T
h
e
f
leet
s
ize
le
s
s
t
h
a
n
th
r
ee
a
m
b
u
lan
ce
s
is
o
m
i
tted
b
ec
au
s
e
o
f
h
i
g
h
AR
T
(
>
7
0
m
i
n
u
te
s
)
.
Fo
r
ea
ch
m
o
d
el,
t
w
o
d
if
f
er
e
n
t
cr
itical
d
is
ta
n
ce
s
ar
e
u
s
ed
.
Firstl
y
,
R
m
a
x
=1
0
an
d
R
m
in
=
3
.
3
ar
e
u
s
ed
.
Fo
r
th
e
s
ec
o
n
d
ca
s
e,
R
m
a
x
=6
an
d
R
m
in
=
2
ar
e
ap
p
lied
.
No
te
th
at
R
m
i
n
is
o
n
l
y
ap
p
licab
le
to
GC
L
P
.
T
h
u
s
,
a
to
tal
o
f
3
6
s
i
m
u
latio
n
s
ar
e
p
er
f
o
r
m
ed
.
5.
RE
SU
L
T
AND
DI
SCUS
SI
O
N
5
.
1
.
Am
bu
la
nce
Str
a
t
eg
ic
L
o
ca
t
io
n
Fig
u
r
es
1
a
n
d
2
s
h
o
w
t
h
e
r
es
u
lts
o
f
t
h
e
b
es
t
a
m
b
u
la
n
ce
lo
c
atio
n
s
f
o
u
n
d
f
o
r
6
a
m
b
u
lan
ce
s
b
ase
d
o
n
MC
L
P
an
d
GC
L
P
m
o
d
els
r
esp
ec
tiv
el
y
.
T
h
e
s
m
all
cir
cles
in
b
lack
ar
e
th
e
id
en
tifie
d
s
tr
ateg
ic
a
m
b
u
lan
c
e
lo
ca
tio
n
s
.
T
h
e
r
ad
ii
f
o
r
s
m
all
an
d
b
ig
cir
cles
ar
e
R
m
i
n
an
d
R
m
a
x
r
esp
ec
tiv
e
l
y
.
De
m
an
d
s
ar
e
ca
teg
o
r
is
ed
w
it
h
d
if
f
er
e
n
t
co
lo
u
r
s
,
w
i
th
r
ed
d
en
o
tin
g
t
h
e
h
i
g
h
e
s
t
d
e
m
a
n
d
an
d
th
en
f
o
llo
w
ed
b
y
o
th
er
co
lo
u
r
s
as
f
o
llo
w
s
:
r
ed
>
b
lu
e
>
lig
h
t
b
lu
e
>
y
ello
w
>
g
r
ee
n
.
I
n
Fig
u
r
e
1
,
MCL
P
atte
m
p
ts
to
m
a
x
i
m
i
s
e
co
v
er
ag
e
b
y
co
v
er
in
g
all
d
em
a
n
d
s
b
y
cir
cles
w
i
th
r
ad
iu
s
R
m
a
x
.
No
te
th
at
it
h
a
s
co
v
er
ed
1
0
0
%
o
f
th
e
d
e
m
an
d
s
b
ased
o
n
ML
C
P
co
v
er
ag
e
d
ef
in
itio
n
.
W
e
ca
n
d
ed
u
ce
f
r
o
m
Fi
g
u
r
e
1
th
at
w
h
e
n
ex
tr
a
a
m
b
u
la
n
ce
s
ar
e
ad
d
e
d
,
h
ar
d
ly
an
y
i
m
p
r
o
v
e
m
en
t
ca
n
b
e
ac
h
iev
ed
b
ec
au
s
e
th
e
d
e
m
an
d
s
h
av
e
b
ee
n
co
n
s
id
er
ed
as
f
u
ll
y
co
v
er
ed
.
I
n
o
th
er
w
o
r
d
s
,
th
e
f
u
n
ctio
n
s
in
M
C
L
P
ar
e
in
ca
p
ab
le
o
f
o
p
tim
i
s
in
g
t
h
e
ex
tr
a
a
m
b
u
la
n
ce
s
f
o
r
f
u
r
th
er
co
v
er
a
g
e
i
m
p
r
o
v
e
m
e
n
t.
F
ig
u
re
1
.
M
CL
P
,
1
0
0
%
c
o
v
e
re
d
(
Rm
a
x
=
1
0
k
m
)
F
ig
u
re
2
.
G
CL
P
,
9
0
.
7
2
%
c
o
v
e
re
d
(
R
max
=
1
0
k
m
,
R
min
=
3
.
3
k
m
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
E
C
E
I
SS
N:
2088
-
8708
C
o
mp
a
r
is
o
n
o
f E
merg
en
cy
Med
ica
l S
ervices D
elive
r
y
P
erfo
r
ma
n
ce
u
s
in
g
…
(
Mo
h
d
Ha
fiz
A
z
iz
a
n
)
2795
5
.
2
.
Si
m
ula
t
io
n Re
s
ult
I
n
Fi
g
u
r
e
2
,
GC
L
P
tr
ies
to
p
o
s
itio
n
a
m
b
u
la
n
ce
s
a
s
clo
s
e
as
p
o
s
s
ib
le
to
t
h
e
ar
ea
s
w
ith
a
h
i
g
h
n
u
m
b
er
o
f
d
e
m
a
n
d
s
.
T
h
is
is
b
ec
au
s
e
w
it
h
i
n
R
m
i
n
,
t
h
e
ar
ea
is
co
n
s
i
d
er
ed
as
f
u
ll
y
co
v
er
ed
,
w
h
ile
f
o
r
th
e
ar
ea
b
et
w
ee
n
R
m
in
an
d
R
m
a
x
,
t
h
e
ar
ea
i
s
co
n
s
id
er
ed
as
p
ar
tia
ll
y
co
v
er
ed
.
As
a
n
y
d
e
m
a
n
d
n
o
t
co
v
e
r
ed
b
y
cir
cles
w
it
h
r
ad
iu
s
R
m
i
n
i
s
co
n
s
id
er
ed
as
n
o
t
f
u
ll
y
co
v
er
ed
,
t
h
er
e
is
s
til
l
r
o
o
m
f
o
r
i
m
p
r
o
v
e
m
e
n
t
w
h
e
n
ex
tr
a
a
m
b
u
lan
ce
s
ar
e
ad
d
ed
.
Fig
u
r
e
3
s
h
o
w
s
t
h
at
AR
T
f
o
r
GC
L
P
is
s
h
o
r
ter
t
h
a
n
M
C
L
P
.
On
e
r
ea
s
o
n
f
o
r
p
o
o
r
r
esu
lts
o
f
MC
L
P
is
th
e
in
ab
il
it
y
o
f
th
e
m
o
d
el
to
f
u
r
t
h
er
i
m
p
r
o
v
e
t
h
e
co
v
er
ag
e
w
it
h
e
x
tr
a
a
m
b
u
lan
ce
s
,
w
h
e
n
it
h
as
r
ea
ch
ed
f
u
ll
co
v
er
ag
e.
Fo
r
GC
L
P
,
f
u
r
t
h
er
o
p
tim
is
atio
n
ca
n
s
till
b
e
ac
h
ie
v
ed
w
it
h
ex
tr
a
a
m
b
u
la
n
ce
s
b
ec
au
s
e
it
n
ee
d
s
m
o
r
e
a
m
b
u
la
n
ce
s
to
r
ea
ch
f
u
ll
co
v
er
ag
e.
Fig
u
r
e
4
s
h
o
w
s
t
h
e
r
esu
lt
s
w
it
h
co
v
er
ag
e
r
ad
iu
s
,
R
m
a
x
=6
k
m
.
GC
L
P
s
co
r
es
lo
w
er
A
R
T
ex
ce
p
t
w
it
h
a
f
leet
s
ize
o
f
f
o
u
r
a
m
b
u
la
n
ce
s
.
I
n
g
en
er
al,
GC
L
P
p
er
f
o
r
m
s
b
etter
th
an
MC
L
P
ev
en
w
h
en
MC
L
P
h
as
y
et
to
r
ea
ch
f
u
ll
co
v
er
a
g
e.
Fo
r
R
m
ax
=6
k
m
,
a
f
leet
s
ize
o
f
ei
g
h
t
i
s
r
eq
u
ir
ed
f
o
r
MC
L
P
to
r
ea
ch
f
u
ll
co
v
er
a
g
e.
Fig
u
r
e
s
5
an
d
6
s
h
o
w
t
h
e
to
tal
d
is
ta
n
ce
tr
av
elled
b
y
a
m
b
u
la
n
ce
s
o
f
v
ar
io
u
s
f
leet
s
izes.
As
ca
n
b
e
s
ee
n
,
t
h
e
to
tal
tr
av
el
d
is
tan
ce
ca
n
b
e
h
i
g
h
l
y
a
f
f
e
cted
b
y
t
h
e
s
elec
tio
n
o
f
th
e
a
m
b
u
la
n
ce
lo
ca
tio
n
m
o
d
el
s
.
Ho
w
e
v
er
,
f
o
r
m
o
s
t o
f
th
e
ca
s
es,
to
tal
tr
a
v
el
d
is
ta
n
ce
f
o
r
GC
L
P
is
lo
w
er
t
h
an
M
C
L
P
.
Fig
u
r
e
3
.
Av
er
ag
e
AR
T
v
s
.
Am
b
u
la
n
ce
Fleet
Size
(
R
max
=
1
0
k
m
,
R
min
=
3
.
3
k
m
)
Fig
u
r
e
4
.
Av
er
ag
e
AR
T
v
s
.
Am
b
u
la
n
ce
Fleet
Size
(
R
max
=
6
k
m
,
R
min
=
2
k
m
)
Fig
u
r
e
5
.
T
o
tal
Am
b
u
la
n
ce
T
r
av
elled
Dis
ta
n
ce
v
s
.
Am
b
u
lan
ce
Flee
t Size
(
R
max
=
1
0
k
m
,
R
min
=
3
.
3
k
m
)
Fig
u
r
e
6
.
T
o
tal
Am
b
u
la
n
ce
T
r
av
elled
Dis
ta
n
ce
v
s
.
Am
b
u
lan
ce
Flee
t Size
(
R
max
=
6
k
m
,
R
min
=
2
k
m
)
6.
CO
NCLU
SI
O
N
T
h
er
e
ar
e
a
n
u
m
b
er
o
f
li
m
it
atio
n
s
i
n
th
is
r
esear
ch
.
T
h
e
f
ir
s
t
d
r
a
w
b
ac
k
is
th
e
u
s
e
o
f
E
u
clid
ea
n
d
is
tan
ce
.
A
ct
u
al
d
is
tan
ce
o
f
th
e
r
o
ad
n
et
w
o
r
k
s
h
o
u
ld
b
e
u
s
ed
s
o
th
at
t
h
e
r
esu
lt
s
ca
n
r
ef
lect
th
e
r
ea
l
li
f
e
p
er
f
o
r
m
a
n
ce
.
Facto
r
s
th
at
a
f
f
e
ct
th
e
tr
av
el
ti
m
e
o
f
a
m
b
u
lan
c
es
s
u
c
h
as
r
o
ad
ty
p
es
an
d
tr
af
f
ic
co
n
d
itio
n
s
m
u
s
t
also
b
e
co
n
s
id
er
ed
.
I
f
all
th
e
ab
o
v
e
-
m
en
t
io
n
ed
p
r
o
p
o
s
als
ar
e
i
m
p
le
m
e
n
ted
,
th
e
A
R
T
is
ex
p
ec
ted
to
b
e
h
ig
h
er
th
an
t
h
e
c
u
r
r
en
t si
m
u
latio
n
r
es
u
lts
.
Desp
ite
t
h
e
m
en
t
io
n
ed
li
m
itati
o
n
s
i
n
t
h
e
r
esear
ch
w
o
r
k
,
t
h
e
s
i
m
u
lat
io
n
r
es
u
lt
s
h
a
v
e
clea
r
l
y
r
ef
lec
ted
th
e
ad
v
a
n
ta
g
es
o
f
GC
L
P
o
v
er
c
lass
ical
MC
L
P
i
n
E
MS
d
eliv
er
y
.
G
C
L
P
is
ca
p
ab
le
o
f
o
p
ti
m
is
i
n
g
e
x
tr
a
a
m
b
u
la
n
ce
s
i
n
t
h
e
s
ce
n
ar
io
s
w
h
ic
h
ar
e
d
ee
m
ed
as
f
u
ll
y
c
o
v
er
ed
in
MC
L
P
.
A
lt
h
o
u
g
h
t
h
e
i
m
p
r
o
v
e
m
e
n
t
is
m
ar
g
i
n
al,
a
n
y
AR
T
i
m
p
r
o
v
e
m
en
t in
e
m
er
g
en
c
y
ca
s
e
s
w
o
u
ld
d
ir
ec
tl
y
in
cr
ea
s
e
t
h
e
s
u
r
v
iv
al
r
ate.
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
.
5
,
Octo
b
er
201
7
:
2
7
9
1
–
2
7
9
7
2796
ACK
NO
WL
E
D
G
E
M
E
NT
S
T
h
e
au
t
h
o
r
s
w
o
u
ld
li
k
e
to
ac
k
n
o
w
led
g
e
t
h
e
f
i
n
a
n
cial
s
u
p
p
o
r
t
f
r
o
m
F
u
n
d
a
m
en
ta
l
R
esear
ch
Gr
a
n
t
Sch
e
m
e
(
v
o
te
n
o
R
.
J
1
3
0
0
0
0
.
7
8
2
3
.
4
F3
1
4
)
o
f
th
e
Min
i
s
tr
y
o
f
Hig
h
er
E
d
u
ca
tio
n
(
MO
HE
)
,
an
d
R
esear
c
h
Un
i
v
er
s
it
y
Gr
an
t
(
v
o
te
o
n
Q.
J
1
3
0
0
0
0
.
2
5
2
3
.
0
5
H5
9
)
f
r
o
m
R
e
s
ea
r
ch
Ma
n
a
g
e
m
en
t
C
en
tr
e
(
R
MC)
o
f
U
n
iv
er
s
iti
T
ek
n
o
lo
g
i M
ala
y
s
ia.
RE
F
E
R
E
NC
E
S
[1
]
R.
S
á
n
c
h
e
z
-
M
a
n
g
a
s,
A
.
G
a
rc
í
a
-
F
e
rrre
r,
A
.
d
e
Ju
a
n
,
a
n
d
A
.
M
.
A
rro
y
o
,
“
T
h
e
P
ro
b
a
b
il
it
y
o
f
De
a
th
in
Ro
a
d
T
ra
ff
i
c
A
c
c
id
e
n
ts.
Ho
w
Im
p
o
rta
n
t
is
a
Qu
ick
M
e
d
ica
l
Re
sp
o
n
se
?
”
Ac
c
id
.
A
n
a
l.
P
re
v
.
,
v
o
l.
4
2
,
n
o
.
4
,
p
p
.
1
0
4
8
–
5
6
,
Ju
l
.
2
0
1
0
.
[2
]
R.
P
.
G
o
n
z
a
lez
,
G
.
R.
Cu
m
m
in
g
s,
H.
a
P
h
e
lan
,
M
.
S
.
M
u
lek
a
r,
a
n
d
C.
B.
R
o
d
n
in
g
,
“
Do
e
s
In
c
re
a
se
d
Eme
rg
e
n
c
y
M
e
d
ica
l
S
e
rv
ice
s
P
re
h
o
sp
i
tal
T
im
e
Aff
e
c
t
P
a
ti
e
n
t
M
o
rtalit
y
in
Ru
ra
l
M
o
to
r
V
e
h
icle
Cra
sh
e
s
?
A
S
tate
w
id
e
A
n
a
l
y
si
s
,
”
Am
.
J.
S
u
rg
.
,
v
o
l.
1
9
7
,
n
o
.
1
,
p
p
.
3
0
–
4
,
Ja
n
.
2
0
0
9
.
[3
]
R.
B.
Vu
k
m
ir,
“
S
u
rv
iv
a
l
f
ro
m
P
re
h
o
sp
i
tal
Ca
rd
iac
A
rre
s
t
is
Crit
ica
ll
y
De
p
e
n
d
e
n
t
u
p
o
n
R
e
sp
o
n
se
T
i
m
e
,
”
Re
su
sc
it
a
ti
o
n
,
v
o
l.
6
9
,
n
o
.
2
,
p
p
.
2
2
9
–
3
4
,
M
a
y
2
0
0
6
.
[4
]
M
.
Ca
stré
n
,
R.
Ka
rlsten
,
F
.
L
ip
p
e
rt
,
E.
F
.
Ch
risten
se
n
,
E.
Bo
v
im,
a
M
.
Kv
a
m
,
I.
Ro
b
e
rtso
n
-
S
tee
l,
J.
Ov
e
rto
n
,
T
.
Kra
f
t,
L
.
En
g
e
rstro
m
,
a
n
d
L
.
G
a
r
c
ia
-
Ca
stril
l
Rie
g
o
,
“
Re
c
o
m
m
e
n
d
e
d
G
u
id
e
li
n
e
s
f
o
r
Re
p
o
rti
n
g
on
Em
e
r
g
e
n
c
y
M
e
d
ica
l
Disp
a
tch
w
h
e
n
Co
n
d
u
c
t
in
g
Re
se
a
r
c
h
in
Eme
rg
e
n
c
y
M
e
d
icin
e
:
th
e
Utste
in
st
y
le.,
”
Re
su
sc
it
a
ti
o
n
,
v
o
l.
7
9
,
n
o
.
2
,
p
p
.
1
9
3
–
7
,
No
v
.
2
0
0
8
.
[5
]
A
.
K.
M
a
r
sd
e
n
,
“
Ge
tt
in
g
th
e
Rig
h
t
Am
b
u
lan
c
e
to
th
e
Rig
h
t
P
a
ti
e
n
t
a
t
th
e
Rig
h
t
T
i
m
e
,
”
A
c
c
id
.
Eme
rg
.
Nu
rs.,
v
o
l.
3
,
n
o
.
4
,
p
p
.
1
7
7
–
8
3
,
Oc
t.
1
9
9
5
.
[6
]
U.
K.
N.
S
tatist
ics
,
Am
b
u
lan
c
e
S
e
rv
ice
s E
n
g
lan
d
2
0
0
8
–
2
0
0
9
.
NH
S
In
f
o
rm
a
ti
o
n
Ce
n
tre,
2
0
0
9
.
[7
]
P
e
ter
T
.
P
o
n
s
a
n
d
V
in
c
e
n
t
J.
M
a
rk
o
v
c
h
ick
,
“
Ei
g
h
t
M
in
u
tes
Or
L
e
ss
:
Do
e
s
T
h
e
Am
b
u
lan
c
e
Re
sp
o
n
se
T
im
e
G
u
id
e
li
n
e
Im
p
a
c
t
T
r
a
u
m
a
P
a
ti
e
n
t
Ou
tco
m
e
?
,
”
J.
E
m
e
r
g
.
M
e
d
.
,
v
o
l
.
2
3
,
n
o
.
1
,
p
p
.
4
3
–
4
8
,
2
0
0
2
.
[8
]
J.
J.
Be
rn
a
rd
o
,
“
Ca
se
S
tu
d
y
De
v
e
lo
p
i
n
g
a
n
d
V
a
li
d
a
ti
n
g
a
De
c
isio
n
S
u
p
p
o
rt
S
y
ste
m
f
o
r
L
o
c
a
ti
n
g
E
m
e
rg
e
n
c
y
M
e
d
ica
l
V
e
h
icle
s
in
L
o
u
isv
il
le ,
Ke
n
tu
c
k
y
,
”
v
o
l.
7
5
,
p
p
.
5
6
7
–
5
8
1
,
1
9
9
4
.
[9
]
L
.
Bro
tco
rn
e
,
G
.
L
a
p
o
rte,
a
n
d
F
.
S
e
m
e
t,
“
Am
b
u
lan
c
e
L
o
c
a
ti
o
n
a
n
d
Re
lo
c
a
ti
o
n
M
o
d
e
ls
,
”
E
u
r.
J.
Op
e
r.
Re
s.,
v
o
l.
1
4
7
,
n
o
.
3
,
p
p
.
4
5
1
–
4
6
3
,
Ju
n
.
2
0
0
3
.
[1
0
]
R.
Ch
u
rc
h
a
n
d
C.
Re
v
e
ll
e
,
“
T
h
e
M
a
x
i
m
a
l
Co
v
e
rin
g
L
o
c
a
ti
o
n
P
ro
b
lem
,
”
v
o
l.
3
2
,
p
p
.
1
0
1
–
1
1
8
,
1
9
7
4
.
[1
1
]
D.
J.
Eato
n
,
M
.
S
.
Da
sk
in
,
D.
S
imm
o
n
s,
B.
Bu
ll
o
c
h
,
G
.
J
a
n
s
m
a
,
S
.
In
terf
a
c
e
s,
C.
T
i
m
s,
P
.
P
a
p
e
rs,
J.
F
e
b
,
J.
Eato
n
,
a
n
d
S
.
Da
sk
in
,
“
De
term
in
in
g
Eme
rg
e
n
c
y
M
e
d
ica
l
in
A
u
stin
,
T
e
x
a
s
V
e
h
icle
De
p
lo
y
m
e
n
t,
”
In
terf
a
c
e
s
(
P
ro
v
id
e
n
c
e
).
,
v
o
l.
1
5
,
n
o
.
1
,
p
p
.
9
6
–
1
0
8
,
1
9
8
5
.
[1
2
]
P
.
M
it
r
o
p
o
u
lo
s,
I
.
M
it
ro
p
o
u
lo
s,
I
.
G
ian
n
ik
o
s,
a
n
d
A
.
S
isso
u
ra
s,
“
A
Bio
b
jec
ti
v
e
M
o
d
e
l
f
o
r
th
e
L
o
c
a
ti
o
n
a
l
P
lan
n
i
n
g
o
f
Ho
sp
it
a
ls
a
n
d
He
a
lt
h
Ce
n
ters
,
”
H
e
a
lt
h
Ca
re
M
a
n
a
g
.
S
c
i.
,
v
o
l
.
9
,
n
o
.
2
,
p
p
.
1
7
1
–
1
7
9
,
Ju
l
.
2
0
0
6
.
[1
3
]
G
.
C.
M
o
o
re
a
n
d
C.
Re
v
e
ll
e
,
“
T
h
e
Hie
ra
rc
h
ica
l
S
e
rv
ice
L
o
c
a
ti
o
n
P
r
o
b
lem
,
”
M
a
n
a
g
e
.
S
c
i.
,
v
o
l.
2
8
,
n
o
.
7
,
p
p
.
7
7
5
–
7
8
0
,
1
9
8
2
.
[1
4
]
V
.
M
a
rian
o
v
a
n
d
D.
S
e
rra
,
“
Hie
ra
rc
h
ica
l
L
o
c
a
ti
o
n
–
a
ll
o
c
a
ti
o
n
M
o
d
e
ls
f
o
r
C
o
n
g
e
ste
d
S
y
ste
m
s
,
”
Eu
r.
J.
Op
e
r.
Re
s.,
v
o
l.
1
3
5
,
n
o
.
1
,
p
p
.
1
9
5
–
2
0
8
,
No
v
.
2
0
0
1
.
[1
5
]
J.
M
.
G
lea
so
n
,
“
A
S
e
t
Co
v
e
rin
g
A
p
p
ro
a
c
h
to
B
u
s S
t
o
p
L
o
c
a
ti
o
n
,
”
O
m
e
g
a
,
v
o
l.
3
,
n
o
.
5
,
p
p
.
6
0
5
–
6
0
8
,
Oc
t.
1
9
7
5
.
[1
6
]
M
.
H.
A
z
iza
n
,
C.
S
.
L
im
,
W
.
A
.
L
.
W
.
M
.
Ha
tt
a
,
T
.
L
.
G
o
,
a
n
d
S
.
S
.
T
e
o
h
,
“
S
im
u
latio
n
o
f
Em
e
r
g
e
n
c
y
M
e
d
ica
l
S
e
rv
ice
s
De
li
v
e
r
y
P
e
rf
o
r
m
a
n
c
e
B
a
se
d
on
Re
a
l
M
a
p
,
”
In
t
.
J.
E
n
g
.
T
e
c
h
n
o
l.
,
v
o
l.
5
,
n
o
.
3
,
p
p
.
2
6
2
0
–
2
6
2
7
,
2
0
1
3
.
[1
7
]
M
.
G
e
n
d
re
a
u
,
“
S
o
lv
i
n
g
a
n
Am
b
u
lan
c
e
L
o
c
a
ti
o
n
M
o
d
e
l
by
T
a
b
u
S
e
a
rc
h
,
”
L
o
c
a
t.
S
c
i.
,
v
o
l.
5
,
n
o
.
2
,
p
p
.
7
5
–
8
8
,
1
9
9
7
.
[1
8
]
J.
M
.
L
e
e
a
n
d
Y.
H.
L
e
e
,
“
T
a
b
u
b
a
se
d
h
e
u
risti
c
s
f
o
r
th
e
g
e
n
e
ra
li
z
e
d
h
iera
rc
h
ica
l
c
o
v
e
rin
g
lo
c
a
ti
o
n
p
r
o
b
lem
,
”
Co
m
p
u
t.
In
d
.
E
n
g
.
,
v
o
l
.
5
8
,
n
o
.
4
,
p
p
.
6
3
8
–
6
4
5
,
M
a
y
2
0
1
0
.
[1
9
]
R.
D.
G
a
lv
a
o
,
L
.
G
.
A
.
Esp
e
jo
,
a
n
d
B.
Bo
f
fe
y
,
“
A
Co
m
p
a
riso
n
o
f
L
a
g
ra
n
g
e
a
n
a
n
d
S
u
rro
g
a
te
Re
lax
a
ti
o
n
s
f
o
r
th
e
M
a
x
i
m
a
l
Co
v
e
rin
g
L
o
c
a
ti
o
n
P
ro
b
lem
,
”
Eu
r.
J.
O
p
e
r.
Re
s.,
v
o
l.
1
2
4
,
p
p
.
3
7
7
–
3
8
9
,
2
0
0
0
.
[2
0
]
M
.
Da
sk
in
,
Ne
t
w
o
rk
a
n
d
Disc
re
t
e
L
o
c
a
ti
o
n
:
M
o
d
e
ls,
A
lg
o
rit
h
m
s,
a
n
d
A
p
p
li
c
a
ti
o
n
s.
Ca
n
a
d
a
:
Jo
h
n
W
il
e
y
a
n
d
S
o
n
s,
1
9
9
5
.
[2
1
]
C.
Re
V
e
ll
e
,
M
.
S
c
h
o
lss
b
e
rg
,
a
n
d
J.
W
il
li
a
m
s,
“
S
o
lv
in
g
th
e
M
a
x
i
m
a
l
Co
v
e
rin
g
L
o
c
a
ti
o
n
P
ro
b
le
m
w
it
h
He
u
risti
c
Co
n
c
e
n
trati
o
n
,
”
C
o
m
p
u
t.
Op
e
r.
R
e
s.,
v
o
l.
3
5
,
n
o
.
2
,
p
p
.
4
2
7
–
4
3
5
,
F
e
b
.
2
0
0
8
.
[2
2
]
M
.
H.
F
a
z
e
l
Zara
n
d
i,
S
.
Da
v
a
ri,
a
n
d
S
.
a
.
Ha
d
d
a
d
S
i
sa
k
h
t,
“
T
h
e
L
a
rg
e
S
c
a
le
M
a
x
i
m
a
l
Co
v
e
rin
g
L
o
c
a
ti
o
n
P
ro
b
lem
,
”
S
c
i.
Ira
n
.
,
v
o
l.
1
8
,
n
o
.
6
,
p
p
.
1
5
6
4
–
1
5
7
0
,
De
c
.
2
0
1
1
.
[2
3
]
W
.
A
.
L
.
W
.
M
.
Ha
tt
a
,
C.
S
.
L
im
,
A
.
F
.
Z.
A
b
id
in
,
M
.
H.
A
z
iz
a
n
,
a
n
d
S
.
S
.
T
e
o
h
,
“
S
o
lv
in
g
M
a
x
i
m
a
l
Co
v
e
rin
g
L
o
c
a
ti
o
n
w
it
h
P
a
rti
c
le S
w
a
r
m
Op
ti
m
iz
a
ti
o
n
,
”
In
t.
J.
En
g
.
T
e
c
h
n
o
l
.
,
v
o
l.
5
,
n
o
.
4
,
p
p
.
3
3
0
1
–
3
3
0
6
,
2
0
1
3
.
[2
4
]
O.
Be
rm
a
n
,
Z.
Dre
z
n
e
r,
a
n
d
D.
Kra
ss
,
“
G
e
n
e
ra
li
z
e
d
c
o
v
e
ra
g
e
:
N
e
w
De
v
e
lo
p
m
e
n
ts
in
C
o
v
e
rin
g
L
o
c
a
ti
o
n
M
o
d
e
ls
,
”
Co
m
p
u
t.
Op
e
r.
Re
s.,
v
o
l.
3
7
,
n
o
.
1
0
,
p
p
.
1
6
7
5
–
1
6
8
7
,
Oc
t.
2
0
1
0
.
[2
5
]
R.
Ch
u
rc
h
a
n
d
K.
R
o
b
e
rts,
“
G
e
n
e
ra
li
z
e
d
Co
v
e
ra
g
e
M
o
d
e
ls
a
n
d
P
u
b
li
c
F
a
c
il
it
y
L
o
c
a
ti
o
n
,
”
P
a
p
.
Re
g
.
S
c
i.
A
ss
o
c
.
,
v
o
l.
5
3
,
n
o
.
1
,
p
p
.
1
1
7
–
1
3
5
,
1
9
8
3
.
[2
6
]
O.
Be
rm
a
n
a
n
d
D.
Kra
ss
,
“
T
h
e
Ge
n
e
ra
li
z
e
d
M
a
x
ima
l
Co
v
e
rin
g
L
o
c
a
ti
o
n
P
r
o
b
lem
,
”
Co
m
p
u
t.
Op
e
r.
Re
s.,
v
o
l.
2
9
,
n
o
.
6
,
p
p
.
5
6
3
–
5
8
1
,
M
a
y
2
002.
[2
7
]
O.
Be
rm
a
n
,
D.
Kra
ss
,
a
n
d
Z
.
Dre
z
n
e
r,
“
T
h
e
G
ra
d
u
a
l
Co
v
e
rin
g
De
c
a
y
L
o
c
a
ti
o
n
P
ro
b
lem
o
n
a
Ne
tw
o
rk
,
”
Eu
r.
J.
Op
e
r.
Re
s.,
v
o
l.
1
5
1
,
n
o
.
3
,
p
p
.
4
7
4
–
4
8
0
,
De
c
.
2
0
0
3
.
[2
8
]
O.
Ka
ra
sa
k
a
l
a
n
d
E.
K.
Ka
ra
sa
k
a
l,
“
A
M
a
x
i
m
a
l
Co
v
e
rin
g
L
o
c
a
ti
o
n
M
o
d
e
l
in
th
e
P
re
se
n
c
e
o
f
P
a
rti
a
l
Co
v
e
ra
g
e
,
”
Co
m
p
u
t.
Op
e
r.
Re
s.,
v
o
l.
3
1
,
n
o
.
9
,
p
p
.
1
5
1
5
–
1
5
2
6
,
A
u
g
.
2
0
0
4
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
E
C
E
I
SS
N:
2088
-
8708
C
o
mp
a
r
is
o
n
o
f E
merg
en
cy
Med
ica
l S
ervices D
elive
r
y
P
erfo
r
ma
n
ce
u
s
in
g
…
(
Mo
h
d
Ha
fiz
A
z
iz
a
n
)
2797
[2
9
]
Z.
Dre
z
n
e
r,
G
.
O.
W
e
so
lo
w
sk
y
,
a
n
d
T
.
Dre
z
n
e
r,
“
T
h
e
G
r
a
d
u
a
l
Co
v
e
rin
g
P
r
o
b
lem
,
”
Na
v
.
Re
s.
L
o
g
ist
.
,
v
o
l.
5
1
,
n
o
.
6
,
p
p
.
8
4
1
–
8
5
5
,
S
e
p
.
2
0
0
4
.
[3
0
]
H.
A
.
Ei
se
lt
a
n
d
V
.
M
a
rian
o
v
,
“
G
ra
d
u
a
l
L
o
c
a
ti
o
n
S
e
t
Co
v
e
rin
g
w
it
h
S
e
rv
ice
Qu
a
li
t
y
,
”
S
o
c
io
e
c
o
n
.
P
lan
n
.
S
c
i.
,
v
o
l.
4
3
,
n
o
.
2
,
p
p
.
1
2
1
–
1
3
0
,
J
u
n
.
2
0
0
9
.
[3
1
]
J.
G
o
ld
b
e
rg
,
R.
Die
tri
c
h
,
J.
M
.
C
h
e
n
,
M
.
M
it
w
a
si,
T
.
V
a
len
z
u
e
la,
a
n
d
E.
Criss,
“
A
S
im
u
latio
n
M
o
d
e
l
f
o
r
Ev
a
lu
a
ti
n
g
a
S
e
t
Of
E
m
e
r
g
e
n
c
y
V
e
h
icle
Ba
s
e
L
o
c
a
t
io
n
s
:
De
v
e
lo
p
m
e
n
t,
V
a
li
d
a
ti
o
n
,
a
n
d
Us
a
g
e
,
”
S
o
c
io
e
c
o
n
.
P
la
n
n
.
S
c
i.
,
v
o
l
.
2
4
,
n
o
.
2
,
p
p
.
1
2
5
–
1
4
1
,
1
9
9
0
.
[3
2
]
A
.
C.
S
w
o
v
e
lan
d
,
D.
U
y
e
n
o
,
I.
V
e
rti
n
sk
y
,
a
n
d
R.
V
ick
so
n
,
“
A
m
b
u
lan
c
e
L
o
c
a
ti
o
n
:
A
P
ro
b
a
b
il
i
stic
En
u
m
e
ra
ti
o
n
A
p
p
ro
a
c
h
,
”
v
o
l.
2
0
,
n
o
.
4
,
p
p
.
6
8
6
–
6
9
8
,
2
0
1
4
.
[3
3
]
S
.
I.
Ha
re
w
o
o
d
,
“
Em
e
rg
e
n
c
y
Am
b
u
lan
c
e
De
p
l
o
y
m
e
n
t
in
Ba
rb
a
d
o
s
:
a
m
u
lt
i
-
o
b
jec
ti
v
e
a
p
p
ro
a
c
h
,
”
J.
Op
e
r.
Re
s.
S
o
c
.
,
v
o
l.
5
3
,
n
o
.
2
,
p
p
.
1
8
5
–
1
9
2
,
F
e
b
.
2
0
0
2
.
[3
4
]
S
.
I
n
g
o
lf
ss
o
n
,
A
.
,
Erk
u
t,
E.
,
B
u
d
g
e
,
“
S
im
u
latio
n
o
f
S
in
g
le
S
tart
S
ta
ti
o
n
f
o
r
Ed
m
o
n
t
o
n
EM
S
,
”
J.
Op
e
r
.
Re
s.
S
o
c
.
,
v
o
l.
5
4
,
n
o
.
7
,
p
p
.
7
3
6
–
7
4
6
,
2
0
0
3
.
[3
5
]
M
.
S
.
M
a
x
w
e
ll
,
S
.
G
.
H
e
n
d
e
rso
n
,
a
n
d
H.
T
o
p
a
lo
g
lu
,
“
Am
b
u
lan
c
e
Re
d
e
p
lo
y
m
e
n
t
:
A
n
A
p
p
ro
x
i
m
a
te
D
y
n
a
m
i
c
P
r
o
g
ra
m
m
in
g
A
p
p
ro
a
c
h
,
”
W
in
ter S
im
u
l.
…,
p
p
.
1
8
5
0
–
1
8
6
0
,
2
0
0
9
.
[3
6
]
C.
S
.
L
im
,
R.
M
a
m
a
t,
a
n
d
T
.
Bra
u
n
l
,
“
Im
p
a
c
t
o
f
Am
b
u
lan
c
e
Disp
a
tch
P
o
l
icie
s
o
n
P
e
rf
o
rm
a
n
c
e
o
f
E
m
e
r
g
e
n
c
y
M
e
d
ica
l
S
e
rv
ice
s,” IE
EE
T
ra
n
s.
In
tell.
T
ra
n
sp
.
S
y
st.,
v
o
l.
1
2
,
n
o
.
2
,
p
p
.
6
2
4
–
6
3
2
,
Ju
n
.
2
0
1
1
.
[3
7
]
S
.
G
.
He
n
d
e
rso
n
a
n
d
A
.
J.
M
a
so
n
,
“
Am
b
u
lan
c
e
S
e
rv
ice
P
lan
n
i
n
g
:
S
im
u
latio
n
a
n
d
Da
ta V
isu
a
li
sa
ti
on
,
”
in
Op
e
ra
ti
o
n
s
Re
se
a
rc
h
a
n
d
He
a
lt
h
Ca
re
,
W
.
P
.
Bra
n
d
e
a
u
,
M
.
L
.
,
S
a
in
f
o
rt,
F
.
a
n
d
P
iers
k
a
ll
a
,
Ed
.
S
p
rin
g
e
r
US,
2
0
0
5
,
p
p
.
7
7
–
1
0
2
.
[3
8
]
A
.
G
h
a
d
e
ri,
M
.
S
.
Ja
b
a
la
m
e
li
,
F
.
Ba
rz
in
p
o
u
r
,
a
n
d
R.
Ra
h
m
a
n
ian
i,
“
A
n
Eff
i
c
ien
t
H
y
b
rid
P
a
rti
c
le
S
w
a
r
m
Op
ti
m
iza
ti
o
n
A
lg
o
rit
h
m
f
o
r
S
o
lv
in
g
th
e
Un
c
a
p
a
c
it
a
ted
C
o
n
ti
n
u
o
u
s
L
o
c
a
ti
o
n
-
A
ll
o
c
a
ti
o
n
P
r
o
b
lem
,
”
Ne
tw
o
rk
s
S
p
a
t.
Eco
n
.
,
v
o
l.
1
2
,
n
o
.
3
,
p
p
.
4
2
1
–
4
3
9
,
Ju
l.
2
0
1
1
.
[3
9
]
T
.
L
.
G
o
,
C.
S
.
L
i
m
,
K.
A
.
Da
n
a
p
a
las
in
g
a
m
,
M
.
L
o
o
n
g
,
P
.
T
a
n
,
a
n
d
C.
W
.
T
a
n
,
“
Ju
rn
a
l
Tek
n
o
lo
g
i
F
u
ll
p
a
p
e
r
A
Re
v
ie
w
o
n
De
v
e
lo
p
m
e
n
t
a
n
d
Op
t
im
iz
a
ti
o
n
o
f
Em
e
rg
e
n
c
y
M
e
d
ica
l
S
e
rv
ice
s in
M
a
la
y
sia
,
”
v
o
l.
3
,
p
p
.
9
3
–
9
6
,
2
0
1
4
.
[4
0
]
L
.
M
.
Be
il
lo
n
,
B.
-
O.
S
u
se
ru
d
,
I
.
Ka
rlb
e
rg
,
a
n
d
J.
He
rli
tz,
“
Do
e
s
A
m
b
u
lan
c
e
Us
e
Diffe
r
Be
t
w
e
e
n
Ge
o
g
ra
p
h
ic
A
re
a
s
?
A
S
u
rv
e
y
o
f
Am
b
u
lan
c
e
Us
e
in
S
p
a
rs
e
ly
a
n
d
De
n
se
l
y
P
o
p
u
late
d
A
re
a
s
,
”
Am
.
J.
E
m
e
r
g
.
M
e
d
.
,
v
o
l.
2
7
,
n
o
.
2
,
p
p
.
202
–
1
1
,
M
a
r.
2
0
0
9
.
[4
1
]
A
.
Kh
o
rra
m
-
M
a
n
e
sh
,
K.
M
.
L
e
n
n
q
u
ist,
A
.
He
d
e
li
n
,
M
.
Ki
h
lg
re
n
,
a
n
d
P
.
Örte
n
w
a
ll
,
“
P
r
e
h
o
sp
it
a
l
T
riag
e
,
Disc
re
p
a
n
c
y
in
P
rio
ri
ty
-
se
tt
in
g
Be
twe
e
n
E
m
e
r
g
e
n
c
y
M
e
d
ica
l
D
isp
a
tch
Ce
n
tre
a
n
d
Am
b
u
lan
c
e
Cre
ws
,
”
Eu
r.
J.
T
ra
u
m
a
E
m
e
r
g
.
S
u
rg
.
,
v
o
l.
3
7
,
n
o
.
1
,
p
p
.
7
3
–
7
8
,
M
a
y
2
0
1
0
.
Evaluation Warning : The document was created with Spire.PDF for Python.