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
.
1
,
Feb
r
u
ar
y
201
7
,
p
p
.
41
7
~
42
3
I
SS
N:
2
0
8
8
-
8708
,
DOI
: 1
0
.
1
1
5
9
1
/
i
j
ec
e
.
v7
i
1
.
p
p
4
1
7
-
4
2
3
417
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
ra
tive
Ana
ly
sis
of
M
etaheuri
stic App
ro
a
ches
f
o
r
M
a
k
espa
n Mini
miz
a
tion
f
o
r No
Wa
it
Flow
Shop
Sche
duling
Proble
m
L
a
x
m
i
A.
B
ew
o
o
r
1
,
V.
Cha
n
dra
P
ra
k
a
s
h
2
,
Sa
g
a
r
U.
Sa
p
ka
l
3
1,
2
Co
m
p
u
ter S
c
ien
c
e
&
En
g
g
.
De
p
t.
,
K.
L
.
Un
iv
e
rsity
,
G
u
n
tu
r
-
5
0
0
0
0
2
,
I
n
d
ia
3
M
e
c
h
a
n
ica
l
En
g
g
.
De
p
t.
,
W
.
C.
o
.
E.
,
S
h
iv
a
ji
Un
iv
e
rsity
,
S
a
n
g
a
li
-
4
1
6
4
1
5
,
In
d
ia
Art
icle
I
nfo
AB
ST
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
J
u
n
2
2
,
2
0
1
6
R
ev
i
s
ed
A
u
g
2
9
,
2
0
1
6
A
cc
ep
ted
Sep
1
3
,
2
0
1
6
T
h
is
p
a
p
e
r
p
r
o
v
id
e
s
c
o
m
p
a
ra
ti
v
e
a
n
a
l
y
sis
o
f
v
a
rio
u
s
m
e
t
a
h
e
u
risti
c
a
p
p
ro
a
c
h
e
s
f
o
r
m
-
m
a
c
h
in
e
n
o
wa
it
f
lo
w
sh
o
p
sc
h
e
d
u
li
n
g
(NW
F
S
S
)
p
ro
b
lem
w
it
h
m
a
k
e
sp
a
n
a
s
a
n
o
p
ti
m
a
li
t
y
c
rit
e
rio
n
.
NW
F
S
S
p
ro
b
lem
is
N
P
h
a
rd
a
n
d
b
ru
te
f
o
rc
e
m
e
th
o
d
u
n
a
b
le
t
o
f
in
d
th
e
so
l
u
ti
o
n
s
so
a
p
p
ro
x
im
a
te
so
lu
ti
o
n
s
a
re
f
o
u
n
d
w
it
h
m
e
tah
e
u
risti
c
a
l
g
o
rit
h
m
s.
T
h
e
o
b
jec
ti
v
e
i
s
to
f
in
d
o
u
t
t
h
e
sc
h
e
d
u
li
n
g
se
q
u
e
n
c
e
o
f
jo
b
s
to
m
in
i
m
ize
to
tal
c
o
m
p
letio
n
ti
m
e
.
In
o
rd
e
r
to
m
e
e
t
th
e
o
b
jec
ti
v
e
c
rit
e
rio
n
,
e
x
isti
n
g
m
e
tah
e
u
risti
c
tec
h
n
iq
u
e
s
v
iz.
T
a
b
u
Se
a
rc
h
(T
S
),
G
e
n
e
ti
c
A
l
g
o
rit
h
m
(
GA
)
a
n
d
P
a
rti
c
le
S
w
a
r
m
O
p
ti
m
iza
ti
o
n
(P
S
O)
a
re
im
p
le
m
e
n
ted
f
o
r
s
m
a
ll
a
n
d
larg
e
siz
e
d
p
ro
b
lem
s
a
n
d
e
ffe
c
ti
v
e
n
e
ss
o
f
th
e
se
tec
h
n
iq
u
e
s are
m
e
a
su
re
d
w
it
h
sta
ti
stica
l
m
e
tri
c
.
K
ey
w
o
r
d
:
Ma
k
esp
a
n
Me
tah
e
u
r
is
tic
No
w
ait
f
lo
w
s
h
o
p
NP
h
ar
d
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
:
L
a
x
m
i
A
.
B
e
w
o
o
r
,
C
o
m
p
u
ter
Scien
ce
&
E
n
g
g
.
D
ep
t.,
K.
L
.
Un
i
v
er
s
i
t
y
,
Gu
n
tu
r
-
5
0
0
0
0
2
,
I
n
d
ia
.
E
m
ail
:
la
x
m
iab
e
w
o
o
r
@
g
m
ail.
co
m
1.
I
NT
RO
D
UCT
I
O
N
Ma
n
u
f
ac
t
u
r
i
n
g
s
c
h
ed
u
li
n
g
is
co
n
ce
r
n
ed
w
it
h
s
e
tti
n
g
th
e
ti
m
etab
le
f
o
r
th
e
p
r
o
ce
s
s
in
g
o
f
g
iv
e
n
s
et
o
f
j
o
b
s
o
n
th
e
s
e
t
o
f
m
ac
h
in
e
s
in
o
r
d
er
to
o
p
tim
ize
g
i
v
en
m
ea
s
u
r
e
o
f
p
er
f
o
r
m
a
n
ce
.
E
f
f
icie
n
t
s
ch
ed
u
lin
g
i
s
n
ee
d
o
f
an
h
o
u
r
f
o
r
m
a
n
u
f
ac
t
u
r
i
n
g
in
d
u
s
tr
y
to
s
u
r
v
iv
e
i
n
to
d
ay
's
i
n
te
n
s
el
y
co
m
p
etiti
v
e
b
u
s
in
es
s
en
v
ir
o
n
m
e
n
t.
Ma
n
u
f
ac
t
u
r
i
n
g
s
ch
ed
u
li
n
g
i
s
b
r
o
ad
ly
clas
s
i
f
ied
in
to
f
lo
w
s
h
o
p
s
ch
ed
u
li
n
g
,
j
o
b
s
h
o
p
s
ch
ed
u
lin
g
an
d
Op
en
s
h
o
p
s
ch
ed
u
lin
g
.
I
n
r
ec
en
t
y
ea
r
s
,
a
co
n
s
id
er
ab
le
am
o
u
n
t
o
f
in
ter
est
h
as
s
ee
n
i
n
n
o
w
ait
s
c
h
ed
u
li
n
g
p
r
o
b
le
m
n
o
t
o
n
l
y
f
r
o
m
r
esear
c
h
a
s
p
ec
t
b
u
t
also
b
ec
a
u
s
e
o
f
w
id
e
v
ar
iet
y
o
f
i
n
d
u
s
tr
ial
ap
p
licatio
n
s
.
No
w
a
it
f
lo
w
s
h
o
p
s
ch
ed
u
lin
g
i
s
o
n
e
o
f
t
h
e
v
ar
ian
t
o
f
f
lo
w
s
h
o
p
s
ch
ed
u
li
n
g
,
i
n
w
h
ic
h
j
o
b
m
u
s
t
b
e
p
r
o
ce
s
s
ed
f
r
o
m
s
tar
t
to
f
i
n
is
h
,
w
it
h
o
u
t a
n
y
in
ter
r
u
p
tio
n
a
n
d
p
r
e
-
e
m
p
tio
n
o
n
o
r
b
et
w
ee
n
m
ac
h
in
e
s
.
A
p
p
licatio
n
s
o
f
n
o
-
w
ait
f
lo
w
s
h
o
p
s
c
h
ed
u
li
n
g
(
NW
FS
S)
ca
n
b
e
f
o
u
n
d
i
n
m
a
n
y
in
d
u
s
tr
ie
s
s
u
ch
a
s
s
teel
i
n
d
u
s
tr
y
,
p
last
ic
m
o
u
ld
in
g
i
n
d
u
s
tr
y
,
p
r
o
ce
s
s
i
n
d
u
s
tr
ies,
ch
e
m
ical
a
n
d
p
h
ar
m
ac
e
u
tical
i
n
d
u
s
tr
ies,
co
n
cr
ete
w
ar
e
p
r
o
d
u
ct
io
n
,
elec
tr
o
n
ic
i
n
d
u
s
tr
y
,
Fo
o
d
in
d
u
s
tr
y
etc.
Hall
an
d
Sris
k
a
n
d
ar
aj
ah
[
1
]
p
r
o
v
id
ed
a
d
etailed
s
u
r
v
e
y
o
f
ap
p
licatio
n
s
a
n
d
r
elate
d
r
esear
ch
p
r
o
b
le
m
s
o
f
t
h
e
n
o
-
w
a
it
f
lo
w
s
h
o
p
s
c
h
ed
u
li
n
g
.
Su
b
s
eq
u
e
n
tl
y
,
NW
FS
S
p
r
o
b
le
m
is
ad
d
r
ess
ed
f
r
o
m
r
esear
c
h
p
o
in
t
a
s
w
ell
b
ec
a
u
s
e
p
r
o
b
le
m
i
s
NP
h
ar
d
in
n
at
u
r
e
an
d
tr
ea
ted
as
an
ex
a
m
p
le
o
f
co
m
b
i
n
ato
r
i
al
o
p
tim
izatio
n
.
T
h
is
t
y
p
e
o
f
c
lass
o
f
p
r
o
b
le
m
s
f
in
d
s
an
o
p
tim
al
s
o
l
u
tio
n
f
r
o
m
a
d
iv
er
s
e
s
ea
r
c
h
-
s
p
ac
e.
I
n
t
h
is
c
ase,
th
e
s
ea
r
ch
-
s
p
ac
e
o
f
ca
n
d
i
d
ate
s
o
lu
tio
n
s
g
r
o
w
s
ex
p
o
n
e
n
tiall
y
a
s
t
h
e
s
ize
o
f
th
e
p
r
o
b
le
m
in
cr
ea
s
es,
a
n
d
it
is
v
er
y
d
if
f
ic
u
lt
to
m
a
k
e
an
ex
te
n
s
i
v
e
s
ea
r
c
h
f
o
r
t
h
e
o
p
ti
m
al
s
o
lu
tio
n
w
it
h
tr
ad
itio
n
al
e
n
u
m
er
ati
v
e
m
et
h
o
d
s
.
I
n
ca
s
e
o
f
NW
FS
S,
d
ec
is
i
o
n
ab
o
u
t
o
p
ti
m
al
s
eq
u
e
n
ce
o
f
g
i
v
e
n
n
j
o
b
s
to
b
e
p
r
o
ce
s
s
ed
o
n
g
iv
e
n
m
m
ac
h
i
n
es
s
h
o
u
ld
b
e
tak
e
n
f
r
o
m
n
!
co
m
b
in
at
io
n
s
o
f
j
o
b
s
eq
u
en
ce
s
f
o
r
m
i
n
i
m
izi
n
g
(
o
r
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
.
1
,
Feb
r
u
ar
y
2
0
1
7
:
41
7
–
42
3
418
m
ax
i
m
izin
g
)
th
e
o
p
ti
m
a
lit
y
c
r
iter
io
n
.
M
ak
esp
an
,
to
tal
f
lo
w
ti
m
e,
m
ea
n
f
lo
w
ti
m
e,
id
le
m
ac
h
in
e
ti
m
e,
to
tal
tar
d
in
ess
,
n
u
m
b
er
o
f
tar
d
y
j
o
b
s
,
in
-
p
r
o
ce
s
s
in
v
e
n
to
r
y
co
s
t,
an
d
co
s
t
o
f
b
ein
g
late
ar
e
s
o
m
e
o
f
th
e
o
p
ti
m
al
it
y
cr
iter
io
n
in
co
n
te
x
t
w
it
h
NW
FS
S
s
c
h
ed
u
li
n
g
p
r
o
b
lem
.
Ma
k
esp
an
i
s
o
n
e
o
f
th
e
m
o
s
t
i
m
p
o
r
tan
t
p
er
f
o
r
m
a
n
ce
m
ea
s
u
r
es
b
ec
au
s
e
it
d
ec
id
es
to
tal
len
g
th
o
f
s
c
h
ed
u
le.
I
n
p
r
ac
tice
a
p
r
o
d
u
ct
ca
n
b
e
d
eliv
er
ed
if
all
th
e
s
u
b
s
eq
u
en
t
j
o
b
s
r
eq
u
ir
e
d
to
m
ak
e
t
h
at
p
r
o
d
u
ct
g
et
e
x
ec
u
t
ed
w
i
th
i
n
g
i
v
e
n
s
c
h
ed
u
le.
T
h
er
ef
o
r
e,
m
i
n
i
m
izi
n
g
m
ak
e
s
p
an
i
s
a
v
er
y
i
m
p
o
r
tan
t
o
b
j
ec
tiv
e
f
o
r
s
ch
ed
u
li
n
g
i
n
m
a
n
y
NW
F
SS
s
y
s
te
m
s
.
I
n
r
ec
en
t
y
ea
r
s
,
Me
ta
h
eu
r
i
s
tics
ar
e
p
r
ef
er
r
ed
m
o
r
e
th
a
n
h
eu
r
i
s
tic
f
o
r
s
o
l
v
i
n
g
co
m
b
in
ato
r
ial
o
p
tim
i
za
tio
n
p
r
o
b
lem
s
b
ec
a
u
s
e
th
e
y
g
u
id
e
a
n
d
m
o
d
i
f
y
h
eu
r
i
s
tics
an
d
less
p
r
u
n
ed
to
g
et
s
t
u
ck
i
n
lo
ca
l
o
p
ti
m
a.
T
h
e
class
ical
m
eta
h
e
u
r
is
tic
al
g
o
r
ith
m
s
o
f
t
h
i
s
t
y
p
e
in
cl
u
d
e
An
t
C
o
lo
n
y
Op
ti
m
izatio
n
(
AC
O)
,
E
v
o
lu
tio
n
ar
y
A
l
g
o
r
ith
m
s
(
E
A
)
,
P
ar
ticle
S
w
ar
m
Op
ti
m
izatio
n
(
P
SO
)
,
an
d
Gen
etic
Alg
o
r
it
h
m
s
(
G
A
)
etc.
[
2
]
.
R
esear
ch
er
s
m
ad
e
f
e
w
atte
m
p
ts
f
o
r
co
m
p
a
r
ativ
e
an
al
y
s
is
o
f
m
etah
e
u
r
is
ti
c
ap
p
r
o
ac
h
es
f
o
r
v
ar
io
u
s
o
p
tim
izatio
n
p
r
o
b
lem
s
.
Hass
a
n
et
al.
[
3
]
co
m
p
ar
ed
p
er
f
o
r
m
an
ce
o
f
G
A
a
n
d
P
SO
f
o
r
s
et
o
f
b
en
c
h
m
ar
k
te
s
t
p
r
o
b
lem
s
an
d
d
esi
g
n
op
tim
izatio
n
p
r
o
b
le
m
s
o
f
tele
s
co
p
e
ar
r
ay
co
n
f
i
g
u
r
atio
n
an
d
s
p
ac
ec
r
af
t
r
eliab
ilit
y
-
b
ased
d
esig
n
p
r
o
b
le
m
.
T
h
e
au
th
o
r
s
clai
m
ed
th
at
P
SO
h
a
s
th
e
s
a
m
e
ef
f
ec
ti
v
en
e
s
s
as
t
h
at
o
f
G
A
f
o
r
f
i
n
d
in
g
th
e
tr
u
e
g
lo
b
al
o
p
ti
m
al
s
o
lu
tio
n
b
u
t
w
it
h
s
i
g
n
i
f
ican
tl
y
b
etter
co
m
p
u
t
atio
n
al
ef
f
ic
i
en
c
y
.
S
tatis
t
ical
an
al
y
s
is
a
n
d
f
o
r
m
al
h
y
p
o
th
e
s
is
test
i
n
g
w
er
e
ap
p
lied
to
v
a
lid
at
e
n
u
m
b
er
o
f
co
m
p
ar
i
s
io
n
o
f
f
u
n
ct
io
n
e
v
al
u
atio
n
s
o
f
G
A
an
d
P
SO.
J
am
ian
et
al.
[
4
]
s
tu
d
ied
th
at
th
e
s
ize
o
f
Di
s
tr
ib
u
ted
Gen
er
atio
n
(
DG)
i
s
cr
u
cial
i
n
o
r
d
er
to
r
ed
u
ce
th
e
i
m
p
ac
t
o
f
in
s
tall
in
g
a
DG
in
th
e
d
is
tr
ib
u
t
io
n
Net
wo
r
k
.
T
h
e
y
i
m
p
le
m
en
ted
P
SO
an
d
ev
o
lu
tio
n
ar
y
P
SO
(
E
P
SO)
f
o
r
f
o
r
m
u
lati
n
g
o
p
tim
izatio
n
tec
h
n
iq
u
e
to
r
eg
u
la
te
th
e
DG
’
s
o
u
tp
u
t
to
co
m
p
u
te
its
o
p
ti
m
a
l
s
ize
s
o
th
at
p
o
w
er
lo
s
s
g
et
s
r
ed
u
ce
d
an
d
v
o
ltag
e
g
e
t r
eg
u
la
ted
.
T
h
e
m
ain
o
b
j
ec
tiv
e
o
f
t
h
is
p
ap
er
is
to
co
m
p
ar
e
ef
f
ec
ti
v
e
n
e
s
s
o
f
s
o
m
e
m
eta
h
eu
r
i
s
tic
tec
h
n
iq
u
e
s
f
o
r
m
i
n
i
m
izi
n
g
m
a
k
esp
a
n
f
o
r
N
W
FS
S
p
r
o
b
le
m
.
T
h
e
r
e
m
ai
n
i
n
g
p
ap
er
is
o
r
g
a
n
ized
as
f
o
ll
o
w
s
:
Sectio
n
2
b
r
ief
s
ab
o
u
t
liter
atu
r
e
r
ev
ie
w
,
Secti
o
n
3
d
ef
in
es
N
W
FS
S
p
r
o
b
le
m
p
r
ec
is
el
y
,
Sectio
n
4
d
is
cu
s
s
es
co
m
p
u
tatio
n
al
ex
p
er
ien
ce
s
a
n
d
Sectio
n
5
d
is
c
u
s
s
es
m
ea
n
i
n
g
f
u
l c
o
n
clu
s
io
n
s
.
2.
L
I
T
E
R
AT
U
RE
R
E
VI
E
W
E
n
u
m
er
ati
v
e
m
et
h
o
d
s
g
o
t
f
ai
lu
r
e
w
h
en
t
h
e
j
o
b
s
ize
in
cr
e
ases
w
h
ic
h
g
en
er
ate
s
t
h
e
n
e
ce
s
s
it
y
o
f
d
ev
elo
p
in
g
ap
p
r
o
x
i
m
atio
n
al
g
o
r
ith
m
s
.
So
atte
m
p
ts
w
er
e
m
a
d
e
f
o
r
d
ev
elo
p
in
g
h
e
u
r
is
tic
al
g
o
r
ith
m
s
f
o
r
f
i
n
d
in
g
n
ea
r
o
p
ti
m
al
s
o
lu
tio
n
b
u
t
s
ti
ll
th
e
h
e
u
r
is
t
ic
ap
p
r
o
ac
h
es
s
u
f
f
er
ed
w
it
h
co
s
t
l
y
d
e
v
elo
p
m
en
t
o
f
s
p
ec
ialized
alg
o
r
ith
m
w
h
ich
h
i
n
d
er
w
h
e
n
ap
p
l
y
i
n
g
to
r
ea
l
li
f
e
p
r
o
b
le
m
s
.
T
h
is
m
aj
o
r
p
r
o
b
lem
ca
n
b
e
s
o
lv
ed
w
it
h
m
eta
h
eu
r
i
s
tic
ap
p
r
o
ac
h
es
w
h
i
ch
ar
e
w
id
el
y
g
e
n
er
ic
w
it
h
r
esp
ec
t
to
ty
p
e
o
f
p
r
o
b
le
m
.
T
h
e
p
ap
er
p
r
o
v
id
es
a
d
ec
ad
e
liter
atu
r
e
s
tu
d
y
o
f
co
n
tr
ib
u
tio
n
b
y
v
ar
io
u
s
r
esear
ch
er
s
f
o
r
s
o
lv
in
g
s
c
h
ed
u
li
n
g
p
r
o
b
lem
s
.
P
r
o
d
u
ctio
n
s
ch
ed
u
lin
g
p
r
o
b
l
e
m
s
w
er
e
s
t
u
d
ied
m
aj
o
r
ly
w
it
h
t
w
o
o
p
tim
alit
y
cr
iter
ia
to
tal
f
lo
w
ti
m
e
(
T
FT)
an
d
to
tal
co
m
p
let
io
n
ti
m
e
(
m
a
k
esp
an
)
.
So
th
e
r
e
v
ie
w
o
f
all
t
h
e
li
ter
atu
r
e
p
er
tin
e
n
t
to
t
h
e
p
r
o
b
lem
u
n
d
er
s
t
u
d
y
w
it
h
m
ak
e
s
p
an
i
s
p
r
esen
ted
in
t
h
i
s
s
ec
tio
n
.
Lu
an
d
Gu
[
3
]
c
o
n
s
id
er
ed
th
e
r
ea
l
-
w
o
r
ld
p
r
o
b
le
m
s
i
n
th
e
B
a
tch
P
lan
t
I
n
d
u
s
tr
y
an
d
i
n
tr
o
d
u
ce
d
f
u
zz
y
p
r
o
ce
s
s
in
g
t
i
m
e
a
n
d
d
u
e
d
at
e
w
i
n
d
o
w
i
n
to
t
h
e
Flo
w
s
h
o
p
s
ch
ed
u
li
n
g
p
r
o
b
le
m
s
.
T
h
e
f
u
zz
y
Flo
w
s
h
o
p
s
ch
ed
u
lin
g
m
o
d
el
w
it
h
m
in
i
m
i
zin
g
th
e
m
a
k
esp
a
n
an
d
m
in
i
m
izin
g
th
e
p
e
n
altie
s
o
f
t
h
e
E
m
w
a
s
s
et
u
p
b
ased
o
n
th
e
t
h
eo
r
y
o
f
f
u
zz
y
p
r
o
g
r
am
m
in
g
,
b
y
ap
p
l
y
in
g
t
h
e
M
ax
i
m
u
m
Me
m
b
er
s
h
ip
F
u
n
ct
i
o
n
s
o
f
Me
a
n
Va
lu
e
(
MM
FMV)
.
T
h
e
p
r
o
p
o
s
ed
m
o
d
el
co
n
v
er
t
t
h
e
f
u
zz
y
o
p
ti
m
izat
io
n
p
r
o
b
le
m
to
th
e
g
en
er
al
o
p
ti
m
izatio
n
p
r
o
b
lem
an
d
f
u
r
t
h
er
s
o
lv
ed
w
i
th
i
m
p
r
o
v
ed
s
i
m
u
lated
an
n
ea
li
n
g
(
S
A
)
al
g
o
r
ith
m
.
Xia
an
d
Wu
[
4
]
d
ev
elo
p
ed
ap
p
r
o
x
im
a
tio
n
a
lg
o
r
it
h
m
f
o
r
th
e
p
r
o
b
lem
o
f
f
in
d
i
n
g
t
h
e
m
i
n
i
m
u
m
m
ak
e
s
p
an
i
n
th
e
j
o
b
-
s
h
o
p
s
ch
ed
u
lin
g
en
v
ir
o
n
m
e
n
t.
T
h
e
n
ew
al
g
o
r
ith
m
w
as
b
ased
o
n
th
e
p
r
in
cip
le
o
f
p
ar
ticle
s
w
ar
m
o
p
ti
m
izat
io
n
(
P
SO)
a
n
d
f
o
r
h
i
g
h
s
ea
r
c
h
e
f
f
icie
n
c
y
an
d
Si
m
u
la
ted
A
n
n
ea
li
n
g
(
S
A
)
to
av
o
id
tr
ap
i
n
lo
ca
l
o
p
ti
m
a.
B
y
r
ea
s
o
n
ab
l
y
co
m
b
i
n
i
n
g
t
h
ese
t
w
o
d
i
f
f
er
e
n
t
s
ea
r
ch
al
g
o
r
ith
m
s
,
a
g
en
e
r
al,
f
ast
an
d
ea
s
il
y
i
m
p
le
m
en
ted
h
y
b
r
id
o
p
ti
m
izat
io
n
alg
o
r
it
h
m
HP
SO
w
a
s
d
ev
e
lo
p
ed
.
L
i
u
et
al
.
[
5
]
p
r
o
p
o
s
ed
an
ef
f
e
ctiv
e
h
y
b
r
id
al
g
o
r
ith
m
b
ased
o
n
p
ar
ticle
s
w
ar
m
o
p
ti
m
izatio
n
(
P
SO)
f
o
r
no
-
w
ai
t
f
lo
w
s
h
o
p
s
ch
ed
u
li
n
g
w
it
h
th
e
cr
iter
io
n
to
m
i
n
i
m
iz
e
th
e
m
a
x
i
m
u
m
co
m
p
letio
n
ti
m
e.
T
h
e
alg
o
r
ith
m
u
s
e
s
r
an
d
o
m
k
e
y
e
n
co
d
in
g
s
ch
e
m
e
alo
n
g
w
it
h
lo
ca
l
s
e
ar
ch
b
ased
o
n
t
h
e
Na
w
az
-
E
n
s
co
r
e
-
Ha
m
(
NE
H)
h
eu
r
i
s
tic
an
d
s
i
m
u
lated
a
n
n
ea
l
in
g
(
S
A
)
w
i
th
a
n
ad
ap
tiv
e
m
et
a
-
L
a
m
ar
c
k
ia
n
lear
n
i
n
g
s
tr
ate
g
y
.
L
i
e
t
al
.
[
6
]
p
r
o
p
o
s
ed
an
o
b
j
e
ctiv
e
i
n
cr
e
m
en
t
h
eu
r
i
s
tic
m
et
h
o
d
n
o
-
w
ait
f
lo
w
s
h
o
p
s
w
it
h
m
ak
e
s
p
a
n
m
i
n
i
m
izatio
n
.
T
h
e
p
r
o
p
o
s
ed
m
o
d
el
ca
n
ca
lcu
late
m
a
k
esp
an
o
f
a
n
e
w
s
c
h
ed
u
le
d
ir
ec
tl
y
f
r
o
m
t
h
at
o
f
it
s
p
ar
en
t
an
d
d
ec
is
io
n
co
u
ld
b
e
m
ad
e
ab
o
u
t
q
u
alit
y
o
f
n
e
w
s
c
h
ed
u
le
i
n
co
m
p
ar
is
o
n
w
it
h
i
ts
p
ar
en
t.
A
d
d
itio
n
all
y
co
m
p
o
s
i
te
h
e
u
r
is
tic
b
ased
o
n
m
ak
e
s
p
an
i
n
cr
e
m
e
n
t
w
a
s
p
r
o
p
o
s
ed
w
h
ich
n
ee
d
s
m
in
i
m
al
C
P
U
ti
m
e.
P
an
et
al
.
[
7
]
d
ev
elo
p
ed
a
d
i
s
cr
ete
p
ar
ticle
s
w
ar
m
o
p
ti
m
iz
atio
n
(
DP
SO)
alg
o
r
ith
m
i
s
p
r
esen
ted
to
s
o
lv
e
t
h
e
n
o
-
w
ait
f
lo
w
s
h
o
p
s
ch
ed
u
li
n
g
p
r
o
b
lem
w
i
th
b
o
th
m
ak
esp
a
n
a
n
d
to
tal
f
lo
w
ti
m
e
cr
iter
ia.
T
h
e
p
ar
ticles
in
t
h
is
al
g
o
r
ith
m
w
er
e
r
ep
r
esen
ted
as
d
is
cr
ete
j
o
b
p
er
m
u
tatio
n
s
an
d
a
n
e
w
p
o
s
itio
n
u
p
d
ate
m
et
h
o
d
is
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
E
C
E
I
SS
N:
2
0
8
8
-
8708
C
o
mp
a
r
a
tive
A
n
a
lysi
s
o
f
Meta
h
eu
r
is
tic
A
p
p
r
o
a
ch
es
f
o
r
Ma
ke
s
p
a
n
Min
imiz
a
tio
n
f
o
r
N
W
F
S
S
…
(
La
xmi
A
.
B
.
)
419
d
ev
elo
p
ed
b
ased
o
n
th
e
d
is
cr
ete
d
o
m
ai
n
.
Fu
r
t
h
er
DP
S
O
alg
o
r
it
h
m
w
as
h
y
b
r
id
ized
w
ith
t
h
e
v
ar
iab
le
n
eig
h
b
o
u
r
h
o
o
d
d
escen
t (
VND)
alg
o
r
ith
m
f
o
r
i
m
p
r
o
v
i
n
g
th
e
s
o
lu
tio
n
q
u
alit
y
.
Sh
a
an
d
Sh
u
[
8
]
p
r
esen
ted
a
n
e
w
p
ar
ticle
s
w
ar
m
o
p
ti
m
izat
io
n
(
P
SO)
f
o
r
th
e
o
p
en
s
h
o
p
s
ch
ed
u
lin
g
p
r
o
b
lem
to
m
i
n
i
m
is
e
m
ak
e
s
p
an
.
I
n
th
e
p
r
o
p
o
s
ed
m
et
h
o
d
th
e
p
ar
ticle
p
o
s
itio
n
w
er
e
r
ep
r
esen
ted
u
s
in
g
p
r
io
r
ities
,
an
d
th
e
p
ar
ticle
m
o
v
e
m
e
n
t
u
s
i
n
g
an
i
n
s
er
t
o
p
er
ato
r
.
A
m
o
d
if
ied
p
ar
a
m
eter
iz
ed
ac
tiv
e
s
ch
ed
u
le
g
en
er
atio
n
al
g
o
r
ith
m
(
m
P
-
AS
G)
w
a
s
u
s
ed
to
d
ec
o
d
e
a
p
ar
ticle
p
o
s
itio
n
in
to
a
s
c
h
ed
u
le
an
d
alg
o
r
it
h
m
w
a
s
h
y
b
r
id
ized
w
ith
b
ea
m
s
ea
r
ch
.
P
an
et
al
.
[
9
]
d
ev
elo
p
ed
a
n
o
v
el
h
y
b
r
id
d
is
cr
ete
p
ar
ticle
s
w
a
r
m
o
p
ti
m
izatio
n
(
HDP
SO)
alg
o
r
ith
m
to
s
o
lv
e
th
e
n
o
-
w
ait
f
lo
w
s
h
o
p
s
ch
ed
u
li
n
g
p
r
o
b
lem
s
f
o
r
m
in
i
m
izatio
n
o
f
th
e
m
a
x
i
m
u
m
co
m
p
letio
n
ti
m
e.
Dis
cr
ete
p
ar
ticle
s
w
ar
m
o
p
tim
izatio
n
(
DP
SO)
alg
o
r
ith
m
b
ased
o
n
p
er
m
u
tatio
n
r
ep
r
esen
tatio
n
a
n
d
a
lo
ca
l
s
ea
r
ch
al
g
o
r
it
h
m
b
ased
o
n
t
h
e
in
s
er
t
n
ei
g
h
b
o
u
r
h
o
o
d
w
er
e
in
te
g
r
ated
f
o
r
e
n
h
an
ce
t
h
e
s
e
ar
ch
in
g
ab
il
it
y
an
d
b
alan
cin
g
t
h
e
ex
p
lo
r
atio
n
an
d
ex
p
lo
itatio
n
.
P
an
et
al.
[
1
0
]
p
r
o
p
o
s
ed
im
p
r
o
v
ed
iter
ati
v
e
g
r
ee
d
y
al
g
o
r
ith
m
f
o
r
m
in
i
m
iz
in
g
m
a
k
esp
an
.
T
h
e
p
r
o
p
o
s
ed
m
e
th
o
d
u
s
es
a
n
i
m
p
r
o
v
ed
Na
w
az
-
E
n
s
co
r
e
-
Ha
m
(
NE
H)
h
eu
r
i
s
tic
f
o
r
co
n
s
tr
u
cti
n
g
s
o
lu
t
io
n
s
in
i
ti
all
y
an
d
f
o
r
s
ea
r
c
h
in
g
s
eq
u
e
n
ce
s
w
it
h
a
s
i
m
p
le
lo
ca
l
s
ea
r
c
h
al
g
o
r
ith
m
w
a
s
i
n
co
r
p
o
r
ated
in
to
th
e
iter
ated
g
r
ee
d
y
alg
o
r
ith
m
to
p
er
f
o
r
m
ex
p
lo
itat
io
n
.
L
a
h
a
an
d
C
h
a
k
r
ab
o
r
t
y
[
1
1
]
p
r
esen
ted
a
n
e
w
co
n
s
tr
u
cti
v
e
h
eu
r
i
s
tic,
b
ased
o
n
t
h
e
p
r
in
ci
p
le
o
f
j
o
b
in
s
er
tio
n
,
f
o
r
m
i
n
i
m
izi
n
g
m
a
k
esp
an
i
n
n
o
-
w
ait
p
er
m
u
tatio
n
f
lo
w
s
h
o
p
s
c
h
ed
u
li
n
g
p
r
o
b
le
m
s
.
T
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
co
n
s
tr
u
ct
s
n
-
j
o
b
s
eq
u
en
ce
s
i
n
cr
e
m
en
ta
ll
y
,
b
y
u
s
i
n
g
s
h
i
f
t
n
ei
g
h
b
o
u
r
h
o
o
d
m
ec
h
an
is
m
i
n
g
e
n
er
atin
g
th
e
in
itial
s
eq
u
e
n
ce
.
A
n
a
l
y
t
ica
l
ex
p
r
ess
io
n
s
f
o
r
th
e
to
tal
n
u
m
b
er
o
f
p
ar
tial
an
d
co
m
p
lete
s
eq
u
en
ce
s
g
e
n
er
ated
b
y
t
h
e
al
g
o
r
ith
m
s
ar
e
d
er
iv
ed
.
Ku
o
et
al
.
[
1
2
]
d
ev
elo
p
ed
a
n
e
w
h
y
b
r
id
p
ar
ticle
s
w
ar
m
o
p
tim
izatio
n
m
o
d
el
(
HP
SO)
th
at
w
a
s
co
m
b
i
n
i
n
g
r
a
n
d
o
m
-
k
e
y
(
R
K)
en
co
d
in
g
s
ch
e
m
e,
f
o
r
ex
p
l
o
itin
g
t
h
e
g
lo
b
al
s
ea
r
ch
a
b
ilit
y
o
f
P
SO
an
d
in
d
iv
id
u
al
e
n
h
an
ce
m
e
n
t
(
I
E
)
s
ch
e
m
e
f
o
r
en
h
a
n
ci
n
g
t
h
e
lo
c
al
s
ea
r
ch
ab
ilit
y
o
f
p
ar
ticles,
a
n
d
p
ar
ticle
s
w
ar
m
o
p
tim
izatio
n
(
P
SO)
f
o
r
s
o
lv
in
g
th
e
f
lo
w
-
s
h
o
p
s
ch
ed
u
lin
g
p
r
o
b
lem
(
FS
SP
)
w
ith
m
a
k
esp
a
n
as
an
o
b
j
ec
tiv
e
.
T
h
e
o
b
j
ec
tiv
e
o
f
FS
SP
is
to
f
i
n
d
a
n
ap
p
r
o
p
r
iate
s
eq
u
en
ce
o
f
j
o
b
s
in
o
r
d
er
to
m
in
i
m
ize
m
a
k
esp
an
.
Gao
et
al.
[
1
3
]
p
r
esen
ted
a
d
is
cr
ete
h
ar
m
o
n
y
s
ea
r
c
h
(
DHS)
alg
o
r
ith
m
f
o
r
s
o
l
v
i
n
g
n
o
-
w
ait
f
lo
w
s
h
o
p
s
ch
ed
u
lin
g
p
r
o
b
lem
s
w
it
h
m
a
k
esp
an
cr
iter
io
n
.
T
h
e
p
r
o
p
o
s
e
d
m
o
d
el
b
u
ild
s
h
ar
m
o
n
y
t
h
at
w
a
s
r
ep
r
ese
n
ted
as
a
d
is
cr
ete
j
o
b
p
er
m
u
tatio
n
an
d
h
eu
r
i
s
tic
m
et
h
o
d
s
w
er
e
u
s
e
d
to
in
itialize
t
h
e
h
ar
m
o
n
y
m
e
m
o
r
y
later
w
it
h
d
y
n
a
m
icall
y
r
eg
r
o
u
p
in
g
m
ec
h
an
i
s
m
,
t
h
e
h
ar
m
o
n
y
m
e
m
o
r
y
w
as
d
iv
id
ed
in
to
s
e
v
er
al
g
r
o
u
p
s
f
o
r
s
h
ar
i
n
g
in
f
o
r
m
atio
n
r
ec
ip
r
o
ca
lly
.
Var
i
ab
le
n
eig
h
b
o
u
r
h
o
o
d
s
ea
r
ch
alg
o
r
ith
m
w
a
s
d
ev
elo
p
ed
an
d
e
m
b
ed
d
ed
in
th
e
DHS
f
o
r
p
r
o
v
id
in
g
a
b
alan
ce
b
et
w
e
en
th
e
g
lo
b
al
ex
p
lo
r
atio
n
an
d
l
o
ca
l e
x
p
lo
r
atio
n
.
C
h
a
k
ar
av
ar
t
h
y
e
t
al.
[
1
4
]
d
e
r
iv
ed
s
o
lu
t
io
n
f
o
r
n
-
j
o
b
,
m
-
m
ac
h
in
e
lo
t
s
tr
ea
m
i
n
g
p
r
o
b
lem
in
a
f
lo
w
s
h
o
p
w
it
h
eq
u
al
s
ize
s
u
b
l
o
ts
f
o
r
m
i
n
i
m
izi
n
g
t
h
e
m
a
k
esp
an
an
d
to
tal
f
lo
w
ti
m
e
as
o
b
j
ec
tiv
e
f
u
n
ctio
n
s
.
T
h
e
y
p
r
o
p
o
s
ed
a
Dif
f
er
en
t
ial
E
v
o
l
u
tio
n
A
lg
o
r
it
h
m
(
DE
A
)
a
n
d
P
a
r
ticle
S
w
ar
m
Op
ti
m
izatio
n
(
PS
O)
to
ev
o
lv
e
b
est
s
eq
u
en
ce
f
o
r
m
a
k
e
s
p
an
/to
tal
f
lo
w
ti
m
e
cr
iter
io
n
.
T
h
e
p
r
o
p
o
s
ed
m
eth
o
d
w
a
s
u
s
in
g
t
h
e
DE
A
a
n
d
P
SO
alg
o
r
ith
m
s
f
o
r
d
is
cr
ete
lo
t stre
a
m
i
n
g
w
i
th
eq
u
a
l s
u
b
lo
ts
.
Do
n
ald
et
al
.
[
15
]
p
r
o
p
o
s
ed
Di
s
cr
ete
Self
-
Or
g
an
iz
in
g
Mig
r
at
in
g
A
l
g
o
r
it
h
m
(
SOM
A
)
w
h
ic
h
is
a
class
o
f
s
w
ar
m
h
eu
r
i
s
tic
b
ased
o
n
th
e
co
m
p
eti
tiv
e
–
co
o
p
er
ativ
e
b
eh
av
io
u
r
o
f
in
telli
g
en
t
cr
ea
tu
r
es
s
o
lv
in
g
a
co
m
m
o
n
p
r
o
b
le
m
.
T
h
e
p
r
o
p
o
s
ed
m
o
d
el
w
a
s
d
is
cr
ete
v
ar
ia
n
t
o
f
SOM
A
f
o
r
s
o
lv
in
g
p
er
m
u
tati
v
e
v
ar
ian
t
s
o
f
s
ch
ed
u
lin
g
p
r
o
b
lem
s
.
T
h
e
m
e
th
o
d
lo
o
k
s
f
o
r
th
e
b
est
in
d
i
v
i
d
u
al
f
r
o
m
t
h
e
o
p
ti
m
ized
m
o
d
el’
s
h
y
p
er
s
p
ac
e
f
o
r
m
i
n
i
m
izi
n
g
m
a
k
es
p
an
.
Sh
ao
et
al.
[
1
6
]
d
ev
elo
p
e
d
a
h
y
b
r
id
d
is
cr
ete
p
ar
ticle
s
w
ar
m
o
p
ti
m
izat
io
n
(
DP
SO)
an
d
s
i
m
u
late
d
an
n
ea
l
in
g
(
S
A
)
al
g
o
r
ith
m
to
i
d
en
tify
an
ap
p
r
o
x
i
m
atio
n
o
f
t
h
e
P
ar
eto
f
r
o
n
t
f
o
r
f
lex
ib
le
j
o
b
-
s
h
o
p
s
ch
ed
u
li
n
g
p
r
o
b
lem
(
FJ
SP
)
.
T
h
e
p
r
o
p
o
s
e
d
m
et
h
o
d
ad
d
r
ess
ed
FJ
SP
w
it
h
m
a
k
esp
an
,
m
a
x
i
m
al
m
ac
h
i
n
e
w
o
r
k
lo
ad
an
d
to
tal
w
o
r
k
lo
ad
m
in
i
m
izat
io
n
.
I
n
t
h
e
p
r
o
p
o
s
ed
h
y
b
r
id
alg
o
r
it
h
m
,
DP
SO
w
as
s
i
g
n
i
f
ica
n
t
f
o
r
g
lo
b
al
s
ea
r
ch
an
d
S
A
w
a
s
u
s
ed
f
o
r
lo
ca
l
s
ea
r
ch
a
n
d
P
ar
eto
r
an
k
in
g
a
n
d
cr
o
w
d
in
g
d
is
tan
ce
m
et
h
o
d
w
er
e
in
co
r
p
o
r
ated
f
o
r
f
i
n
d
in
g
t
h
e
f
it
n
es
s
o
f
p
ar
ticles.
T
h
e
lo
c
al
b
est
p
o
s
itio
n
s
o
f
p
ar
ticles
w
er
e
u
s
ed
to
s
to
r
e
t
h
e
f
i
x
e
d
n
u
m
b
er
o
f
n
o
n
-
d
o
m
i
n
ated
s
o
l
u
tio
n
s
.
T
h
e
r
ev
ie
w
o
f
li
ter
atu
r
e
r
ev
ea
l
s
th
a
t,
th
o
u
g
h
t
h
e
s
ch
ed
u
li
n
g
p
r
o
b
lem
h
as
m
a
n
y
p
r
ac
tical
ap
p
licatio
n
s
,
th
e
liter
at
u
r
e
r
elate
d
w
ith
t
h
i
s
is
v
er
y
li
m
ited
.
T
h
e
m
aj
o
r
liter
atu
r
e
f
o
cu
s
es
o
n
v
ar
iet
y
o
f
f
l
o
w
s
h
o
p
s
ch
ed
u
li
n
g
p
r
o
b
lem
b
u
t
NW
FS
S
h
a
s
co
m
p
ar
at
iv
el
y
les
s
ad
d
r
ess
ed
s
ch
ed
u
li
n
g
p
r
o
b
lem
.
I
t
is
al
s
o
o
b
s
er
v
ed
th
at,
li
m
ited
ef
f
o
r
t
s
w
er
e
atte
m
p
ted
to
c
o
m
p
ar
e
d
if
f
er
e
n
t
m
e
tah
e
u
r
is
t
ic
f
o
r
NW
FS
S
p
r
o
b
le
m
w
it
h
m
a
k
e
s
p
an
as
a
n
o
p
tim
a
lit
y
cr
i
ter
ia.
A
d
d
itio
n
a
l
l
y
atte
m
p
ts
s
h
o
u
ld
b
e
m
ad
e
f
o
r
in
v
est
ig
atio
n
o
f
e
f
f
icien
t
al
g
o
r
ith
m
f
o
r
s
o
lv
i
n
g
NW
FS
S p
r
o
b
lem
w
i
th
m
a
k
esp
an
as a
n
o
b
j
ec
tiv
e
f
u
n
ctio
n
f
o
r
lar
g
e
s
ize
p
r
o
b
lem
.
I
n
li
n
e
w
i
th
t
h
e
s
aid
o
b
j
ec
tiv
e,
t
h
i
s
p
ap
er
p
r
esen
t
s
a
co
m
p
ar
ati
v
e
a
n
al
y
s
i
s
o
f
m
et
ah
eu
r
i
s
tic
alg
o
r
ith
m
s
f
o
r
m
-
m
ac
h
i
n
e
N
W
FS
S
f
o
r
f
i
n
d
i
n
g
o
p
ti
m
al
s
c
h
ed
u
li
n
g
s
eq
u
e
n
ce
co
n
s
id
er
in
g
m
i
n
i
m
izat
io
n
o
f
m
ak
e
s
p
an
as
a
n
o
b
j
ec
tiv
e
c
r
iter
io
n
.
I
n
o
r
d
er
to
f
i
n
d
t
h
e
n
ea
r
o
p
ti
m
al
s
o
l
u
tio
n
,
f
o
r
NW
FS
S
p
r
o
b
lem
,
p
o
p
u
latio
n
b
ased
tec
h
n
iq
u
e
v
i
z.
T
ab
u
Sear
ch
(
T
S),
ev
o
lu
tio
n
ar
y
tec
h
n
iq
u
e
v
iz.
Gen
et
ic
Alg
o
r
ith
m
(
G
A
)
a
n
d
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
.
1
,
Feb
r
u
ar
y
2
0
1
7
:
41
7
–
42
3
420
p
o
p
u
latio
n
b
ased
e
v
o
lu
tio
n
ar
y
tech
n
iq
u
e
v
iz.
P
ar
ticle
S
w
ar
m
Op
ti
m
izat
io
n
(
P
SO)
ar
e
e
v
a
lu
ated
f
o
r
s
m
all
a
n
d
lar
g
e
p
r
o
b
lem
s
ize.
3.
NO
WAIT
F
L
O
W
SH
O
P
S
CH
E
DU
L
I
N
G
P
RO
B
L
E
M
(
NWF
SS
P
)
T
h
e
n
o
-
w
ait
f
lo
w
s
h
o
p
s
c
h
ed
u
li
n
g
p
r
o
b
lem
(
NW
FS
SP
)
ca
n
b
e
d
escr
ib
ed
as
f
o
llo
w
s
.
Gi
v
en
n
j
o
b
s
ar
e
t
o
b
e
p
r
o
ce
s
s
ed
s
eq
u
en
tial
l
y
o
n
m
ac
h
in
e
s
1
,
2
,
.
.
m
.
P
r
o
ce
s
s
in
g
ti
m
e
(
P
T
ij
)
o
f
i
th
j
o
b
o
n
j
th
m
ac
h
in
e
s
is
g
i
v
en
.
E
v
er
y
m
ac
h
i
n
e
ca
n
p
r
o
ce
s
s
at
m
o
s
t
o
n
e
j
o
b
at
g
iv
en
in
s
ta
n
ce
.
T
h
e
p
r
o
ce
s
s
in
g
s
eq
u
en
ce
o
f
j
o
b
s
is
s
am
e
o
n
ev
er
y
m
ac
h
i
n
e.
A
j
o
b
ca
n
n
o
t
b
e
s
tar
ted
o
n
n
ex
t
m
ac
h
i
n
e
u
n
le
s
s
it
h
as
co
m
p
leted
it
s
p
r
o
ce
s
s
in
g
o
n
ea
r
lier
m
ac
h
in
e.
I
n
ad
d
itio
n
to
th
at,
n
o
w
ait
ad
d
s
th
e
co
n
s
tr
ai
n
t
o
n
FS
SP
th
at
o
n
ce
th
e
j
o
b
s
g
et
s
tar
ted
o
n
o
n
e
m
ac
h
in
e
it
s
h
o
u
ld
co
m
p
lete
i
ts
p
r
o
ce
s
s
in
g
o
n
ev
er
y
m
ac
h
i
n
e
w
ith
o
u
t
an
y
in
ter
r
u
p
tio
n
o
r
p
r
e
-
e
m
p
tio
n
.
I
n
o
r
d
er
to
s
atis
f
y
th
i
s
co
n
s
tr
ai
n
t
,
p
r
o
ce
s
s
in
g
o
f
f
ir
s
t
j
o
b
is
d
el
a
y
ed
at
t
h
e
s
tar
t
s
o
t
h
at
t
h
er
e
m
u
s
t
b
e
n
o
w
a
itin
g
ti
m
e
b
et
w
ee
n
p
r
o
ce
s
s
in
g
o
f
a
n
y
co
n
s
ec
u
ti
v
e
o
p
er
atio
n
s
o
f
ea
ch
o
f
n
j
o
b
s
.
So
,
a
d
elay
m
atr
i
x
(
d
el
m
at)
n
ee
d
s
to
b
e
ca
lcu
lated
as p
er
s
tated
in
Eq
u
atio
n
1
to
s
o
lv
e
NW
FS
S
p
r
o
b
lem
.
No
m
e
ncla
t
ure
n
Nu
m
b
er
o
f
j
o
b
s
m
Nu
m
b
er
o
f
m
ac
h
in
e
s
PT
ij
P
r
o
ce
s
s
in
g
ti
m
e
o
f
i
th
j
o
b
o
n
j
th
m
ac
h
in
e
d
el
m
at
d
elay
m
atr
i
x
(k
-
1
,
i
’
)
s
eq
u
e
n
ce
o
r
d
er
o
f
jo
b
s
o
n
m
m
ac
h
i
n
es{j
1
,j
2
,
.
.
j
n
}
C
m
a
x
m
ak
e
s
p
an
(
∑
)
∑
)
)
)
(
1
)
w
h
er
e,
(k
-
1
,
i
’
)
is
s
eq
u
en
ce
o
r
d
er
o
f
jo
b
o
n
m
m
ac
h
in
e
s
.
d
el
m
at
(
i,k
)
is
m
i
n
i
m
u
m
d
ela
y
t
i
m
e
b
et
w
ee
n
s
tar
t
o
f
j
o
b
i
a
n
d
s
tar
t
o
f
j
o
b
k
an
d
j
o
b
k
f
o
llo
w
s
j
o
b
i
.
(
1
≤
i
≤
n
,
1
≤
k
≤
n
,
i
≠
k
)
.
Dela
y
m
atr
ix
r
ep
r
esen
ts
d
el
a
y
ti
m
e
b
et
w
ee
n
c
u
r
r
en
t
j
o
b
an
d
n
e
x
t
j
o
b
to
b
e
s
u
b
m
itted
.
d
el
m
at
(j
(i
-
1
)
,j
i
)
d
en
o
te
m
i
n
i
m
u
m
d
ela
y
o
n
t
h
e
f
ir
s
t
m
ac
h
in
e
b
et
w
ee
n
t
h
e
s
tar
t
o
f
t
w
o
co
n
s
e
cu
ti
v
e
j
o
b
s
i a
n
d
i
-
1
.
Fo
r
th
e
g
iv
e
n
m
atr
ix
o
f
s
ize
n
(
j
o
b
s
)
x
m
(
m
ac
h
in
e
s
)
w
ith
p
r
o
ce
s
s
in
g
ti
m
e
P
T
ij
g
en
er
ates
(
n
!
)
n
u
m
b
er
o
f
f
ea
s
ib
le
s
eq
u
en
ce
o
f
s
o
lu
t
io
n
s
f
r
o
m
w
h
ich
o
p
ti
m
al
s
eq
u
e
n
ce
is
to
b
e
c
h
o
s
e
n
.
T
h
e
p
r
o
b
le
m
i
s
to
d
eter
m
in
e
a
s
eq
u
en
ce
o
f
n
j
o
b
s
w
h
ich
g
i
v
e
s
m
i
n
i
m
u
m
m
a
k
esp
an
.
T
h
e
f
o
r
m
u
la
m
a
k
esp
a
n
is
g
iv
e
n
as p
er
E
q
u
atio
n
2
.
)
∑
)
)
∑
)
(
2
)
4.
CO
M
P
UT
AT
I
O
NAL
E
XP
E
RIE
N
CE
T
h
e
Me
tah
eu
r
i
s
tics
u
n
d
er
co
n
s
id
er
atio
n
ar
e
co
d
ed
in
J
av
a
a
n
d
r
u
n
o
n
I
n
tel
C
o
r
e
i5
,
8
GB
R
A
M,
2
.
2
0
GHz
P
C
.
T
h
e
ex
p
er
i
m
en
tatio
n
is
ca
r
r
ied
o
u
t in
t
w
o
p
h
a
s
es.
I
n
f
ir
s
t p
h
a
s
e,
s
m
all
p
r
o
b
le
m
s
i
ze
s
w
it
h
n
u
m
b
er
o
f
j
o
b
s
(
n
)
=6
,
8
,
1
0
,
1
2
an
d
n
u
m
b
e
r
o
f
m
ac
h
i
n
es(
m
)
=5
,
1
0
,
1
5
,
2
0
,
2
5
a
r
e
co
n
s
i
d
er
ed
.
Seco
n
d
p
h
ase
co
m
p
r
is
e
s
o
f
lar
g
e
p
r
o
b
le
m
s
izes
w
it
h
n
=2
0
,
4
0
,
6
0
,
8
0
,
1
0
0
an
d
m
=5
,
1
0
,
1
5
,
2
0
,
2
5
.
E
ac
h
p
r
o
b
lem
s
ize
i
n
b
o
th
p
h
a
s
es
h
a
v
e
th
ir
t
y
in
d
ep
en
d
en
t
p
r
o
b
lem
i
n
s
tan
ce
s
.
E
ac
h
p
r
o
b
lem
i
n
s
tan
ce
co
r
r
esp
o
n
d
s
to
a
n
ew
p
r
o
ce
s
s
i
n
g
ti
m
e
m
atr
i
x
(
PT
ij
)
g
en
er
a
ted
r
an
d
o
m
l
y
u
s
in
g
u
n
i
f
o
r
m
d
i
s
tr
ib
u
tio
n
u
(
1
,
9
9
)
w
h
ich
i
s
u
s
ed
m
o
r
e
o
f
ten
i
n
r
esear
c
h
co
m
m
u
n
it
y
[
1
7
]
.
T
h
e
ex
p
er
im
en
tatio
n
m
ea
s
u
r
es
th
e
p
er
f
o
r
m
an
ce
b
y
a
v
er
ag
e
r
elativ
e
p
er
ce
n
tag
e
d
ev
iat
io
n
(
AR
P
D)
f
o
r
s
m
all
p
r
o
b
lem
s
ize
w
h
er
e
d
ev
iatio
n
is
ca
lcu
lated
w
i
th
r
esp
e
ct
to
r
es
u
lts
o
b
tain
ed
f
r
o
m
m
eta
h
eu
r
i
s
tics
a
n
d
r
es
u
lts
o
b
tain
ed
f
r
o
m
en
u
m
er
ati
v
e
ap
p
r
o
ac
h
.
T
h
e
p
er
f
o
r
m
a
n
ce
f
o
r
la
r
g
e
p
r
o
b
lem
s
ize
i
s
m
ea
s
u
r
ed
w
it
h
AR
P
D
w
h
er
e
d
ev
iatio
n
is
ca
lcu
lated
f
r
o
m
r
esu
lt
s
o
b
tai
n
ed
f
r
o
m
m
eta
h
e
u
r
is
tic
a
n
d
r
es
u
lt
o
f
b
est
m
eta
h
eu
r
i
s
tic
,
w
h
ich
i
s
m
etah
eu
r
i
s
tic
g
iv
in
g
les
s
v
al
u
e
o
f
m
a
k
esp
an
at
t
h
at
p
r
o
b
le
m
i
n
s
ta
n
ce
.
E
q
u
atio
n
3
ex
p
lain
s
f
o
r
m
u
la
u
s
ed
f
o
r
AR
P
D
f
o
r
s
m
all
p
r
o
b
le
m
s
ize.
A
RP
D = (
100 /
k)
∑
(
3
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
E
C
E
I
SS
N:
2
0
8
8
-
8708
C
o
mp
a
r
a
tive
A
n
a
lysi
s
o
f
Meta
h
eu
r
is
tic
A
p
p
r
o
a
ch
es
f
o
r
Ma
ke
s
p
a
n
Min
imiz
a
tio
n
f
o
r
N
W
F
S
S
…
(
La
xmi
A
.
B
.
)
421
W
h
er
e
,
m
etah
e
u
r
is
tic
d
en
o
t
es
th
e
o
b
j
ec
tiv
e
f
u
n
ctio
n
v
alu
e
o
b
tai
n
ed
f
o
r
i
th
in
s
ta
n
ce
b
y
a
Me
tah
e
u
r
is
tic
s
(
P
SO,
G
A
an
d
T
S
in
t
h
is
ca
s
e
)
.
Op
ti
m
al
is
t
h
e
o
p
ti
m
al
s
o
lu
tio
n
v
al
u
e
w
it
h
e
n
u
m
er
ati
v
e
ap
p
r
o
ac
h
an
d
k
is
n
u
m
b
er
o
f
p
r
o
b
lem
i
n
s
tan
ce
s
.
T
ab
le
1
g
i
v
es
co
m
p
ar
ativ
e
an
a
l
y
s
is
u
s
in
g
AR
P
D
v
a
lu
e
s
o
f
m
eta
h
eu
r
i
s
tic
al
g
o
r
ith
m
s
w
i
th
en
u
m
er
ati
v
e
ap
p
r
o
ac
h
ap
p
lied
f
o
r
s
m
all
s
ized
p
r
o
b
lem
.
T
ab
le
2
.
C
o
m
p
ar
is
o
n
o
f
A
R
P
D
V
alu
es o
f
M
etah
e
u
r
is
tic
A
l
g
o
r
ith
m
s
w
i
th
E
n
u
m
er
ati
v
e
Ap
p
r
o
ac
h
P
r
o
b
l
e
m Si
z
e
A
R
P
D
n
(
j
o
b
s)
m(
mac
h
i
n
e
s)
N
o
.
o
f
i
n
s
t
a
n
c
e
s
PSO
TS
GA
6
5
30
6
.
5
0
2
7
.
0
9
1
1
.
3
3
10
30
4
.
5
4
2
2
.
1
4
7
.
3
1
15
30
4
.
7
2
2
2
.
3
5
7
.
2
2
20
30
3
.
3
7
1
9
.
0
0
5
.
4
5
25
30
4
.
2
1
1
7
.
6
6
5
.
7
6
8
5
30
1
0
.
0
8
2
9
.
1
1
1
1
.
1
0
10
30
8
.
4
7
2
8
.
1
3
1
0
.
4
5
15
30
8
.
9
2
2
9
.
0
0
1
1
.
3
6
20
30
7
.
8
5
2
3
.
8
5
1
0
.
0
9
25
30
7
.
0
5
2
3
.
2
7
7
.
7
9
10
5
30
1
4
.
3
0
3
2
.
0
3
1
3
.
1
9
10
30
1
2
.
7
9
3
3
.
4
0
1
5
.
1
0
15
30
1
4
.
0
7
3
1
.
9
9
1
3
.
4
2
20
30
1
1
.
4
0
2
8
.
0
3
1
5
.
7
6
25
30
1
0
.
7
1
2
5
.
9
9
1
2
.
9
1
12
5
30
1
5
.
6
5
3
4
.
0
2
1
9
.
5
2
10
30
1
6
.
8
8
3
7
.
7
1
1
8
.
8
7
15
30
1
6
.
6
2
3
6
.
9
5
1
9
.
7
1
20
30
1
3
.
2
0
3
1
.
4
0
3
1
.
9
4
25
30
1
4
.
6
6
3
1
.
3
7
1
6
.
8
9
E
q
u
atio
n
4
p
r
o
v
id
es f
o
r
m
u
la
f
o
r
A
R
P
D
f
o
r
lar
g
e
p
r
o
b
le
m
s
i
ze
.
AR
P
D
=
(
1
0
0
/ k
)
∑
(
4
)
W
h
er
e,
m
eta
h
e
u
r
is
tic
d
en
o
t
es
th
e
o
b
j
ec
tiv
e
f
u
n
c
tio
n
v
alu
e
o
b
tain
ed
f
o
r
i
th
in
s
t
an
ce
b
y
a
Me
tah
e
u
r
is
tic
s
(
P
SO,
GA
an
d
T
S
in
th
is
ca
s
e)
.
B
est
is
o
p
tim
al
s
o
lu
tio
n
v
alu
e
o
b
tain
ed
f
o
r
th
at
in
s
ta
n
ce
(
P
SO
in
m
aj
o
r
it
y
o
f
ca
s
es)
an
d
k
i
s
th
e
n
u
m
b
er
o
f
p
r
o
b
le
m
in
s
ta
n
ce
s
f
o
r
a
p
r
o
b
lem
s
ize.
V
alu
e
o
f
k
is
3
0
f
o
r
ex
p
er
i
m
e
n
tatio
n
p
u
r
p
o
s
e.
T
a
b
le
2
g
iv
es
co
m
p
ar
ativ
e
a
n
al
y
s
i
s
o
f
m
eta
h
e
u
r
is
tic
al
g
o
r
ith
m
s
w
it
h
b
est
m
eta
h
eu
r
i
s
tic
ap
p
r
o
ac
h
u
s
i
n
g
AR
P
D
v
al
u
es a
p
p
lied
f
o
r
lar
g
e
s
ized
p
r
o
b
lem
.
C
lear
l
y
,
th
e
b
est
p
o
s
s
ib
le
p
er
f
o
r
m
a
n
ce
i
s
ac
h
ie
v
ed
w
h
en
AR
P
D
=
0.
T
h
e
“
b
est”
u
s
ed
in
th
e
AR
P
D
ca
lcu
latio
n
s
is
b
etter
o
f
t
h
e
t
w
o
co
m
p
etito
r
s
f
o
r
th
e
p
r
o
b
lem
u
n
d
er
co
n
s
id
er
atio
n
.
T
h
e
r
esu
lts
tab
u
lated
u
s
in
g
AR
P
D
m
etr
ic
in
T
ab
le
1
s
h
o
w
s
th
at
,
in
ca
s
e
o
f
6
0
0
p
r
o
b
lem
in
s
ta
n
ce
s
,
P
SO
o
u
tp
er
f
o
r
m
s
T
S
an
d
G
A
in
1
0
0
%
an
d
9
0
%
o
f
t
h
e
ca
s
es
r
esp
ec
tiv
el
y
.
T
ab
le
2
r
ev
ea
ls
th
at
,
i
n
ca
s
e
o
f
7
5
0
p
r
o
b
lem
i
n
s
ta
n
ce
s
,
P
SO
p
er
f
o
r
m
s
b
est
th
a
n
T
S
i
n
1
0
0
%
o
f
t
h
e
ca
s
e
s
a
n
d
b
etter
th
a
n
G
A
i
n
8
0
%
o
f
th
e
ca
s
es.
B
y
o
b
s
er
v
i
n
g
th
e
AR
P
D
v
al
u
es
tab
u
la
ted
in
T
ab
le
1
an
d
2
,
it
ca
n
b
e
co
n
clu
d
ed
th
at,
P
SO
alg
o
r
ith
m
co
n
v
er
g
e
s
to
n
ea
r
o
p
ti
m
al
s
o
l
u
tio
n
as
co
m
p
ar
ed
to
G
A
an
d
T
S
,
w
h
ich
co
r
o
b
o
lates
w
ith
t
h
e
co
n
cl
u
s
io
n
s
m
ad
e
b
y
ea
r
lier
r
e
s
ea
r
ch
er
s
[
2
0
-
2
2
]
.
P
r
o
g
r
am
m
i
n
g
ef
f
o
r
ts
e
x
p
er
ien
ce
t
h
at
P
SO
co
n
v
er
g
e
s
to
o
p
ti
m
al
s
o
lu
tio
n
w
i
t
h
less
e
f
f
o
r
ts
of
tu
n
i
n
g
p
ar
a
m
e
te
r
s
.
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
.
1
,
Feb
r
u
ar
y
2
0
1
7
:
41
7
–
42
3
422
T
ab
le
3
.
C
o
m
p
ar
is
o
n
o
f
A
R
P
D
v
al
u
es o
f
m
eta
h
eu
r
i
s
tic
w
it
h
b
est alg
o
r
ith
m
P
r
o
b
l
e
m Si
z
e
A
R
P
D
n
(
j
o
b
s)
m(
mac
h
i
n
e
s)
N
o
.
o
f
i
n
s
t
a
n
c
e
s
PSO
TS
GA
20
5
30
0
1
1
.
6
3
2
.
1
2
10
30
0
1
5
.
1
4
2
.
1
4
15
30
0
1
6
.
0
2
2
.
0
9
20
30
0
1
3
.
6
8
1
.
9
8
25
30
0
1
2
.
5
2
1
.
5
4
40
5
30
0
9
.
9
9
0
.
9
1
10
30
0
1
0
.
8
3
0
.
7
8
15
30
0
1
1
.
3
0
1
.
0
7
20
30
0
1
0
.
7
0
0
.
9
3
25
30
0
9
.
8
7
1
.
2
2
60
5
30
0
.
4
6
8
.
5
2
0
10
30
0
9
.
0
2
0
.
2
2
15
30
0
9
.
6
8
0
.
8
9
20
30
0
9
.
1
9
0
.
3
3
25
30
0
8
.
2
5
0
.
0
1
80
5
30
0
7
.
3
3
0
.
1
5
10
30
0
8
.
3
8
0
.
6
9
15
30
0
8
.
3
1
0
.
3
2
20
30
0
8
.
3
8
0
.
1
4
25
30
0
.
0
7
7
.
6
5
0
1
0
0
5
30
0
.
5
7
6
.
3
6
0
10
30
0
7
.
5
5
0
.
0
0
15
30
0
.
0
3
7
.
1
8
0
20
30
0
7
.
9
8
0
.
5
3
25
30
0
7
.
0
6
0
.
2
7
5.
CO
NCLU
SI
O
N
I
n
t
h
is
p
ap
er
,
p
er
f
o
r
m
a
n
ce
s
i
n
ter
m
s
o
f
m
i
n
i
m
izatio
n
o
f
t
h
e
m
ak
e
s
p
an
o
f
NW
FS
S
p
r
o
b
le
m
u
s
i
n
g
th
r
ee
m
eta
h
e
u
r
is
tic
al
g
o
r
ith
m
s
w
er
e
ex
a
m
i
n
ed
.
E
m
p
ir
ical
r
esu
lt
s
o
n
a
lar
g
e
n
u
m
b
er
o
f
tes
t
p
r
o
b
lem
s
s
h
o
w
ed
th
at
th
e
s
o
l
u
tio
n
s
o
f
f
er
ed
b
y
u
s
e
o
f
P
SO
ar
e
f
o
u
n
d
s
ig
n
i
f
i
ca
n
tl
y
b
etter
th
a
n
G
A
an
d
T
S.
Mo
r
eo
v
er
,
P
SO
co
n
v
er
g
e
s
m
o
r
e
to
th
e
o
p
ti
m
al
s
o
lu
tio
n
w
i
th
f
air
l
y
less
c
o
m
p
u
tatio
n
ti
m
e
an
d
less
o
v
er
h
ea
d
o
f
p
ar
am
ete
r
s
etti
n
g
as c
o
m
p
ar
ed
w
it
h
o
th
er
m
etah
e
u
r
is
tic
tech
n
iq
u
es v
iz.
GA
a
n
d
T
S.
A
l
s
o
,
r
elev
an
t
p
u
b
lis
h
ed
liter
atu
r
e
is
s
ile
n
t
ab
o
u
t
t
h
e
u
s
e
o
f
P
SO
as
co
m
b
i
n
ato
r
ial
alg
o
r
ith
m
s
f
o
r
o
p
tim
izatio
n
i
n
g
e
n
er
al
a
n
d
in
ca
s
e
o
f
n
o
w
a
it
f
lo
w
s
h
o
p
in
p
ar
ticu
lar
.
T
h
e
r
esu
lt
s
o
f
c
o
m
p
ar
iti
v
e
a
n
al
y
s
is
p
r
esen
ted
in
t
h
is
p
ap
er
ad
v
o
ca
tes
t
h
e
s
a
id
r
esear
ch
g
ap
o
f
u
s
e
o
f
P
SO
a
s
a
f
u
t
u
r
e
s
co
p
e
f
o
r
d
ev
elo
p
in
g
a
n
e
w
v
ar
ian
t o
f
P
SO to
i
m
p
r
o
v
e
th
e
s
o
lu
tio
n
q
u
alit
y
i
n
ca
s
e
o
f
NW
FS
S.
RE
F
E
R
E
NC
E
S
[1
]
Ha
ll
N
G
,
S
risk
a
n
d
a
ra
jah
C.
”
A
s
u
rv
e
y
o
f
m
a
c
h
in
e
sc
h
e
d
u
li
n
g
p
r
o
b
lem
s
w
it
h
b
lo
c
k
in
g
a
n
d
n
o
-
w
a
it
in
p
ro
c
e
ss
”
,
Op
e
ra
ti
o
n
Res
e
a
rc
h
,
v
o
l
.
4
4
,
p
p
5
1
0
-
5
2
5
,
1
9
9
6
[2
]
Blu
m
,
C.
,
Ro
li
,
“
A
.
M
e
tah
e
u
risti
c
s
in
c
o
m
b
in
a
to
rial
o
p
ti
m
iza
ti
o
n
:
Ov
e
rv
ie
w
a
n
d
c
o
n
c
e
p
tu
a
l
c
o
m
p
a
riso
n
”
,
ACM
Co
mp
u
t
in
g
S
u
rv
e
y
s
,
p
p
2
6
8
–
3
0
8
,
2
0
0
3
.
[3
]
R.
Ha
ss
a
n
,
B.
Co
h
a
n
im
,
O.
W
e
c
k
,
G.
V
e
n
ter,
“
A
Co
m
p
a
riso
n
o
f
P
a
rti
c
le
S
w
a
r
m
Op
ti
m
iz
a
ti
o
n
a
n
d
th
e
G
e
n
e
ti
c
A
l
g
o
rit
h
m
”
,
Pro
c
e
e
d
in
g
s
o
f
t
h
e
st
ru
c
tu
ra
l
d
y
n
a
mic
s a
n
d
ma
ter
ia
l
c
o
n
fer
e
n
c
e
;
v
o
l.
4
6
,
2
0
0
5
.
[4
]
Ja
m
ian
J.
M
u
sta
f
a
M
.
M
o
k
h
li
s
H.Ba
h
a
ru
d
i
n
M
.
“
Im
p
li
m
e
n
tatio
n
o
f
Ev
o
lu
ti
o
n
a
ry
P
a
rti
c
le
S
wa
r
m
Op
ti
m
iza
ti
o
n
in
Distrib
u
te
d
G
e
n
e
ra
ti
o
n
S
izi
n
g
”
,
In
t.
J
.
o
f
E
lec
trica
l
En
g
g
.
a
n
d
C
o
m
p
u
ter
S
c
i
;
V
o
l.
2
,
No
.
1
,
p
p
-
1
3
7
-
1
4
6
.
2
0
1
2
.
[5
]
L
u
Zh
e
n
g
,
X
i
n
g
sh
e
n
g
G
u
.
“
F
u
z
z
y
P
ro
d
u
c
ti
o
n
S
c
h
e
d
u
li
n
g
i
n
N
o
-
w
a
it
F
lo
w
sh
o
p
to
M
i
n
im
ize
th
e
m
a
k
e
sp
a
n
w
it
h
E/
T
Co
n
stra
in
ts
u
sin
g
S
A
”
,
Pro
c
e
e
d
in
g
s
o
f
th
e
5
'
W
o
rI
d
Co
n
g
re
ss
o
n
In
tell
ig
e
n
t
Co
n
tro
l
a
n
d
Au
t
o
m
a
ti
o
n
,
vo
l
4,
p
p
2
9
0
9
-
2
9
1
3
,
2
0
0
4
[6
]
W
.
X
ia
a
n
d
Z.
W
u
.
“
A
h
y
b
rid
p
a
rti
c
le
sw
a
r
m
o
p
ti
m
i
z
a
ti
o
n
a
p
p
ro
a
c
h
f
o
r
th
e
j
o
b
-
s
h
o
p
sc
h
e
d
u
li
n
g
p
r
o
b
lem
”
,
In
t
.
J
.
Ad
v
.
M
a
n
u
f.
T
e
c
h
n
o
l
.
,
v
o
l.
2
9
p
p
.
3
6
0
–
3
6
6
,
2
0
0
5
.
[7
]
B.
L
iu
,
L
.
W
a
n
g
,
a
n
d
Y.H.
Jin
.
“
A
n
e
ffe
c
ti
v
e
h
y
b
rid
p
a
rti
c
le
s
w
a
r
m
o
p
ti
m
iz
a
ti
o
n
f
o
r
n
o
-
w
a
it
f
lo
w
sh
o
p
sc
h
e
d
u
li
n
g
”
,
In
t.
J
.
Ad
v
.
M
a
n
u
f.
T
e
c
h
n
o
l.
,
v
o
l
3
1
,
p
p
1
0
0
1
–
1
0
1
1
,
2
0
0
6
.
[8
]
X
iao
p
in
g
L
i,
Qia
n
W
a
n
g
,
Ch
e
n
g
W
u
,
S
h
ih
-
W
e
iL
in
,
Ku
o
-
Ch
i
n
g
Yin
g
,
“
A
n
Eff
icie
n
t
M
e
th
o
d
f
o
r
No
-
W
a
it
F
lo
w
S
h
o
p
S
c
h
e
d
u
li
n
g
to
M
i
n
im
ize
M
a
k
e
sp
a
n
”
,
Pro
c
e
e
d
in
g
s
o
f
th
e
1
0
th
I
n
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
Co
mp
u
ter
S
u
p
p
o
rte
d
Co
o
p
e
ra
ti
v
e
W
o
rk
in
De
sig
n
,
pp
.
1
-
6
.
2
0
0
6
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
E
C
E
I
SS
N:
2
0
8
8
-
8708
C
o
mp
a
r
a
tive
A
n
a
lysi
s
o
f
Meta
h
eu
r
is
tic
A
p
p
r
o
a
ch
es
f
o
r
Ma
ke
s
p
a
n
Min
imiz
a
tio
n
f
o
r
N
W
F
S
S
…
(
La
xmi
A
.
B
.
)
423
[9
]
Q.K.
P
a
n
,
M
.
F
a
ti
h
T
a
sg
e
ti
re
n
,
a
n
d
Y.C
.
L
ian
g
,
“
A
d
isc
re
te
p
a
rti
c
le
sw
a
r
m
o
p
ti
m
iza
ti
o
n
a
lg
o
rit
h
m
f
o
r
th
e
n
o
-
w
a
it
f
lo
ws
h
o
p
sc
h
e
d
u
li
n
g
p
ro
b
lem
”
,
Co
mp
u
t
.
Op
e
r.
Res
.
v
o
l.
3
5
(
9
)
p
p
.
2
8
0
7
–
2
8
3
9
.
2
0
0
8
[1
0
]
D.Y.
S
h
a
a
n
d
C.
Y.
Hs
u
.
”
A
n
e
w
p
a
rti
c
le
s
w
a
r
m
o
p
ti
m
iza
ti
o
n
f
o
r
th
e
o
p
e
n
sh
o
p
sc
h
e
d
u
li
n
g
p
ro
b
l
e
m
”
,
Co
mp
u
t.
Op
e
r.
Res
;
v
o
l.
3
5
(
1
0
)
p
p
3
2
4
3
–
3
2
6
1
,
2
0
0
8
[1
1
]
Qu
a
n
-
Ke
P
a
n
,
L
in
g
W
a
n
g
,
M
.
F
a
ti
h
T
a
sg
e
ti
re
n
,
Ba
o
-
Hu
a
Zh
a
o
.
“
A
h
y
b
rid
d
isc
re
te
p
a
rti
c
le
sw
a
rm
o
p
ti
m
iza
ti
o
n
a
lg
o
rit
h
m
f
o
r
th
e
n
o
-
w
a
it
f
lo
w
sh
o
p
sc
h
e
d
u
li
n
g
p
ro
b
lem
w
it
h
m
a
k
e
sp
a
n
c
rit
e
rio
n
”
,
I
n
t.
J
.
Ad
v
a
n
c
e
d
M
a
n
u
f
a
c
tu
ri
n
g
T
e
c
h
n
o
l
o
g
y
;
v
o
l.
3
8
p
p
.
3
3
7
-
3
4
7
.
2
0
0
8
.
[1
2
]
Qu
a
n
-
Ke
P
a
n
,
L
in
g
W
a
n
g
,
Ba
o
-
Hu
a
Zh
a
o
,
”
A
n
im
p
ro
v
e
d
it
e
ra
ted
g
re
e
d
y
a
lg
o
rit
h
m
f
o
r
th
e
n
o
-
w
a
it
f
lo
w
sh
o
p
sc
h
e
d
u
li
n
g
p
ro
b
lem
w
it
h
m
a
k
e
sp
a
n
c
rit
e
rio
n
”,
In
t.
J
.
Ad
v
a
n
c
e
d
M
a
n
u
fa
c
t
u
rin
g
T
e
c
h
n
o
l
o
g
y
;
v
o
l.
3
8
.
P
p
7
7
8
-
7
8
6
,
2
0
0
8
.
[1
3
]
Dip
a
k
L
a
h
a
a
n
d
Ud
a
y
K.
Ch
a
k
r
a
b
o
rty
,
“
A
c
o
n
stru
c
ti
v
e
h
e
u
risti
c
f
o
r
m
in
i
m
izin
g
m
a
k
e
sp
a
n
in
n
o
-
w
a
it
f
lo
w
sh
o
p
sc
h
e
d
u
li
n
g
”
,
In
t.
J
.
Ad
v
a
n
c
e
d
M
a
n
u
f
a
c
tu
rin
g
T
e
c
h
n
o
lo
g
,
v
o
l.
;
44
p
p
.
9
7
–
1
0
9
,
2
0
0
9
.
[1
4
]
I.
H.
Ku
o
,
S
.
J.
Ho
r
n
g
,
T
.
W
.
Ka
o
,
T
.
L
.
L
in
,
C.
L
.
L
e
e
,
T
.
Tera
n
o
,
a
n
d
Y.
P
a
n
,
“
A
n
e
ff
icie
n
t
f
lo
w
-
s
h
o
p
sc
h
e
d
u
li
n
g
a
lg
o
rit
h
m
b
a
se
d
o
n
a
h
y
b
rid
p
a
rt
icle
s
w
a
r
m
o
p
ti
m
iz
a
ti
o
n
m
o
d
e
l”
,
Exp
e
rt
S
y
st.
Ap
p
l
.
v
o
l
.
3
6
(3
)
p
p
.
7
0
2
7
–
7
0
3
2
,
2
0
0
9
.
[1
5
]
Ka
izh
o
u
G
a
o
,
S
h
e
n
g
x
ian
X
ie,
H
u
a
Jia
n
g
,
Ju
n
q
i
n
g
L
i.
“
Disc
re
te
H
a
r
m
o
n
y
S
e
a
rc
h
A
lg
o
rit
h
m
f
o
r
th
e
No
W
a
it
F
lo
w
S
h
o
p
S
c
h
e
d
u
l
in
g
P
r
o
b
lem
w
it
h
M
a
k
e
sp
a
n
Crit
e
rio
n
”
,
L
NCS
6
8
3
8
I
CIC,
p
p
.
5
9
2
–
5
9
9
,
2
0
1
1
.
[1
6
]
G
.
V
ij
a
y
c
h
a
k
a
ra
v
a
rth
y
,
S
.
M
a
rim
u
th
u
,
a
n
d
a
.
Na
v
e
e
n
S
a
it
,
“
P
e
rf
o
r
m
a
n
c
e
e
v
a
lu
a
ti
o
n
o
f
p
ro
p
o
s
e
d
Diff
e
re
n
ti
a
l
Ev
o
lu
ti
o
n
a
n
d
P
a
rti
c
le
S
w
a
r
m
O
p
ti
m
iza
ti
o
n
a
lg
o
rit
h
m
s
f
o
r
s
c
h
e
d
u
li
n
g
m
-
m
a
c
h
in
e
f
lo
w
sh
o
p
s
w
it
h
lo
t
stre
a
m
in
g
”
,
J
.
In
tell.
M
a
n
u
f
.
v
o
l.
2
4
(1
),
p
p
.
1
7
5
–
1
9
1
.
2
0
1
1
.
[1
7
]
Do
n
a
ld
D.,
Iv
a
n
Z,
M
a
g
d
a
len
a
B
,
Da
v
e
n
d
ra
,
Ro
m
a
n
S
,
Ro
m
a
n
J.
“
Disc
re
te
S
e
lf
-
Org
a
n
izin
g
M
ig
ra
t
in
g
A
l
g
o
rit
h
m
f
o
r
f
lo
w
-
sh
o
p
sc
h
e
d
u
li
n
g
w
it
h
n
o
-
w
a
it
m
a
k
e
sp
a
n
”
,
M
a
th
e
ma
ti
c
a
l
a
n
d
C
o
mp
u
ter
M
o
d
e
li
n
g
,
v
o
l.
5
7
p
p
.
1
0
0
–
1
1
0
,
2
0
1
3
.
[1
8
]
X
.
S
h
a
o
,
W
.
L
iu
,
Q.
L
iu
,
a
n
d
C.
Zh
a
n
g
,
“
H
y
b
rid
d
isc
re
te
p
a
rti
c
le
sw
a
r
m
o
p
ti
m
iz
a
ti
o
n
f
o
r
m
u
lt
i
-
o
b
j
e
c
ti
v
e
f
l
e
x
ib
le
jo
b
-
sh
o
p
sc
h
e
d
u
li
n
g
p
ro
b
lem
”
,
In
t.
J
.
A
d
v
.
M
a
n
u
f.
T
e
c
h
n
o
l
,
v
o
l
.
6
7
.
P
p
2
8
8
5
–
2
9
0
1
.
2
0
1
3
.
[1
9
]
S
.
U.
S
a
p
k
a
l
a
n
d
Di
p
a
k
L
a
h
a
,
"
A
n
Eff
ici
e
n
t
He
u
risti
c
A
lg
o
rit
h
m
f
o
r
m
-
M
a
c
h
in
e
No
-
W
a
it
F
lo
w
S
h
o
p
s
"
,
Pro
c
e
e
d
in
g
s
o
f
t
h
e
In
ter
n
a
ti
o
n
a
l
M
u
lt
ico
n
fer
e
n
c
e
o
f
En
g
in
e
e
rs
a
n
d
Co
mp
u
ter
sc
ien
ti
st
;
v
o
l.
1.
2
0
1
1
.
[2
0
]
Na
ss
e
r
A
.
M
a
h
m
o
u
d
S
.
Ho
r
b
a
ty
M
.
“
A
Co
m
p
a
ra
ti
v
e
S
tu
d
y
o
f
M
e
ta
-
h
e
u
risti
c
A
lg
o
rit
h
m
s
f
o
r
S
o
lv
in
g
Qu
a
d
ra
ti
c
a
ss
ig
n
m
e
n
t
P
r
o
b
lem
”
,
In
t.
J
.
o
f
A
d
v
a
n
c
e
d
C
o
mp
u
ter
S
c
ien
c
e
a
n
d
A
p
p
li
c
a
ti
o
n
s
,
V
o
l.
5
,
No
.
1
,
pp
-
1
-
6
.
2
0
1
4
.
[2
1
]
A
ro
ra
T
.
G
i
g
ra
s
Y.
“
A
S
u
rv
e
y
o
f
Co
m
p
a
riso
n
b
e
twe
e
n
V
a
rio
u
s
M
e
tah
e
u
risti
c
T
e
c
h
n
iq
u
e
s
f
o
r
P
a
th
P
lan
n
i
n
g
P
r
o
b
lem
”
,
In
t.
J
.
o
f
C
o
mp
u
ter
En
g
in
e
e
rin
g
&
S
c
ien
c
e
,
Vo
l.
3
No
.
2,
p
p
6
2
-
6
6
.
2
0
1
3
.
[2
2
]
A
d
h
a
n
i
G
,
Bu
o
n
o
A
,
F
a
q
ih
A
.
“
Op
ti
m
iza
ti
o
n
o
f
S
u
p
p
o
rt
V
e
c
to
r
Re
g
re
ss
io
n
u
sin
g
Ge
n
e
ti
c
A
lg
o
rit
h
m
a
n
d
P
a
rti
c
le
S
w
a
r
m
Op
ti
m
iza
ti
o
n
f
o
r
Ra
in
f
a
ll
P
re
d
icti
o
n
in
Dry
S
e
a
so
n
”
,
In
t.
J
.
o
f
El
e
c
trica
l
En
g
g
.
a
n
d
Co
m
p
u
t
e
r
S
c
i
.
V
o
l.
1
2
,
No
.
1
1
,
p
p
.
7
2
1
2
-
7
2
1
9
.
2
0
1
4
B
I
O
G
RAP
H
I
E
S
O
F
AUTH
O
RS
M
r
s.
La
x
m
i
A
.
Be
w
o
o
r
is
re
s
e
a
rc
h
sc
h
o
lar
a
t
K.
L
.
Un
iv
e
rsit
y
,
G
u
n
tu
r.
S
h
e
is
w
o
rk
in
g
a
s
A
s
sista
n
t
p
ro
f
e
ss
o
r
a
t
V
ish
w
a
k
a
r
m
a
In
st.o
f
In
f
o
.
T
e
c
h
.
f
ro
m
l
a
st
9
y
e
a
rs.
S
h
e
h
a
s
to
tal
1
4
y
e
a
rs
o
f
tea
c
h
in
g
e
x
p
e
rien
c
e
.
He
r
re
se
a
rc
h
in
tere
st
in
c
lu
d
e
s
A
lg
o
rit
h
m
ic
A
n
a
l
y
sis
a
n
d
A
rti
f
icia
l
In
telli
g
e
n
c
e
.
Dr
.
V.
C
h
a
n
d
r
a
Pr
a
k
a
s
h
is
wo
rk
in
g
a
s
a
P
r
o
f
e
ss
o
r
a
t
K.L
.
U
n
iv
e
rsity
,
G
u
n
tu
r.
His
re
se
a
rc
h
in
tere
st i
n
c
l
u
d
e
s A
rti
f
icia
l
In
telli
g
e
n
c
e
a
n
d
Da
ta M
in
in
g
.
Dr
.
S
a
g
a
r
U.
S
a
p
k
a
l
is
w
o
rk
in
g
a
s
a
As
so
c
iate
P
ro
f
e
ss
o
r
a
t
W
a
l
c
h
a
n
d
c
o
l
leg
e
o
f
En
g
in
e
e
rin
g
,
S
a
n
g
a
li
.
His res
e
a
rc
h
in
ters
t
in
c
l
u
d
e
s o
p
t
im
iza
ti
o
n
a
n
d
m
a
n
u
f
a
c
tu
rin
g
.
Evaluation Warning : The document was created with Spire.PDF for Python.