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]
–
[
2
1
]
.
I
n
th
e
p
r
o
p
o
s
ed
m
eth
o
d
th
er
e
is
n
o
n
ee
d
to
estab
lis
h
th
e
r
elativ
e
im
p
o
r
ta
n
ce
am
o
n
g
th
e
attr
ib
u
tes.
I
n
f
ac
t,
th
is
m
eth
o
d
d
o
es
n
o
t
n
e
ed
to
ca
lcu
late
th
e
weig
h
ts
o
f
th
e
attr
ib
u
tes
in
v
o
lv
ed
in
d
ec
is
io
n
m
a
k
in
g
.
T
h
is
m
eth
o
d
is
u
s
ef
u
l
wh
en
c
o
n
f
licts
o
cc
u
r
wh
en
d
eter
m
in
in
g
r
elativ
e
attr
ib
u
te
s
.
I
n
th
e
PS
I
m
eth
o
d
,
th
e
r
e
s
u
lts
ar
e
o
b
tain
ed
th
r
o
u
g
h
m
in
im
al
an
d
s
im
p
l
e
ca
lcu
latio
n
s
as
it
is
b
a
s
ed
o
n
s
tatis
tical
co
n
ce
p
ts
with
o
u
t
attr
ib
u
te
weig
h
ts
.
PS
I
i
s
o
n
e
o
f
th
e
m
eth
o
d
s
u
s
ed
f
o
r
ca
n
d
id
ate
s
elec
tio
n
.
PS
I
is
a
t
o
o
l
th
at
in
teg
r
ates
d
ata
f
r
o
m
m
u
ltip
le
cr
iter
ia
in
th
e
s
elec
ti
o
n
p
r
o
ce
s
s
.
Alth
o
u
g
h
PS
I
h
as
b
ee
n
u
s
ed
in
v
ar
io
u
s
co
n
tex
ts
,
in
clu
d
in
g
em
p
lo
y
ee
s
elec
tio
n
,
th
is
ap
p
r
o
ac
h
h
as
n
o
t
b
ee
n
f
u
lly
ex
p
lo
r
ed
in
th
e
c
o
n
tex
t o
f
r
aw
m
ater
ial
s
u
p
p
ly
s
taf
f
s
elec
tio
n
.
T
h
is
r
esear
ch
aim
s
to
f
ill
th
e
g
ap
b
y
an
al
y
zin
g
th
e
u
s
e
o
f
P
SI
in
r
aw
m
ater
ial
s
u
p
p
ly
s
taf
f
s
elec
tio
n
.
B
y
u
tili
zin
g
PS
I
,
th
is
r
esear
c
h
ca
n
also
ac
h
iev
e
s
ev
er
al
o
b
jectiv
es
in
clu
d
in
g
e
v
alu
atin
g
th
e
ef
f
ec
tiv
en
ess
o
f
PS
I
in
r
aw
m
ater
ial
s
u
p
p
ly
s
t
af
f
s
elec
tio
n
,
id
en
tify
in
g
k
ey
v
ar
iab
les
th
at
af
f
ec
t
th
e
s
u
cc
ess
o
f
s
u
p
p
ly
s
taf
f
s
elec
tio
n
,
an
aly
zin
g
th
e
im
p
a
ct
o
f
u
s
in
g
PS
I
o
n
s
u
p
p
ly
c
h
ain
ef
f
icien
c
y
an
d
co
m
p
an
y
p
r
o
d
u
ctiv
ity
,
a
n
d
p
r
o
v
id
i
n
g
p
r
ac
tical
g
u
id
an
ce
f
o
r
o
r
g
an
izatio
n
s
th
at
wan
t
to
ad
o
p
t
PS
I
in
r
aw
m
ater
ial
s
u
p
p
ly
s
taf
f
s
elec
tio
n
.
B
y
b
r
id
g
in
g
th
is
k
n
o
wled
g
e
g
ap
an
d
an
aly
zin
g
th
e
ap
p
licatio
n
o
f
PS
I
in
r
aw
m
ater
ial
s
u
p
p
ly
s
taf
f
s
elec
tio
n
,
th
is
r
esear
ch
co
n
tr
ib
u
tes
to
t
h
e
p
r
ac
tical
a
n
d
th
eo
r
etica
l
u
n
d
er
s
tan
d
in
g
o
f
ef
f
icien
t
an
d
s
u
s
tain
ab
le
s
u
p
p
ly
ch
ain
m
an
ag
e
m
en
t.
As
s
u
ch
,
th
e
r
esu
lts
o
f
th
is
s
tu
d
y
ar
e
e
x
p
ec
ted
to
p
r
o
v
i
d
e
v
alu
a
b
le
in
s
ig
h
ts
to
in
d
u
s
tr
ies
an
d
o
r
g
an
izatio
n
s
th
at
d
ep
en
d
o
n
a
r
eliab
le
s
u
p
p
ly
o
f
r
aw
m
a
ter
ials
.
2.
RE
S
E
ARCH
M
E
T
H
O
D
2
.
1
.
Resea
rc
h sta
g
es
B
e
c
a
u
s
e
t
h
i
s
r
e
s
e
a
r
c
h
u
s
e
s
t
h
e
c
o
n
c
e
p
t
o
f
a
n
e
x
p
e
r
i
m
e
n
t
a
l
a
p
p
r
o
a
c
h
.
F
i
g
u
r
e
1
e
x
p
l
a
i
n
s
h
o
w
t
o
d
o
t
h
is
r
e
s
e
a
r
c
h
.
T
h
e
f
i
r
s
t
t
h
i
n
g
t
h
a
t
i
s
d
o
n
e
s
t
a
r
t
s
f
r
o
m
t
h
e
d
a
t
a
c
o
l
l
e
c
t
i
o
n
s
t
a
g
e
,
p
r
o
b
l
e
m
a
n
a
l
y
s
i
s
,
p
r
o
b
l
e
m
f
o
r
m
u
l
a
t
i
o
n
,
a
n
d
P
S
I
a
l
g
o
r
i
t
h
m
c
a
l
c
u
l
a
t
i
o
n
m
e
t
h
o
d
w
i
t
h
t
h
e
r
e
s
u
l
t
s
o
f
t
h
e
a
n
a
l
y
s
i
s
w
h
i
c
h
t
h
e
n
r
e
s
u
l
t
s
i
n
c
o
n
c
l
u
s
i
o
n
s
i
n
d
e
t
e
r
m
i
n
i
n
g
r
a
w
m
a
t
e
r
i
a
l
s
t
a
f
f
s
e
l
e
c
t
i
o
n
.
T
h
e
f
o
l
l
o
w
i
n
g
c
a
n
b
e
s
e
e
n
i
n
F
i
g
u
r
e
1
t
h
e
s
t
a
g
e
s
i
n
t
h
e
r
e
s
e
a
r
c
h
.
Fig
u
r
e
1
.
R
esear
ch
s
tag
es
2
.
2
.
M
et
ho
d pre
f
er
ence
s
elec
t
io
n ind
ex
Me
th
o
d
PS
I
is
a
m
eth
o
d
th
at
at
th
e
s
tag
e
o
f
ca
lcu
latin
g
th
e
cr
iter
ia
weig
h
t in
d
ex
is
d
eter
m
in
ed
b
y
th
e
in
f
o
r
m
atio
n
co
n
tain
e
d
in
t
h
e
d
ec
is
io
n
m
atr
ix
,
with
th
e
s
tan
d
ar
d
d
ev
iatio
n
o
r
en
tr
o
p
y
m
eth
o
d
it
will
b
e
ab
le
t
o
id
en
tify
th
e
cr
iter
ia
weig
h
ts
o
b
jectiv
ely
.
T
h
e
PS
I
m
eth
o
d
c
o
n
s
id
er
s
b
o
th
th
e
r
elativ
e
im
p
o
r
tan
ce
o
f
cr
iter
ia
an
d
th
e
v
ar
iab
ilit
y
in
th
e
d
ata,
allo
win
g
d
ec
is
io
n
-
m
a
k
er
s
to
m
ak
e
in
f
o
r
m
ed
a
n
d
u
n
b
iased
d
ec
is
io
n
s
.
B
y
u
s
in
g
th
e
s
tan
d
ar
d
d
ev
iatio
n
o
r
en
tr
o
p
y
m
eth
o
d
,
th
e
PS
I
m
eth
o
d
q
u
an
tifie
s
th
e
d
is
p
er
s
io
n
o
r
u
n
c
er
tain
ty
in
th
e
d
ata,
p
r
o
v
id
i
n
g
a
m
o
r
e
o
b
jectiv
e
a
n
d
r
eliab
le
ass
ess
m
en
t
o
f
th
e
cr
iter
ia
weig
h
ts
.
T
h
is
ap
p
r
o
ac
h
h
elp
s
to
a
v
o
i
d
p
o
ten
tial
b
iases
th
at
ca
n
ar
is
e
f
r
o
m
s
u
b
jectiv
e
j
u
d
g
m
e
n
ts
in
th
e
d
ec
is
io
n
-
m
ak
in
g
p
r
o
ce
s
s
,
u
ltima
tely
lead
in
g
to
m
o
r
e
r
o
b
u
s
t
an
d
f
air
o
u
t
co
m
es.
Ad
d
itio
n
ally
,
th
e
PS
I
m
eth
o
d
p
r
o
v
i
d
es
a
s
y
s
tem
atic
f
r
am
ewo
r
k
f
o
r
d
ec
is
io
n
an
aly
s
is
,
m
ak
in
g
it
a
v
alu
ab
le
to
o
l
in
v
ar
io
u
s
f
ie
ld
s
,
in
clu
d
in
g
b
u
s
in
ess
,
en
g
in
ee
r
in
g
,
a
n
d
p
u
b
lic
p
o
licy
.
T
h
e
f
o
llo
win
g
ar
e
th
e
ca
lcu
latio
n
s
tep
s
ap
p
ly
in
g
th
e
PS
I
m
eth
o
d
[
2
2
]
–
[
2
8
]
,
n
am
el
y
:
‒
Dete
r
m
in
e
th
e
p
r
o
b
lem
:
d
eter
m
in
e
th
e
o
b
jectiv
es
a
n
d
id
en
ti
f
y
th
e
attr
ib
u
tes
a
n
d
alter
n
ativ
es
in
v
o
lv
ed
in
th
e
d
ec
is
io
n
-
m
ak
i
n
g
p
r
o
b
lem
.
D
a
t
a
c
o
l
l
e
c
t
i
o
n
P
r
o
b
l
e
m
a
n
a
l
y
si
s
F
o
r
mu
l
a
t
i
o
n
o
f
t
h
e
p
r
o
b
l
e
m
P
S
I
a
l
g
o
r
i
t
h
m
c
a
l
c
u
l
a
t
i
o
n
R
e
su
l
t
s
a
n
a
l
y
si
s
R
e
su
l
t
s
a
n
d
c
o
n
c
l
u
si
o
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
S
u
p
p
ly
c
h
a
in
efficien
cy
tr
a
n
s
f
o
r
ma
tio
n
:
a
n
a
lysi
s
o
f ra
w
ma
teri
a
l sta
ff select
io
n
…
(
A
mru
lla
h
)
2461
‒
Fo
r
m
u
late
a
d
ec
is
io
n
m
atr
ix
:
t
h
is
s
tep
in
v
o
lv
es
co
n
s
tr
u
ctin
g
a
m
atr
ix
b
ased
o
n
all
a
v
ailab
l
e
in
f
o
r
m
atio
n
th
at
d
escr
ib
es
th
e
attr
ib
u
tes
o
f
th
e
p
r
o
b
lem
.
E
ac
h
d
ec
is
io
n
m
atr
ix
s
er
ies
is
a
llo
ca
ted
to
o
n
e
alter
n
ativ
e
an
d
ea
ch
co
lu
m
n
to
o
n
e
attr
ib
u
te.
T
h
er
e
f
o
r
e.
th
e
X
ij
elem
en
ts
o
f
th
e
X
d
ec
is
io
n
m
atr
ix
as
s
ig
n
attr
ib
u
t
e
v
alu
es
to
t
h
e
o
r
ig
in
al
v
alu
es.
So
,
if
t
h
e
n
u
m
b
e
r
o
f
alter
n
ati
v
es
is
M
an
d
th
e
n
u
m
b
er
o
f
a
ttrib
u
tes
is
N
th
en
th
e
d
ec
is
io
n
m
atr
ix
as a
n
NM
m
atr
ix
ca
n
b
e
r
ep
r
esen
ted
as (
1
)
.
=
[
11
12
21
22
…
1
…
2
⋮
⋮
1
2
⋮
⋮
…
]
(1
)
‒
No
r
m
aliza
tio
n
o
f
t
h
e
d
ec
is
io
n
m
atr
ix
:
i
f
attr
ib
u
te
is
ty
p
eb
e
n
e
f
its
th
en
a
lar
g
er
v
alu
e
is
d
esire
d
wh
ich
ca
n
b
e
n
o
r
m
alize
d
as (
2
)
:
=
(
2
)
If
th
e
attr
ib
u
te
is
ty
p
ec
o
s
t th
en
a
s
m
aller
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alu
e
is
d
esire
d
wh
i
ch
ca
n
b
e
n
o
r
m
alize
d
as (
3
)
.
=
(
3
)
W
h
er
e
X
ij
is
th
e
attr
ib
u
te
s
ize
(
i=1
,
2
,
.
.
.
N
an
d
j=1
,
2
,
.
.
.
M)
.
‒
C
alcu
late
v
alu
e
m
ea
n
f
r
o
m
n
o
r
m
alize
d
d
ata:
i
n
th
is
s
tep
,
th
e
v
alu
e
o
f
th
e
n
o
r
m
al
d
ata
f
o
r
ea
ch
attr
ib
u
te
is
ca
lcu
lated
b
y
th
e
(
4
)
.
=
1
∑
=
1
(
4
)
‒
C
alcu
late
th
e
v
alu
e
o
f
th
e
v
ar
iatio
n
in
p
er
ce
p
tio
n
:
i
n
t
h
is
s
tep
,
th
e
p
r
ef
er
en
ce
v
ar
iatio
n
v
alu
e
b
etwe
en
th
e
v
alu
es o
f
ea
c
h
attr
ib
u
te
is
ca
lcu
lated
u
s
in
g
th
e
(
5
)
.
∅
=
∑
[
11
−
]
2
=
1
(
5
)
‒
Dete
r
m
in
e
th
e
d
ev
iatio
n
in
th
e
p
r
ef
er
e
n
ce
v
alu
e
Ω
=
1
−
∅
(
6
)
‒
Dete
r
m
in
es th
e
weig
h
t o
f
t
h
e
cr
iter
ia
=
Ω
∑
Ω
=
1
(
7
)
T
h
e
to
tal
v
alu
e
o
f
all
th
e
c
r
iter
ia
f
o
r
th
e
weig
h
t o
f
all
attr
ib
u
t
es sh
o
u
ld
b
e
o
n
e,
f
o
r
ex
a
m
p
le
∑
Ω
=
1
.
‒
C
alcu
late
PS
I
:
t
o
s
elec
t in
d
ex
p
r
ef
er
en
ce
s
f
o
r
ea
c
h
alter
n
ativ
e
,
u
s
e
th
e
(
8
)
.
=
∑
=
1
(
8
)
‒
Select
th
e
ap
p
r
o
p
r
iate
alter
n
ati
v
e
f
o
r
t
h
e
g
iv
e
n
ap
p
licatio
n
2
.
3
.
Sy
s
t
e
m
a
na
ly
s
is
T
h
e
s
y
s
tem
an
aly
s
is
in
th
is
r
esear
ch
is
ca
r
r
ied
o
u
t
b
y
ap
p
ly
in
g
th
e
PSI
f
o
r
th
e
s
elec
tio
n
o
f
r
aw
m
ater
ial
s
u
p
p
lier
s
taf
f
.
T
h
e
s
a
m
p
le
d
ata
u
s
ed
in
th
is
s
tu
d
y
c
o
m
es
f
r
o
m
ce
r
tain
c
r
iter
ia
th
a
t
p
lay
an
im
p
o
r
tan
t
r
o
le
in
th
e
p
r
o
ce
s
s
o
f
s
elec
tin
g
r
aw
m
ater
ial
s
u
p
p
lier
s
taf
f
[
2
9
]
,
[
3
0
]
.
T
ab
le
1
s
h
o
ws
th
e
cr
iter
ia
u
s
ed
in
t
h
is
s
tu
d
y
.
T
h
e
a
p
p
lied
cr
iter
ia
h
av
e
b
ee
n
id
e
n
tifie
d
as
k
ey
d
eter
m
in
an
ts
in
ass
ess
in
g
an
d
s
elec
tin
g
s
u
itab
le
ca
n
d
id
ates
f
o
r
th
e
p
o
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itio
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a
n
d
th
is
r
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ch
f
o
cu
s
es
o
n
an
aly
zin
g
d
ata
b
ased
o
n
th
e
cr
iter
ia
to
ea
s
e
th
e
d
ec
is
io
n
-
m
ak
in
g
p
r
o
ce
s
s
in
th
e
s
elec
tio
n
o
f
r
aw
m
ater
ial
s
u
p
p
ly
s
taf
f
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Af
ter
th
at,
in
T
ab
le
2
,
th
e
d
ata
th
at
h
as
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o
b
tain
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d
f
r
o
m
th
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ed
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en
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Fig
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2
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th
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at
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ater
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y
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eter
m
in
in
g
th
e
av
er
ag
e
v
al
u
e
ac
h
iev
e
d
.
Evaluation Warning : The document was created with Spire.PDF for Python.
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.
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f
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o
n
v
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r
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lts
ID
N
a
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C1
C2
C3
C4
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3
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n
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k
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4
5
5
4
5
4
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6
Fig
u
r
e
2
.
I
n
itial r
an
k
in
g
v
is
u
al
izatio
n
Fro
m
th
e
cr
iter
ia
th
at
h
av
e
b
ee
n
k
n
o
wn
a
n
d
th
e
d
ata
th
at
h
as
b
ee
n
s
u
cc
ess
f
u
lly
c
o
n
v
e
r
ted
in
to
a
L
ik
er
t
s
ca
le
with
a
v
alu
e
r
an
g
e
o
f
1
to
5
,
it
s
h
o
u
l
d
b
e
th
at
i
f
y
o
u
l
o
o
k
at
t
h
is
d
ata
wh
ich
h
as
d
eter
m
in
ed
th
e
av
er
ag
e
v
alu
e
o
b
tain
ed
b
y
ea
c
h
ca
n
d
i
d
ate
f
o
r
r
aw
m
ater
ial
s
u
p
p
ly
s
taf
f
,
it
ca
n
b
e
co
n
clu
d
e
d
d
ir
ec
tly
wh
o
will
b
e
s
elec
ted
as
r
aw
m
ater
ia
l
s
u
p
p
ly
s
taf
f
,
n
am
ely
R
am
li
an
d
Mu
liy
o
n
o
with
an
av
er
a
g
e
v
alu
e
o
f
4
.
8
wh
o
g
e
t
th
e
h
ig
h
est
s
co
r
e,
b
u
t
in
th
e
s
elec
tio
n
d
ec
is
io
n
it
is
n
o
t
allo
wed
f
o
r
2
o
r
m
o
r
e
ca
n
d
id
ates
wh
o
h
av
e
th
e
s
am
e
v
alu
e
an
d
p
o
s
itio
n
b
ec
au
s
e
i
t
is
ce
r
tain
t
h
at
o
n
ly
1
ca
n
d
id
ate
will
b
e
s
elec
ted
to
o
cc
u
p
y
th
at
p
o
s
itio
n
.
T
h
er
ef
o
r
e
,
th
is
r
esear
ch
will
s
o
lv
e
th
e
p
r
o
b
lem
s
th
at
o
f
ten
o
cc
u
r
in
th
e
ca
s
e
o
f
s
elec
tin
g
r
a
w
m
ater
ial
s
u
p
p
lier
s
taf
f
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d
will
also
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e
a
p
p
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t
o
o
th
er
ca
s
es
an
d
f
r
o
m
t
h
is
r
e
s
ea
r
ch
we
will
also
u
n
d
e
r
s
tan
d
h
o
w
PS
I
wo
r
k
s
in
d
ep
th
.
T
h
e
PS
I
m
eth
o
d
h
e
r
e
h
as
its
o
wn
u
n
iq
u
en
ess
f
r
o
m
o
t
h
er
m
eth
o
d
s
,
n
am
ely
in
th
e
p
r
o
ce
s
s
o
f
weig
h
tin
g
th
e
v
alu
e
o
f
th
e
c
r
iter
ia
will
b
e
d
eter
m
in
ed
d
i
r
ec
tly
f
r
o
m
th
e
PS
I
ca
lcu
latio
n
p
r
o
ce
s
s
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er
e
f
o
r
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t
h
e
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h
e
w
e
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g
h
t
i
n
g
o
f
t
h
e
c
r
i
t
e
r
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a
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s
u
s
u
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l
l
y
d
e
t
e
r
m
i
n
e
d
a
t
t
h
e
b
e
g
i
n
n
i
n
g
w
i
t
h
a
s
c
a
l
e
o
f
0
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1
o
r
0
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4
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4
4
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4
4
.
6
4
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4
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2
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6
4
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4
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6
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8
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t
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n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
S
u
p
p
ly
c
h
a
in
efficien
cy
tr
a
n
s
f
o
r
ma
tio
n
:
a
n
a
lysi
s
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w
ma
teri
a
l sta
ff select
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…
(
A
mru
lla
h
)
2463
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
3
.
1
.
Resul
t
s
o
f
a
pp
lica
t
io
n o
f
P
SI
m
et
ho
d
C
o
m
p
letio
n
with
th
e
PS
I
m
eth
o
d
r
e
f
er
s
to
t
h
e
p
r
o
ce
s
s
o
f
m
a
k
in
g
a
d
ec
is
io
n
o
r
s
elec
tio
n
b
a
s
ed
o
n
t
h
e
ca
lcu
lated
PS
I
s
co
r
e
o
f
th
e
ca
n
d
id
ate.
Af
ter
th
e
d
ata
is
co
llected
,
an
d
th
e
PS
I
m
eth
o
d
is
ap
p
lied
to
ass
ess
th
e
s
u
itab
ilit
y
o
f
ea
ch
ca
n
d
id
ate,
t
h
e
f
in
aliza
tio
n
s
tag
e
b
eg
in
s
.
Du
r
in
g
th
e
f
in
aliza
tio
n
p
h
ase,
th
e
d
ec
is
io
n
m
ak
er
an
aly
ze
s
th
e
ca
n
d
id
ate'
s
PS
I
s
co
r
e
an
d
co
n
s
id
er
s
v
ar
io
u
s
f
ac
to
r
s
to
m
ak
e
an
in
f
o
r
m
ed
d
ec
i
s
io
n
.
T
h
ese
f
ac
to
r
s
m
ay
in
clu
d
e
s
p
ec
if
ic
r
eq
u
i
r
em
en
ts
th
at
ap
p
ly
to
a
g
iv
en
cr
ite
r
io
n
.
C
o
m
p
letio
n
with
th
e
PS
I
m
eth
o
d
allo
ws
d
ec
is
io
n
m
ak
er
s
to
s
tr
ea
m
lin
e
th
e
s
elec
tio
n
p
r
o
ce
s
s
b
y
tak
in
g
in
to
ac
co
u
n
t
th
e
o
b
jec
tiv
e
PS
I
s
co
r
e
an
d
th
e
s
u
b
jec
tiv
e
f
ac
to
r
s
th
at
in
f
lu
e
n
ce
th
e
f
in
al
d
ec
is
io
n
.
B
y
u
s
in
g
th
e
PS
I
m
eth
o
d
,
co
m
p
a
n
ies
ca
n
en
s
u
r
e
a
f
air
a
n
d
s
y
s
tem
atic
ap
p
r
o
ac
h
to
s
elec
tin
g
ca
n
d
id
ates
f
o
r
t
h
e
s
ettlem
en
t
p
r
o
ce
s
s
,
av
o
id
in
g
b
ias
an
d
s
u
b
jectiv
ity
.
I
n
th
e
en
d
th
e
s
ettlem
en
t
with
th
e
PS
I
m
eth
o
d
h
elp
s
co
m
p
an
ies
m
ak
e
o
p
tim
al
d
ec
i
s
io
n
s
b
y
co
n
s
id
er
in
g
o
b
jectiv
e
d
ata
an
d
cr
iter
ia.
T
h
is
allo
ws
d
ec
is
io
n
m
ak
er
s
to
id
en
tify
ca
n
d
id
ates
with
th
e
h
ig
h
est
PS
I
s
co
r
es,
in
d
icatin
g
th
eir
s
u
itab
ilit
y
to
co
m
p
lete
th
e
r
o
le
b
ased
o
n
th
e
d
ata
an
aly
ze
d
.
Usi
n
g
th
is
m
eth
o
d
,
co
m
p
a
n
ies
ca
n
in
c
r
ea
s
e
th
e
ef
f
ec
tiv
e
n
ess
an
d
ef
f
icien
cy
o
f
th
eir
co
m
p
letio
n
p
r
o
ce
s
s
es,
lead
in
g
to
b
etter
r
esu
lts
an
d
s
u
cc
ess
f
u
l
co
m
p
letio
n
s
.
T
h
e
f
o
llo
win
g
ar
e
th
e
r
esu
lts
o
f
ap
p
ly
in
g
th
e
PS
I
m
eth
o
d
to
th
e
d
ata:
‒
C
r
e
a
t
e
d
e
c
i
s
i
o
n
m
a
t
r
i
x
:
t
h
e
d
e
c
i
s
i
o
n
m
a
t
r
i
x
b
a
s
e
d
o
n
t
h
e
r
e
s
u
l
t
s
o
f
c
o
n
v
e
r
s
i
o
n
o
f
a
l
t
e
r
n
a
t
i
v
e
v
a
l
u
e
s
a
s
i
n
(
9
)
.
M
a
tr
ix
=
[
3
3
3
4
3
4
5
5
4
4
5
5
4
4
4
4
5
4
5
5
5
5
3
5
4
5
3
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4
3
3
3
5
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4
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4
4
4
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5
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5
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5
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5
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4
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5
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5
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4
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3
4
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4
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]
(
9
)
‒
Fin
d
th
e
m
ax
im
u
m
an
d
m
in
i
m
u
m
o
f
ea
ch
alter
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e
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h
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g
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a
T
a
b
le
3
o
f
m
ax
im
u
m
a
n
d
m
in
im
u
m
v
al
u
es f
o
r
ea
c
h
alter
n
ativ
e
.
T
ab
le
3.
Ma
x
im
u
m
an
d
m
in
im
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m
v
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es
M
a
x
i
m
u
m
v
a
l
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e
M
i
n
i
m
u
m
v
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3
5
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5
3
‒
No
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m
alizin
g
t
h
e
d
ec
is
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n
m
atr
ix
:
in
th
e
(
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3
0
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a
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atr
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o
r
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aliza
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ativ
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ac
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in
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ty
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No
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m
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I
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60
(
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23
=
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3
=
5
5
=
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(
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0
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33
=
33
3
=
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5
=
0
.
80
(
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233
=
233
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=
5
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(
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No
r
m
aliza
tio
n
f
o
r
cr
iter
io
n
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V
:
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=
14
4
=
4
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80
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3
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24
=
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4
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4
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80
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4
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34
=
34
4
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80
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=
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No
r
m
aliza
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n
f
o
r
cr
iter
io
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V
:
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=
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3
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=
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80
(
2
8
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35
=
35
5
=
4
5
=
0
.
80
(
2
9
)
235
=
235
5
=
5
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=
1
(
3
0
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T
h
e
(
3
1
)
is
th
e
o
v
er
all
d
ec
is
io
n
m
atr
ix
n
o
r
m
aliza
tio
n
r
esu
lt
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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tif
I
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tell
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SS
N:
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8
9
3
8
S
u
p
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ly
c
h
a
in
efficien
cy
tr
a
n
s
f
o
r
ma
tio
n
:
a
n
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lysi
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teri
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(
A
mru
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h
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2465
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a
t
ri
x
=
[
0
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60
0
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60
0
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60
0
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80
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0
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80
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]
(
3
1
)
‒
C
a
l
c
u
l
a
t
i
n
g
t
h
e
a
v
e
r
a
g
e
v
a
l
u
e
o
f
m
a
t
r
i
x
:
d
o
t
h
e
s
u
m
o
f
m
a
t
r
i
x
a
v
e
r
a
g
e
v
a
l
u
e
s
o
f
e
a
c
h
at
t
r
i
b
u
te
a
s
i
n
(
3
2
)
.
=
1
∑
=
1
=
[
19
.
20
20
.
20
19
.
80
19
.
60
19
.
60
]
(
3
2
)
C
alcu
latin
g
th
e
m
ea
n
v
al
u
e
o
f
th
e
r
esu
lts
o
b
tain
ed
ab
o
v
e
,
n
a
m
el
y:
=
1
∑
=
1
=
1
23
×
19
.
20
=
0
.
834783
(
3
3
)
=
1
∑
=
1
=
1
23
×
20
.
20
=
0
.
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(
3
4
)
=
1
∑
=
1
=
1
23
×
19
.
80
=
0
.
860870
(
3
5
)
=
1
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=
1
=
1
23
×
19
.
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=
0
.
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(
3
6
)
=
1
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=
1
=
1
23
×
19
.
60
=
0
.
852174
(
3
7
)
‒
C
alcu
latin
g
p
r
ef
er
e
n
ce
v
ar
iati
o
n
v
alu
es:
d
eter
m
in
e
th
e
p
r
e
f
er
en
ce
v
ar
iatio
n
v
alu
e
in
r
el
atio
n
to
ea
c
h
cr
iter
io
n
u
s
in
g
t
h
e
(
3
8
)
.
Her
e
ar
e
th
e
p
r
e
f
er
en
ce
v
ar
iatio
n
v
a
lu
es (
∅
)
as in
(
3
8
)
.
∅
=
[
0
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]
(
3
8
)
T
h
en
ad
d
u
p
th
e
r
esu
lts
o
f
th
e
r
an
k
v
alu
es
in
th
e
p
r
e
f
er
en
ce
v
ar
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n
m
atr
ix
(
∅
)
.
T
h
e
r
esu
lt
o
f
th
e
s
u
m
o
f
th
e
p
r
ef
er
e
n
ce
v
ar
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n
m
a
tr
ices is as in
(
3
9
)
:
∅
=
[
0
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452174
0
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539130
0
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594783
0
.
41
7
39
1
0
.
417391
]
(
3
9
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
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8
9
3
8
I
n
t J Ar
tif
I
n
tell
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Vo
l.
1
4
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No
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3
,
J
u
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2
0
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5
:
2
4
5
9
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2
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2466
‒
Def
in
in
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(
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Evaluation Warning : The document was created with Spire.PDF for Python.
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DATA AV
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estrictio
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ata,
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y
with
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m
is
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f
r
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m
ater
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s
taf
f
.
RE
F
E
R
E
NC
E
S
[
1
]
P
.
H
e
l
o
a
n
d
Y
.
H
a
o
,
“
A
r
t
i
f
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e
n
t
a
n
d
s
u
p
p
l
y
c
h
a
i
n
m
a
n
a
g
e
m
e
n
t
:
a
n
e
x
p
l
o
r
a
t
o
r
y
c
a
s
e
st
u
d
y
,
”
Pr
o
d
u
c
t
i
o
n
P
l
a
n
n
i
n
g
a
n
d
C
o
n
t
ro
l
,
v
o
l
.
3
3
,
n
o
.
1
6
,
p
p
.
1
5
7
3
–
1
5
9
0
,
2
0
2
2
,
d
o
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:
1
0
.
1
0
8
0
/
0
9
5
3
7
2
8
7
.
2
0
2
1
.
1
8
8
2
6
9
0
.
[
2
]
M
.
P
o
u
r
n
a
d
e
r
,
H
.
G
h
a
d
e
r
i
,
A
.
H
a
ssan
z
a
d
e
g
a
n
,
a
n
d
B
.
F
a
h
i
m
n
i
a
,
“
A
r
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l
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e
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s
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n
su
p
p
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y
c
h
a
i
n
man
a
g
e
me
n
t
,
”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
rn
a
l
o
f
Pro
d
u
c
t
i
o
n
Ec
o
n
o
m
i
c
s
,
v
o
l
.
2
4
1
,
2
0
2
1
,
d
o
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:
1
0
.
1
0
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6
/
j
.
i
j
p
e
.
2
0
2
1
.
1
0
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2
5
0
.
Evaluation Warning : The document was created with Spire.PDF for Python.