I
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3
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1
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1162
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h
ttp
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ee
cs
.
ia
esco
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e.
co
m
O
ptima
l
la
nd dist
ribution
for a
mbi
g
uo
us pro
fit
veg
et
a
ble crops
using
multi
-
o
b
jec
tive fuz
zy
linea
r p
ro
g
ra
mm
ing
P
ra
na
v
Dix
it
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So
ha
n L
a
l Ty
a
g
i
D
e
p
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r
t
me
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t
o
f
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a
t
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m
a
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i
c
s,
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R
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i
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u
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o
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a
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c
h
n
o
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o
g
y
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D
e
l
h
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-
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C
R
C
a
m
p
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s,
M
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a
r
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h
a
z
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a
b
a
d
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I
n
d
i
a
Art
icle
I
nfo
AB
S
T
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
May
1
,
2
0
2
4
R
ev
is
ed
No
v
6
,
2
0
2
4
Acc
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ted
No
v
11
,
2
0
2
4
De
c
isio
n
s
in
a
g
ricu
l
tu
re
h
a
d
b
e
e
n
d
riv
e
n
b
y
m
e
th
o
d
ica
l
p
la
n
n
i
n
g
t
o
in
c
re
a
se
y
ield
s
t
o
c
a
ter
to
th
e
n
e
e
d
s
o
f
o
v
e
rwh
e
lmin
g
p
o
p
u
lat
io
n
s
wh
il
e
a
lso
a
ll
o
win
g
fa
rm
e
rs t
o
p
ro
s
p
e
r.
All
o
c
a
ti
n
g
lan
d
t
o
v
a
ri
o
u
s
c
ro
p
s
b
y
m
a
k
in
g
u
se
o
f
li
m
it
e
d
re
so
u
rc
e
s
is
b
e
c
o
m
i
n
g
a
c
ru
c
ial
c
h
a
ll
e
n
g
e
f
o
r
a
c
h
ie
v
in
g
h
i
g
h
e
r
p
ro
fit
s.
To
m
a
k
e
c
ro
p
p
in
g
p
a
tt
e
rn
d
e
c
isio
n
s
,
fa
rm
e
rs
trad
it
i
o
n
a
ll
y
re
ly
o
n
e
x
p
e
rien
c
e
,
in
stin
c
t,
a
n
d
c
o
m
p
a
riso
n
s
wit
h
t
h
e
ir
n
e
ig
h
b
o
rs.
S
in
c
e
p
r
o
fit
v
a
ries
d
e
p
e
n
d
in
g
o
n
m
a
n
y
fa
c
to
rs,
in
tu
it
io
n
a
n
d
e
x
p
e
rien
c
e
u
su
a
ll
y
c
a
n
n
o
t
g
u
a
ra
n
t
e
e
o
p
ti
m
a
l
(m
a
x
imu
m
)
p
ro
fit
s.
A
n
u
m
b
e
r
o
f
re
se
a
rc
h
stu
d
ies
o
n
li
n
e
a
r
p
ro
g
ra
m
m
in
g
(LP
)
h
a
v
e
sh
o
w
n
o
p
ti
m
u
m
c
ro
p
p
i
n
g
p
a
tt
e
rn
s
wh
e
n
c
ro
p
p
rice
s
(p
r
o
fit
s)
a
re
fix
e
d
.
Ve
g
e
tab
le
c
ro
p
s,
a
lso
k
n
o
w
n
a
s
c
a
sh
c
ro
p
s,
a
re
su
b
jec
t
to
a
h
ig
h
d
e
g
re
e
o
f
p
rice
v
o
latil
i
ty
o
wi
n
g
t
o
th
e
fa
c
t
th
a
t
t
h
e
ir
p
ro
d
u
c
t
i
o
n
is co
stl
y
a
n
d
th
e
y
c
a
rry
a
sig
n
ifi
c
a
n
t
risk
o
f
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o
t
b
e
in
g
p
r
o
fit
a
b
le,
d
e
sp
it
e
th
e
fa
c
t
t
h
a
t
t
h
e
y
p
r
o
v
id
e
h
ig
h
e
r
e
a
rn
in
g
s
t
h
a
n
fo
o
d
c
r
o
p
s.
Th
e
n
e
t
re
tu
r
n
s
o
f
c
ro
p
s
in
a
g
ric
u
lt
u
re
a
re
g
re
a
tl
y
imp
a
c
ted
b
y
p
rice
u
n
c
e
rt
a
in
ty
.
Wi
t
h
t
h
e
u
se
o
f
th
e
o
p
ti
m
iza
ti
o
n
t
o
o
l
TORA,
a
ste
p
-
by
-
ste
p
p
ro
c
e
ss
is
sh
o
wn
i
n
t
h
is
p
a
p
e
r
t
o
so
l
v
e
th
e
m
o
d
e
l
a
n
d
m
a
n
a
g
e
th
e
v
o
latil
it
y
in
v
e
g
e
tab
le
c
ro
p
p
r
o
fit
a
b
i
li
ty
u
sin
g
fu
z
z
y
m
u
lt
i
-
o
b
jec
ti
v
e
li
n
e
a
r
p
r
o
g
ra
m
m
in
g
(F
M
OLP
).
K
ey
w
o
r
d
s
:
Fu
zz
y
m
u
lti
-
o
b
jectiv
e
lin
ea
r
p
r
o
g
r
a
m
m
in
g
L
an
d
d
is
tr
ib
u
tio
n
Max
-
m
in
ap
p
r
o
ac
h
T
OR
A
W
eig
h
ted
av
er
ag
e
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
So
h
an
L
al
T
y
a
g
i
Dep
ar
tm
en
t o
f
Ma
th
em
atics,
SR
M
I
n
s
titu
te
o
f
Scien
ce
an
d
T
ec
h
n
o
lo
g
y
,
Delh
i
-
NC
R
C
am
p
u
s
Mo
d
in
ag
ar
,
Gh
az
iab
ad
,
Uttar
Pra
d
esh
-
2
0
1
2
0
4
,
I
n
d
ia
E
m
ail:
d
r
s
o
h
an
ty
a
g
i@
g
m
ail.
co
m
1.
I
NT
RO
D
UCT
I
O
N
T
h
er
e
is
a
s
u
b
s
tan
tial
r
elatio
n
s
h
ip
b
etwe
en
th
e
q
u
a
n
tity
o
f
y
ield
g
en
er
ated
b
y
ag
r
icu
ltu
r
al
f
ar
m
s
an
d
th
e
d
em
a
n
d
f
o
r
th
at
p
r
o
d
u
ct,
wh
ich
in
tu
r
n
in
f
lu
en
ce
s
th
e
m
ar
k
et
p
r
icin
g
.
A
c
o
n
v
e
n
tio
n
al
tech
n
iq
u
e
is
o
f
ten
f
o
llo
wed
b
y
f
ar
m
e
r
s
wh
en
it
co
m
es
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cr
o
p
p
in
g
p
atter
n
s
o
r
th
e
allo
ca
tio
n
o
f
lan
d
to
d
if
f
er
en
t
cr
o
p
s
.
T
h
is
d
is
tr
ib
u
tio
n
o
f
lan
d
is
d
eter
m
in
ed
b
y
th
e
r
eso
u
r
ce
s
th
at
ar
e
av
ailab
le.
I
t
h
as
b
ee
n
n
o
te
d
th
at
th
e
n
et
r
etu
r
n
p
e
r
ac
r
e
f
o
r
v
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g
etab
le
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ield
s
,
o
f
t
en
k
n
o
wn
as
ca
s
h
cr
o
p
s
,
is
h
ig
h
er
th
a
n
th
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n
et
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r
n
p
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ac
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f
o
r
f
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o
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cr
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th
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u
g
h
o
u
t
th
e
c
o
u
r
s
e
o
f
th
e
last
d
ec
ad
e.
T
h
e
m
a
x
im
izin
g
o
f
p
r
o
f
its
will
th
u
s
b
e
th
e
p
r
im
ar
y
aim
o
f
ev
e
r
y
f
ar
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er
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eg
ar
d
less
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f
th
e
k
in
d
o
f
v
eg
etab
le
cr
o
p
th
at
is
b
ein
g
cu
ltiv
ated
.
B
y
u
tili
zin
g
th
e
o
p
er
atio
n
s
r
esear
ch
ap
p
r
o
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,
s
p
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if
ically
with
lin
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r
p
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r
a
m
m
in
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p
r
o
b
l
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(
L
PP
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,
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teg
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p
r
o
g
r
am
m
in
g
p
r
o
b
lem
(
I
PP
)
,
ass
ig
n
m
en
t
p
r
o
b
lem
(
AP)
,
a
n
d
tr
an
s
p
o
r
tatio
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p
r
o
b
lem
(
T
P
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,
ag
r
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al
m
an
ag
e
m
en
t
s
y
s
tem
s
ar
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ab
le
to
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ess
th
e
is
s
u
es
o
f
allo
ca
tin
g
lan
d
f
o
r
v
ar
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o
u
s
cr
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p
s
,
m
ax
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izin
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t
h
e
p
r
o
d
u
ctio
n
o
f
cr
o
p
s
,
m
ax
im
izin
g
p
r
o
f
its
,
an
d
m
in
im
izin
g
p
r
o
d
u
ctio
n
c
o
s
ts
.
T
h
ese
i
s
s
u
es
ar
e
ad
d
r
es
s
ed
in
o
r
d
er
to
m
ax
im
ize
p
r
o
f
its
an
d
m
in
im
ize
p
r
o
d
u
ctio
n
c
o
s
ts
.
T
h
ese
is
s
u
es
in
th
e
ag
r
icu
ltu
r
al
s
ec
to
r
wer
e
f
ir
s
t
r
ep
r
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as
a
s
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g
le
-
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lin
ea
r
p
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r
a
m
m
in
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b
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wh
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ey
wer
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ap
p
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g
o
al
at
a
tim
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we
v
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d
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f
f
er
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d
to
b
e
ad
d
r
ess
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co
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cu
r
r
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n
tly
wh
ile
ad
h
e
r
in
g
to
th
e
s
am
e
s
et
o
f
r
estrictio
n
s
.
T
h
is
is
b
e
ca
u
s
e
th
e
s
itu
atio
n
o
f
r
ea
l
-
tim
e
is
s
u
es
th
at
in
clu
d
e
s
ev
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f
ac
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is
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ch
a
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in
g
.
Du
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f
ac
t
th
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m
ax
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o
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cr
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
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J
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&
C
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m
p
Sci
I
SS
N:
2
5
0
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4
7
52
Op
tima
l la
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d
d
is
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1163
p
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a
n
d
o
m
v
a
r
iab
le,
an
d
atte
m
p
ts
ar
e
m
ad
e
to
d
ev
elo
p
o
p
tim
u
m
p
la
n
s
th
at
en
s
u
r
e
a
r
etu
r
n
.
I
n
a
s
im
ilar
v
ein
,
th
e
m
an
ag
em
en
t
o
f
ag
r
ib
u
s
in
e
s
s
p
lan
s
a
t
th
e
lev
el
o
f
f
ar
m
er
s
is
v
er
y
n
ec
ess
ar
y
to
o
b
tain
ass
u
r
ed
r
etu
r
n
s
d
esp
ite
v
ar
iatio
n
s
in
p
r
ice.
Fo
o
d
g
r
ain
p
r
ices
ar
e
o
f
ten
s
tab
le
an
d
p
r
o
v
id
e
a
r
eliab
le
r
etu
r
n
o
n
in
v
e
s
tm
en
t.
T
h
is
is
d
u
e
to
g
o
v
er
n
m
en
t
s
u
p
p
o
r
t
p
r
ice
s
in
m
an
y
co
u
n
tr
ies,
s
u
ch
a
s
I
n
d
ia.
I
n
c
o
n
tr
ast,
v
eg
eta
b
le
p
r
ices
ar
e
m
o
r
e
u
n
p
r
e
d
ict
ab
le
an
d
th
e
co
s
t
o
f
g
r
o
win
g
th
em
is
also
q
u
ite
ex
p
en
s
iv
e.
I
n
ac
tu
ality
,
v
e
g
etab
l
e
cr
o
p
p
in
g
in
v
o
lv
es
m
an
ag
in
g
a
n
u
m
b
er
o
f
ex
p
e
n
s
es,
in
clu
d
in
g
ca
p
ital
in
v
estme
n
ts
in
f
er
tili
ze
r
s
,
p
esti
cid
es,
in
s
ec
ticid
es,
lab
o
r
co
s
ts
,
an
d
th
e
co
s
t
o
f
tr
an
s
p
o
r
tatio
n
.
Occ
asio
n
ally
,
th
e
u
n
p
r
ed
icted
p
r
o
d
u
ctio
n
o
f
th
e
s
a
m
e
p
r
o
d
u
ce
s
f
r
o
m
n
ea
r
b
y
r
e
g
io
n
s
will
also
af
f
ec
t
m
ar
k
et
r
ates
b
ec
au
s
e
th
er
e
is
a
lack
o
f
s
to
r
ag
e
f
ac
ilit
ies
.
W
h
en
tak
in
g
in
to
co
n
s
id
er
atio
n
th
e
v
o
latilit
y
o
f
v
eg
etab
le
p
r
ices,
ad
eq
u
ate
lan
d
p
lan
n
in
g
ca
n
b
e
u
n
d
er
ta
k
en
in
o
r
d
er
to
ac
h
iev
e
o
p
tim
al
r
etu
r
n
s
.
Su
r
p
r
is
in
g
ly
,
v
eg
etab
le
p
r
ices
m
ig
h
t
ch
a
n
g
e
o
n
a
d
aily
b
asis
ev
en
th
r
o
u
g
h
o
u
t
th
e
s
am
e
s
ea
s
o
n
.
T
h
e
n
u
m
e
r
ical
ex
am
p
le
u
s
ed
in
th
is
r
esear
ch
is
b
ased
o
n
th
e
lik
elih
o
o
d
o
f
o
cc
u
r
r
e
n
ce
(
p
r
o
b
ab
ilit
y
)
o
f
cr
is
p
p
r
o
f
it c
o
e
f
f
i
cien
ts
o
v
er
an
o
b
s
er
v
ab
le
p
e
r
io
d
.
2.
L
I
T
E
R
AT
U
RE
R
E
VI
E
W
So
il
q
u
ality
,
cr
o
p
r
o
tatio
n
,
cli
m
ate
co
n
d
itio
n
s
,
d
is
ea
s
e
m
an
ag
em
en
t,
m
ar
k
et
d
em
a
n
d
,
a
n
d
i
r
r
ig
atio
n
s
.
ar
e
th
e
f
ac
to
r
s
to
b
e
co
n
s
id
er
e
d
wh
en
p
lan
n
in
g
v
eg
eta
b
le
f
a
r
m
in
g
.
Sin
ce
L
PP
is
p
er
h
ap
s
t
h
e
m
o
s
t
s
ig
n
i
f
ica
n
t
an
d
well
-
s
tu
d
ied
o
p
tim
izatio
n
is
s
u
e,
it
is
b
ein
g
e
m
p
lo
y
e
d
f
o
r
a
wid
e
r
an
g
e
o
f
m
a
n
u
f
ac
t
u
r
in
g
,
d
is
tr
ib
u
tio
n
,
m
ar
k
etin
g
,
a
n
d
p
o
lic
y
d
ec
is
io
n
-
m
ak
in
g
co
n
ce
r
n
s
.
Nu
m
er
o
u
s
s
tr
ateg
ies
h
av
e
b
ee
n
estab
lis
h
ed
in
m
an
a
g
em
en
t
s
cien
ce
to
d
escr
ib
e
t
h
e
ch
alle
n
g
es
o
f
m
u
lti
-
o
b
jectiv
e
d
ec
is
io
n
-
m
ak
in
g
.
T
h
e
o
u
tp
u
t
f
u
n
ct
io
n
is
th
e
p
r
im
ar
y
p
u
r
p
o
s
e
o
f
ag
r
icu
ltu
r
al
lan
d
;
y
et,
as
tech
n
o
lo
g
y
a
d
v
an
ce
s
,
th
e
r
is
k
ass
o
ciate
d
with
lan
d
o
u
t
p
u
t
is
r
is
in
g
,
p
ar
ticu
lar
ly
in
p
lace
s
th
at
p
r
o
d
u
ce
a
lo
t
o
f
g
r
ain
an
d
in
u
r
b
an
s
u
b
u
r
b
s
.
T
h
e
d
ev
e
lo
p
m
en
t
o
f
a
n
ec
o
n
o
m
ic,
s
o
cial,
an
d
ec
o
lo
g
ical
en
v
ir
o
n
m
e
n
t
is
ess
en
tial
f
o
r
co
o
r
d
in
atin
g
t
h
e
d
ev
elo
p
m
e
n
t
o
f
ag
r
icu
ltu
r
al
la
n
d
-
u
s
e
p
atter
n
s
,
an
d
m
an
ag
er
s
s
h
o
u
ld
b
e
ab
le
to
g
et
tr
u
s
two
r
th
y
in
f
o
r
m
atio
n
t
o
s
u
p
p
o
r
t
th
eir
d
ec
is
io
n
-
m
ak
i
n
g
th
r
o
u
g
h
s
p
ec
if
ic
ap
p
r
o
ac
h
es.
T
o
in
cr
ea
s
e
p
r
o
d
u
ctio
n
a
n
d
e
f
f
icien
cy
,
o
p
tim
al
lan
d
allo
ca
tio
n
f
o
r
v
eg
etab
l
es
r
eq
u
ir
es
ca
r
e
f
u
l
p
lan
n
in
g
an
d
c
o
n
s
id
er
atio
n
o
f
s
ev
er
al
cr
iter
ia.
Ma
r
k
et
p
r
ices
ar
e
o
f
te
n
im
p
ac
ted
b
y
s
ev
er
al
v
ar
iab
les in
clu
d
i
n
g
co
n
s
u
m
er
an
d
f
ar
m
er
s
en
tim
e
n
ts
.
Fin
an
cial
p
l
an
n
in
g
,
b
y
its
v
er
y
n
atu
r
e,
is
ex
tr
em
ely
co
n
f
lictu
al,
in
clu
d
in
g
a
m
u
ltit
u
d
e
o
f
o
b
jectiv
es
with
i
n
tr
icate
f
in
an
cial
r
elatio
n
s
h
ip
s
.
B
ec
au
s
e
o
f
th
is
,
th
e
f
in
an
cial
in
d
u
s
tr
y
is
r
ely
in
g
m
o
r
e
a
n
d
m
o
r
e
o
n
m
ath
em
ati
ca
l
m
o
d
els
to
ex
tr
ac
t
t
h
e
m
o
s
t
v
alu
e
f
r
o
m
co
m
p
lex
ity
.
Ad
d
it
io
n
ally
,
a
g
r
icu
ltu
r
e
n
o
w
h
o
ld
s
a
lar
g
e
m
ar
k
et
s
h
a
r
e
in
th
e
wo
r
ld
,
a
n
d
s
ev
er
al
c
o
r
p
o
r
ate
en
titi
es
f
in
an
ce
f
ar
m
er
s
to
en
s
u
r
e
th
e
s
m
o
o
th
o
p
er
atio
n
o
f
th
eir
s
u
p
p
ly
ch
ai
n
.
T
h
er
e
f
o
r
e,
at
th
e
f
a
r
m
er
le
v
el
,
o
p
tim
al
lan
d
u
s
ag
e
a
n
d
ag
r
ic
u
ltu
r
al
p
atter
n
s
ar
e
r
eq
u
ir
ed
.
T
o
e
n
s
u
r
e
m
ax
im
u
m
p
r
o
f
ita
b
ilit
y
d
esp
ite
f
lu
ctu
a
tin
g
p
r
ices,
th
e
f
a
r
m
er
m
u
s
t
cu
ltiv
ate
an
d
s
ell
th
e
v
eg
etab
le
cr
o
p
s
o
v
er
th
e
f
u
ll
s
ea
s
o
n
,
aim
in
g
f
o
r
th
e
h
i
g
h
est
p
o
s
s
ib
le
weig
h
ted
r
etu
r
n
.
T
o
a
d
d
r
ess
o
p
tim
al
f
ar
m
p
lan
n
in
g
,
[
1
]
d
ev
is
ed
th
e
L
P
a
p
p
r
o
ac
h
.
T
h
e
id
ea
o
f
f
u
zz
y
in
d
ec
is
io
n
m
ak
in
g
was
in
itially
in
tr
o
d
u
ce
d
b
y
Z
ad
e
h
[
2
]
.
Fu
zz
y
lin
ea
r
p
r
o
g
r
am
m
i
n
g
is
s
u
es
h
av
e
b
ee
n
f
o
r
m
u
l
ated
an
d
r
eso
lv
ed
u
s
in
g
f
u
z
zy
s
et
th
eo
r
y
[
3
]
.
A
p
ar
am
etr
ic
ap
p
r
o
ac
h
ca
n
b
e
f
o
u
n
d
in
[
4
]
.
Su
m
p
s
i
et
a
l.
[
5
]
E
m
p
lo
y
ed
f
u
zz
y
g
o
al
p
r
o
g
r
am
m
in
g
m
eth
o
d
o
lo
g
ies
to
ad
d
r
ess
a
f
ar
m
p
lan
n
in
g
is
s
u
e.
A
f
r
esh
a
p
p
r
o
ac
h
was
p
r
o
v
i
d
ed
f
o
r
d
ea
lin
g
with
f
u
zz
y
v
ar
i
ab
le
is
s
u
es
in
lin
ea
r
p
r
o
g
r
a
m
m
in
g
b
y
Ma
lek
i
et
a
l.
[
6
]
.
I
n
th
eir
s
tu
d
y
o
f
cr
o
p
p
lan
n
in
g
u
n
d
e
r
v
a
g
u
en
ess
.
I
t
o
h
et
a
l.
[
7
]
ass
u
m
ed
th
at
p
r
o
f
it
co
ef
f
icie
n
ts
ar
e
d
is
cr
ete
r
an
d
o
m
v
a
r
iab
les.
Gan
esan
an
d
Vee
r
am
an
i
[
8
]
,
p
r
o
p
o
s
ed
a
s
tu
d
y
ab
o
u
t
f
u
zz
y
lin
ea
r
p
r
o
g
r
a
m
m
in
g
u
s
in
g
f
u
zz
y
v
ar
iab
les
an
d
tr
ap
ez
o
i
d
al
m
em
b
er
s
h
ip
f
u
n
ctio
n
r
e
p
r
esen
tin
g
d
if
f
er
e
n
t
m
em
b
er
s
h
ip
f
u
n
ctio
n
s
f
o
r
m
u
lti
-
o
b
jectiv
e
FLPP
r
esp
ec
tiv
ely
.
W
ein
tr
au
b
an
d
R
o
m
er
o
[
9
]
E
m
p
h
asized
th
e
p
r
esen
t
is
s
u
es
an
d
r
esear
ch
p
r
io
r
ities
f
o
r
th
e
f
ield
o
f
a
g
r
ic
u
ltu
r
e
an
d
f
o
r
estry
as
well
as
th
e
ap
p
licatio
n
o
f
o
p
er
atio
n
s
r
esear
c
h
m
o
d
els
to
ev
alu
ate
h
is
to
r
ical
p
e
r
f
o
r
m
an
ce
in
th
ese
ar
ea
s
.
T
an
k
o
l
et
a
l.
[
1
0
]
f
o
c
u
s
ed
o
n
en
v
ir
o
n
m
en
tal
co
n
s
eq
u
en
ce
s
,
r
is
k
an
d
u
n
ce
r
ta
in
ty
co
n
ce
r
n
s
f
o
r
n
u
m
er
o
u
s
cr
iter
ia
an
d
p
la
n
n
in
g
c
h
allen
g
es
at
th
e
f
ar
m
an
d
r
e
g
io
n
al
s
ec
to
r
l
ev
el,
as
well
as
th
e
cr
ea
tio
n
o
f
an
im
al
d
iets
an
d
f
ee
d
in
g
m
ater
ials
.
R
o
s
s
[
1
1
]
s
u
g
g
ested
to
s
o
lv
e
lin
ea
r
o
p
t
im
izatio
n
p
r
o
b
lem
s
to
f
in
d
o
p
tim
al
s
o
lu
tio
n
f
o
r
s
ev
er
al
o
b
jectiv
e
f
u
n
ctio
n
s
.
Sen
th
ilk
u
m
ar
a
n
d
R
ajen
d
r
an
[
1
2
]
h
as
d
ev
elo
p
ed
t
h
e
tech
n
i
q
u
e
to
s
o
lv
e
FLPP
co
n
s
is
tin
g
o
f
f
u
zz
y
v
ar
ia
b
les
b
y
u
s
in
g
p
ar
am
etr
ic
f
o
r
m
.
Gar
g
an
d
Sin
g
h
[
1
3
]
Pre
s
en
ted
a
m
eth
o
d
f
o
r
s
o
lv
in
g
MO
L
P
b
y
b
u
ild
i
n
g
u
p
th
e
m
em
b
er
s
h
ip
f
u
n
ctio
n
u
s
in
g
th
e
Ma
x
-
Min
s
tr
ateg
y
an
d
clai
m
e
d
to
p
r
o
v
id
e
b
etter
r
esu
lts
th
an
I
to
h
et
a
l.
[
1
4
]
p
r
esen
ted
d
if
f
e
r
en
t
m
em
b
er
s
h
ip
f
u
n
cti
o
n
s
f
o
r
m
u
lti
o
b
jectiv
e
FLPP.
L
o
n
e
et
a
l.
[
1
5
]
g
a
v
e
an
ap
p
r
o
ac
h
f
o
r
f
in
d
in
g
o
p
tim
u
m
allo
ca
tio
n
f
o
r
FLPP
b
y
u
s
in
g
t
h
e
tr
ap
ez
o
id
al
m
em
b
er
s
h
ip
f
u
n
ctio
n
.
A
y
ea
r
ly
ag
r
icu
ltu
r
al
p
lan
f
o
r
s
ev
er
al
cr
o
p
s
was
s
u
g
g
este
d
b
y
Sh
ar
m
a
et
a
l.
[
1
6
]
af
ter
t
h
ey
in
v
esti
g
ated
th
e
FGP
f
o
r
th
e
ag
r
ic
u
ltu
r
al
lan
d
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
8
,
No
.
2
,
May
20
2
5
:
1
1
6
2
-
1
1
6
9
1164
allo
ca
tio
n
p
r
o
b
lem
.
B
h
a
r
ati
a
n
d
Sin
g
h
[
1
7
]
p
r
o
p
o
s
ed
c
o
m
p
u
tatio
n
al
alg
o
r
ith
m
f
o
r
th
e
r
eso
lu
tio
n
o
f
m
u
lti
-
o
b
jectiv
e
g
o
al
p
r
o
g
r
am
m
in
g
i
n
in
ter
v
al
v
alu
ed
f
u
zz
y
p
r
o
g
r
am
m
in
g
m
eth
o
d
.
R
en
et
a
l.
[
1
8
]
p
r
o
p
o
s
ed
m
u
lti
-
o
b
jectiv
e
s
to
ch
asti
c
f
u
zz
y
p
r
o
g
r
am
m
in
g
m
eth
o
d
s
th
at
ca
n
b
e
u
s
ed
to
f
in
d
th
e
o
p
tim
al
allo
c
atio
n
o
f
a
g
r
icu
ltu
r
al
wate
r
.
An
alg
o
r
ith
m
b
ased
o
n
t
h
e
s
u
p
er
io
r
ity
an
d
in
f
er
io
r
ity
m
ea
s
u
r
es
tech
n
iq
u
e
(
SIM
M)
is
p
r
o
v
id
e
d
to
ad
d
r
ess
f
u
zz
y
m
u
lti
-
o
b
jectiv
e
lin
ea
r
f
r
ac
tio
n
al
p
r
o
g
r
am
m
in
g
(
FM
OL
FP
)
is
s
u
es
b
y
[
1
9
]
.
Mitlif
an
d
Hu
s
s
ein
[
2
0
]
o
p
tim
ized
th
e
g
o
al
f
u
n
ctio
n
b
y
u
s
in
g
th
e
r
an
k
in
g
f
u
n
ctio
n
an
d
f
u
zz
y
f
r
ac
tio
n
al
p
r
o
g
r
a
m
m
in
g
in
d
ec
is
io
n
-
m
ak
in
g
.
L
an
d
allo
ca
tio
n
f
o
r
o
p
tim
u
m
p
r
o
d
u
ct
io
n
p
la
n
n
in
g
th
r
o
u
g
h
m
u
lti
-
o
b
jectiv
e
L
PP
is
s
u
g
g
ested
b
y
B
asu
m
atar
y
an
d
Mitr
a
[
2
1
]
.
W
an
g
[
2
2
]
d
escr
ib
e
d
a
m
ath
e
m
a
tical
m
o
d
el
o
f
f
u
zz
y
L
PP
u
n
d
e
r
th
e
r
estrictio
n
s
o
f
elastic
co
n
s
tr
ain
ts
.
Hak
m
an
ag
e
et
a
l.
[
2
3
]
p
r
o
p
o
s
ed
m
u
lti
-
c
r
o
p
c
u
ltiv
atio
n
p
r
o
g
r
am
m
in
g
ap
p
r
o
ac
h
b
y
f
u
zz
y
g
o
al
p
r
o
g
r
a
m
m
in
g
.
Fo
r
th
e
p
u
r
p
o
s
e
o
f
r
a
n
k
in
g
tr
ian
g
u
lar
f
u
z
zy
n
u
m
b
er
s
,
a
u
n
iq
u
e
r
an
k
i
n
g
f
u
n
ctio
n
tech
n
iq
u
e
o
f
o
r
d
in
ar
y
f
u
zz
y
n
u
m
b
e
r
s
is
u
s
ed
i
n
[
2
4
]
.
K
h
an
a
n
d
A
f
tab
[
2
5
]
also
u
s
ed
f
u
zz
y
p
r
o
g
r
am
m
in
g
to
e
x
p
lain
m
u
lti
-
o
b
jectiv
e
g
o
al
p
r
o
g
r
am
m
in
g
to
im
p
r
o
v
e
a
g
r
icu
ltu
r
e
cr
o
p
p
r
o
d
u
ctio
n
.
Fak
h
r
ah
m
a
d
et
a
l.
[
2
6
]
ad
d
r
ess
ed
t
h
e
cu
r
r
en
t
tech
n
iq
u
e
f
o
r
p
r
ed
ictin
g
n
eig
h
b
o
r
h
o
o
d
s
atis
f
ac
tio
n
u
n
d
er
am
b
ig
u
o
u
s
co
n
d
itio
n
s
.
Ma
h
m
o
o
d
ir
ad
[
2
7
]
in
tr
o
d
u
ce
d
a
n
in
n
o
v
ativ
e
m
et
h
o
d
f
o
r
r
eso
lv
i
n
g
lin
ea
r
p
r
o
g
r
am
m
in
g
is
s
u
es
u
s
in
g
in
tu
itio
n
is
tic
f
u
zz
y
n
u
m
b
e
r
s
.
Ma
h
m
u
d
et
a
l.
[
2
8
]
cr
ea
te
d
a
m
o
d
elin
g
s
y
s
tem
th
at
u
s
es m
ac
h
in
e
lear
n
in
g
to
f
o
r
ec
ast ac
tiv
ity
co
n
ce
n
tr
atio
n
.
T
h
e
o
b
jectiv
e
o
f
th
is
s
tu
d
y
is
to
p
r
o
d
u
ce
at
least
th
e
b
est
weig
h
ted
r
etu
r
n
wh
ile
tak
in
g
in
to
ac
co
u
n
t
th
e
u
n
s
tab
le
p
r
ice
o
f
v
eg
eta
b
le
cr
o
p
s
an
d
th
e
u
n
ce
r
tain
t
y
o
f
ea
r
n
in
g
s
o
win
g
to
s
ev
er
al
f
ac
to
r
s
.
I
t
is
,
th
er
ef
o
r
e,
p
o
s
s
ib
le
to
in
clu
d
e
u
n
ce
r
tain
ty
in
th
e
p
lan
n
in
g
m
o
d
el
u
s
in
g
th
e
p
r
o
p
o
s
ed
f
u
zz
y
s
et
b
ased
q
u
a
n
titativ
e
tech
n
iq
u
e.
A
n
u
m
er
ical
illu
s
tr
atio
n
aid
s
i
n
th
e
r
esear
ch
e
r
s
’
clea
r
u
n
d
er
s
tan
d
in
g
o
f
th
e
m
o
d
el
’
s
s
o
lv
ab
i
lity
.
3.
M
E
T
H
O
D
L
et
u
s
ex
am
in
e
th
e
s
itu
atio
n
wh
er
e
th
er
e
ar
e
“
n
”
p
r
o
d
u
cib
l
e
cr
o
p
s
an
d
th
e
co
r
r
esp
o
n
d
in
g
p
r
o
f
its
f
o
r
th
ese
cr
o
p
s
ar
e
1
,
2
,
3
,
…
,
p
er
u
n
it
ar
ea
an
d
th
e
ass
o
ciate
d
p
r
o
b
ab
ilit
y
.
T
h
e
v
ar
iab
les
,
,
an
d
r
ep
r
esen
t
th
e
cr
o
p
cu
ltiv
atio
n
ar
ea
,
lab
o
r
h
o
u
r
s
wo
r
k
ed
,
an
d
wate
r
u
n
its
r
eq
u
ir
ed
to
cu
ltiv
at
e
cr
o
p
at
th
e
u
n
it
ar
ea
,
r
esp
ec
tiv
ely
.
A
f
ar
m
’
s
lan
d
is
r
estricte
d
an
d
m
u
s
t
b
e
le
s
s
th
an
o
r
eq
u
al
to
“
A
”
ac
r
es;
th
is
is
k
n
o
wn
as
a
“
lan
d
co
n
s
tr
ain
t
”
1
+
2
+
3
+
⋯
+
.
B
ec
au
s
e
th
e
r
e
is
a
ca
p
o
n
th
e
to
tal
n
u
m
b
er
o
f
lab
o
r
h
o
u
r
s
th
at
m
ay
b
e
wo
r
k
ed
,
th
e
s
u
m
o
f
th
e
f
o
l
lo
win
g
:
1
1
+
2
2
+
3
3
+
⋯
+
m
u
s
t
b
e
less
th
an
o
r
eq
u
al
to
a
f
ix
e
d
“
T.
”
T
h
is
is
k
n
o
wn
as a
“
lab
o
r
co
n
s
tr
ain
t.
”
W
ater
m
ay
also
b
e
co
n
s
id
er
ed
a
lim
itatio
n
w
h
en
d
ea
lin
g
with
“
W
”
u
n
its
.
T
h
e
eq
u
ati
o
n
:
1
1
+
2
2
+
3
3
+
⋯
+
ca
n
b
e
c
o
n
s
id
er
ed
a
“
wate
r
co
n
s
tr
ain
t
”
as
th
e
to
tal
n
ee
d
n
ee
d
s
to
b
e
m
o
d
if
ied
with
in
th
e
lim
it.
Giv
en
th
e
ab
o
v
e
lim
itatio
n
s
an
d
th
e
p
r
esen
ce
o
f
d
is
cr
ete
cr
is
p
an
d
f
u
zz
y
r
a
n
d
o
m
p
r
o
f
it
co
e
f
f
icie
n
ts
,
o
u
r
o
b
jectiv
e
is
to
d
eter
m
in
e
th
e
ch
o
ice
v
a
r
iab
les
th
at
will
r
esu
lt
in
th
e
m
ax
im
u
m
p
r
o
f
it (
P
).
Ma
x
im
ize
P Su
b
ject
to
1
+
2
+
3
+
⋯
.
.
+
≤
A
(
L
an
d
c
o
n
s
tr
ain
t)
1
1
+
2
2
+
3
3
+
⋯
.
.
+
≤
T
(
L
ab
o
u
r
c
o
n
s
tr
ain
t)
(
1
)
1
1
+
2
2
+
3
3
+
⋯
.
.
+
≤
W
(
W
ater
co
n
s
tr
ain
t)
11
1
+
12
2
+
13
3
+
⋯
.
.
+
1
≥
21
1
+
22
2
+
23
3
+
⋯
.
.
+
2
≥
31
1
+
32
2
+
33
3
+
⋯
.
.
+
3
≥
1
1
+
2
2
+
3
3
+
⋯
.
.
+
≥
3.
1
.
F
uzzy
pro
g
ra
m
m
ing
a
nd
m
a
x
-
m
in a
pp
ro
a
ch
I
n
a
f
u
zz
y
c
o
n
tex
t,
a
d
ec
is
io
n
is
g
en
er
ally
s
ee
n
as
a
m
em
b
er
s
h
ip
f
u
n
ctio
n
-
b
ased
f
u
zz
y
o
b
jectiv
e
f
u
n
ctio
n
.
C
o
n
s
tr
ain
ts
ar
e
tr
ea
ted
in
a
s
im
ilar
f
ash
io
n
.
W
h
en
th
er
e
ar
e
s
ev
er
al
o
b
jectiv
es,
a
p
r
o
ce
s
s
f
o
r
ch
o
o
s
in
g
a
ctiv
ities
ar
is
es
th
at
s
im
u
ltan
eo
u
s
ly
f
u
lf
ills
all
o
f
th
e
r
estrictio
n
s
an
d
o
b
jectiv
e
f
u
n
ctio
n
s
.
T
h
is
p
r
o
ce
d
u
r
e
m
ay
b
e
th
o
u
g
h
t
o
f
as
a
co
m
b
in
ati
o
n
o
f
f
u
zz
y
o
b
jectiv
e
f
u
n
ctio
n
s
an
d
f
u
zz
y
co
n
s
tr
ain
ts
.
T
h
e
d
ec
is
io
n
is
f
u
r
th
er
o
p
tim
ized
to
a
d
eg
r
ee
o
f
s
atis
f
ac
ti
o
n
u
s
in
g
th
e
m
em
b
er
s
h
ip
f
u
n
ctio
n
o
f
th
e
s
o
lu
tio
n
s
et.
Us
in
g
Z
im
m
er
m
an
n
’
s
(
1
9
7
8
)
m
a
x
-
m
in
f
u
zz
y
p
r
o
g
r
am
m
in
g
tech
n
iq
u
e,
th
e
c
u
r
r
en
t
s
tu
d
y
o
n
o
p
tim
izatio
n
u
n
d
er
v
ag
u
e
n
ess
in
ag
r
icu
ltu
r
al
p
r
o
d
u
ctio
n
m
an
ag
em
en
t
h
as
b
ee
n
ex
am
in
e
d
in
th
e
ca
s
e
wh
er
e
p
r
o
f
it
co
ef
f
ici
en
t
s
ar
e
d
is
cr
ete,
cr
is
p
r
an
d
o
m
v
ar
iab
les.
He
ass
er
ts
th
at
if
th
e
o
b
jectiv
e
f
u
n
ctio
n
is
(
2
)
.
M
a
x
/
M
i
n
Z
n
(
,
)
=
,
=
1
,
2
,
3
,
…
.
,
Su
b
ject
to
(
2)
(
,
)
=
≤
an
d
≥
0
W
h
er
e
=
(
1
,
2
,
…
…
)
is
th
e
p
r
o
f
it/co
s
t
co
ef
f
icien
ts
v
ec
to
r
of
th
e
k
th
o
b
jectiv
e
f
u
n
ctio
n
,
=
[
1
,
2
,
3
,
…
.
.
]
T
is
th
e
v
ec
to
r
of
to
tal
a
v
a
i
l
a
b
l
e
r
eso
u
r
ce
s
=
[
1
,
2
,
3
,
…
.
.
]
T
is
d
ec
is
io
n
v
a
r
i
a
b
l
e
v
e
c
t
o
r
an
d
A
=
[
]
×
is
co
ef
f
icien
t
m
atr
ix
.
He
s
u
g
g
ested
t
h
e
m
ax
-
m
i
n
o
p
e
r
ato
r
to
e
x
p
lain
t
h
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
Op
tima
l la
n
d
d
is
tr
ib
u
tio
n
f
o
r
a
mb
ig
u
o
u
s
p
r
o
fit ve
g
eta
b
le
cr
o
p
s
u
s
in
g
mu
lti
-
o
b
jective
…
(
P
r
a
n
a
v
Dixit
)
1165
MO
L
P
p
r
o
b
lem
a
n
d
c
o
n
s
id
er
ed
th
e
eq
u
atio
n
as
f
in
d
,
s
u
ch
th
at
n
(
)
≥
0
∀
k,
x
∈
wh
er
e
0
∀k
is
r
elate
d
o
b
jectiv
es
an
d
m
ax
im
izin
g
e
ac
h
o
f
th
e
o
b
jectiv
e
f
u
n
ctio
n
s
is
th
e
aim
.
Her
e,
th
e
f
u
zz
y
co
n
s
tr
ain
ts
ar
e
th
e
o
b
jectiv
e
f
u
n
ctio
n
s
in
(
2
)
.
(
I
f
th
e
f
u
zz
y
co
n
s
tr
ain
ts
’
to
le
r
an
ce
s
ar
e
p
r
o
v
id
ed
,
o
n
e
m
a
y
d
eter
m
in
e
th
ei
r
m
em
b
er
s
h
ip
f
u
n
ctio
n
µ
(
),
∀
an
d
th
en
t
h
e
m
em
b
e
r
s
h
ip
f
u
n
ctio
n
o
f
a
f
ea
s
ib
le
s
o
lu
tio
n
s
et
d
ef
in
es it
,
(
)
=
min
{
(
1
(
)
,
2
(
)
,
3
(
)
…
(
)
}
(
3
)
n
o
w,
wh
en
a
d
ec
is
io
n
m
ak
e
r
r
ea
ch
es
a
co
n
clu
s
io
n
with
a
m
ax
im
u
m
µ
,
th
e
p
r
o
b
lem
will
b
e
tr
an
s
f
o
r
m
e
d
in
to
Max
µ
(
)
,
Su
b
ject
to
Ma
x
[
m
in
k
µ
r
(
)
]
s
u
c
h
th
at
∈
,
let
α
=
m
in
k
µ
r
(
)
be
th
e
o
v
er
all
s
atis
f
ac
to
r
y
lev
el
o
f
co
m
p
r
o
m
is
e.
T
h
e
s
u
b
s
eq
u
en
t
m
o
d
el
is
d
er
i
v
ed
as M
ax
α
s
u
c
h
th
at
α
≤
µ
r
(
)
,
∀r
,
∈
Fo
r
th
e
p
u
r
p
o
s
e
o
f
esti
m
atin
g
th
e
m
em
b
er
s
h
ip
f
u
n
ctio
n
s
o
f
th
e
o
b
jectiv
e
f
u
n
ctio
n
s
,
th
e
p
ay
o
f
f
tab
le
o
f
th
e
p
o
s
itiv
e
id
ea
l so
lu
tio
n
(
PI
S)
is
co
n
s
tr
u
cted
u
s
in
g
th
is
m
eth
o
d
.
I
t is ass
u
m
ed
th
at
th
e
m
e
m
b
er
s
h
ip
f
u
n
ctio
n
s
b
elo
n
g
t
o
th
e
ca
teg
o
r
y
o
f
n
o
n
-
d
ec
r
ea
s
in
g
lin
ea
r
o
r
h
y
p
er
b
o
li
c
f
u
n
ctio
n
s
,
a
m
o
n
g
o
th
er
p
o
s
s
ib
ilit
ies.
3.
2
.
Co
m
pu
t
a
t
io
na
l
a
pp
ro
a
c
h
f
o
r
s
o
lv
ing
a
f
uzzy
m
ulti
-
o
bje
ct
iv
e
lin
ea
r
pro
g
ra
mm
ing
(
M
O
L
P
)
pro
blem
Her
e
is
a
c
o
m
p
u
tatio
n
al
tec
h
n
iq
u
e
em
p
lo
y
in
g
f
u
zz
y
m
u
lti
-
o
b
jectiv
e
lin
ea
r
p
r
o
g
r
am
m
in
g
f
o
r
a
ca
s
e
wh
er
e
p
r
o
f
it c
o
ef
f
icien
ts
ar
e
d
is
cr
ete,
cr
is
p
v
ar
iab
les.
1)
So
lv
e
th
e
s
am
e
s
et
o
f
r
estrictio
n
s
as st
ated
in
(
3
.
1
.
1
)
,
a
n
d
s
o
lv
e
ea
ch
g
o
al
f
u
n
ctio
n
in
d
ep
en
d
en
tly
.
2)
Dete
r
m
in
e
th
e
co
r
r
esp
o
n
d
in
g
v
alu
e
o
f
ea
ch
o
b
jectiv
e
f
u
n
ctio
n
f
o
r
ea
ch
s
o
l
u
tio
n
b
y
u
s
in
g
th
e
r
esu
lt
th
at
was
d
eter
m
in
ed
in
s
tep
1
.
3)
C
r
ea
te
a
tab
le
o
f
Po
s
itiv
e
I
d
e
al
So
lu
tio
n
s
(
PIS)
a
f
ter
o
b
tain
in
g
th
e
lo
wer
a
n
d
u
p
p
e
r
lim
it
s
,
′
an
d
’’
,
f
o
r
ea
ch
o
b
jectiv
e
f
u
n
ctio
n
f
r
o
m
s
tep
2
.
4)
C
o
n
s
id
er
a
lin
ea
r
an
d
n
o
n
-
d
ec
r
ea
s
in
g
m
em
b
er
s
h
i
p
f
u
n
ctio
n
b
etwe
en
,
′′
(
)
=
{
1
(
)
=
′
(
)
−
′
′′
−
′
′
≤
(
)
≤
′′
0
(
)
<
′
5)
C
o
n
v
er
t m
u
lti
-
o
b
jectiv
e
lin
ea
r
p
r
o
g
r
am
m
in
g
to
L
PP
as
Ma
x
Su
b
jecte
d
to
1
+
2
+
3
+
⋯
.
.
+
≤
A
(
L
an
d
c
o
n
s
tr
ain
t)
1
1
+
2
2
+
3
3
+
⋯
.
.
+
≤
T
(
L
a
b
o
u
r
co
n
s
tr
ain
t)
(
4
)
1
1
+
2
2
+
3
3
+
⋯
.
.
+
≤
W
(
W
ater
co
n
s
tr
ain
t)
(
)
=
(
)
−
′
∗
−
′
≥
∀
∈
w
h
er
e
Z
n
(
x
)
=
1
1
+
2
2
+
3
3
+
⋯
.
.
+
,
th
is
eq
u
atio
n
c
an
b
e
r
ewr
itten
as
Ma
x
Su
b
jecte
d
to
1
+
2
+
3
+
⋯
.
.
+
≤
A
(
L
an
d
c
o
n
s
tr
ain
t)
1
1
+
2
2
+
3
3
+
⋯
.
.
+
≤
T
(
L
a
b
o
u
r
co
n
s
tr
ain
t
1
1
+
2
2
+
3
3
+
⋯
.
.
+
≤
W
(
W
ater
co
n
s
tr
ain
t)
11
1
+
12
2
+
13
3
+
⋯
.
.
+
1
–
(
1
′′
−
1
,
)
=
1
,
(
5
)
21
1
+
22
2
+
23
3
+
⋯
.
.
+
2
–
(
2
′′
−
2
,
)
=
2
,
31
1
+
32
2
+
33
3
+
⋯
.
.
+
3
–
(
3
′′
−
3
,
)
=
3
,
1
1
+
2
2
+
3
3
+
⋯
.
.
+
–
(
′′
−
,
)
=
,
6)
So
lv
e
th
e
eq
u
atio
n
with
th
e
h
e
lp
o
f
T
OR
A
s
o
f
twar
e.
7)
Fin
ally
,
th
e
ass
u
r
ed
an
ticip
ated
y
ield
m
ay
b
e
co
m
p
u
ted
as
∑
(
)
=
1
wh
er
e
(
)
is
th
e
ith
o
b
jectiv
e
f
u
n
ctio
n
’
s
v
alu
e
at
th
e
d
ec
is
io
n
-
v
ar
iab
le
v
alu
es f
o
u
n
d
b
y
s
o
l
v
in
g
th
e
e
q
u
atio
n
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
8
,
No
.
2
,
May
20
2
5
:
1
1
6
2
-
1
1
6
9
1166
4.
NUM
E
RIC
AL
I
L
L
US
T
RA
T
I
O
N
4
.
1
.
Num
er
ic
a
l illus
t
ra
t
io
n
-
pro
blem
des
cr
iptio
n
A
f
ar
m
er
in
ten
d
e
d
to
cu
ltiv
ate
a
v
ar
iety
o
f
v
e
g
etab
le
cr
o
p
s
v
iz.
p
o
tato
,
to
m
ato
,
b
r
in
jal
a
n
d
o
n
io
n
in
a
p
ar
ticu
lar
s
ea
s
o
n
o
n
h
is
2
5
ac
r
es
o
f
ar
ab
le
lan
d
.
B
ased
o
n
h
is
o
wn
ex
p
er
ien
ce
,
h
e
esti
m
ated
th
at
th
er
e
ar
e
4
5
0
h
o
u
r
s
o
f
lab
o
r
av
ailab
le
with
h
im
an
d
5
0
ac
r
es
o
f
wate
r
ac
c
ess
ib
le.
T
h
e
T
ab
le
1
g
iv
es
th
e
p
r
o
f
it
co
e
f
f
icien
ts
(
in
lak
h
r
u
p
ee
s
)
,
t
h
e
am
o
u
n
t
o
f
wate
r
n
ee
d
ed
,
an
d
th
e
am
o
u
n
t
o
f
lab
o
r
h
o
u
r
s
n
ec
ess
ar
y
f
o
r
e
ac
h
cr
o
p
o
n
an
ac
r
e
o
f
lan
d
.
T
o
en
s
u
r
e
n
et
r
etu
r
n
s
f
r
o
m
p
r
o
f
it
co
e
f
f
icien
t
v
o
latilit
y
,
h
o
w
m
a
n
y
ac
r
es
m
u
s
t
h
e
ta
k
e
in
to
ac
co
u
n
t
f
o
r
ea
ch
cr
o
p
?
T
ab
le
1
.
Pro
f
it,
lab
o
r
an
d
wate
r
co
n
s
tr
ain
ts
b
u
ild
in
g
d
ata
f
o
r
th
e
en
tire
d
u
r
atio
n
o
f
cr
o
p
B
r
i
n
j
a
l
P
o
t
a
t
o
O
n
i
o
n
To
ma
t
o
P
r
o
b
a
b
i
l
i
t
y
P
r
o
f
i
t
c
o
e
f
f
i
c
i
e
n
t
(
i
n
0
0
0
0
’
s)
1
st
sc
e
n
a
r
i
o
0
.
7
4
0
.
6
7
1
.
4
4
1
.
9
8
4
0
%
P
r
o
f
i
t
c
o
e
f
f
i
c
i
e
n
t
(
i
n
0
0
0
0
’
s)
2
nd
sc
e
n
a
r
i
o
1
.
1
6
0
.
8
6
1
.
6
3
2
.
5
2
2
5
%
P
r
o
f
i
t
c
o
e
f
f
i
c
i
e
n
t
(
i
n
0
0
0
0
,
s)
3
rd
sce
n
a
r
i
o
1
.
3
7
1
.
1
1
2
.
1
4
1
.
4
2
2
0
%
P
r
o
f
i
t
c
o
e
f
f
i
c
i
e
n
t
(
i
n
0
0
0
0
’
s)
4
th
sce
n
a
r
i
o
1
.
7
2
1
.
3
0
2
.
6
2
1
.
6
8
1
5
%
R
e
q
u
i
r
e
d
l
a
b
o
r
h
o
u
r
s (
p
e
r
a
c
r
e
)
27
18
19
20
R
e
q
u
i
r
e
d
w
a
t
e
r
p
e
r
a
c
r
e
1
.
8
1
.
3
1
.
7
1
.
8
4
Her
e,
we
u
s
e
th
e
wo
r
k
in
g
m
et
h
o
d
f
r
o
m
s
ec
tio
n
3
.
3
to
d
em
o
n
s
tr
ate
h
o
w
to
s
o
lv
e
t
h
e
p
r
o
b
le
m
.
L
et
1
,
2
,
3
an
d
4
r
ep
r
esen
t
th
e
n
u
m
b
e
r
o
f
ac
r
es
to
b
e
tak
en
in
to
co
n
s
id
er
a
tio
n
f
o
r
t
o
m
ato
es,
p
o
tato
es,
o
n
io
n
an
d
b
r
in
jal,
r
esp
ec
tiv
ely
.
T
h
e
p
r
o
b
lem
to
b
e
s
o
lv
ed
tr
an
s
f
o
r
m
s
in
to
Ma
x
im
ize
Z
1
=
0
.
74
1
+
0
.
67
2
+
1
.
44
3
+
1
.
98
4
Ma
x
im
ize
Z
2
=
1
.
16
1
+
0
.
86
2
+
1
.
63
3
+
2
.
52
4
Ma
x
im
ize
Z
3
=
1
.
37
1
+
1
.
11
2
+
2
.
14
3
+
1
.
42
4
(
6
)
Ma
x
im
ize
Z
4
=
1
.
72
1
+
1
.
30
2
+
2
.
62
3
+
1
.
68
4
Su
b
ject
to
co
n
s
tr
ain
ts
1
+
2
+
3
+
4
≤
2
5
(
lan
d
r
e
s
tr
ictio
n
)
27
1
+
18
2
+
19
3
+
20
4
≤
4
5
0
(
lab
o
r
r
e
s
tr
ic
tion
)
(
7
)
1
.
8
1
+
1
.
3
2
+
1
.
7
3
+
1
.
84
4
≤
5
0
(
wate
r
r
estrictio
n
)
Usi
n
g
th
e
o
p
tim
izatio
n
p
r
o
g
r
a
m
T
OR
A,
th
e
o
p
tim
al
s
o
lu
tio
n
is
g
iv
e
n
to
th
is
c
r
is
p
L
P
Pro
b
lem
f
o
r
th
e
s
p
ec
if
ied
o
b
jectiv
e
f
u
n
ctio
n
s
with
r
esp
ec
t
to
th
e
co
n
s
tr
ain
ts
.
T
ab
le
2
p
r
o
v
id
es
a
s
u
m
m
a
r
y
o
f
th
e
f
o
u
r
o
p
tim
al
r
esu
lts
.
T
ab
le
2
.
So
lu
tio
n
at
ea
ch
o
b
je
ctiv
e
f
u
n
ctio
n
M
a
x
Z
1
M
a
x
Z
2
M
a
x
Z
3
M
a
x
Z
4
1
0
0
0
0
2
0
0
0
0
3
0
0
2
3
.
6
8
2
3
.
6
8
4
2
2
.
5
2
2
.
5
0
0
T
h
e
s
o
lu
tio
n
s
f
o
r
ea
c
h
o
b
jectiv
e
f
u
n
ctio
n
th
at
h
as
b
ee
n
s
o
lv
ed
with
r
esp
ec
t
to
co
n
s
tr
ain
ts
u
s
i
n
g
(
4
.
1
.
2
)
m
ay
b
e
ar
r
an
g
ed
t
o
g
et
s
tep
3
in
s
ec
tio
n
3
.
3
.
T
h
ese
s
o
lu
tio
n
s
ar
e
d
escr
ib
ed
as
p
o
s
itiv
e
i
d
ea
l
s
o
lu
tio
n
(
PIS)
.
T
h
ese
s
o
lu
tio
n
s
ar
e
s
h
o
wn
in
T
ab
le
3
.
T
ab
le
3
.
Po
s
itiv
e
id
ea
l so
lu
tio
n
M
a
x
Z
1
M
a
x
Z
2
M
a
x
Z
3
M
a
x
Z
4
M
a
x
M
i
n
M
a
x
-
M
i
n
Z
1
4
4
.
5
5
4
4
.
5
5
3
4
.
1
0
3
4
.
1
0
4
4
.
5
5
3
4
.
1
0
1
0
.
4
5
Z
2
5
6
.
7
5
6
.
7
3
8
.
6
3
8
.
6
5
6
.
7
3
8
.
6
1
8
.
1
Z
3
3
1
.
9
5
3
1
.
9
5
5
0
.
6
8
5
0
.
6
8
5
0
.
6
8
3
1
.
9
5
1
8
.
7
3
Z
4
3
7
.
8
3
7
.
8
6
2
.
0
4
6
2
.
0
4
6
2
.
0
4
3
7
.
8
2
4
.
2
4
x
1
x
2
x
3
x
4
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
Op
tima
l la
n
d
d
is
tr
ib
u
tio
n
f
o
r
a
mb
ig
u
o
u
s
p
r
o
fit ve
g
eta
b
le
cr
o
p
s
u
s
in
g
mu
lti
-
o
b
jective
…
(
P
r
a
n
a
v
Dixit
)
1167
No
w,
g
iv
en
L
PP
ca
n
b
e
r
ef
o
r
m
u
lated
as p
er
o
u
r
d
is
cu
s
s
ed
s
o
lv
in
g
p
r
o
ce
d
u
r
e
Ma
x
im
ize
∝
Su
b
ject
to
co
n
s
tr
ain
ts
1
+
2
+
3
+
4
≤
2
5
27
1
+
18
2
+
19
3
+
20
4
≤
4
5
0
1
.
8
1
+
1
.
3
2
+
1
.
7
3
+
1
.
84
4
≤
5
0
0
.
74
1
+
0
.
67
2
+
1
.
44
3
+
1
.
98
4
−
10
.
45
∝
≥
34
.
10
(
8
)
1
.
16
1
+
0
.
86
2
+
1
.
63
3
+
2
.
52
4
−
18
.
10
≫
38
.
60
1
.
37
1
+
1
.
11
2
+
2
.
14
3
+
1
.
42
4
−
18
.
73
≫
31
.
95
1
.
72
1
+
1
.
30
2
+
2
.
62
3
+
1
.
68
4
−
24
.
24
≥
37
.
8
So
lv
in
g
th
e
a
b
o
v
e
c
o
n
s
tr
ain
ts
with
th
e
h
elp
o
f
th
e
o
p
tim
izatio
n
s
o
f
twar
e
T
OR
A,
we
f
in
d
t
h
e
s
o
lu
tio
n
1
=
0
,
2
=
0
,
3
=
1
1
.
8
4
,
4
=
1
1
.
2
5
,
α
=
0
.
5
Af
ter
th
at,
we
m
ay
s
ee
th
e
o
p
ti
m
al
r
esp
o
n
s
e
th
at
T
OR
A
h
as
p
r
o
v
id
ed
.
T
h
e
co
m
p
u
tatio
n
o
f
th
e
o
p
tim
al
r
etu
r
n
ar
e
s
h
o
wn
in
T
a
b
le
4
.
T
h
is
tab
le
also
p
r
o
v
id
e
weig
h
te
d
av
er
ag
e
af
ter
tak
in
g
r
esp
ec
ti
v
e
p
r
o
b
ab
ilit
ies
in
to
co
n
s
id
er
atio
n
.
T
ab
le
4
.
R
etu
r
n
ca
lc
u
latio
n
an
d
weig
h
ted
a
v
er
ag
e
D
e
c
i
s
i
o
n
v
a
r
i
a
b
l
e
s
C
o
n
st
r
a
i
n
t
s
R
e
t
u
r
n
c
a
l
c
u
l
a
t
i
o
n
P
r
o
b
a
b
i
l
i
t
y
1
=
0
C
o
n
st
r
a
i
n
t
1
=
2
3
.
0
9
Z
1
=
3
9
.
3
2
4
0
%
2
= 0
C
o
n
st
r
a
i
n
t
2
=
4
4
1
.
9
6
Z
2
=
4
7
.
6
5
2
5
%
3
=
1
1
.
8
4
C
o
n
st
r
a
i
n
t
3
=
4
1
.
3
9
Z
3
=
4
1
.
3
1
2
0
%
4
=
1
1
.
2
5
C
o
n
st
r
a
i
n
t
4
=
3
4
.
1
0
Z
4
=
4
9
.
9
2
1
5
%
α =
0
.
5
C
o
n
st
r
a
i
n
t
5
=
3
8
.
6
0
C
o
n
st
r
a
i
n
t
6
=
3
1
.
9
5
C
o
n
st
r
a
i
n
t
7
=
3
7
.
8
W
e
i
g
h
t
e
d
a
v
e
r
a
g
e
4
3
.
3
9
4
.
2
.
Resul
t
a
nd
dis
cus
s
io
n
C
r
o
p
o
u
tp
u
t
m
ax
im
izatio
n
d
o
es
n
o
t
g
u
ar
a
n
tee
p
r
o
f
it
m
ax
i
m
izatio
n
.
Op
tim
al
o
u
tco
m
es
r
eq
u
ir
e
th
e
cr
ea
tio
n
o
f
n
o
v
el
s
tr
ateg
ies
th
at
ca
n
ef
f
ec
tiv
ely
tack
le
th
e
c
o
m
p
lex
p
r
o
b
lem
o
f
d
ec
is
io
n
m
ak
in
g
.
C
o
n
s
id
er
in
g
th
e
g
iv
en
p
r
o
b
lem
h
a
v
in
g
d
if
f
er
en
t
p
r
o
b
ab
ilit
ies
f
o
r
p
r
o
f
it
c
o
ef
f
icien
ts
,
we
ap
p
lied
th
e
Ma
x
-
m
in
a
p
p
r
o
ac
h
f
o
r
s
o
lv
in
g
th
e
f
u
zz
y
L
PP
.
D
if
f
er
en
t
o
p
tim
al
o
u
tco
m
es
ar
e
o
b
t
ain
ed
co
r
r
esp
o
n
d
in
g
to
d
if
f
er
en
t
p
r
o
b
ab
ilit
ies,
s
o
weig
h
ted
av
er
a
g
e
h
as
b
ee
n
tak
en
in
to
co
n
s
id
er
atio
n
.
T
ab
le
1
d
em
o
n
s
tr
ates
th
e
d
ata
o
f
c
o
n
s
tr
ain
ts
.
Usi
n
g
T
OR
A,
s
o
lu
tio
n
o
f
ea
c
h
o
b
jectiv
e
f
u
n
ctio
n
an
d
PIS
a
r
e
o
b
tain
ed
in
T
a
b
le
s
2
a
n
d
3
r
esp
ec
tiv
ely
.
T
h
e
o
p
tim
u
m
s
o
l
u
tio
n
th
at
s
atis
f
ies
f
o
u
r
o
b
jectiv
e
f
u
n
ctio
n
s
(
4
.
1
.
1
)
s
im
u
ltan
eo
u
s
ly
is
1
=
0
,
2
=
0
,
3
=
1
1
.
8
4
an
d
4
=
1
1
.
2
5
(
s
h
o
wn
in
T
ab
le
4
)
wh
ich
m
ea
n
s
th
at
th
e
f
ar
m
e
r
h
as t
o
cu
ltiv
ate
o
n
io
n
an
d
to
m
ato
at
1
1
.
8
4
an
d
1
1
.
2
5
ac
r
es o
f
lan
d
r
esp
ec
tiv
ely
in
o
r
d
e
r
to
g
e
t
g
u
ar
a
n
teed
av
e
r
ag
e
n
et
ea
r
n
i
n
g
s
o
f
R
s
.
4
.
3
3
9
lak
h
s
(
weig
h
t
ed
av
er
a
g
e)
i
n
s
p
ite
o
f
in
co
n
s
is
ten
t p
r
ices.
T
h
e
f
o
u
r
th
s
et
o
f
p
r
o
f
it c
o
e
f
f
icien
ts
,
wh
ich
o
cc
u
r
s
o
n
ly
1
5
% o
f
th
e
tim
e,
d
eter
m
in
es th
e
m
ax
im
u
m
p
r
o
f
it.
T
h
e
p
r
esen
ted
s
tu
d
y
h
as
b
o
th
s
tr
en
g
th
s
an
d
lim
itatio
n
.
T
h
e
u
s
e
o
f
th
is
ap
p
r
o
ac
h
m
a
y
p
r
o
v
id
e
d
ir
ec
t
g
u
id
an
ce
to
f
ar
m
er
s
in
t
h
eir
d
ec
is
io
n
-
m
ak
in
g
p
r
o
ce
s
s
es.
T
h
e
an
aly
s
is
ac
co
u
n
ts
f
o
r
t
h
e
r
an
g
e
in
p
o
s
s
ib
le
o
u
tco
m
es
b
y
u
s
in
g
a
weig
h
te
d
av
er
ag
e
a
n
d
tak
in
g
in
to
ac
c
o
u
n
t
v
ar
i
o
u
s
p
r
o
b
ab
ilit
ies
f
o
r
p
r
o
f
it
co
ef
f
icien
ts
.
T
h
is
ap
p
r
o
ac
h
m
a
y
b
e
ad
ap
te
d
to
ac
co
m
m
o
d
ate
a
lar
g
e
r
n
u
m
b
er
o
f
co
n
s
tr
ain
ts
;
h
o
wev
er
,
we
o
n
ly
ex
p
lo
r
ed
th
r
ee
co
n
s
tr
ain
ts
h
er
e.
T
h
e
p
r
o
b
ab
ilit
y
d
is
tr
ib
u
tio
n
s
f
o
r
p
r
o
f
it
co
ef
f
icien
ts
ar
e
ass
u
m
ed
b
y
th
e
m
o
d
el
to
b
e
co
n
s
tan
t a
cr
o
s
s
tim
e
wh
ich
wo
r
k
s
as lim
itatio
n
f
o
r
s
tu
d
y
.
B
ased
o
n
v
ar
io
u
s
p
r
o
f
it
co
ef
f
i
cien
t
p
r
o
b
ab
ilit
ies,
th
e
s
tu
d
y
y
ield
s
s
ev
er
al
id
ea
l
r
esu
lts
.
T
h
e
r
esear
ch
f
in
d
s
an
o
p
tim
u
m
s
o
lu
tio
n
th
at
co
n
cu
r
r
en
tly
f
u
lf
ils
f
o
u
r
o
b
ject
iv
e
f
u
n
ctio
n
s
b
y
tak
in
g
weig
h
t
ed
av
er
a
g
e
o
f
th
ese
r
esu
lts
.
Ma
x
-
m
in
ap
p
r
o
ac
h
m
ay
b
e
p
r
ac
tical,
b
alan
ce
d
an
d
ad
ap
tab
le
f
o
r
m
o
r
e
co
m
p
lex
s
ce
n
ar
io
s
o
f
cr
o
p
allo
ca
tio
n
.
As
a
f
u
tu
r
e
p
r
o
s
p
e
ctiv
e,
i
t
ca
n
b
e
u
s
ed
ef
f
icien
tly
in
o
p
tim
ized
r
eso
u
r
ce
m
an
a
g
em
en
t,
cr
o
p
y
ield
p
r
ed
ictio
n
,
p
est
an
d
d
is
ea
s
e
m
an
ag
em
en
t,
an
d
ad
a
p
tiv
e
ir
r
i
g
atio
n
s
y
s
tem
.
Po
licy
m
a
k
er
s
m
ay
u
s
e
f
u
zz
y
m
ax
-
m
in
m
eth
o
d
o
l
o
g
ies
to
d
ev
elo
p
p
o
licies
th
at
ex
h
ib
it
more
r
esil
ien
ce
to
u
n
ce
r
tain
ties
in
a
g
r
icu
ltu
r
e,
in
clu
d
in
g
m
ar
k
et
v
o
latilit
y
,
e
n
v
ir
o
n
m
en
t
al
s
h
if
ts
,
an
d
tech
n
o
l
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.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
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0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
8
,
No
.
2
,
May
20
2
5
:
1
1
6
2
-
1
1
6
9
1168
5.
CO
NCLU
SI
O
N
I
n
th
e
c
o
n
tem
p
o
r
ar
y
ag
r
ic
u
ltu
r
al
p
r
o
d
u
ctio
n
s
y
s
tem
,
th
e
ev
alu
atio
n
o
f
co
m
p
r
o
m
is
e
o
p
tio
n
s
is
o
f
ten
u
s
ed
wh
ile
m
ak
in
g
c
h
o
ices
p
er
tain
in
g
to
p
a
r
ticu
lar
o
b
jectiv
es,
r
ath
er
th
an
o
n
ly
p
r
io
r
itiz
in
g
th
e
m
ax
im
u
m
-
attain
ab
le
alter
n
ativ
e.
I
n
s
im
u
latin
g
th
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ag
r
icu
ltu
r
al
cr
o
p
p
in
g
p
atter
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th
e
c
u
r
r
e
n
t
s
tu
d
y
to
o
k
in
to
ac
c
o
u
n
t
a
f
ew
co
n
tr
ib
u
tin
g
asp
ec
ts
.
T
h
e
r
esear
ch
s
h
o
ws
h
o
w,
wh
ile
tak
in
g
in
to
ac
co
u
n
t
th
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is
k
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an
d
u
n
c
er
tain
ties
r
elate
d
to
p
r
ice
f
lu
ctu
atio
n
in
v
e
g
etab
le
cr
o
p
s
,
FMOL
P
m
ay
b
e
u
s
ed
t
o
id
en
tify
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e
b
est
p
lan
tin
g
p
a
tter
n
s
th
at
o
p
tim
ize
f
ar
m
er
s
’
in
co
m
e.
Fu
zz
y
l
o
g
ic
is
u
s
ed
in
th
is
wo
r
k
to
h
an
d
l
e
th
e
in
h
er
e
n
t
u
n
ce
r
tain
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y
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d
p
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v
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s
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f
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eg
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le
cr
o
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s
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wh
ich
ar
e
n
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t
o
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io
u
s
ly
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latile
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m
ar
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et
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is
k
s
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p
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o
d
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s
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FMOL
P e
n
ab
les a
m
eth
o
d
ical
an
d
d
ata
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o
r
ien
ted
a
p
p
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o
ac
h
to
d
ec
is
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n
-
m
ak
in
g
in
ag
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icu
ltu
r
e,
r
e
d
u
cin
g
th
e
d
e
p
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n
d
en
ce
o
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in
t
u
itio
n
an
d
ex
p
er
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ce
alo
n
e.
T
h
er
ef
o
r
e,
th
e
q
u
an
titativ
e
te
ch
n
iq
u
es
b
ased
o
n
f
u
zz
y
s
ets
th
at
h
av
e
b
ee
n
cr
ea
ted
m
a
y
in
clu
d
e
u
n
ce
r
tain
ty
in
t
h
e
p
lan
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in
g
m
o
d
el.
A
n
u
m
e
r
ical
illu
s
tr
atio
n
aid
s
in
th
e
r
esear
ch
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s
’
clea
r
u
n
d
er
s
tan
d
in
g
o
f
th
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m
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d
el
’
s
s
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lv
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h
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f
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zz
y
m
ax
-
m
in
tech
n
iq
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in
ag
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ic
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p
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m
is
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f
ield
th
at
h
as
s
ev
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al
p
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ten
tial
ap
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licatio
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s
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I
t
is
m
o
tiv
ated
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y
th
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ee
d
f
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m
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ate
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ad
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ak
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esen
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o
f
u
n
ce
r
tain
t
y
.
RE
F
E
R
E
NC
E
S
[
1
]
R
.
K
r
i
s
h
n
a
,
“
T
h
e
o
p
t
i
ma
l
i
t
y
o
f
l
a
n
d
a
l
l
o
c
a
t
i
o
n
:
a
c
a
s
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st
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d
y
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f
t
h
e
P
u
n
j
a
b
,
”
I
n
d
i
a
n
J
o
u
rn
a
l
o
f
Ag
r
i
c
u
l
t
u
r
a
l
E
c
o
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m
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c
s
,
v
o
l
.
1
8
,
n
o
.
1
,
p
p
.
6
3
–
7
3
,
1
9
6
3
.
[
2
]
L.
A
.
Za
d
e
h
,
“
F
u
z
z
y
s
e
t
s
,
”
I
n
f
o
rm
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t
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o
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l
,
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l
.
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o
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p
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,
Ju
n
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1
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0
0
1
9
-
99
5
8
(
6
5
)
9
0
2
4
1
-
X.
[
3
]
H.
-
J.
Z
i
mm
e
r
m
a
n
n
,
“
F
u
z
z
y
p
r
o
g
r
a
m
mi
n
g
a
n
d
l
i
n
e
a
r
p
r
o
g
r
a
mm
i
n
g
w
i
t
h
sev
e
r
a
l
o
b
j
e
c
t
i
v
e
f
u
n
c
t
i
o
n
s,
”
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u
zzy
S
e
t
s
a
n
d
S
y
st
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m
s
,
v
o
l
.
1
,
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o
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1
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p
p
.
4
5
–
5
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,
J
a
n
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1
9
7
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d
o
i
:
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0
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0
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6
5
-
0
1
1
4
(
7
8
)
9
0
0
3
1
-
3.
[
4
]
S
.
C
h
a
n
a
s,
“
F
u
z
z
y
p
r
o
g
r
a
mm
i
n
g
i
n
m
u
l
t
i
o
b
j
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c
t
i
v
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l
i
n
e
a
r
p
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g
r
a
mm
i
n
g
—
a
p
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a
m
e
t
r
i
c
a
p
p
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o
a
c
h
,
”
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z
zy
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t
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y
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m
s
,
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l
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2
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2
-
0.
[
5
]
J.
S
u
m
p
si
,
F
.
A
ma
d
o
r
,
a
n
d
C
.
R
o
me
r
o
,
“
O
n
f
a
r
mers
’
o
b
j
e
c
t
i
v
e
s
:
a
m
u
l
t
i
-
c
r
i
t
e
r
i
a
a
p
p
r
o
a
c
h
,
”
Eu
r
o
p
e
a
n
J
o
u
r
n
a
l
o
f
O
p
e
r
a
t
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o
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a
l
Re
se
a
rc
h
,
v
o
l
.
9
6
,
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o
.
1
,
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.
6
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–
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n
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)
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X.
[
6
]
H
.
R
.
M
a
l
e
k
i
,
M
.
Ta
t
a
,
a
n
d
M
.
M
a
s
h
i
n
c
h
i
,
“
L
i
n
e
a
r
p
r
o
g
r
a
mm
i
n
g
w
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t
h
f
u
z
z
y
v
a
r
i
a
b
l
e
s,
”
Fu
zz
y
S
e
t
s
a
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d
S
y
s
t
e
m
s
,
v
o
l
.
1
0
9
,
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o
.
1
,
p
p
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2
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–
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3
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n
.
2
0
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d
o
i
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1
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0
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6
5
-
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1
4
(
9
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)
0
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0
6
6
-
9.
[
7
]
T.
I
t
o
h
,
H
.
I
sh
i
i
,
a
n
d
T.
N
a
n
se
k
i
,
“
A
mo
d
e
l
o
f
c
r
o
p
p
l
a
n
n
i
n
g
u
n
d
e
r
u
n
c
e
r
t
a
i
n
t
y
i
n
a
g
r
i
c
u
l
t
u
r
a
l
m
a
n
a
g
e
me
n
t
,
”
I
n
t
e
rn
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
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l
.
8
1
–
8
2
,
p
p
.
5
5
5
–
5
5
8
,
Ja
n
.
2
0
0
3
,
d
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i
:
1
0
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1
0
1
6
/
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0
9
2
5
-
5
2
7
3
(
0
2
)
0
0
2
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3
-
9.
[
8
]
K
.
G
a
n
e
s
a
n
a
n
d
P
.
V
e
e
r
a
ma
n
i
,
“
F
u
z
z
y
l
i
n
e
a
r
p
r
o
g
r
a
ms w
i
t
h
t
r
a
p
e
z
o
i
d
a
l
f
u
z
z
y
n
u
m
b
e
r
s
,
”
A
n
n
a
l
s
o
f
O
p
e
r
a
t
i
o
n
s
R
e
se
a
rc
h
,
v
o
l
.
1
4
3
,
n
o
.
1
,
p
p
.
3
0
5
–
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1
5
,
M
a
r
.
2
0
0
6
,
d
o
i
:
1
0
.
1
0
0
7
/
s
1
0
4
7
9
-
0
0
6
-
7
3
9
0
-
1.
[
9
]
A
.
W
e
i
n
t
r
a
u
b
a
n
d
C
.
R
o
m
e
r
o
,
“
O
p
e
r
a
t
i
o
n
s
r
e
sea
r
c
h
m
o
d
e
l
s
a
n
d
t
h
e
ma
n
a
g
e
m
e
n
t
o
f
a
g
r
i
c
u
l
t
u
r
a
l
a
n
d
f
o
r
e
st
r
y
r
e
so
u
r
c
e
s
:
a
r
e
v
i
e
w
a
n
d
c
o
m
p
a
r
i
so
n
,
”
I
n
t
e
r
f
a
c
e
s
,
v
o
l
.
3
6
,
n
o
.
5
,
p
p
.
4
4
6
–
4
5
7
,
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c
t
.
2
0
0
6
,
d
o
i
:
1
0
.
1
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8
7
/
i
n
t
e
.
1
0
6
0
.
0
2
2
2
.
[
1
0
]
L.
T
a
n
k
o
l
,
C
.
E.
O
n
y
e
n
w
e
a
k
u
,
a
n
d
A
.
C
.
N
w
o
s
u
,
“
O
p
t
i
m
u
m
c
r
o
p
c
o
m
b
i
n
a
t
i
o
n
s
u
n
d
e
r
l
i
mi
t
e
d
r
e
s
o
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r
c
e
s
c
o
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d
i
t
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o
n
s:
a
m
i
c
r
o
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l
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v
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l
st
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d
y
i
n
Y
a
u
r
i
,
K
e
b
b
i
S
t
a
t
e
,
N
i
g
e
r
i
a
,
”
T
h
e
N
i
g
e
ri
a
n
A
g
ri
c
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t
u
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l
J
o
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a
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,
v
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l
.
3
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,
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o
.
1
,
p
p
.
1
–
6
,
2
0
0
6
.
[
1
1
]
T.
J.
R
o
ss,
F
u
zzy
l
o
g
i
c
w
i
t
h
e
n
g
i
n
e
e
ri
n
g
a
p
p
l
i
c
a
t
i
o
n
s
.
Jo
h
n
W
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l
e
y
& S
o
n
s
,
2
0
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0
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d
o
i
:
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0
.
1
0
0
2
/
9
7
8
1
1
1
9
9
9
4
3
7
4
.
[
1
2
]
P
.
S
e
n
t
h
i
l
k
u
mar
a
n
d
G
.
R
a
j
e
n
d
r
a
n
,
“
O
n
t
h
e
s
o
l
u
t
i
o
n
o
f
f
u
z
z
y
l
i
n
e
a
r
p
r
o
g
r
a
mm
i
n
g
p
r
o
b
l
e
m,
”
A
p
p
l
i
e
d
M
a
t
h
e
m
a
t
i
c
a
l
S
c
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e
n
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e
s
,
v
o
l
.
3
,
n
o
.
4
9
,
p
p
.
2
4
1
1
–
2
4
1
9
,
2
0
0
9
.
[
1
3
]
A
.
G
a
r
g
a
n
d
S
.
R
.
S
i
n
g
h
,
“
O
p
t
i
m
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z
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t
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o
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u
n
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n
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y
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n
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r
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c
t
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o
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l
a
n
n
i
n
g
,
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c
o
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c
e
p
t
p
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k
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