NLP for CP
Addressing Constraint Programming with Natural Language Processing
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Correct
predictions are in
blue
. If we detect only a subset of a labelled sentence, we highlight the caught part as
blue
, the missing part
light blue.
False positives
are in
green
and
false negatives
are in
red
.
Problem Flow_Shop_Scheduling — Constraint detection
We
consider
the
following
scheduling
problem
.
A
set
$
N
=
-LCB-
1
,
...
,
n
-RCB-
of
$
n
$
jobs
has
to
be
processed
on
a
single
machine
,
which
can
handle
at
most
one
job
at
a
time
.
Each
job
$
j
$
has
a
positive
processing
time
,
$
p_j
>
0
$
,
and
a
nonnegative
weight
,
$
w_j
\
ge
0
$
,
and
we
want
to
find
a
schedule
of
the
jobs
that
minimizes
the
weighted
sum
of
job
completion
times
,
$
\
sum
_
-LCB-
j
\
in
N
-RCB-
w_j
C_j
-RCB-
.
Here
,
$
C_j
$
denotes
the
time
at
which
job
$
j
$
is
completed
in
a
feasible
schedule
.
We
focus
on
the
case
when
the
jobs
have
to
be
consistent
with
precedence
constraints
;
the
precedence
constraints
are
given
in
the
form
of
a
directed
acyclic
graph
-LRB-
i.e.
,
a
partial
order
-RRB-
G
=
-LRB-
N
,
P
-RRB-
,
where
$
-LRB-
i
,
j
-RRB-
\
in
P$
implies
that
job
i
must
be
completed
before
job
j
can
be
started
;
we
assume
that
G
is
transitively
closed
;
i.e.
,
if
$
-LRB-
i
,
j
-RRB-
,
-LRB-
j
,
k
-RRB-
\
in
P$
,
then
$
-LRB-
i
,
k
-RRB-
\
in
P$
.
Problem Flow_Shop_Scheduling — Detection of the decisions and objects to be modeled
We
consider
the
following
scheduling
problem
.
A
set
$
N
=
-LCB-
1
,
...
,
n
-RCB-
of
$
n
$
jobs
has
to
be
processed
on
a
single
machine
,
which
can
handle
at
most
one
job
at
a
time
.
Each
job
$
j
$
has
a
positive
processing
time
,
$
p_j
>
0
$
,
and
a
nonnegative
weight
,
$
w_j
\
ge
0
$
,
and
we
want
to
find
a
schedule
of
the
jobs
that
minimizes
the
weighted
sum
of
job
completion
times
,
$
\
sum
_
-LCB-
j
\
in
N
-RCB-
w_j
C_j
-RCB-
.
Here
,
$
C_j
$
denotes
the
time
at
which
job
$
j
$
is
completed
in
a
feasible
schedule
.
We
focus
on
the
case
when
the
jobs
have
to
be
consistent
with
precedence
constraints
;
the
precedence
constraints
are
given
in
the
form
of
a
directed
acyclic
graph
-LRB-
i.e.
,
a
partial
order
-RRB-
G
=
-LRB-
N
,
P
-RRB-
,
where
$
-LRB-
i
,
j
-RRB-
\
in
P$
implies
that
job
i
must
be
completed
before
job
j
can
be
started
;
we
assume
that
G
is
transitively
closed
;
i.e.
,
if
$
-LRB-
i
,
j
-RRB-
,
-LRB-
j
,
k
-RRB-
\
in
P$
,
then
$
-LRB-
i
,
k
-RRB-
\
in
P$
.
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