In
this
paper,
we
solve
a
distributed
flow
shop
group
scheduling
problem
with
heterogeneous
factories,
which
call
the
(DHFGSP).
The
objective
is
to
minimize
maximum
energy
consumption
cost.
Although
DHFGSP
very
meaningful
for
today's
production
situation,
it
has
not
captured
attention
so
far.
Firstly,
in
mixed
integer
linear
model
developed.
Secondly,
four
heuristic
methods
are
presented
based
on
specific
rules
and
cost
criteria
features.
Thirdly,
an
effective
iterative
greedy
algorithm
Q-learning
(Q_PIG)
proposed.
Q_PIG,
family
ordering
rule
proposed
exchange
families
other
factories
explore
better
quality
solutions
during
local
search.
Moreover,
embedded
select
operations
(family
or
job
operations)
current
solution.
embedding
of
enables
solution
execute
high-quality
operations.
Comprehensive
experiments
show
that
Q_PIG
solved.
