Mathematics,
Год журнала:
2025,
Номер
13(5), С. 877 - 877
Опубликована: Март 6, 2025
This
paper
addresses
a
distributed
permutation
flowshop
scheduling
problem
with
an
order
acceptance
strategy
in
heterogeneous
factories.
Each
has
related
revenue
and
due
date,
several
machines
are
operated
each
factory,
they
have
distinct
sequence-dependent
setup
time.
We
select/reject
production
orders,
assign
the
selected
orders
to
factories,
determine
manufacturing
sequence
factory
maximize
total
profit.
To
optimally
solve
problem,
we
formulate
as
mixed
integer
linear
programming
model
find
optimal
solution
for
small-sized
experiments.
Then,
propose
two
population-based
algorithms,
genetic
algorithm
particle
swarm
optimization
large-sized
proved
that
proposed
effectively
efficiently
solves
guarantee
near
through
computational
Finally,
conduct
sensitivity
analysis
of
observe
relationship
between
selection,
revenue,
tardiness
cost.
Engineering Optimization,
Год журнала:
2024,
Номер
unknown, С. 1 - 23
Опубликована: Апрель 12, 2024
With
the
complexity
involved
in
manufacturing
products,
many
companies
use
multiple
processes
to
complete
product
processing.
Most
studies
have
been
concerned
with
single
production
but
neglected
widespread
joint
flowshop
scheduling
problem
(JFSP).
In
this
article,
a
cooperative
grey
wolf
optimizer
(CGWO)
is
developed
solve
JFSP.
First,
according
features
of
JFSP,
corresponding
mathematical
model
constructed,
and
three
collaborative
strategies
random
generation
are
proposed
initialize
population.
process
searching
for
prey,
discretized
search
prey
update
mechanism
proposed,
which
conducive
balancing
exploration
exploitation.
An
energy-saving
strategy
decrease
energy
consumption.
Moreover,
four
local
mechanisms
different
optimization
objectives
enhance
performance
method
attacking
prey.
The
results
show
that
CGWO
effective
solving
