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.
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.
