Applied Sciences,
Journal Year:
2022,
Volume and Issue:
12(3), P. 1491 - 1491
Published: Jan. 29, 2022
In
recent
years,
the
application
of
artificial
intelligence
has
been
revolutionizing
manufacturing
industry,
becoming
one
key
pillars
what
called
Industry
4.0.
this
context,
we
focus
on
job
shop
scheduling
problem
(JSP),
which
aims
at
productions
orders
to
be
carried
out,
but
considering
reduction
energy
consumption
as
a
objective
fulfill.
Finding
best
combination
machines
and
jobs
performed
is
not
trivial
becomes
even
more
involved
when
several
objectives
are
taken
into
account.
Among
them,
improvement
savings
may
conflict
with
other
objectives,
such
minimization
makespan.
paper,
provide
an
in-depth
review
existing
literature
multi-objective
optimization
metaheuristics,
in
consumption.
We
systematically
reviewed
critically
analyzed
most
relevant
features
both
formulations
algorithms
solve
them
effectively.
The
manuscript
also
informs
empirical
results
main
findings
our
bibliographic
critique
performance
comparison
among
representative
evolutionary
solvers
applied
diversity
synthetic
test
instances.
ultimate
goal
article
carry
out
critical
analysis,
finding
good
practices
opportunities
for
further
that
stem
from
current
knowledge
vibrant
research
area.
International Journal of Production Research,
Journal Year:
2022,
Volume and Issue:
61(3), P. 1013 - 1038
Published: July 4, 2022
Lot
streaming
is
the
most
widely
used
technique
to
facilitate
overlap
of
successive
operations.
Considering
consistent
sublots
and
machine
breakdown,
this
study
investigates
multi-objective
hybrid
flowshop
rescheduling
problem
with
(MOHFRP_CS),
which
aims
at
optimising
total
completion
time,
starting
time
deviations
operations,
average
adjustment
sublot
sizes
simultaneously.
By
introducing
decomposition
strategy
effective
migrating
birds
optimisation
framework,
paper
develops
a
algorithm
based
on
(MMBO/D).
In
MMBO/D,
decomposed
into
series
sub-problems,
its
solutions
are
initialised
by
Glover
operator
further
optimised
variable
neighbourhood
descent
strategy.
The
weights
assigned
sub-problems
adapted
dynamically
according
weight
strategy,
global
update
employed
solutions.
A
novel
sharing
benefiting
mechanism
proposed
implement
coevolution
among
different
sub-problems.
Competitive
mechanisms
modified
considering
similar
improve
population
quality.
criterion
designed
check
whether
subproblem
stuck
in
local
optima.
comprehensive
computational
results
demonstrate
that
MMBO/D
outperforms
other
state-of-the-art
evolutionary
algorithms
(MOEAs)
for
addressed
problem.
Applied Sciences,
Journal Year:
2022,
Volume and Issue:
12(3), P. 1491 - 1491
Published: Jan. 29, 2022
In
recent
years,
the
application
of
artificial
intelligence
has
been
revolutionizing
manufacturing
industry,
becoming
one
key
pillars
what
called
Industry
4.0.
this
context,
we
focus
on
job
shop
scheduling
problem
(JSP),
which
aims
at
productions
orders
to
be
carried
out,
but
considering
reduction
energy
consumption
as
a
objective
fulfill.
Finding
best
combination
machines
and
jobs
performed
is
not
trivial
becomes
even
more
involved
when
several
objectives
are
taken
into
account.
Among
them,
improvement
savings
may
conflict
with
other
objectives,
such
minimization
makespan.
paper,
provide
an
in-depth
review
existing
literature
multi-objective
optimization
metaheuristics,
in
consumption.
We
systematically
reviewed
critically
analyzed
most
relevant
features
both
formulations
algorithms
solve
them
effectively.
The
manuscript
also
informs
empirical
results
main
findings
our
bibliographic
critique
performance
comparison
among
representative
evolutionary
solvers
applied
diversity
synthetic
test
instances.
ultimate
goal
article
carry
out
critical
analysis,
finding
good
practices
opportunities
for
further
that
stem
from
current
knowledge
vibrant
research
area.
