Journal of Cloud Computing Advances Systems and Applications,
Год журнала:
2024,
Номер
13(1)
Опубликована: Ноя. 5, 2024
With
the
dawn
of
Industry
5.0
upon
us,
smart
factory
emerges
as
a
pivotal
element,
playing
crucial
role
in
realm
intelligent
manufacturing.
Meanwhile,
mobile
edge
computing
is
proposed
to
alleviate
computational
burden
presented
by
substantial
workloads
factories.
Nonetheless,
it
very
challenging
effectively
incorporate
resources
improve
efficiency
resource
deployment
Accordingly,
we
devise
novel
approach
based
on
Proximal
Policy
Optimization
algorithm
with
Self-Attention
Mechanism
implement
allocation
MEC-Empowered
Smart
Factories.
More
specifically,
self-attention
mechanism
incorporated
enable
dynamic
focus
state
information,
accelerates
convergence
and
facilitates
global
control.
A
great
number
experiments
conducted
both
simulated
real
datasets
have
verified
superiority
our
compared
state-of-the-art
baselines.
Symmetry,
Год журнала:
2024,
Номер
16(6), С. 641 - 641
Опубликована: Май 22, 2024
In
today’s
customer-centric
economy,
the
demand
for
personalized
products
has
compelled
corporations
to
develop
manufacturing
processes
that
are
more
flexible,
efficient,
and
cost-effective.
Flexible
job
shops
offer
organizations
agility
cost-efficiency
traditional
lack.
However,
dynamics
of
modern
manufacturing,
including
machine
breakdown
new
order
arrivals,
introduce
unpredictability
complexity.
This
study
investigates
multiplicity
dynamic
flexible
shop
scheduling
problem
(MDFJSP)
with
arrivals.
To
address
this
problem,
we
incorporate
fluid
model
propose
a
randomized
adaptive
search
(FRAS)
algorithm,
comprising
construction
phase
local
phase.
Firstly,
in
phase,
heuristic
an
online
tracking
policy
generates
high-quality
initial
solutions.
Secondly,
employ
improved
tabu
procedure
enhance
efficiency
solution
space,
incorporating
symmetry
considerations.
The
results
numerical
experiments
demonstrate
superior
effectiveness
FRAS
algorithm
solving
MDFJSP
when
compared
other
algorithms.
Specifically,
proposed
demonstrates
quality
relative
existing
algorithms,
average
improvement
29.90%;
exhibits
acceleration
speed,
increase
1.95%.
Frontiers in Industrial Engineering,
Год журнала:
2025,
Номер
3
Опубликована: Янв. 27, 2025
The
advent
of
Industry
4.0
and
the
emerging
5.0
have
fundamentally
transformed
manufacturing
systems,
introducing
unprecedented
levels
complexity
in
production
scheduling.
This
is
further
amplified
by
integration
cyber-physical
Internet
Things,
Artificial
Intelligence,
human-centric
approaches,
necessitating
more
sophisticated
optimization
methods.
paper
aims
to
provide
a
comprehensive
perspective
on
application
metaheuristic
algorithms
shop
scheduling
problems
within
context
5.0.
Through
systematic
review
recent
literature
(2015–2024),
we
analyze
categorize
various
including
Evolutionary
Algorithms
(EAs),
swarm
intelligence,
hybrid
methods,
that
been
applied
address
complex
challenges
smart
environments.
We
specifically
examine
how
these
handle
multiple
competing
objectives
such
as
makespan
minimization,
energy
efficiency,
costs,
human-machine
collaboration,
which
are
crucial
modern
industrial
settings.
Our
survey
reveals
several
key
findings:
1)
metaheuristics
demonstrate
superior
performance
handling
multi-objective
compared
standalone
algorithms;
2)
bio-inspired
show
promising
results
addressing
environments;
3)
tri-objective
higher-order
warrant
in-depth
exploration;
4)
there
an
trend
towards
incorporating
human
factors
sustainability
optimization,
aligned
with
principles.
Additionally,
identify
research
gaps
propose
future
directions,
particularly
areas
real-time
adaptation,
sustainability-aware
algorithms.
provides
insights
for
researchers
practitioners
field
scheduling,
offering
structured
understanding
current
methodologies
evolution
from
