Frontiers in Sustainable Development,
Journal Year:
2025,
Volume and Issue:
5(3), P. 227 - 237
Published: March 22, 2025
Currently,
automobile
production
in
workshops
faces
demands
for
multi-variety,
small-batch,
and
rapid
delivery.
As
a
key
auxiliary
link,
optimizing
the
performance
of
workshop
material
scheduling
system
can
enhance
efficiency
economic
benefits.
With
expansion
enterprise
scale
complexity
requirements,
multi-AGV
handling
systems
have
become
an
effective
solution
to
optimize
processes
save
costs
due
their
parallel
collaboration
advantages.
However,
NP-hard
nature
this
problem,
traditional
exact
algorithms
often
perform
poorly
when
dealing
with
complex
large-scale
problems.
Therefore,
paper
explores
applications
intelligent
such
as
genetic
algorithms,
artificial
neural
networks,
particle
swarm
optimization,
proposes
novel
efficient
solutions
methods
mixed-model
assembly
workshops.
In
addition,
address
problem
large
state
space
schemes,
also
discusses
potential
emerging
technologies
reinforcement
learning.
Through
these
studies,
it
aims
processes,
reduce
costs,
promote
development
manufacturing
industry.
Energies,
Journal Year:
2023,
Volume and Issue:
16(3), P. 1512 - 1512
Published: Feb. 3, 2023
We
have
analyzed
127
publications
for
this
review
paper,
which
discuss
applications
of
Reinforcement
Learning
(RL)
in
marketing,
robotics,
gaming,
automated
cars,
natural
language
processing
(NLP),
internet
things
security,
recommendation
systems,
finance,
and
energy
management.
The
optimization
use
is
critical
today’s
environment.
mainly
focus
on
the
RL
application
Traditional
rule-based
systems
a
set
predefined
rules.
As
result,
they
may
become
rigid
unable
to
adjust
changing
situations
or
unforeseen
events.
can
overcome
these
drawbacks.
learns
by
exploring
environment
randomly
based
experience,
it
continues
expand
its
knowledge.
Many
researchers
are
working
RL-based
management
(EMS).
utilized
such
as
optimizing
smart
buildings,
hybrid
automobiles,
grids,
managing
renewable
resources.
contributes
achieving
net
zero
carbon
emissions
sustainable
In
context
technology,
be
optimize
regulation
building
heating,
ventilation,
air
conditioning
(HVAC)
reduce
consumption
while
maintaining
comfortable
atmosphere.
EMS
accomplished
teaching
an
agent
make
judgments
sensor
data,
temperature
occupancy,
modify
HVAC
system
settings.
has
proven
beneficial
lowering
usage
buildings
active
research
area
buildings.
used
electric
vehicles
(HEVs)
learning
optimal
control
policy
maximize
battery
life
fuel
efficiency.
acquired
remarkable
position
gaming
applications.
majority
security-related
operate
simulated
recommender
provide
good
suggestions
accuracy
diversity.
This
article
assists
novice
comprehending
foundations
reinforcement
Journal of Industrial Information Integration,
Journal Year:
2024,
Volume and Issue:
38, P. 100582 - 100582
Published: Feb. 9, 2024
Smart
manufacturing
scheduling
(SMS)
requires
a
high
degree
of
flexibility
to
successfully
cope
with
changes
in
operational
decision
level
planning
processes
today's
production
environments,
which
are
usually
subject
uncertainty.
In
such
unique
and
complex
scenario
as
the
real
job
shop,
modelling
SMS
Markov
process
(MDP),
its
approach
by
deep
reinforcement
learning
(DRL),
is
research
field
growing
interest
given
characteristics.
It
allows
us
consider
achieving
levels
promoting
automation,
autonomy
making,
ability
act
time
when
faced
disturbances
disruptions
highly
dynamic
environment.
This
paper
addresses
problem
quasi-realistic
shop
environment
characterised
machines
receiving
jobs
from
buffers
that
accumulate
numerous
using
wide
variety
parts
multimachine
routes
diverse
number
operation
phases
developing
digital
twin
based
on
MDP
DRL
methodology.
approached
by:
OpenAI
Gym;
designing
an
observation
space
18
features;
action
composed
three
priority
heuristic
rules;
shaping
single
reward
function
multi-objective
characteristic;
implementation
proximal
policy
optimisation
(PPO)
algorithm
Stable
Baselines
3
library.
approach,
dubbed
smart
(JS-SMS),
deterministic
formulation
implementation.
The
model
subjected
validation
comparing
it
several
best-known
rules.
main
findings
this
methodology
allow
replicate,
great
extent,
positive
aspects
rules
mitigate
negative
ones,
achieves
more
balanced
behaviour
most
measures
established
performance
indicators
outperforms
perspective.
Finally,
further
oriented
stochastic
approaches
address
reality
Industry
4.0
context.
