Energy Science & Engineering,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 30, 2025
ABSTRACT
Optimization
scheduling
plays
a
pivotal
role
in
construction
projects,
significantly
influencing
both
the
overall
project
schedule
and
its
efficiency.
This
study
focuses
on
optimizing
of
electric
highway
engineering
projects
within
roadbed
construction.
The
research
considers
multiple
earthmoving
processes
optimizes
working
time
each
piece
equipment,
taking
into
account
capacity
speed
limited
week.
is
further
contextualized
by
use
regional
time‐of‐use
(TOU)
electricity
pricing.
A
sophisticated
optimization
model
developed
to
simulate
optimal
machinery
operation,
striking
balance
between
energy
consumption
work
paper
introduces
an
innovative
algorithm,
improved
crested
porcupine
optimizer
(ICPO),
which
incorporates
Latin
hypercube
sampling
for
population
initialization.
To
enhance
algorithmic
effectiveness,
combined
strategy
parallel
compact
processing
employed.
approach
reduces
number
iterations
required
consequently
lowers
consumption.
Rigorous
analysis
comparison
with
existing
algorithms
demonstrate
that
ICPO
iteration
count
financial
expenditure.
Simulation
results
validate
accuracy
practicality
proposed
showing
reduction
over
7%
IEEE Transactions on Industrial Informatics,
Год журнала:
2024,
Номер
20(6), С. 8662 - 8672
Опубликована: Март 21, 2024
The
dynamic
job-shop
scheduling
problem
(DJSSP)
is
an
advanced
form
of
the
classical
(JSSP),
incorporating
events
that
make
it
even
more
challenging.
This
article
proposes
a
novel
approach
involving
deep
reinforcement
learning
and
graph
neural
networks
to
solve
this
optimization
problem.
To
effectively
model
DJSSP,
we
use
disjunctive
graph,
designing
specific
node
features
reflect
unique
characteristics
JSSP
with
machine
breakdowns
stochastic
job
arrivals.
Our
proposed
method
can
dynamically
adapt
occurrence
disruptions,
ensuring
accurately
reflects
current
state
environment.
Furthermore,
attention
mechanism
prioritize
crucial
nodes
while
discarding
irrelevant
ones.
study
applies
learn
embeddings,
serving
as
input
for
actor–critic
model.
proximal
policy
then
utilized
train
model,
which
assists
in
operations
machines.
We
conducted
extensive
experiments
static
public
environments.
Experimental
results
indicate
our
superior
state-of-the-art
methods.
