Applied Sciences,
Год журнала:
2023,
Номер
13(18), С. 10427 - 10427
Опубликована: Сен. 18, 2023
The
advent
of
electric
and
flying
vehicles
(EnFVs)
has
brought
significant
advancements
to
the
transportation
industry,
offering
improved
sustainability,
reduced
congestion,
enhanced
mobility.
However,
efficient
routing
messages
in
EnFVs
presents
unique
challenges
that
demand
specialized
algorithms
address
their
specific
constraints
objectives.
This
study
analyzes
several
case
studies
investigate
effectiveness
genetic
(GAs)
optimizing
for
EnFVs.
major
contributions
this
research
lie
demonstrating
capability
GAs
handle
complex
optimization
problems
with
multiple
objectives,
enabling
simultaneous
consideration
factors
like
energy
efficiency,
travel
time,
vehicle
utilization.
Moreover,
offer
a
flexible
adaptive
approach
finding
near-optimal
solutions
dynamic
systems,
making
them
suitable
real-world
EnFV
networks.
While
show
promise,
there
are
also
limitations,
such
as
computational
complexity,
difficulty
capturing
constraints,
potential
sub-optimal
solutions.
Addressing
these
challenges,
highlights
future
directions,
including
integration
real-time
data
updates,
hybrid
approaches
other
techniques,
uncertainty
risk
management,
scalability
large-scale
problems,
enhancing
efficiency
sustainability
routing.
By
exploring
avenues,
researchers
can
further
improve
EnFVs,
paving
way
seamless
into
modern
systems.
Applied Sciences,
Год журнала:
2025,
Номер
15(8), С. 4220 - 4220
Опубликована: Апрель 11, 2025
Unmanned
aerial
vehicles
(UAVs)
are
increasingly
deployed
in
dynamic
environments
for
applications
such
as
surveillance,
delivery,
and
data
collection,
where
efficient
task
allocation
path
planning
critical
to
minimizing
mission
completion
time
while
managing
limited
energy
resources.
This
paper
proposes
a
novel
approach
that
integrates
management
into
rolling
horizon
framework
UAV
planning.
We
introduce
an
enhanced
Particle
Swarm
Optimization
(PSO)
algorithm,
incorporating
adaptive
perturbation
strategies
local
search
mechanism
based
on
simulated
annealing,
optimize
assignments
routes.
The
enables
the
system
adapt
evolving
demands
over
time.
Energy
consumption
is
explicitly
modeled,
accounting
flight,
computation,
recharging
at
designated
stations,
ensuring
practical
applicability.
Extensive
simulations
demonstrate
proposed
method
reduces
makespan
significantly
compared
conventional
static
approaches,
effectively
balancing
usage
requirements.
These
results
highlight
potential
of
our
real-world
operations
settings.
Mathematics,
Год журнала:
2023,
Номер
11(4), С. 901 - 901
Опубликована: Фев. 10, 2023
In
the
context
of
carbon
neutralization,
electric
vehicle
and
energy
storage
market
is
growing
rapidly.
As
a
result,
battery
recycling
an
important
work
with
consideration
advent
retirement
resource
constraints,
environmental
factors,
regional
price
factors.
Based
on
theoretical
research
intelligent
algorithm
mathematical
models,
integer
programming
model
urban
power
reverse
supply
chain
scheduling
was
established
goal
highest
customer
satisfaction
least
total
cost
logistics
distribution,
to
study
influence
resources
operation
status
built
city
center
dismantling
chain.
The
includes
load,
demand
point
range,
service
capacity
constraints.
This
collected
image
data,
conducted
analysis,
further
designed
improved
Non-dominated
Sorting
Genetic
Algorithm-II
(NSGA-II)
optimization
suitable
solve
global
problem
by
introducing
improvement
strategy
convergence
rate,
particle
search,
traditional
elite
individual
retention.
results
verified
practicability
model,
ability
problem,
speed
through
comparing
obtained
from
basic
algorithm.
A
reasonable
comprehensive
solution
for
location
path
also
obtained.
Multi-objective
carried
out
in
scheduling,
facility
construction,
construction.
integrated
software
were
compared.
We
found
that
scheme
provided
can
significantly
reduce
enterprise.
new
insights
enterprises
effectively
utilize
optimize
battery.
