The
Vehicle
Routing
Problem
(VRP)
is
related
to
determining
the
route
of
several
vehicles
distribute
goods
customers
efficiently
and
minimize
transportation
costs
or
optimize
other
objective
functions.
VRP
variations
will
continue
emerge
as
manufacturing
industry
production
distribution
problems
become
increasingly
complex.
Meta-heuristic
methods
have
emerged
a
powerful
solution
overcome
complexity
VRP.
This
article
provides
comprehensive
review
use
meta-heuristic
in
solving
challenges
faced.
A
popular
presented,
including
Simulated
Annealing,
Genetic
Algorithm,
Particle
Swarm
Optimization,
Ant
Colony
Optimization.
advantages
each
method
its
role
complex
are
discussed
detail.
Challenges
that
may
be
encountered
using
meta-heuristics
for
VRPs
analyzed,
along
with
strategies
these
challenges.
also
recommends
further
research
includes
adaptation
more
variants,
incorporation
methods,
parameter
optimization,
practical
implementation
real-world
scenarios.
Overall,
this
explains
important
intelligent
solutions
logistics
Intelligent Decision Technologies,
Journal Year:
2025,
Volume and Issue:
unknown
Published: May 19, 2025
Particle
Swarm
Optimization
(PSO)
remains
straightforward
and
has
many
scientific
engineering
applications.
Most
real-world
optimization
problems
are
nonlinear
discrete
with
local
constraints.
The
PSO
algorithm
encounters
issues
such
as
inefficient
solutions
early
convergence.
It
works
best
well-tuned
attribute
weights,
improving
case
retrieval
accuracy.
Using
case-based
reasoning
to
optimize
pressure
vessel
models
improves
performance,
resulting
in
predictions
closer
true
values
fulfilling
requirements.
When
developed
for
a
group
of
Wheeled
Mobile
Robots
(WMR),
Fault
Tolerant
Formation
Control
(FTFC)
technique
is
designed
protect
against
serious
actuator
defects.
At
the
outset
study,
WMRs
arranged
very
orderly.
severe
faults
impede
certain
robots,
functioning
wheeled
mobile
robots
(WMRs)
adjust
their
formation
reduce
consequences
malfunction.
An
ideal
assignment
assigns
new
duties
each
robot,
followed
by
evolutionary
algorithms
design
pathways
reconfigured
positions.
CPTD
approach
uses
piecewise
linear
approximation
overcome
obstacles
continuous
switch
inputs.
This
method
combines
Genetic
Algorithm
(GAPSO),
an
effective
strategy
dynamic
reconfiguration
path
optimization.
holistic
reduces
time
required
achieve
configuration
while
considering
physical
restrictions
avoiding
collisions.
Finally,
tests
performed
verify
proposed
Algorithm's
efficacy
compared
existing
methods.
GAPSO
will
average
relative
error
reduction
2%,
accuracy
improve
96%,
maximum
performance
be
achieved
95%,
F1
score
develop
training
cure
rate
94%.
IEEE Access,
Journal Year:
2024,
Volume and Issue:
12, P. 33400 - 33419
Published: Jan. 1, 2024
The
time-dependent
green
vehicle
routing
problem
with
time
windows
is
a
further
deepening
of
the
research
on
problems
windows.
Its
simultaneous
consideration
transportation
time,
carbon
emissions,
and
customer
satisfaction
under
variables
makes
it
more
challenging
to
solve
than
traditional
problems.
This
work
proposes
multi-objective
optimization
algorithm
that
combines
learnable
crossover
strategy
adaptive
search
based
reinforcement
learning
overcome
local
optima,
poor
convergence,
reduced
variety
solutions
plague
algorithms
when
solving
this
problem.
proposed
approach
solves
in
two
stages:
In
first
stage,
hybrid
initialization
used
generate
initial
high
quality
diversity,
strategies
are
explore
solution
space
improve
convergence
by
characteristics
pareto
solutions.
second
designed
for
searching
later
stage
algorithm.
experimental
results
show
better
obtained
approach,
effectiveness
superiority
over
existing
methods
terms
diversity
demonstrated
through
comparisons.
The
Vehicle
Routing
Problem
(VRP)
is
related
to
determining
the
route
of
several
vehicles
distribute
goods
customers
efficiently
and
minimize
transportation
costs
or
optimize
other
objective
functions.
VRP
variations
will
continue
emerge
as
manufacturing
industry
production
distribution
problems
become
increasingly
complex.
Meta-heuristic
methods
have
emerged
a
powerful
solution
overcome
complexity
VRP.
This
article
provides
comprehensive
review
use
meta-heuristic
in
solving
challenges
faced.
A
popular
presented,
including
Simulated
Annealing,
Genetic
Algorithm,
Particle
Swarm
Optimization,
Ant
Colony
Optimization.
advantages
each
method
its
role
complex
are
discussed
detail.
Challenges
that
may
be
encountered
using
meta-heuristics
for
VRPs
analyzed,
along
with
strategies
these
challenges.
also
recommends
further
research
includes
adaptation
more
variants,
incorporation
methods,
parameter
optimization,
practical
implementation
real-world
scenarios.
Overall,
this
explains
important
intelligent
solutions
logistics
