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
Biomimetics,
Journal Year:
2024,
Volume and Issue:
9(4), P. 242 - 242
Published: April 18, 2024
Due
to
the
high
pollution
of
transportation
sector,
nowadays
role
electric
vehicles
has
been
noticed
more
and
by
governments,
organizations,
environmentally
friendly
people.
On
other
hand,
problem
vehicle
routing
(EVRP)
widely
studied
in
recent
years.
This
paper
deals
with
an
extended
version
EVRP,
which
(EVs)
deliver
goods
customers.
The
limited
battery
capacity
EVs
causes
their
operational
domains
be
less
than
those
gasoline
vehicles.
For
this
purpose,
several
charging
stations
are
considered
study
for
EVs.
In
addition,
depending
on
domain,
a
full
charge
may
not
needed,
reduces
operation
time.
Therefore,
partial
recharging
is
also
taken
into
account
present
research.
formulated
as
multi-objective
integer
linear
programming
model,
whose
objective
functions
include
economic,
environmental,
social
aspects.
Then,
preemptive
fuzzy
goal
method
(PFGP)
exploited
exact
solve
small-sized
problems.
Also,
two
hybrid
meta-heuristic
algorithms
inspired
nature,
including
MOSA,
MOGWO,
MOPSO,
NSGAII_TLBO,
utilized
large-sized
results
obtained
from
solving
numerous
test
problems
demonstrate
that
algorithm
can
provide
efficient
solutions
terms
quality
non-dominated
all
performance
was
compared
four
indexes:
time,
MID,
MOCV,
HV.
Moreover,
statistical
analysis
performed
investigate
whether
there
significant
difference
between
algorithms.
indicate
MOSA
performs
better
time
index.
NSGA-II-TLBO
outperforms
HV
indexes.
IEEE Access,
Journal Year:
2023,
Volume and Issue:
11, P. 127043 - 127056
Published: Jan. 1, 2023
With
the
rapid
increase
in
demand
for
fresh
products,
cold
chain
logistics
has
become
an
important
mode
of
transportation.
Logistics
enterprises
are
faced
with
problem
cost
control
and
improvement
customer
satisfaction.
In
light
this,
we
present
a
bi-objective
optimization
vehicle
routing
model
logistics,
which
aims
to
reduce
total
costs
improve
To
solve
intricate
model,
propose
hybrid
algorithm
called
Simulated
Annealing
Non-dominated
Sorting
Genetic
Algorithm
II
(SA-NSGA-II)
algorithm,
amalgamates
simulated
annealing
NSGA-II
enabling
efficient
resolution
problem.
Extensive
numerical
experiments
validate
effectiveness
proposed
by
exhibiting
solutions
lower
higher
satisfaction
levels.
Furthermore,
conduct
sensitivity
analysis
explore
impact
speed
on
both
satisfaction,
shedding
trade-offs
between
various
objectives.
