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
Mathematics,
Journal Year:
2024,
Volume and Issue:
12(7), P. 1059 - 1059
Published: April 1, 2024
Supply
Chain
(SC)
Optimization
is
a
key
activity
in
today’s
industry
with
the
goal
of
increasing
operational
efficiency,
reducing
costs,
and
improving
customer
satisfaction.
Traditional
optimization
methods
often
struggle
to
effectively
use
resources
while
handling
complex
dynamic
chain
networks.
This
paper
introduces
novel
biomimetic
metaheuristic
algorithm
called
Wombat
Algorithm
(WOA)
for
supply
optimization.
replicates
natural
behaviors
observed
wombats
living
wild,
particularly
focusing
on
their
foraging
tactics
evasive
maneuvers
towards
predators.
The
theory
WOA
described
then
mathematically
modeled
two
phases:
(i)
exploration
based
simulation
wombat
movements
during
trying
find
food
(ii)
exploitation
simulating
when
diving
nearby
tunnels
defend
against
its
effectiveness
addressing
challenges
assessed
by
CEC
2017
test
suite
across
various
problem
dimensions,
including
10,
30,
50,
100.
findings
indicate
that
demonstrates
strong
ability
manage
exploitation,
maintains
balance
between
them
throughout
search
phase
deliver
optimal
solutions
problems.
A
total
twelve
well-known
algorithms
are
upon
performance
process.
outcomes
simulations
reveal
outperforms
other
algorithms,
achieving
superior
results
most
benchmark
functions
securing
top
ranking
as
efficient
optimizer.
Using
Wilcoxon
rank
sum
statistical
analysis,
it
has
been
proven
significantly.
put
twenty-two
constrained
problems
from
2011
four
engineering
design
showcase
solve
real-world
demonstrate
excels
applications
delivering
outperforming
competitors.
Operations Research Perspectives,
Journal Year:
2024,
Volume and Issue:
12, P. 100303 - 100303
Published: April 28, 2024
The
green
vehicle
routing
problem
(GVRP)
has
been
a
prominent
topic
in
the
literature
on
logistics
and
transportation,
leading
to
extensive
research
previous
review
studies
covering
various
aspects.
Operations
seen
development
of
exact
approximation
approaches
for
different
extensions
GVRP.
This
paper
presents
an
up-to-date
thorough
GVRP
spanning
from
2016
2023,
encompassing
458
papers.
significant
contribution
lies
updated
solution
algorithms
applied
both
single-objective
multi-objective
Notably,
92.58%
papers
introduced
mathematical
model
GVRP,
with
many
researchers
adopting
mixed
integer
linear
programming
as
preferred
modeling
approach.
findings
indicate
that
metaheuristics
hybrid
are
most
employed
addressing
Among
approaches,
combination
metaheuristics-metaheuristics
is
particularly
favored
by
researchers.
Furthermore,
large
neighborhood
search
(LNS)
its
variants
(especially
adaptive
search)
emerges
widely
adopted
algorithm
These
proposed
within
metaheuristic
where
A-/LNS
often
combined
other
algorithms.
Conversely,
predominant
NSGA-II
being
frequently
algorithm.
Researchers
utilize
GAMS
CPLEX
optimization
software
solvers.
MATLAB
commonly
language
implementing
Applied Artificial Intelligence,
Journal Year:
2024,
Volume and Issue:
38(1)
Published: March 27, 2024
This
paper
presents
a
model
and
heuristic
solution
algorithms
for
the
Green
Vehicle
Routing
Problem
with
Flexible
Time
Windows.
A
scenario
of
new
vehicle
routing
is
analyzed
in
which
customers
are
asked
to
provide
alternative
time
windows
offer
flexibility
help
route
planners
find
more
fuel-efficient
routes
("green
delivery").
Customers
can
rank
their
preferred
as
first,
second,
third.
The
optimization
aims
reduce
tour
costs,
promote
electromobility
over
fossil
fuels,
such
diesel,
meet
customer
preferences
when
possible
affordable.
study
incorporates
multi-objective
three
objectives,
overall
cost,
use
fuel,
satisfaction.
For
problem,
set
realistic
benchmark
problems
created
four
mainstream
solvers
applied
Pareto
front
approximation:
NSGA-II,
NSGA-III,
MOEA/D,
SMS-EMOA.
These
compared
terms
effectiveness
achieving
objectives
minimizing
travel
promoting
electromobility,
meeting
preferences.
uses
five
different
single-vehicle
planning.
Two
major
findings
that
selection
metaheuristic
make
big
difference
algorithm
performance.
resulting
3-D
fronts
reveal
nature
this
class
problems:
Interestingly,
flexible
windows,
most
users
still
be
delivered
only
small
concessions
other
objectives.
However,
using
one
window
per
user
lead
an
increasingly
drastic
cost
fuel
consumption.
Sustainability,
Journal Year:
2025,
Volume and Issue:
17(6), P. 2700 - 2700
Published: March 18, 2025
With
increasingly
diverse
customer
demands
and
the
rapid
growth
of
new
energy
logistics
industry,
establishing
a
sustainable
responsive
network
is
critical.
In
multi-depot
network,
adopting
collaborative
distribution
resource
sharing
can
significantly
improve
operational
efficiency.
This
study
proposes
collaboration
for
electric
vehicle
(EV)
routing
problem
with
time
windows
dynamic
demands.
A
bi-objective
optimization
model
formulated
to
minimize
total
operating
costs
number
EVs.
To
solve
model,
novel
hybrid
algorithm
combining
mini-batch
k-means
clustering
an
improved
multi-objective
differential
evolutionary
(IMODE)
proposed.
integrates
genetic
operations
non-dominated
sorting
operation
enhance
solution
quality.
The
strategies
dynamically
inserting
charging
stations
are
embedded
within
identify
Pareto-optimal
solutions
effectively.
algorithm’s
efficacy
applicability
verified
through
comparisons
algorithm,
particle
swarm
ant
colony
optimization,
tabu
search.
Additionally,
case
company
in
Chongqing
City,
China
demonstrates
that
proposed
method
reduces
improves
Sensitivity
analysis
considering
different
demand
response
modes
provides
insights
reducing
enhancing
findings
offer
essential
promoting
environmentally
resource-efficient
city.
