Spectrum of Decision Making and Applications.,
Год журнала:
2024,
Номер
2(1), С. 157 - 164
Опубликована: Ноя. 23, 2024
Vendor
Managed
Inventory
(VMI)
is
a
widely
adopted
strategy
in
supply
chain
management,
where
the
vendor
assumes
responsibility
for
maintaining
inventory
levels
at
customer’s
location.
This
paper
presents
model
to
solve
VMI
problem,
focusing
on
optimizing
replenishment
and
reducing
overall
costs.
The
study
employs
heuristic
approach,
breaking
down
problem
into
manageable
phases,
including
clustering
customers,
determining
service
sequence
lists,
delivery
routes.
applied
practical
case
study,
demonstrating
its
effectiveness
minimizing
stockouts
while
efficient
levels.
also
examines
key
factors
like
quantity
optimization,
route
scheduling,
cost
minimization.
model's
demonstrated
by
specific
performance
criteria,
such
as
reduced
stockouts,
improved
levels,
minimized
transportation
findings
indicate
that
suggested
can
significantly
enhance
efficiency,
providing
organizations
with
solid
framework
enhancing
their
procedures.
These
enhancements
are
accomplished
preserving
simplicity
usefulness
without
need
overly
complex
technological
systems.
Advances in information security, privacy, and ethics book series,
Год журнала:
2024,
Номер
unknown, С. 53 - 76
Опубликована: Июль 26, 2024
Reinforcement
learning
(RL)
allows
defense
mechanisms
to
adapt
changing
threats
and
has
shown
promise
in
tackling
cyber
security
issues.
This
study
presents
a
thorough
introduction
which
includes
foundations,
uses,
difficulties
RL
security.
The
efficacy
of
making
decisions
is
also
emphasized
the
introduction.
Then
foundation
for
comprehending
RL's
use
security,
fundamentals
technology,
algorithm
classifications
clarified.
then
delves
into
number
applications
issues
discussed.
Along
with
prospects
improving
safeguards
through
application
methodologies,
successfully
manage
increasing
threats,
future
research
directions
are
proposed
integration
blockchain
technology
generative
adversarial
networks
(GANs).
work
emphasizes
importance
supporting
improve
defenses.
Energies,
Год журнала:
2024,
Номер
17(4), С. 849 - 849
Опубликована: Фев. 11, 2024
Many
control
algorithms
have
been
applied
to
manage
the
flow
of
products
in
supply
chains.
However,
era
thriving
globalization,
even
a
small
disruption
can
be
fatal
for
some
companies.
On
other
hand,
rising
environmental
impact
rapid
industry
is
imposing
limitations
on
energy
usage
and
waste
generation.
Therefore,
taking
into
account
mentioned
perspectives,
there
need
explore
research
directions
that
concern
product
perishability
together
with
different
demand
patterns
their
uncertain
character.
This
study
aims
propose
robust
approach
combines
neural
networks
optimal
controller
tuning
use
both
fuzzy
logic.
Firstly,
forecast
generated,
following
which
parameters
are
optimized,
uncertainty.
As
part
verification
designated
controller,
sensitivity
parameter
changes
has
determined
using
OAT
method.
It
turns
out
proposed
provide
significant
reductions
compared
well-known
POUT
method
while
maintaining
low
stocks,
high
fill
rate,
providing
lower
most
considered
cases.
The
effectiveness
this
verified
by
dataset
from
worldwide
retailer.
simulation
results
show
effectively
improve
perishable
inventories.
Science & Technology Indonesia,
Год журнала:
2024,
Номер
9(1), С. 148 - 155
Опубликована: Янв. 22, 2024
Good
management
of
goods
is
needed
so
that
the
inventory
activities
a
business
can
run
smoothly
as
part
supply
chain
which
aims
to
monitor
flow
stock
from
purchasing
process,
and
storage
point
sale.
In
terms
or
supplies
pharmaceutical
goods,
conditions
such
shortages
stockouts
must
also
be
considered
are
matter
control,
management,
security.
this
study,
an
model
formulated
with
deterioration
damage
occurs
due
length
time
when
stored
linear
demand
level.
optimal
solution,
it
reaches
zero
(t1)
0.34
cycle
(T1)
0.83
average
minimum
total
cost
(TC)
$445.25
per
completed
by
WolframAlpha
software.
Sensitivity
analysis
changes
value
results
in
increases
for
all
parameters.
increasing
function
variables
(a
b),
produces
t1
T1
stable
values.
An
increase
each
item
(DC)
constant
rate
(theta)
value,
but
increases.
The
costs
(h)
decrease
T1.
(s)
Advances in logistics, operations, and management science book series,
Год журнала:
2024,
Номер
unknown, С. 295 - 312
Опубликована: Апрель 22, 2024
The
integration
of
Deep
Reinforcement
Learning
(DRL)
into
the
realm
robotics
and
autonomous
systems
has
emerged
as
a
groundbreaking
paradigm
shift,
empowering
machines
to
tackle
intricate
tasks
through
interaction
with
their
environments.
This
chapter
offers
comprehensive
examination
current
research
landscape
at
intersection
DRL
within
this
dynamic
field.
navigates
conceptualization
explores
its
diverse
applications
in
controlling
object
manipulation.
showcases
autonomy
adaptability
enabled
by
while
addressing
prevalent
challenges
such
sample
efficiency,
safety
concerns,
scalability.
In
conclusion,
serves
valuable
resource
for
future
researchers
practitioners
intrigued
robotics.
It
synthesizes
knowledge,
underscores
significant
progress
made,
maps
out
exciting
avenues
further
exploration,
ultimately
propelling
advancement
robotic
era
machine
learning
artificial
intelligence.
