EUREKA Physics and Engineering,
Год журнала:
2024,
Номер
1, С. 27 - 35
Опубликована: Янв. 31, 2024
Electrical
energy
is
now
widely
recognized
as
an
essential
part
of
life
for
humans,
it
powers
many
daily
amenities
and
devices
that
people
cannot
function
without.
Examples
these
include
traffic
signals,
medical
equipment
in
hospitals,
electrical
appliances
used
homes
offices,
public
transportation.
The
process
generates
electricity
can
pollute
the
air.
Even
though
natural
gas
power
plants
derived
from
fossil
fuels,
nevertheless
produce
air
pollutants
involving
particulate
matter
(PM),
nitrogen
oxides
(NOx),
carbon
monoxide
(CO),
which
affect
human
health
cause
environmental
problems.
Numerous
researchers
have
devoted
significant
efforts
to
developing
methods
not
only
facilitate
monitoring
current
quality
but
also
possess
capability
predict
impacts
this
increasing
rise.
primary
pollution
issues
associated
with
generation
combustion
fuels.
objective
study
was
create
three
multiple
linear
regression
models
using
artificial
intelligence
(AI)
technology
data
collected
sensors
positioned
around
generator.
precisely
amount
would
produce.
highly
accurate
forecasted
proved
valuable
determining
operational
parameters
resulted
minimal
emissions.
predicted
values
were
mean
squared
error
(MSE)
0.008,
absolute
(MAE)
0.071,
percentage
(MAPE)
0.006
turbine
yield
(TEY).
For
CO,
MSE
2.029,
MAE
0.791,
MAPE
0.934.
NOx,
69.479,
6.148,
0.096.
results
demonstrate
developed
a
high
level
accuracy
identifying
conditions
result
emissions,
exception
NOx.
NOx
model
relatively
lower,
may
still
be
estimate
pattern
emissions
Supply Chain Analytics,
Год журнала:
2023,
Номер
4, С. 100040 - 100040
Опубликована: Сен. 25, 2023
This
study
uses
a
two-level
programming
model
to
present
Stackelberg
game.
The
problems
consist
of
two
levels
decision-making,
each
level
having
its
objective
function.
model's
first
player
(leader)
includes
the
supplier
and
manufacturer,
while
second
(follower)
distributor,
customer,
revival
centers.
proposed
is
determine
optimal
amount
products
components
in
network
segment,
minimizing
system's
total
costs
optimizing
transportation
system.
research
(1)
considers
environmental
factors
supply
chain
wooden
products,
(2)
game
theory
for
players,
(3)
provides
competition
mechanism
players
where
do
not
share
their
functions
due
information
security.
compared
with
Genetic
Algorithm
(GA)
Gray
Wolf
Optimization
(GWO)
meta-heuristic
algorithms.
We
show
calculation
error
GWO
algorithm
less
than
that
GA.
Therefore,
it
can
better
predict
behavior
long
term.
results
lower
production
case
no
shortage.
Decision Analytics Journal,
Год журнала:
2024,
Номер
12, С. 100492 - 100492
Опубликована: Июнь 8, 2024
This
study
investigates
the
ensemble
machine
learning
models
to
predict
mechanical
properties
of
3D-printed
Polylactic
Acid
(PLA)
specimens.
We
studied
effects
five
process
parameters,
including
build
orientation,
infill
angle,
layer
thickness,
printing
speed,
and
nozzle
temperature,
on
printed
parts
tensile
strength
surface
roughness.
Machine
are
developed
using
experimental
data
collected
from
27
Gradient
Boosting
Regression,
Extreme
Adaptive
Random
Forest
Extremely
Randomized
Tree
Regression
were
during
modeling
stage
roughness
parts.
research
demonstrates
effectiveness
model
in
providing
accurate
predictions
with
root
mean
square
error
(RMSE)
1.03,
absolute
(MAE)
0.82,
percentage
(MAPE)
2.20%.
Similarly,
shows
better
accuracy
predicting
having
RMSE
0.408,
MAE
0.31,
MAPE
9.28%.
Moreover,
comparative
confirms
that
techniques
more
useful
than
traditional
support
vector
k-nearest
neighbor
for
The
results
highlight
a
novel
approach
identifying
complex
correlations
dataset,
establishing
foundation
improved
product
design
property
optimization
through
adjustment
parameters
combination.
Decision Analytics Journal,
Год журнала:
2024,
Номер
12, С. 100498 - 100498
Опубликована: Июль 5, 2024
Economic
uncertainty
has
been
increasing,
as
evidenced
by
recent
fluctuations
in
global
markets
and
unpredictable
economic
indicators
such
volatile
demand,
stock
market
fluctuations,
interest
rates.
profitability
working
capital
efficiency
are
pivotal
of
a
business's
financial
health,
both
which
adversely
impacted
uncertainty.
However,
these
metrics
may
diverge
distinct
objectives
drive
them.
There
exists
gap
the
literature
regarding
effective
strategies
for
managing
trade-off
between
under
This
study
addresses
this
introducing
simulation-based
optimization
model
that
integrates
system
dynamics
simulation
genetic
algorithms.
The
proposed
aims
to
balance
within
inventory
management
partial
trade
credit.
A
real
case
demonstrates
model's
applicability
reveals
its
superiority
over
conventional
modeling.
With
capacity
inform
strategic
tactical
decision-making,
emerges
valuable
tool
supply
chain
managers
seeking
ensure
stability
amidst
volatility.
Decision Analytics Journal,
Год журнала:
2024,
Номер
10, С. 100400 - 100400
Опубликована: Янв. 17, 2024
This
paper
proposes
a
Linear
Programming-based
Bi-Objective
time
series
Forecasting
Algorithm
that
helps
forecast
sub-annual
short
univariate
time-series
data.
The
proposed
algorithm
generates
forecasts
optimized
for
pair
of
accuracy
measures
instead
just
one.
is
based
on
the
ϵ
-
constraint-based
multi-objective
optimization
method.
measure
pairs
used
in
this
are
Mean
and
Maximum
Absolute
Errors
Percentage
Errors.
We
compare
performance
with
several
industry-standard
forecasting
methods
using
commonly
reported
literature
three
horizons:
long-term,
medium-term,
short-term.
performs
best
long-
medium-term
horizon
short-time
studied
our
paper.
Across
all
horizons,
has
least
maximum
errors,
reducing
over-and
under-forecast
errors.
can
yield
interpretable
linear
models
quite
flexible.
