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
Decision Analytics Journal,
Год журнала:
2023,
Номер
10, С. 100377 - 100377
Опубликована: Дек. 5, 2023
This
study
identifies
and
examines
the
critical
factors
for
adopting
machine
learning
technologies
in
manufacturing
supply
chains.
Initially,
a
thorough
literature
review
was
employed
to
identify
13
factors,
then
Decision-Making
Trial
Evaluation
Laboratory
(DEMATEL)
methodology
used
analyze
their
cause–effect
relationship.
Next,
qualitative
analysis
concluded
that
'Technology
Integration'
'Forecasting'
are
essential
chains,
'Risk
Management'
is
unaffected
by
causal
'Manufacturing
Processes'
minor
learning.
The
research
findings
aim
guide
practitioners
understanding
influence
of
one
factor
over
other
'cause–effect'
relation
among
them.
strategies
effective
implementation
may
be
deduced.
It
pioneering
which
novel
crucial
determinants
have
been
identified
examined
multi-criteria
environment
using
DEMATEL
approach.
Decision Analytics Journal,
Год журнала:
2024,
Номер
12, С. 100489 - 100489
Опубликована: Июнь 5, 2024
The
effectiveness
of
deep
learning
in
completing
tasks
comprehensively
has
led
to
a
rapid
increase
its
usage.
Deep
encompasses
numerous
diverse
methods,
each
with
own
distinct
characteristics.
aim
this
study
is
synthesize
existing
literature
order
classify
and
identify
an
appropriate
method
for
given
task.
A
systematic
review
was
conducted
as
comprehensive
study,
utilizing
spanning
from
2012
2024.
findings
revealed
that
plays
significant
role
eight
main
tasks,
including
prediction,
design,
evaluation
assessment,
decision-making,
creating
user
instructions,
classification,
identification,
models.
various
such
Convolutional
Neural
Networks
(CNN),
Recurrent
(RNN),
Autoencoders
(AE),
Generative
Adversarial
(GAN),
(DNN),
Backpropagation
(BP),
Feed-Forward
(FFNN),
different
confirmed.
These
provide
researchers
understanding
selecting
effective
methods
specific
tasks.
Journal of Cleaner Production,
Год журнала:
2024,
Номер
468, С. 142922 - 142922
Опубликована: Июнь 15, 2024
A
decarbonized
food
supply
chain
ensures
that
we
have
access
to
safe,
nutritious,
and
affordable
with
a
reduced
carbon
footprint.
It
not
only
helps
in
reducing
greenhouse
gas
emissions
but
also
enhances
security
by
making
the
more
resilient
climate-related
disruptions,
ensuring
stable
production
for
growing
global
population.
Further,
there
is
an
increasing
consumer
demand
sustainably
produced
food,
meeting
this
crucial
maintaining
relevance
competitiveness
market.
Without
well-functioning
chain,
it
would
be
much
harder
farmers,
processors,
distributors,
retailers
promote
improve
public
health.
Decarbonization
complex
process
requires
multifaceted
approach,
entire
from
farm
fork
being
examined.
Technological
advances
such
as
Industry
4.0,
human-centric
solution,
could
answer.
By
combining
power
of
4.0
decarbonization
efforts,
creation
sustainable
efficient
can
promised.
Hence,
study
utilizes
mixed-method
approach
examine
Indian
analyses
factors
motivate
stakeholders
implement
technologies.
uses
opinions
industry
well
academic
experts
employing
integrated
Analytic
hierarchy
(AHP)
Interpretive
structural
modelling
(ISM).
AHP
revealed
"International
community
pressure"
most
critical
factor.
ISM
used
explain
interrelationships
among
identified
factors,
providing
hierarchical
model.
These
key
findings
assist
policymakers
develop
refine
regulations.
help
make
informed
decision
while
allocating
resources
towards
new
Modelling—International Open Access Journal of Modelling in Engineering Science,
Год журнала:
2024,
Номер
5(4), С. 2001 - 2039
Опубликована: Дек. 12, 2024
Efficient
management
of
hospital
evacuations
and
pharmaceutical
supply
chains
is
a
critical
challenge
in
modern
healthcare,
particularly
during
emergencies.
This
study
addresses
these
challenges
by
proposing
novel
bi-objective
optimization
framework.
The
model
integrates
Mixed-Integer
Linear
Programming
(MILP)
approach
with
advanced
machine
learning
techniques
to
simultaneously
minimize
total
costs
maximize
patient
satisfaction.
A
key
contribution
the
incorporation
Gated
Recurrent
Unit
(GRU)
neural
network
for
accurate
drug
demand
forecasting,
enabling
dynamic
resource
allocation
crisis
scenarios.
also
accounts
two
distinct
destinations—receiving
hospitals
temporary
care
centers
(TCCs)—and
includes
specialized
chain
prevent
medicine
shortages.
To
enhance
system
robustness,
probabilistic
patterns
disruption
risks
are
considered,
ensuring
reliability.
solution
methodology
combines
Grasshopper
Optimization
Algorithm
(GOA)
ɛ-constraint
method,
efficiently
addressing
multi-objective
nature
problem.
Results
demonstrate
significant
improvements
cost
reduction,
allocation,
service
levels,
highlighting
model’s
practical
applicability
real-world
research
provides
valuable
insights
optimizing
healthcare
logistics
events,
contributing
both
operational
efficiency
welfare.
IMA Journal of Management Mathematics,
Год журнала:
2024,
Номер
unknown
Опубликована: Окт. 30, 2024
Abstract
Accepted
by:
Aris
Syntetos
Machine
learning
(ML)
has
evolved
into
a
crucial
tool
in
supply
chain
management,
effectively
addressing
the
complexities
associated
with
decision-making
by
leveraging
available
data.
The
utilization
of
ML
markedly
surged
recent
years,
extending
its
influence
across
various
operations,
ranging
from
procurement
to
product
distribution.
In
this
paper,
based
on
systematic
search,
we
provide
comprehensive
literature
review
research
dealing
use
management.
We
present
major
contributions
classifying
them
five
classes
using
processes
operations
reference
framework.
demonstrate
that
applications
management
have
significantly
increased
both
trend
and
diversity
over
substantial
expansion
since
2019.
also
reveals
demand
forecasting
attracted
most
followed
inventory
transportation.
paper
enables
identify
gaps
provides
some
avenues
for
further
research.
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
