Synergy of machine learning and the Einstein Choquet integral with LOPCOW and fuzzy measures for sustainable solid waste management
AIMS Mathematics,
Год журнала:
2025,
Номер
10(1), С. 460 - 498
Опубликована: Янв. 1, 2025
<p>Solid
waste
management
(SWM)
protects
public
health,
the
environment,
and
limited
resources
in
densely
populated
urbanized
countries
such
as
Singapore.
This
work
presents
an
advanced
framework
for
optimizing
SWM
using
mathematical
models
decision-making
techniques,
including
circular
$
q
$-rung
orthopair
fuzzy
set
(C$
$-ROFS)
data,
combined
with
Choquet
integral
(CI)
logarithmic
percentage
change-driven
objective
weighting
(LOPCOW)
methods,
enhanced
by
aggregation
operators
(AOs)
Einstein
weighted
averaging
$-ROFECIWA)
geometric
$-ROFECIWG)
operators.
By
conducting
a
systematic
evaluation,
these
methods
classified
different
alternatives
to
SWM,
evaluating
them
according
criteria
their
environmental
impact,
cost-effectiveness,
reduction
efficiency,
feasibility
of
implementation,
health
safety,
acceptance.
The
C$
$-ROFECIWA
$-ROFECIWG
perform
better
than
previous
approaches
effective
multifaceted
dynamic
scenarios.
comparison
study
demonstrates
that
integration
LOPCOW
offers
conclusions
are
more
reliable
sustainable.
conducted
Singapore
successfully
finds
most
feasible
emphasizes
possibility
implementing
environmentally
sustainable
practices
urban
environment.
research
practical
insights
policymakers
need
improve
enhance
various
environments.</p>
Язык: Английский
Yager’s type weighted power means of q-rung orthopair fuzzy information and their applications to multi-criteria decision making
Computational and Applied Mathematics,
Год журнала:
2025,
Номер
44(2)
Опубликована: Фев. 5, 2025
Язык: Английский
A comprehensive assessment of machine learning models for predictive maintenance using a decision-making framework in the industrial sector
Alexandria Engineering Journal,
Год журнала:
2025,
Номер
120, С. 561 - 583
Опубликована: Фев. 24, 2025
Язык: Английский
Multi-criteria decision-making method based on an integrated model using T-spherical fuzzy aczel-alsina prioritized aggregation operators
Computational and Applied Mathematics,
Год журнала:
2025,
Номер
44(4)
Опубликована: Фев. 28, 2025
Язык: Английский
Enhancing teacher recruitment and retention through decision-making models in education systems
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Апрель 30, 2025
Teacher
recruitment
and
retention
remain
critical
challenges
for
education
systems
worldwide,
with
far-reaching
implications
educational
quality
institutional
sustainability.
Traditional
approaches
often
fail
to
address
the
complexity
of
these
issues,
neglecting
interplay
multiple
conflicting
criteria
inherent
uncertainty
in
decision-making.
This
gap
necessitates
advanced
decision-making
frameworks
that
can
effectively
evaluate
prioritize
strategies
improving
teacher
retention.
To
bridge
this
gap,
study
introduces
a
novel
framework
integrating
intuitionistic
fuzzy
sets
(IFSs)
handle
more
effectively.
The
Entropy
method
is
employed
compute
objective
weights,
while
ranking
comparison
(RANCOM)
determines
subjective
ensuring
balanced
consideration
qualitative
quantitative
factors.
weighted
aggregated
sum
product
assessment
(WASPAS)
then
applied.
validated
through
sensitivity
analysis
assess
its
robustness
comparative
establish
superiority
over
traditional
methods.
results
identify
Golden
Ticket
Salary
Plan
[Formula:
see
text]
as
optimal
strategy,
achieving
highest
(0.3654),
followed
by
(0.3487),
(0.3485),
(0.3400),
(0.2976)
(0.2707).
order
follows:
text].
These
findings
highlight
significance
structured
optimizing
workforce
management.
provides
valuable
insights
policymakers
administrators,
sustainable
advancements
Язык: Английский