University
course
scheduling
(UCS)
is
one
of
the
most
important
and
time-consuming
issues
that
all
educational
institutions
face
yearly.
Most
existing
techniques
to
model
solve
UCS
problems
have
applied
approximate
methods,
which
are
different
in
terms
efficiency,
performance,
optimization
speed.
Accordingly,
this
research
aims
apply
an
exact
method
provide
optimal
solution
problem.
In
other
words,
research,
integer
programming
presented
USC
model,
hard
soft
constraints
include
facilities
classrooms,
courses
levels
compression
students'
curriculum,
outside
faculty
planning
for
them,
limited
time
allocated
professors.
The
objective
maximize
weighted
sum
allocating
available
times
professors
based
on
their
preferences
periods.
To
evaluate
model's
feasibility,
it
implemented
using
GAMS
software.
Finally,
solved
a
larger
dimension
real
data
set
from
college
China
compared
with
current
program
same
college.
obtained
results
show
considering
mathematical
function,
courses'
timetable
reduced
4
days
week
3
working
days.
Moreover,
master
planned
two
days,
groups
do
not
interfere
each
other.
Furthermore,
by
implementing
proposed
case
study,
maximum
teaching
hours
significantly
reduced.
demonstrate
efficiency
speed
accuracy.
Supply Chain Analytics,
Год журнала:
2023,
Номер
3, С. 100031 - 100031
Опубликована: Авг. 10, 2023
This
paper
presents
a
systematic
review
of
the
literature
on
Supply
Chain
Risk
(SCR)
research,
focusing
content-based
analysis.
The
study
comprehensively
examines
general
factors
associated
with
key
themes
and
trends
in
supply
chain
risk
management,
encompassing
identification
assessment
risks,
mitigation
strategies,
influence
emerging
technologies
Management
(SCRM).
provides
an
overview
current
topics
SCRM,
while
also
introducing
categorization
frameworks
to
address
research
gaps
provide
roadmap
for
future
studies,
thereby
generating
valuable
insights
this
field.
highlights
significance
effective
SCRM
ensuring
business
continuity
resilience,
emphasizing
need
organizations
adopt
proactive
approach
management.
concludes
by
identifying
areas
including
development
novel
management
integration
into
practices.
Additionally,
comprehensive
evaluation
each
classification
is
presented,
highlighting
overlooked
aspects
unexplored
domains,
offering
recommendations
potential
next
steps
research.
Engineering Applications of Artificial Intelligence,
Год журнала:
2023,
Номер
126, С. 106839 - 106839
Опубликована: Авг. 2, 2023
This
paper
reviews
the
application
of
metaheuristics
for
optimized
sustainable
supply
chain
management
(SSCM).
explores
potential
to
improve
chain’s
sustainability
while
enhancing
its
efficiency
and
competitiveness.
The
provides
an
overview
principles
SSCM
challenges
businesses
face
in
achieving
management.
It
then
introduces
concept
describes
their
use
solving
complex
optimization
problems.
various
algorithms
applied
analyzes
effectiveness
addressing
SSCM.
also
identifies
key
factors
that
influence
success
using
SSCM,
such
as
choice
algorithm,
problem
complexity,
data
quality.
Finally,
recommendations
future
research
this
area
highlights
promote
review
suggests
can
be
a
valuable
tool
optimizing
improving
operations’
sustainability,
efficiency,
Algorithms,
Год журнала:
2024,
Номер
17(1), С. 34 - 34
Опубликована: Янв. 14, 2024
Heart
disease
is
a
global
health
concern
of
paramount
importance,
causing
significant
number
fatalities
and
disabilities.
Precise
timely
diagnosis
heart
pivotal
in
preventing
adverse
outcomes
improving
patient
well-being,
thereby
creating
growing
demand
for
intelligent
approaches
to
predict
effectively.
This
paper
introduces
an
ensemble
heuristic–metaheuristic
feature
fusion
learning
(EHMFFL)
algorithm
using
tabular
data.
Within
the
EHMFFL
algorithm,
diverse
model
crafted,
featuring
different
subsets
each
heterogeneous
base
learner,
including
support
vector
machine,
K-nearest
neighbors,
logistic
regression,
random
forest,
naive
bayes,
decision
tree,
XGBoost
techniques.
The
primary
objective
identify
most
pertinent
features
leveraging
combined
approach
that
integrates
heuristic
knowledge
Pearson
correlation
coefficient
with
metaheuristic-driven
grey
wolf
optimizer.
second
aggregate
various
learners
through
learning.
performance
rigorously
assessed
Cleveland
Statlog
datasets,
yielding
remarkable
results
accuracy
91.8%
88.9%,
respectively,
surpassing
state-of-the-art
techniques
diagnosis.
These
findings
underscore
potential
enhancing
diagnostic
providing
valuable
clinicians
making
more
informed
decisions
regarding
care.
Algorithms,
Год журнала:
2025,
Номер
18(1), С. 38 - 38
Опубликована: Янв. 10, 2025
Sustainable
logistics
aims
to
balance
economic
efficiency,
environmental
responsibility,
and
social
well-being
in
supply
chain
operations.
This
study
explores
the
use
of
Variable
Neighborhood
Search
(VNS),
a
metaheuristic
optimization
method,
addressing
sustainable
challenges
provides
insights
into
potential
it
has
support
them
by
delivering
efficient
solutions
that
align
with
global
sustainability
goals.
The
review
identifies
key
trends,
including
significant
increase
research
since
2019,
strong
focus
on
routing,
scheduling,
location
problems.
Hybrid
approaches,
combining
VNS
other
methods,
multiobjective
address
trade-offs
between
goals
are
prominent.
most
frequently
applied
versions
closely
those
commonly
used
broader
literature,
reflecting
similar
adoption
proportions.
In
recent
years,
noticeable
studies
incorporating
adaptation
mechanisms
frameworks
emerged.
trend
is
largely
driven
growing
influence
Artificial
Intelligence
approaches
across
numerous
fields
science
engineering,
highlighting
need
for
more
dynamic
intelligent
techniques.
However,
important
gaps
remain.
These
include
limited
consideration
uncertainty
systems,
underrepresentation
sustainability,
lack
standardized
benchmarks
comparing
results.
Future
work
should
these
explore
emerging
applications.
Sensors,
Год журнала:
2024,
Номер
24(5), С. 1667 - 1667
Опубликована: Март 4, 2024
With
the
development
of
agricultural
information
technology,
Internet
Things
and
blockchain
have
become
important
in
traceability
products.
Sensors
collect
real-time
data
production
a
provides
secure
transparent
storage
medium
for
these
data,
which
improves
transparency
credibility
product
traceability.
However,
existing
solutions
are
limited
by
immutability
blockchain,
making
it
difficult
to
delete
erroneous
modify
scope
sharing.
This
damages
is
not
conducive
exchange
sharing
among
enterprises.
In
this
article,
we
propose
an
management
scheme
based
on
redactable
blockchain.
allows
enterprises
encrypt
protect
privacy.
order
facilitate
maintenance
introduce
chameleon
hash
function
provide
modification
capabilities.
Enterprises
can
fix
update
access
permissions
data.
To
improve
efficiency
block
editing,
our
adopts
distributed
editing
method.
method
supports
threshold
operations,
avoiding
single-point-of-failure
issues.
We
save
records
modifications
establish
accountability
mechanisms
identify
malicious
entities.
Finally,
paper
security
analysis
proposed
solution
verify
its
effectiveness
through
experiments.
Compared
with
scheme,
generating
speed
improved
42%
29.3%
at
125
nodes.
Discover Internet of Things,
Год журнала:
2023,
Номер
3(1)
Опубликована: Сен. 28, 2023
Abstract
The
supply
chain
network
is
one
of
the
most
important
areas
focus
in
majority
business
circumstances.
Blockchain
technology
a
feasible
choice
for
secure
information
sharing
network.
Despite
fact
that
maintaining
security
at
all
levels
blockchain
difficult,
cryptographic
methods
are
commonly
used
existing
works.
Effective
management
(SCM)
offers
various
benefits
to
organizations,
such
as
enhanced
customer
satisfaction,
increased
operational
efficiency,
competitive
advantage,
and
cost
reduction.
Potential
SCM
agricultural
food
chains
needs
distributors,
coordination
collaboration
among
farmers,
retailers,
stakeholders.
use
like
Block
Chain
(BC),
sensors,
data
analytics,
can
boost
traceability
visibility,
decrease
waste,
ensure
safety
quality
throughout
chain.
Therefore,
this
study
develops
Hunger
Games
Search
Optimization
with
Deep
Learning
Model
Sustainable
Supply
Management
(HGSODL-ASCM)
technique.
fundamental
goal
HGSODL-ASCM
technique
improve
decision-making
processes
commodity
production
storage
order
optimise
revenue.
In
provided
technique,
bidirectional
long
short-term
memory
(Bi-LSTM)
model
built
determine
amount
productivity
required
maximise
profit.
performance
Bi-LSTM
classification
process,
HGSO
algorithm
has
been
utilized
work.
presented
independently
achieve
policies
via
interaction
complicated
adaptive
environments.
A
brief
set
simulations
were
performed
improved
simulation
results
demonstrated
how
superior
method
other
already
use.
