Biform game analysis with the Owen allocation function for a supply chain game under precedence constraints
Operational Research,
Год журнала:
2025,
Номер
25(2)
Опубликована: Май 16, 2025
Язык: Английский
A Review of Supply Chain Resilience: A Network Modeling Perspective
Applied Sciences,
Год журнала:
2024,
Номер
15(1), С. 265 - 265
Опубликована: Дек. 30, 2024
Against
the
backdrop
of
globalization,
complexity
supply
chains
has
been
increasing,
making
chain
resilience
a
critical
factor
in
ensuring
stable
operation
enterprises,
national
economies,
and
international
trade.
This
paper
adopts
network
modeling
perspective
to
systematically
review
theoretical
foundations
research
progress
resilience,
focusing
on
application
methods.
First,
concept
is
defined,
its
developmental
trajectory
reviewed.
Through
literature
visualization
analysis,
this
study
delves
into
current
state
addressing
challenges
risk
management,
highlighting
importance
techniques
field.
Subsequently,
it
explores
based
complex
networks
agent-based
modeling,
analyzing
their
strengths
limitations
simulating
overall
evolution
dynamic
behavior
individual
entities.
By
integrating
structural
characteristics
with
evaluation
methods,
suggests
potential
directions
for
future
research.
These
include
enhancing
description
firm
behavior,
dynamics
information
networks,
emphasizing
task-oriented
model
design,
thereby
offering
new
perspectives
pathways
managing
way
that
can
generate
significant
positive
externalities
global
economies.
also
indicates
enhanced
produce
multiplier
effect,
benefiting
not
only
firms
but
promoting
economic
stability
growth
across
multiple
countries.
Язык: Английский
Analysis of the Characteristics of Ship Collision-Avoidance Behavior Based on Apriori and Complex Network
Journal of Marine Science and Engineering,
Год журнала:
2024,
Номер
13(1), С. 35 - 35
Опубликована: Дек. 29, 2024
The
exploration
of
ship
collision
avoidance
behavior
characteristics
can
provide
a
theoretical
basis
for
risk
assessment
and
decision-making,
which
is
significant
ensuring
maritime
navigation
safety
the
development
intelligent
ships.
In
order
to
scientifically
effectively
analyze
collision-avoidance
seek
intrinsic
connections
among
feature
parameters(CABFPS),
this
study
proposes
method
that
combines
Apriori
algorithm
complex
network
theory
mine
from
massive
AIS
spatiotemporal
data.
Based
on
obtaining
encounter
samples
CABFPS
data,
used
association
rules
motion
parameters,
maximum
mutual
information
coefficient
employed
represent
correlation
between
parameters.
Complex
networks
different
situations
are
constructed,
topological
indicators
analyzed.
Mutual
applied
identify
key
parameters
affecting
collision-
under
situations.
analysis
using
actual
data
indicates
during
navigation,
relationships
various
closely
linked
prone
influence.
impact
actions
varies
scenarios,
with
relative
distance
DCPA
having
greatest
influence
actions.
This
comprehensively
accurately
correlations
mechanism
actions,
providing
reference
formulation
decisions.
Язык: Английский