An integrated Cognitive Reliability and Error Analysis Method (CREAM) and optimization for enhancing human reliability in blockchain
Decision Analytics Journal,
Год журнала:
2024,
Номер
12, С. 100506 - 100506
Опубликована: Авг. 1, 2024
Minor
errors
in
smart
contract
coding
on
the
blockchain
can
lead
to
significant
and
irreversible
economic
losses
for
transaction
parties.
Therefore,
mitigating
risk
posed
by
is
crucial,
necessitating
development
of
approaches
enhance
human
reliability
coding.
The
Cognitive
Reliability
Error
Analysis
Method
(CREAM)
one
such
approach,
examining
how
environmental
conditions
affect
error
probability
(HEP).
Within
CREAM,
Common
Performance
Conditions
(CPCs)
influence
probability.
This
study
ranks
CPCs
based
their
importance
using
Bayesian
Best
Worst
(BWM).
Two
methods
are
developed
basic
CREAM.
In
first
method,
experts
specify
control
mode
opinions,
experts'
determined
according
level.
second
an
optimization
problem
formulated
select
most
suitable
programs,
enhancing
reliability.
proposed
model
considers
energy,
cost,
organizational
budget
factors
identify
optimal
while
minimizing
risks
costs
associated
with
errors.
A
case
electronics
supply
chain
validates
applicability
efficacy
methods.
Results
from
method
indicate
opportunistic
mode.
contrast,
shows
that
improving
CPC
levels
has
a
more
effect,
shifting
towards
tactical
reducing
HEP
0.00249.
Язык: Английский
Evaluating human error probability in maintenance task: An integrated system dynamics and machine learning approach
Human Factors and Ergonomics in Manufacturing & Service Industries,
Год журнала:
2024,
Номер
35(1)
Опубликована: Окт. 16, 2024
Abstract
Human
error
is
often
implicated
in
industrial
accidents
and
frequently
found
to
be
a
symptom
of
broader
issues
within
the
sociotechnical
system.
Therefore,
research
exploring
human
during
maintenance
activities
important.
This
article
aims
assess
probability
tasks
at
cement
factory
using
Cognitive
Reliability
Error
Analysis
Method
System
Dynamics
modeling.
Given
that
(HEP)
influenced
by
various
common
performance
conditions
(CPCs)
their
sub‐factors,
changes
dynamically
response
other
variables,
SD
method
offers
practical
approach
for
estimating
predicting
behavior
over
time.
study
identifies
quantifies
variables
affecting
HEP,
explores
interactions
feedback
tasks,
assesses
associated
costs.
The
machine
learning
technique
then
used
estimate
relationship
between
HEP
these
optimal
value
function,
0.000772,
determined
identifying
minimum
point
cubic
thereby
minimizing
costs
occupational
accidents.
Determining
crucial
excessive
investing
improved
ergonomics
CPCs
better
performance.
addresses
significant
gap
existing
where
impact
on
has
not
been
estimated
as
function.
Furthermore,
three
scenarios
are
presented
help
managers
allocate
organization's
budget
more
effectively.
Язык: Английский