Journal of Adhesion Science and Technology,
Journal Year:
2024,
Volume and Issue:
unknown, P. 1 - 23
Published: Oct. 9, 2024
The
reconfigurable
manufacturing
system
(RMS)
is
an
advanced
strategy
that
enables
precise
adjustment
of
functionality
and
capacity
to
meet
fluctuating
demands
economically.
RMS
focuses
on
part
families,
allowing
configurations
be
adapted
for
new
requirements.
Optimizing
flow
line
design
produce
various
parts
involves
minimizing
reconfigurations
associated
costs
by
enhancing
operation
sequence
similarity.
This
article
proposes
a
novel
optimization
using
the
Longest
Common
Subsequence
(LCS)
method
reduce
bypassing
moves
machine
idle
times.
study
introduces
similarity
coefficient
derived
from
LCS
employs
average
linkage
hierarchical
clustering
categorize
in
case
study.
Unlike
traditional
methods,
this
approach
considers
material
movements
both
before
initial
after
final
processing
station,
addressing
gaps
move
calculations.
impact
different
weighting
scenarios
Type-II
(ω)
idleness
(β)
was
examined.
For
example,
with
weights
{1.0,
0.6,
0.3,
0.0}
equal
weightings
(α)
set
at
0.5,
threshold
value
0.3
results
eight
clusters,
such
as
Cluster
1
{1,
11,
10,
12}
3
{3,
5,
6,
4,
15,
9,
13,
14,
7,
8}.
Lower
values
lead
fewer
clusters
larger
sizes,
indicating
more
consolidated
family
grouping.
Various
handling
demonstrate
how
affect
sizes.
enhances
efficiency
integrating
comprehensive
considerations
optimizing
based
operational
similarities.
Scientific Reports,
Journal Year:
2025,
Volume and Issue:
15(1)
Published: Jan. 29, 2025
Today,
with
the
increasing
use
of
Internet
Things
(IoT)
in
world,
various
workflows
that
need
to
be
stored
and
processed
on
computing
platforms.
But
this
issue,
causes
an
increase
costs
for
resources
providers,
as
a
result,
system
Energy
Consumption
(EC)
is
also
reduced.
Therefore,
paper
examines
workflow
scheduling
problem
IoT
devices
fog-cloud
environment,
where
reducing
EC
MakeSpan
Time
(MST)
main
objectives,
under
constraints
priority,
deadline
reliability.
order
achieve
these
combination
Aquila
Salp
Swarm
Algorithms
(ASSA)
used
select
best
Virtual
Machines
(VMs)
execution
workflows.
So,
each
iteration
ASSA
execution,
number
VMs
are
selected
by
ASSA.
Then
using
Reducing
(RMST)
technique,
MST
reduced,
while
maintaining
reliability
deadline.
Then,
VM
merging
Dynamic
Voltage
Frequency
Scaling
(DVFS)
technique
output
from
RMST,
static
dynamic
respectively.
Experimental
results
show
effectiveness
proposed
method
compared
previous
methods.
Journal of Computational Design and Engineering,
Journal Year:
2024,
Volume and Issue:
11(5), P. 143 - 163
Published: Aug. 16, 2024
Abstract
This
paper
introduces
the
Quantum
Chimp
Optimization
Algorithm
(QU-ChOA),
which
integrates
(ChOA)
with
quantum
mechanics
principles
to
enhance
optimization
capabilities.
The
study
evaluates
QU-ChOA
across
diverse
domains,
including
benchmark
tests,
IEEE
CEC-06–2019
100-Digit
Challenge,
real-world
problems
from
IEEE-CEC-2020,
and
dynamic
scenarios
IEEE-CEC-2022.
Key
findings
highlight
QU-ChOA’s
competitive
performance
in
both
unimodal
multimodal
functions,
achieving
an
average
success
rate
(SR)
of
88.98%
various
functions.
demonstrates
robust
global
search
abilities,
efficiently
finding
optimal
solutions
fitness
evaluations
(AFEs)
14
012
calculation
duration
58.22
units
fire
detection
applications.
In
outperforms
traditional
algorithms,
a
perfect
SR
100%
Challenge
for
several
underscoring
its
effectiveness
complex
numerical
optimization.
Real-world
applications
significant
improvements
objective
function
values
industrial
processes,
showcasing
versatility
applicability
practical
scenarios.
identifies
gaps
existing
strategies
positions
as
novel
solution
these
challenges.
It
advancements,
such
20%
reduction
AFEs
compared
methods,
illustrating
efficiency
different
tasks.
These
results
establish
promising
tool
addressing
intricate
fields.
Frontiers in Industrial Engineering,
Journal Year:
2025,
Volume and Issue:
3
Published: Jan. 27, 2025
The
advent
of
Industry
4.0
and
the
emerging
5.0
have
fundamentally
transformed
manufacturing
systems,
introducing
unprecedented
levels
complexity
in
production
scheduling.
This
is
further
amplified
by
integration
cyber-physical
Internet
Things,
Artificial
Intelligence,
human-centric
approaches,
necessitating
more
sophisticated
optimization
methods.
paper
aims
to
provide
a
comprehensive
perspective
on
application
metaheuristic
algorithms
shop
scheduling
problems
within
context
5.0.
Through
systematic
review
recent
literature
(2015–2024),
we
analyze
categorize
various
including
Evolutionary
Algorithms
(EAs),
swarm
intelligence,
hybrid
methods,
that
been
applied
address
complex
challenges
smart
environments.
We
specifically
examine
how
these
handle
multiple
competing
objectives
such
as
makespan
minimization,
energy
efficiency,
costs,
human-machine
collaboration,
which
are
crucial
modern
industrial
settings.
Our
survey
reveals
several
key
findings:
1)
metaheuristics
demonstrate
superior
performance
handling
multi-objective
compared
standalone
algorithms;
2)
bio-inspired
show
promising
results
addressing
environments;
3)
tri-objective
higher-order
warrant
in-depth
exploration;
4)
there
an
trend
towards
incorporating
human
factors
sustainability
optimization,
aligned
with
principles.
Additionally,
identify
research
gaps
propose
future
directions,
particularly
areas
real-time
adaptation,
sustainability-aware
algorithms.
provides
insights
for
researchers
practitioners
field
scheduling,
offering
structured
understanding
current
methodologies
evolution
from
