IET Renewable Power Generation,
Journal Year:
2022,
Volume and Issue:
16(8), P. 1535 - 1561
Published: March 25, 2022
Abstract
In
this
paper,
an
efficient
sine
cosine
differential
gradient‐based
optimization
method
is
proposed
for
identifying
unknown
parameters
of
photovoltaic
models.
the
simulation,
parameter
identification
formulated
as
objective
function
to
be
minimized
based
on
error
between
estimated
and
experimental
data.
Based
original
method,
combines
mutation
crossover
evolution
algorithm.
Specifically,
operator
enables
algorithm
avoid
local
optima;
meanwhile,
strategy
encourages
new
individual
calculate
worst
position.
The
simulation
results
demonstrate
that
can
achieve
minimal
root
mean
square
obtain
better
optima
relative
other
algorithms
in
different
cells.
Therefore,
has
great
potential
used
estimating
model
parameters.
International Journal of Production Research,
Journal Year:
2022,
Volume and Issue:
61(4), P. 1233 - 1251
Published: March 24, 2022
Distributed
hybrid
flow
shop
scheduling
(DHFS)
problem
has
attracted
much
attention
in
recent
years;
however,
DHFS
with
actual
processing
constraints
like
assembly
is
seldom
considered
and
reinforcement
learning
hardly
embedded
into
meta-heuristic
for
DHFS.
In
this
study,
a
distributed
(DAHFS)
fabrication,
transportation
mathematic
model
constructed.
A
new
shuffled
frog-learning
algorithm
Q-learning
(QSFLA)
proposed
to
minimise
makespan.
three-string
representation
used.
newly
defined
process
QSFLA
select
search
strategy
dynamically
memeplex
search.
It
composed
of
four
actions
based
on
the
combination
global
search,
neighbourhood
solution
acceptance
rule,
six
states
depicted
by
population
evaluation
elite
diversity,
reward
function.
number
experiments
are
conducted.
The
computational
results
demonstrate
that
can
provide
promising
DAHFS.
Complex System Modeling and Simulation,
Journal Year:
2023,
Volume and Issue:
3(1), P. 32 - 46
Published: March 1, 2023
At
present,
home
health
care
(HHC)
has
been
accepted
as
an
effective
method
for
handling
the
healthcare
problems
of
elderly.
The
HHC
scheduling
and
routing
problem
(HHCSRP)
attracts
wide
concentration
from
academia
industrial
communities.
This
work
proposes
HHCSRP
considering
several
centers,
where
a
group
customers
(i.e.,
patients
elderly)
require
being
assigned
to
centers.
Then,
various
kinds
services
are
provided
by
caregivers
in
different
regions.
By
skill
matching,
customers'
appointment
time,
caregivers'
workload
balancing,
this
article
formulates
optimization
model
with
multiple
objectives
achieve
minimal
service
cost
delay
cost.
To
handle
it,
we
then
introduce
brain
storm
particular
multi-objective
search
mechanisms
(MOBSO)
via
combining
features
investigated
HHCSRP.
Moreover,
perform
experiments
test
effectiveness
designed
method.
Via
comparing
MOBSO
two
excellent
optimizers,
results
confirm
that
developed
significant
superiority
addressing
considered
Journal of Industrial Information Integration,
Journal Year:
2024,
Volume and Issue:
39, P. 100598 - 100598
Published: March 12, 2024
Recent
advancements
in
production
scheduling
have
arisen
response
to
the
need
for
adaptation
dynamic
environments.
This
paper
addresses
challenge
of
real-time
within
context
sustainable
production.
We
redefine
distributed
permutation
flow-shop
problem
using
an
online
mixed-integer
programming
model.
The
proposed
model
prioritizes
minimizing
makespan
while
simultaneously
constraining
energy
consumption,
reducing
number
lost
working
days
and
increasing
job
opportunities
permissible
limits.
Our
approach
considers
machines
operating
different
modes,
ranging
from
manual
automatic,
employs
two
strategies:
predictive-reactive
proactive-reactive
scheduling.
evaluate
rescheduling
policies:
continuous
event-driven.
To
demonstrate
model's
applicability,
we
present
a
case
study
auto
workpiece
manage
complexity
through
various
reformulations
heuristics,
such
as
Lagrangian
relaxation
Benders
decomposition
initial
optimization
well
four
problem-specific
heuristics
considerations.
For
solving
large-scale
instances,
employ
simulated
annealing
tabu
search
metaheuristic
algorithms.
findings
underscore
benefits
strategy
efficiency
event-driven
policy.
By
addressing
challenges
integrating
sustainability
criteria,
this
contributes
valuable
insights
into
Tsinghua Science & Technology,
Journal Year:
2024,
Volume and Issue:
29(5), P. 1249 - 1265
Published: May 2, 2024
With
the
emergence
of
artificial
intelligence
era,
all
kinds
robots
are
traditionally
used
in
agricultural
production.
However,
studies
concerning
robot
task
assignment
problem
agriculture
field,
which
is
closely
related
to
cost
and
efficiency
a
smart
farm,
limited.
Therefore,
Multi-Weeding
Robot
Task
Assignment
(MWRTA)
addressed
this
paper
minimize
maximum
completion
time
residual
herbicide.
A
mathematical
model
set
up,
Multi-Objective
Teaching-Learning-Based
Optimization
(MOTLBO)
algorithm
presented
solve
problem.
In
MOTLBO
algorithm,
heuristic-based
initialization
comprising
an
improved
Nawaz
Enscore,
Ham
(NEH)
heuristic
load-based
generate
initial
population
with
high
level
quality
diversity.
An
effective
teaching-learning-based
optimization
process
designed
dynamic
grouping
mechanism
redefined
individual
updating
rule.
multi-neighborhood-based
local
search
strategy
provided
balance
exploitation
exploration
algorithm.
Finally,
comprehensive
experiment
conducted
compare
proposed
several
state-of-the-art
algorithms
literature.
Experimental
results
demonstrate
significant
superiority
for
solving
under
consideration.
