Symmetry,
Год журнала:
2023,
Номер
15(4), С. 836 - 836
Опубликована: Март 30, 2023
In
real-world
production
processes,
the
same
enterprise
often
has
multiple
factories
or
one
factory
lines,
and
objectives
need
to
be
considered
in
process.
A
dual-population
genetic
algorithm
with
Q-learning
is
proposed
minimize
maximum
completion
time
number
of
tardy
jobs
for
distributed
hybrid
flow
shop
scheduling
problems,
which
have
some
symmetries
machines.
Multiple
crossover
mutation
operators
are
proposed,
only
search
strategy
combination,
including
operator
operator,
selected
each
iteration.
population
assessment
method
provided
evaluate
evolutionary
state
at
initial
after
Two
populations
adopt
different
strategies,
best
first
second
under
guidance
Q-learning.
Experimental
results
show
that
competitive
solving
multi-objective
problems.
IEEE Transactions on Cybernetics,
Год журнала:
2022,
Номер
53(5), С. 3337 - 3350
Опубликована: Авг. 22, 2022
Carbon
peaking
and
carbon
neutrality,
which
are
the
significant
national
strategy
for
sustainable
development,
have
attracted
considerable
attention
from
production
enterprises.
In
this
study,
energy
consumption
is
considered
in
distributed
blocking
flow
shop
scheduling
problem
(DBFSP).
A
hyperheuristic
with
Q
-learning
(HHQL)
presented
to
address
energy-efficient
DBFSP
(EEDBFSP).
employed
select
an
appropriate
low-level
heuristic
(LLH)
a
predesigned
LLH
set
according
historical
information
fed
back
by
LLH.
An
initialization
method,
considers
both
total
tardiness
(TTD)
(TEC),
proposed
construct
initial
population.
The
ε
-greedy
introduced
utilize
learned
knowledge
while
retaining
certain
degree
of
exploration
process
selecting
acceleration
operation
job
on
critical
path
designed
optimize
TTD.
deceleration
noncritical
TEC.
statistical
computational
experimentation
extensive
benchmark
testified
that
HHQL
outperforms
other
comparison
algorithm
regarding
efficiency
significance
solving
EEDBFSP.
IEEE Transactions on Cybernetics,
Год журнала:
2022,
Номер
53(5), С. 3101 - 3113
Опубликована: Март 14, 2022
In
the
actual
production,
insertion
of
new
job
and
machine
preventive
maintenance
(PM)
are
very
common
phenomena.
Under
these
situations,
a
flexible
job-shop
rescheduling
problem
(FJRP)
with
both
PM
is
investigated.
First,
an
imperfect
(IPM)
model
established
to
determine
optimal
plan
for
each
machine,
optimality
proven.
Second,
in
order
jointly
optimize
production
scheduling
planning,
multiobjective
optimization
developed.
Third,
deal
this
model,
improved
nondominated
sorting
genetic
algorithm
III
adaptive
reference
vector
(NSGA-III/ARV)
proposed,
which
hybrid
initialization
method
designed
obtain
high-quality
initial
population
critical-path-based
local
search
(LS)
mechanism
constructed
accelerate
convergence
speed
algorithm.
numerical
simulation,
effect
parameter
setting
on
NSGA-III/ARV
investigated
by
Taguchi
experimental
design.
After
that,
superiority
operators
overall
performance
proposed
demonstrated.
Next,
comparison
two
IPM
models
carried
out,
verifies
effectiveness
model.
Last
but
not
least,
we
have
analyzed
impact
different
effects
decisions
integrated
maintenance-production
schemes.
IEEE Transactions on Systems Man and Cybernetics Systems,
Год журнала:
2024,
Номер
54(5), С. 3207 - 3219
Опубликована: Фев. 13, 2024
The
distributed
no-idle
permutation
flowshop
scheduling
problem
(DNIPFSP)
has
widely
existed
in
various
manufacturing
systems.
makespan
and
total
tardiness
are
optimized
simultaneously
considering
the
variety
of
scales
problems
with
introducing
an
improved
iterative
greedy
(IIG)
algorithm.
variable
neighborhood
descent
(VND)
algorithm
is
applied
to
local
search
method
Two
perturbation
operators
based
on
critical
factory
proposed
as
structure
VND.
In
destruction
phase,
scale
varies
size
problem.
An
insertion
operator-based
strategy
sorts
undeleted
jobs
after
phase.
$Q$
-learning
mechanism
for
selecting
weighting
coefficients
introduced
obtain
a
relatively
small
objective
value.
Finally,
tested
benchmark
suite
compared
other
existing
algorithms.
experiments
show
that
IIG
obtained
more
satisfactory
results.
IEEE Transactions on Industrial Informatics,
Год журнала:
2022,
Номер
19(8), С. 8588 - 8599
Опубликована: Ноя. 9, 2022
Carbon
peaking
and
carbon
neutrality,
which
are
significant
strategies
for
national
sustainable
development,
have
attracted
enormous
attention
from
researchers
in
the
manufacturing
domain.
A
Pareto-based
discrete
Jaya
algorithm
(PDJaya)
is
proposed
to
solve
carbon-efficient
distributed
blocking
flow
shop
scheduling
problem
(CEDBFSP)
with
criteria
of
total
tardiness
emission
this
article.
The
mixed-integer
linear
programming
model
presented
CEDBFSP.
An
effective
constructive
heuristic
produced
generate
initial
population.
new
individual
generated
by
update
mechanism
PDJaya.
self-adaptive
operator
local
search
strategy
designed
enhance
exploitation
capability
critical-path-based
saving
introduced
further
reduce
emissions.
effectiveness
each
PDJaya
verified
compared
state-of-the-art
algorithms
benchmark
suite.
numerical
results
demonstrate
that
efficient
optimizer
solving
IEEE Transactions on Emerging Topics in Computational Intelligence,
Год журнала:
2023,
Номер
7(5), С. 1442 - 1457
Опубликована: Май 8, 2023
With
the
global
energy
shortage,
climate
anomalies,
environmental
pollution
becoming
increasingly
prominent,
saving
scheduling
has
attracted
more
and
concern
than
before.
This
paper
studies
energy-efficient
distributed
hybrid
flow-shop
problem
(DHFSP)
with
blocking
constraints.
Our
aim
is
to
find
job
sequence
low
consumption
as
much
possible
in
a
limited
time.
In
this
paper,
we
formulate
mathematical
model
of
DHFSP
constraints
propose
an
improved
iterative
greedy
(IG)
algorithm
optimize
sequence.
proposed
algorithm,
first,
problem-specific
strategy
presented,
namely,
search
strategy,
which
can
assign
appropriate
jobs
factory
minimize
each
processing
factory.
Next,
new
selection
mechanism
inspired
by
Q-learning
provide
strategic
guidance
for
scheduling.
provides
historical
experience
different
factories.
Finally,
five
types
local
strategies
are
designed
machines
be
scheduled.
These
further
improve
ability
QIG
reduce
caused
blocking.
Simulation
results
statistical
analysis
on
90
test
problems
show
that
superior
several
high-performance
algorithms
convergence
rate
quality
solution.
