A discrete artificial bee colony algorithm and its application in flexible flow shop scheduling with assembly and machine deterioration effect
Applied Soft Computing,
Год журнала:
2024,
Номер
159, С. 111593 - 111593
Опубликована: Апрель 16, 2024
Язык: Английский
Ship pipe production optimization method for solving distributed heterogeneous energy-efficient flexible flowshop scheduling with mobile resource limitation
Expert Systems with Applications,
Год журнала:
2025,
Номер
unknown, С. 126603 - 126603
Опубликована: Янв. 1, 2025
Язык: Английский
A multi-objective Immune Balancing Algorithm for Distributed Heterogeneous Batching-integrated Assembly Hybrid Flowshop Scheduling
Expert Systems with Applications,
Год журнала:
2024,
Номер
259, С. 125288 - 125288
Опубликована: Сен. 12, 2024
Язык: Английский
A Q-learning grey wolf optimizer for a distributed hybrid flowshop rescheduling problem with urgent job insertion
Journal of Applied Mathematics and Computing,
Год журнала:
2025,
Номер
unknown
Опубликована: Янв. 17, 2025
Язык: Английский
Double Deep Q-Network-Based Solution to a Dynamic, Energy-Efficient Hybrid Flow Shop Scheduling System with the Transport Process
Systems,
Год журнала:
2025,
Номер
13(3), С. 170 - 170
Опубликована: Фев. 28, 2025
In
this
paper,
a
dynamic
energy-efficient
hybrid
flow
shop
(TDEHFSP)
scheduling
model
is
proposed,
considering
random
arrivals
of
new
jobs
and
transport
by
transfer
vehicles.
To
simultaneously
optimise
the
maximum
completion
time
total
energy
consumption,
co-evolutionary
approach
(DDQCE)
using
double
deep
Q-network
(DDQN)
introduced,
where
global
local
search
tasks
are
assigned
to
different
populations
use
computational
resources.
addition,
multi-objective
NEW
heuristic
strategy
implemented
generate
an
initial
population
with
enhanced
convergence
diversity.
The
DDQCE
incorporates
based
on
interval
‘left
shift’
turn-on/off
mechanisms,
alongside
rescheduling
manage
disturbances.
36
test
instances
varying
sizes,
simplified
from
excavator
boom
manufacturing
process,
designed
for
comparative
experiments
traditional
algorithms.
experimental
results
demonstrate
that
achieves
40%
more
Pareto-optimal
solutions
compared
NSGA-II
MOEA/D
while
requiring
10%
less
time,
confirming
algorithm
efficiently
solves
TDEHFSP
problem.
Язык: Английский
Q-learning based estimation of distribution algorithm for scheduling distributed heterogeneous flexible flow-shop with mixed buffering limitation
Engineering Applications of Artificial Intelligence,
Год журнала:
2025,
Номер
149, С. 110537 - 110537
Опубликована: Март 12, 2025
Язык: Английский
Cooperative optimisation of production and transportation considering order weight and sequence-dependent setup time
International Journal of Logistics Research and Applications,
Год журнала:
2025,
Номер
unknown, С. 1 - 19
Опубликована: Март 24, 2025
Язык: Английский
A cooperative Q-learning-based memetic algorithm for distributed assembly heterogeneous flexible flowshop scheduling
Expert Systems with Applications,
Год журнала:
2025,
Номер
unknown, С. 128198 - 128198
Опубликована: Май 1, 2025
Язык: Английский
A novel and efficient mathematical optimization model for multi-stage assembly flow shop considering post-processing
Journal of Industrial and Production Engineering,
Год журнала:
2024,
Номер
42(1), С. 1 - 13
Опубликована: Июль 1, 2024
We
addressed
the
multistage
assembly
flow
shop
problem
with
post-processing
and
makespan
minimization,
a
production
environment
commonly
encountered
in
diverse
industries
such
as
automotive,
dental,
medical
equipment,
clothing
manufacturing.
In
this
context,
we
presented
an
innovative
mixed-integer
linear
programming
model
position-based
strategy.
Our
proposed
formulation
demonstrated
remarkable
efficiency
when
compared
to
of
literature.
It
achieved
optimal
solutions
77.16%
instances,
average
optimality
gap
10.59%.
This
study
constitutes
significant
contribution
efficient
resolution
practical
frequently
scheduling
that
has
received
relatively
limited
attention
existing
The
findings
highlight
crucial
role
mathematical
optimization
models
valuable
decision-making
tools
for
within
system.
Язык: Английский
An Improved Constrained Multiobjective Optimization for Energy Multimodal Transport Among Clustering Islands
Mathematics,
Год журнала:
2024,
Номер
12(24), С. 3926 - 3926
Опубликована: Дек. 13, 2024
Clustering
islands
located
close
to
each
other
and
sharing
some
common
characteristics
offer
diverse
unique
opportunities
for
tourism,
trade,
research,
especially
take
a
crucial
part
in
the
military.
Remote
from
inland,
have
relatively
limited
resources,
which
makes
them
dependent
on
imported
energy
sources
such
as
oil
gas
or
renewable
energy.
However,
there
are
few
studies
about
security
of
clustering
islands.
To
this
end,
study
proposes
novel
optimization
framework
that
aims
optimize
use
their
different
types
among
improve
stability
whole
internet
via
multilayer
transportation
network.
The
network
also
enables
serve
emergency
power
situations.
Specifically,
we
construct
an
assignment
model
considers
multimodal
transport,
multiobjective,
multiple
constraints.
address
issue,
develop
unconstrained-individuals
guiding
constrained
multiobjective
algorithm,
named
uiCMOA.
Experimental
results
demonstrate
effectiveness
efficiency
proposed
algorithm.
Язык: Английский