Symmetry,
Journal Year:
2024,
Volume and Issue:
16(6), P. 641 - 641
Published: May 22, 2024
In
today’s
customer-centric
economy,
the
demand
for
personalized
products
has
compelled
corporations
to
develop
manufacturing
processes
that
are
more
flexible,
efficient,
and
cost-effective.
Flexible
job
shops
offer
organizations
agility
cost-efficiency
traditional
lack.
However,
dynamics
of
modern
manufacturing,
including
machine
breakdown
new
order
arrivals,
introduce
unpredictability
complexity.
This
study
investigates
multiplicity
dynamic
flexible
shop
scheduling
problem
(MDFJSP)
with
arrivals.
To
address
this
problem,
we
incorporate
fluid
model
propose
a
randomized
adaptive
search
(FRAS)
algorithm,
comprising
construction
phase
local
phase.
Firstly,
in
phase,
heuristic
an
online
tracking
policy
generates
high-quality
initial
solutions.
Secondly,
employ
improved
tabu
procedure
enhance
efficiency
solution
space,
incorporating
symmetry
considerations.
The
results
numerical
experiments
demonstrate
superior
effectiveness
FRAS
algorithm
solving
MDFJSP
when
compared
other
algorithms.
Specifically,
proposed
demonstrates
quality
relative
existing
algorithms,
average
improvement
29.90%;
exhibits
acceleration
speed,
increase
1.95%.
Processes,
Journal Year:
2024,
Volume and Issue:
12(11), P. 2423 - 2423
Published: Nov. 2, 2024
The
high-quality
development
of
the
manufacturing
industry
necessitates
accelerating
its
transformation
towards
high-end,
intelligent,
and
green
development.
Considering
logistics
resource
constraints,
impact
dynamic
disturbance
events
on
production,
need
for
energy-efficient
integrated
scheduling
production
equipment
automated
guided
vehicles
(AGVs)
in
a
flexible
job
shop
environment
is
investigated
this
study.
Firstly,
static
model
AGVs
(ISPEA)
developed
based
mixed-integer
programming,
which
aims
to
optimize
maximum
completion
time
total
energy
consumption
(EC).
In
recent
years,
reinforcement
learning,
including
deep
learning
(DRL),
has
demonstrated
significant
advantages
handling
workshop
issues
with
sequential
decision-making
characteristics,
can
fully
utilize
vast
quantity
historical
data
accumulated
adjust
plans
timely
manner
changes
conditions
demand.
Accordingly,
DRL-based
approach
introduced
address
common
disturbances
emergency
order
insertions.
Combined
characteristics
ISPEA
problem
an
event-driven
strategy
events,
four
types
agents,
namely
workpiece
selection,
machine
AGV
target
selection
are
set
up,
refine
status
as
observation
inputs
generate
rules
selecting
workpieces,
machines,
AGVs,
targets.
These
agents
trained
offline
using
QMIX
multi-agent
framework,
utilized
solve
problem.
Finally,
effectiveness
proposed
method
validated
through
comparison
solution
performance
other
typical
optimization
algorithms
various
cases.
Scientific Reports,
Journal Year:
2025,
Volume and Issue:
15(1)
Published: Jan. 2, 2025
The
production
stage
of
an
automated
job
shop
is
closely
linked
to
the
guided
vehicle
(AGV),
which
needs
be
planned
in
integrated
manner
achieve
overall
optimization.
In
order
improve
collaboration
between
stages
and
AGV
operation
system,
a
two-layer
scheduling
optimization
model
proposed
for
simultaneous
decision
making
batching
problems,
sequences
obstacle
avoidance.
Under
automatic
path
seeking
mode,
this
paper
adopts
data-driven
Bayesian
network
method
portray
transportation
time
AGVs
based
on
historical
data
control
uncertainty
AGVs.
Meanwhile,
window
established
risk
delay,
constructed
optimize
AGV.
To
solve
model,
we
design
improved
particle
swarm
algorithm
combining
genetic
operators,
crossover
operators
elite
retention
operator.
results
show
that
can
effectively
system
within
floor,
successfully
actual
scale
case
enhance
effectiveness
system.
