Engineering Optimization,
Год журнала:
2024,
Номер
unknown, С. 1 - 29
Опубликована: Авг. 7, 2024
This
article
examines
an
alliance
of
heterogeneous
factories
operating
as
a
production
network,
in
which
jobs
can
be
divided
into
several
sub-jobs
and
independently
processed
distributed
factories.
problem
is
considered
unrelated
parallel
machine
scheduling
with
splitting
(DUPMSP/S).
A
mathematical
model
effective
multi-stage
evolutionary
algorithm
(EMSEA)
are
proposed,
aiming
to
minimize
the
total
tardiness
cost
transportation.
In
EMSEA,
optimization
process
three
stages
according
population
each
generation,
four
problem-based
initial
methods
knowledge-based
local
exploitation
strategies
embedded
improve
its
performance.
Extensive
experiments
conducted
compare
EMSEA
other
algorithms
no
jobs.
The
results
demonstrate
that
most
promising
method
solving
DUPMSP/S,
job
mode
effective.
Journal of Industrial Information Integration,
Год журнала:
2024,
Номер
39, С. 100598 - 100598
Опубликована: Март 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
International Journal of Production Research,
Год журнала:
2024,
Номер
unknown, С. 1 - 29
Опубликована: Май 30, 2024
Flexible
job
shop
scheduling
problem
(FJSP)
with
worker
flexibility
has
gained
significant
attention
in
the
upcoming
Industry
5.0
era
because
of
its
computational
complexity
and
importance
production
processes.
It
is
normally
assumed
that
each
machine
typically
operated
by
one
at
any
time;
therefore,
shop-floor
managers
need
to
decide
on
most
efficient
assignments
for
machines
workers.
However,
processing
time
variable
uncertain
due
fluctuating
environment
caused
unsteady
operating
conditions
learning
effect
Meanwhile,
they
also
balance
workload
while
meeting
efficiency.
Thus
a
dual
resource-constrained
FJSP
worker's
fuzzy
(F-DRCFJSP-WL)
investigated
simultaneously
minimise
makespan,
total
workloads
maximum
workload.
Subsequently,
reinforcement
enhanced
multi-objective
memetic
algorithm
based
decomposition
(RL-MOMA/D)
proposed
solving
F-DRCFJSP-WL.
For
RL-MOMA/D,
Q-learning
incorporated
into
perform
neighbourhood
search
further
strengthen
exploitation
capability
algorithm.
Finally,
comprehensive
experiments
extensive
test
instances
case
study
aircraft
overhaul
are
conducted
demonstrate
effectiveness
superiority
method.
