Mathematics,
Год журнала:
2024,
Номер
12(20), С. 3176 - 3176
Опубликована: Окт. 11, 2024
Production
rescheduling
involves
re-optimizing
production
schedules
in
response
to
disruptions
that
render
the
initial
schedule
inefficient
or
unfeasible.
This
process
requires
simultaneous
consideration
of
multiple
objectives
develop
new
are
both
efficient
and
stable.
However,
existing
review
papers
have
paid
limited
attention
multi-objective
optimization
techniques
employed
this
context.
To
address
gap,
paper
presents
a
systematic
literature
on
rescheduling,
examining
diverse
shop-floor
environments.
Adhering
PRISMA
guidelines,
total
291
were
identified.
From
pool,
studies
meeting
inclusion
criteria
selected
analyzed
provide
comprehensive
overview
problems
tackled,
dynamic
events
managed,
considered,
approaches
discussed
literature.
highlights
primary
methods
used
relation
strategies
disruptive
studied.
Findings
reveal
growing
interest
research
area,
with
“a
priori”
posteriori”
being
most
commonly
implemented
notable
rise
use
latter.
Hybridized
algorithms
shown
superior
performance
compared
standalone
by
leveraging
combined
strengths
mitigating
individual
weaknesses.
Additionally,
“interactive”
“Pareto
pruning”
methods,
as
well
human
factors
flexible
systems,
remain
under-explored.
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.