IET Collaborative Intelligent Manufacturing,
Год журнала:
2024,
Номер
6(1)
Опубликована: Март 1, 2024
Abstract
A
distributed
heterogeneous
permutation
flowshop
scheduling
problem
with
sequence‐dependent
setup
times
(DHPFSP‐SDST)
is
addressed,
which
well
reflects
real‐world
scenarios
in
factories.
The
objective
to
minimise
the
maximum
completion
time
(makespan)
by
assigning
jobs
factories,
and
sequencing
them
within
each
factory.
First,
a
mathematical
model
describe
DHPFSP‐SDST
established.
Second,
four
meta‐heuristics,
including
genetic
algorithms,
differential
evolution,
artificial
bee
colony,
iterated
greedy
(IG)
algorithms
are
improved
optimally
solve
concerned
compared
other
existing
optimisers
literature.
Nawaz‐Enscore‐Ham
(NEH)
heuristic
employed
for
generating
an
initial
solution.
Then,
five
local
search
operators
designed
based
on
characteristics
enhance
algorithms'
performance.
To
choose
appropriately
during
iterations,
Q‐learning‐based
strategy
adopted.
Finally,
extensive
numerical
experiments
conducted
72
instances
using
5
optimisers.
obtained
optimisation
results
comparisons
prove
that
IG
algorithm
along
Q‐learning
selection
shows
better
performance
respect
its
peers.
proposed
exhibits
higher
efficiency
problems.
IEEE Transactions on Systems Man and Cybernetics Systems,
Год журнала:
2024,
Номер
54(6), С. 3321 - 3333
Опубликована: Фев. 21, 2024
This
work
addresses
multiobjective
dynamic
surgery
scheduling
problems
with
considering
uncertain
setup
time
and
processing
time.
When
dealing
them,
researchers
have
to
consider
rescheduling
due
the
arrivals
of
urgent
patients.
The
goals
are
minimize
fuzzy
total
medical
cost,
maximum
completion
time,
maximize
average
patient
satisfaction.
First,
we
develop
a
mathematical
model
for
describing
addressed
problems.
is
expressed
by
triangular
numbers.
Then,
four
meta-heuristics
improved,
eight
variants
developed,
including
artificial
bee
colony,
genetic
algorithm,
teaching-learning-base
optimization,
imperialist
competitive
algorithm.
For
improving
initial
solutions'
quality,
two
initialization
strategies
developed.
Six
local
search
proposed
fine
exploitation
$Q$
-learning
algorithm
used
choose
suitable
among
them
in
iterative
process
meta-heuristics.
states
actions
defined
according
characteristic
Finally,
algorithms
tested
57
instances
different
scales.
analysis
discussions
verify
that
improved
colony
most
one
all
compared
algorithms.
