IEEE Transactions on Cybernetics,
Journal Year:
2021,
Volume and Issue:
53(3), P. 1752 - 1764
Published: Oct. 28, 2021
As
an
extension
of
the
classical
flow-shop
scheduling
problem,
hybrid
problem
(HFSP)
widely
exists
in
large-scale
industrial
production
systems
and
has
been
considered
to
be
challenging
for
its
complexity
flexibility.
Evolutionary
algorithms
based
on
encoding
heuristic
decoding
approaches
are
shown
effective
solving
HFSP.
However,
frequently
used
strategies
can
only
search
a
limited
area
solution
space,
thus
leading
unsatisfactory
performance
during
later
period.
In
this
article,
evolutionary
algorithm
(HEA)
using
two
representations
is
proposed
solve
HFSP
makespan
minimization.
First,
HEA
searches
space
by
permutation-based
representation
methods
find
some
promising
areas.
Afterward,
Tabu
(TS)
procedure
disjunctive
graph
introduced
expand
searching
further
optimization.
Two
neighborhood
structures
focusing
critical
paths
extended
problem-specific
backward
schedules
generate
candidate
solutions
TS.
The
tested
three
public
benchmark
sets
from
existing
literature,
including
567
instances
total,
compared
with
state-of-the-art
algorithms.
Extensive
experimental
results
indicate
that
performs
much
better
than
other
Moreover,
method
finds
new
best
285
hard
instances.
Complex & Intelligent Systems,
Journal Year:
2021,
Volume and Issue:
7(5), P. 2235 - 2253
Published: May 28, 2021
Abstract
Distributed
hybrid
flow
shop
scheduling
problem
(DHFSP)
has
attracted
some
attention;
however,
DHFSP
with
uncertainty
and
energy-related
element
is
seldom
studied.
In
this
paper,
distributed
energy-efficient
(DEHFSP)
fuzzy
processing
time
considered
a
cooperated
shuffled
frog-leaping
algorithm
(CSFLA)
presented
to
optimize
makespan,
total
agreement
index
energy
consumption
simultaneously.
Iterated
greedy,
variable
neighborhood
search
global
are
designed
using
problem-related
features;
memeplex
evaluation
based
on
three
quality
indices
presented,
an
effective
cooperation
process
between
the
best
worst
developed
according
results
performed
by
exchanging
times
ability,
adaptive
population
shuffling
adopted
improve
efficiency.
Extensive
experiments
conducted
computational
validate
that
CSFLA
promising
advantages
solving
DEHFSP.
IEEE Transactions on Cybernetics,
Journal Year:
2021,
Volume and Issue:
53(3), P. 1752 - 1764
Published: Oct. 28, 2021
As
an
extension
of
the
classical
flow-shop
scheduling
problem,
hybrid
problem
(HFSP)
widely
exists
in
large-scale
industrial
production
systems
and
has
been
considered
to
be
challenging
for
its
complexity
flexibility.
Evolutionary
algorithms
based
on
encoding
heuristic
decoding
approaches
are
shown
effective
solving
HFSP.
However,
frequently
used
strategies
can
only
search
a
limited
area
solution
space,
thus
leading
unsatisfactory
performance
during
later
period.
In
this
article,
evolutionary
algorithm
(HEA)
using
two
representations
is
proposed
solve
HFSP
makespan
minimization.
First,
HEA
searches
space
by
permutation-based
representation
methods
find
some
promising
areas.
Afterward,
Tabu
(TS)
procedure
disjunctive
graph
introduced
expand
searching
further
optimization.
Two
neighborhood
structures
focusing
critical
paths
extended
problem-specific
backward
schedules
generate
candidate
solutions
TS.
The
tested
three
public
benchmark
sets
from
existing
literature,
including
567
instances
total,
compared
with
state-of-the-art
algorithms.
Extensive
experimental
results
indicate
that
performs
much
better
than
other
Moreover,
method
finds
new
best
285
hard
instances.
