Algorithms,
Год журнала:
2024,
Номер
17(10), С. 431 - 431
Опубликована: Сен. 26, 2024
Research
on
helicopter
dispatching
has
received
considerable
attention,
particularly
in
relation
to
post-disaster
rescue
operations.
The
survival
chances
of
individuals
trapped
emergency
situations
decrease
as
time
passes,
making
timely
dispatch
crucial
for
successful
missions.
Therefore,
this
study
investigates
a
collaborative
routing
problem
with
window
and
limited
constraints,
solving
it
using
an
improved
iterative
greedy
(IIG)
algorithm.
In
the
proposed
algorithm,
heuristic
initialization
strategy
is
designed
generate
efficient
feasible
initial
solution.
Then,
feasible-first
destruction-construction
applied
enhance
algorithm’s
exploration
ability.
Next,
problem-specific
local
search
developed
improve
effectiveness.
addition,
simulated
annealing
(SA)
method
integrated
acceptance
criterion
avoid
algorithm
from
getting
optima.
Finally,
evaluate
efficacy
IIG,
56
instances
were
generated
based
Solomon
used
simulation
tests.
A
comparative
analysis
was
conducted
against
six
algorithms
existing
studies.
experimental
results
demonstrate
that
performs
well
problem.
International Journal of Production Research,
Год журнала:
2023,
Номер
62(12), С. 4565 - 4594
Опубликована: Окт. 5, 2023
AbstractThis
paper
deals
with
an
overview
of
flowshop
group
scheduling
problems
in
the
manufacturing
environment.
The
aim
this
is
twofold:
(i)
making
a
comprehensive
survey
research
on
systems,
and
(ii)
presenting
bibliometric
analysis.
We
address
general
definition
provide
taxonomy
methodologies
used
previous
literature.
papers
are
presented
from
several
perspectives,
including
utilised
objective
functions,
transformation
problem
structure,
benchmarks
existing
literature,
solution
approaches.
Additionally,
analysis,
keyword
journal
analyses,
conducted
for
articles
published
between
1986
2022.
Finally,
suggestions
future
developments
listed
to
further
consolidate
area.Keywords:
Flowshop
problembibliometric
analysissystematic
analysiscellular
manufacturingVOSviewer
Disclosure
statementNo
potential
conflict
interest
was
reported
by
author(s).Data
Availability
StatementData
sharing
not
applicable
article
as
no
new
data
were
created
or
analysed
study.Correction
StatementThis
has
been
corrected
minor
changes.
These
changes
do
impact
academic
content
article.Additional
informationNotes
contributorsNilgün
İnceNilgün
İnce
Ph.D.
candidate
at
Department
Industrial
Engineering,
Sakarya
University,
Turkey.
She
obtained
BS
degree
industrial
engineering
Kütahya
Dumlupınar
University
MS
systems
management
Warwick
(WMG)
2018.
funded
Republic
Turkey
Ministry
National
Education
during
master
studies
participated
projects
automotive
UK.
Her
interests
include
optimisation,
hyper-heuristics
scheduling.
currently
works
lecturer
Alanya
Alaaddin
Keykubat
University.Derya
DeliktaşDerya
Deliktaş
associate
professor
Engineering
Faculty
received
B.S.
Erciyes
respectively.
did
her
post-doctoral
researcher
supported
Scientific
Technological
Research
Council
(TÜBİTAK)
Computer
Science
Operational
Computational
Optimisation
Learning
(COL)
Lab
School
Nottingham
(UoN)
activities
problems,
assembly
line
balancing
portfolio
artificial
intelligence
methods,
multi-criteria
decision
mining.İhsan
Hakan
Selviİhsan
Selvi
Information
Systems
He
Ph.D.degrees
University.
Missouri
Technology
guest
researcher.
project
executive
(TÜBİTAK).
His
smart
service
information
deep
learning,
optimisation.
editorial
board
Journal
Artificial
Intelligence
Theory
Applications.
roles
assistant
director
Institute
Natural
Sciences.
Mathematics,
Год журнала:
2024,
Номер
12(16), С. 2575 - 2575
Опубликована: Авг. 20, 2024
Demand
fluctuates
in
actual
production.
When
manufacturers
face
demand
under
their
maximum
capacity,
suspension
shifts
are
crucial
for
cost
reduction
and
on-time
delivery.
In
this
case,
needed
to
minimize
idle
time
prevent
inventory
buildup.
Thus,
it
is
essential
integrate
with
scheduling
an
uncertain
production
environment.
This
paper
addresses
the
two-stage
hybrid
flow
shop
problem
(THFSP)
processing
times,
aiming
weighted
sum
of
earliness
tardiness.
We
develop
a
stochastic
integer
programming
model
validate
using
Gurobi
solver.
Additionally,
we
propose
dual-space
co-evolutionary
biased
random
key
genetic
algorithm
(DCE-BRKGA)
parallel
evolution
solutions
scenarios.
Considering
decision-makers’
risk
preferences,
use
both
average
pessimistic
criteria
fitness
evaluation,
generating
two
types
scenario
populations.
Testing
28
datasets,
value
solution
(VSS)
expected
perfect
information
(EVPI)
quantify
benefits.
Compared
scenario,
VSS
shows
that
proposed
achieves
additional
gains
0.9%
69.9%.
Furthermore,
EVPI
indicates
after
eliminating
uncertainty,
yields
potential
improvements
2.4%
20.3%.
These
findings
indicate
DCE-BRKGA
effectively
supports
varying
decision-making
providing
robust
even
without
known
distributions.
