Developing a multi-objective flexible job shop scheduling optimization model using Lexicographic procedure considering transportation time
Deleted Journal,
Год журнала:
2023,
Номер
14(1), С. 57 - 70
Опубликована: Март 1, 2023
Abstract
A
multi-objective
flexible
job
shop
scheduling
problem
(FJSSP)
that
considers
transportation
time
using
mathematical
programming
is
proposed
to
optimise
three
conflicting
objectives:
minimising
makespan,
total
cost,
and
lateness.
The
model
was
developed
verified
in
stages.
In
the
first
stage,
only
one
objective
considered.
minimisation
of
makespan
cost
considered
separately
stage.
second
two
objectives
were
this
instantaneously.
third
a
objectives.
formulated
mixed-integer
nonlinear
(MINLP)
solved
DICOPT
solver
based
on
general
algebraic
modelling
system
(GAMS)
optimisation
software.
This
includes
times
between
machines
FJSSP,
called
“flexible
with
time”
(TT-FJSSP).
gave
better
results
comparison
other
recent
models.
effect
changing
maximum
allowable
deviation
when
optimising
studied
achieve
more-practical
results.
Язык: Английский
Symmetric Two-Workshop Heuristic Integrated Scheduling Algorithm Based on Process Tree Cyclic Decomposition
Electronics,
Год журнала:
2023,
Номер
12(7), С. 1553 - 1553
Опубликована: Март 25, 2023
The
existing
research
on
the
two-workshop
integrated
scheduling
problem
with
symmetrical
resources
does
not
consider
complex
product
attribute
structure
and
objective
situation
of
plant
equipment
resources.
This
results
in
prolongation
makespan
reduction
utilization
rate
general
workshop.
To
solve
above
problems,
a
algorithm
based
process
tree
cyclic
decomposition
(STHIS-PTCD)
was
proposed.
First,
workshop
scheme
sub-tree
strategy
proposed
to
improve
closeness
continuous
processing
further.
Second,
an
operation
allocation
principle
balance
presented.
On
basis
ensuring
advantages
parallel
processing,
it
also
effectively
reduces
idle
time
then
optimizes
overall
effect
both
workshops.
Through
comparison
analysis
all
resource-symmetric
algorithms,
is
best.
Язык: Английский
A Strengthened Dominance Relation NSGA-III Algorithm Based on Differential Evolution to Solve Job Shop Scheduling Problem
Computers, materials & continua/Computers, materials & continua (Print),
Год журнала:
2024,
Номер
78(1), С. 375 - 392
Опубликована: Янв. 1, 2024
The
job
shop
scheduling
problem
is
a
classical
combinatorial
optimization
challenge
frequently
encountered
in
manufacturing
systems.
It
involves
determining
the
optimal
execution
sequences
for
set
of
jobs
on
various
machines
to
maximize
production
efficiency
and
meet
multiple
objectives.
Non-dominated
Sorting
Genetic
Algorithm
III
(NSGA-III)
an
effective
approach
solving
multi-objective
problem.
Nevertheless,
it
has
some
limitations
problems,
including
inadequate
global
search
capability,
susceptibility
premature
convergence,
challenges
balancing
convergence
diversity.
To
enhance
its
performance,
this
paper
introduces
strengthened
dominance
relation
NSGA-III
algorithm
based
differential
evolution
(NSGA-III-SD).
By
incorporating
constrained
simulated
binary
crossover
genetic
operators,
effectively
improves
NSGA-III’s
capability
while
mitigating
issues.
Furthermore,
reinforced
address
trade-off
between
diversity
NSGA-III.
Additionally,
encoding
decoding
methods
discrete
are
proposed,
which
can
improve
overall
performance
without
complex
computation.
validate
algorithm’s
effectiveness,
NSGA-III-SD
extensively
compared
with
other
advanced
algorithms
using
20
test
instances.
experimental
results
demonstrate
that
achieves
better
solution
quality
diversity,
proving
effectiveness
Язык: Английский
A self-learning framework combining association rules and mathematical models to solve production scheduling programs
Production & Manufacturing Research,
Год журнала:
2024,
Номер
12(1)
Опубликована: Март 25, 2024
Data-driven
production
scheduling
and
control
systems
are
essential
for
manufacturing
organisations
to
quickly
adjust
the
demand
a
wide
range
of
bespoke
products,
often
within
short
lead
times.
This
paper
presents
self-learning
framework
that
combines
association
rules
optimization
techniques
create
data-driven
scheduling.
A
new
approach
predicting
interruptions
in
process
through
was
implemented,
using
mathematical
model
sequence
activities
real
or
near
real-time.
The
tested
case
study
ceramics
manufacturer,
updating
confidence
values
by
comparing
planned
actual
recorded
during
control.
It
also
sets
corrective
factor
based
on
value
success
rate
avoid
product
shortages.
results
were
generated
just
1.25
seconds,
resulting
makespan
reduction
9%
6%
compared
two
heuristics
First-In-First-Out
Short
Processing
Time
strategies.
Язык: Английский
Machine Learning prediction model for Dynamic Scheduling of Hybrid Flow-Shop based on Metaheuristic
IFAC-PapersOnLine,
Год журнала:
2024,
Номер
58(19), С. 1228 - 1233
Опубликована: Янв. 1, 2024
Язык: Английский
A Combination of Association Rules and Optimization Model to Solve Scheduling Problems in an Unstable Production Environment
Management and Production Engineering Review,
Год журнала:
2023,
Номер
unknown
Опубликована: Дек. 31, 2023
Production
problems
have
a
significant
impact
on
the
on-time
delivery
of
orders,
resulting
in
deviations
from
planned
scenarios.
Therefore,
it
is
crucial
to
predict
interruptions
during
scheduling
and
find
optimal
production
sequencing
solutions.
This
paper
introduces
selflearning
framework
that
integrates
association
rules
optimisation
techniques
develop
algorithm
capable
learning
past
experiences
anticipating
future
problems.
Association
identify
factors
hinder
process,
while
use
mathematical
models
optimise
sequence
tasks
minimise
execution
time.
In
addition,
establish
correlations
between
parameters
success
rates,
allowing
corrective
for
quantity
be
calculated
based
confidence
values
rates.
The
proposed
solution
demonstrates
robustness
flexibility,
providing
efficient
solutions
Flow-Shop
Job-Shop
with
reduced
calculation
times.
article
includes
two
examples
where
applied.
Язык: Английский