Computational Intelligence,
Journal Year:
2025,
Volume and Issue:
41(2)
Published: March 24, 2025
ABSTRACT
As
manufacturing
faces
evolving
customer
demands,
the
integration
of
Industrial
Internet
Things
(IIoT)
networks
is
crucial
for
enhancing
production
flexibility.
In
this
context,
Seru
Production
System
(SPS)
has
emerged
as
a
highly
adaptable
mode
and
emphasizes
strategic
assignment
cross‐trained
workers,
particularly
in
hybrid
configurations
combining
divisional
rotating
serus.
This
paper
proposes
novel
bi‐objective
mathematical
model
incorporating
learning
effects
to
minimize
makespan
balance
workloads
among
workers.
With
development
Artificial
Intelligence
Generated
Content
(AIGC)
empowered
big
models,
new
breakthroughs
have
industrial
decision‐making.
These
models
utilize
deep
foundational
content
processing
leverage
reinforcement
optimize
strategies.
process
provides
robust
support
achieving
efficient
decision
optimization.
Building
on
concepts
AIGC
training,
study
employs
refine
results
multi‐objective
genetic
algorithms,
thereby
improving
solution
capability
model.
Experimental
demonstrate
that
proposed
algorithm
effectively
optimal
strategies
tuning
crossover
mutation
operations.
Additionally,
numerical
experiments
offer
insights
into
formation
SPS
configurations.
