Mathematics,
Journal Year:
2024,
Volume and Issue:
13(1), P. 102 - 102
Published: Dec. 30, 2024
This
study
investigates
the
integrated
multi-objective
scheduling
problems
of
job
shops
and
material
handling
robots
(MHR)
with
minimising
maximum
completion
time
(makespan),
earliness
or
tardiness,
total
energy
consumption.
The
collaborative
MHR
machines
can
enhance
efficiency
reduce
costs.
First,
a
mathematical
model
is
constructed
to
articulate
concerned
problems.
Second,
three
meta-heuristics,
i.e.,
genetic
algorithm
(GA),
differential
evolution,
harmony
search,
are
employed,
their
variants
seven
local
search
operators
devised
solution
quality.
Then,
reinforcement
learning
algorithms,
Q-learning
state–action–reward–state–action
(SARSA),
utilised
select
suitable
during
iterations.
Three
reward
setting
strategies
designed
for
algorithms.
Finally,
proposed
algorithms
examined
by
solving
82
benchmark
instances.
Based
on
solutions
analysis,
we
conclude
that
GA
integrating
SARSA
first
strategy
most
competitive
one
among
27
compared
