Transactions on Economics Business and Management Research,
Journal Year:
2023,
Volume and Issue:
3, P. 64 - 71
Published: Dec. 25, 2023
This
study
delves
into
the
challenges
and
complexities
of
vegetable
sales
management
in
modern
commercial
environments,
particularly
fresh
food
supermarkets.
By
utilizing
descriptive
statistics
visual
analysis
data,
reveals
correlations
between
different
categories
quantifies
these
using
Pearson's
correlation
coefficient.
Subsequently,
by
integrating
time
series
multi-objective
programming,
a
mathematical
model
is
constructed,
aimed
at
maximizing
profits
under
specific
constraints.
The
innovation
this
research
lies
its
comprehensive
consideration
category-level
application
optimization
algorithms
for
replenishment
pricing
strategies.
uniqueness
paper
integrative
approach
to
problem,
providing
refined
models
advanced
methods.
Finally,
thoroughly
describes
steps
design,
including
data
analysis,
cost
markup
construction
an
based
on
intending
offer
supermarkets
plan
adaptable
market
changes.
Accurate
multi-energy
load
forecasting
for
distributed
systems
is
facing
challenges
due
to
the
complexity
of
coupling
and
inherent
stochasticity.
In
this
regard,
a
novel
stacking
ensemble
learning
model
based
on
reinforcement
(RL)
fusion
proposed.
First,
feature
selection
performed
using
maximal
information
coefficient
(MIC),
data
decomposed
reconstructed
through
complete
empirical
mode
decomposition
with
adaptive
noise
(CEEMDAN)
sample
entropy
(SE).
Subsequently,
fused
models
strong
predictive
capabilities
are
selected
as
base
learners,
RL
deep
deterministic
policy
gradient
(DDPG)
excellent
ability
meta-learner.
Next,
hyperparameters
learners
optimized
an
improved
arctic
puffin
optimization
(APO)
algorithm.
Finally,
constructed
K-fold
cross-validation.
Tests
real-world
datasets
demonstrate
that
proposed
method
achieves
smaller
prediction
errors,
enhanced
robustness,
greater
reliability.
Moreover,
careful
learner
utilization
meta-learner,
up
1.53%
improvement
in
determination
(R²),
36.09%
increase
improves
residual
deviation
(RPD),
102.96%
reduction
rooted
mean
square
error
(RMSE).
