A hybrid machine learning-based decision-making model for viable supplier selection problem considering circular economy dimensions
AmirReza Tajally,
No information about this author
Mahla Zhian Vamarzani,
No information about this author
Mohssen Ghanavati-Nejad
No information about this author
et al.
Environment Development and Sustainability,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Jan. 25, 2025
Language: Английский
Developing Agility, Resilience, and Circular Economy Decision-Making Model Based on Data Envelopment Analysis for Evaluating Medical Equipment Suppliers
M. Mirzayi
No information about this author
Process Integration and Optimization for Sustainability,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Jan. 18, 2025
Language: Английский
Sustainable supplier selection and order allocation problem considering the agility and resilience dimensions: a novel multi-stage data-driven decision-making approach
AmirReza Tajally,
No information about this author
Benyamin Babakhani,
No information about this author
Emaad Jeyzanibrahimzade
No information about this author
et al.
International Journal of Systems Science Operations & Logistics,
Journal Year:
2025,
Volume and Issue:
12(1)
Published: Feb. 5, 2025
Language: Английский
A Multi-stage Machine Learning Model to Design a Sustainable-Resilient-Digitalized Pharmaceutical Supply Chain
Mostafa Jafarian,
No information about this author
Iraj Mahdavi,
No information about this author
Ali Tajdin
No information about this author
et al.
Socio-Economic Planning Sciences,
Journal Year:
2025,
Volume and Issue:
unknown, P. 102165 - 102165
Published: Feb. 1, 2025
Language: Английский
A Heuristic-based Multi-Stage Machine Learning-based Model to Design a Sustainable, Resilient, and Agile Reverse Corn Supply Chain by considering Third-party Recycling
Applied Soft Computing,
Journal Year:
2025,
Volume and Issue:
unknown, P. 113042 - 113042
Published: March 1, 2025
Language: Английский
The customer-based supplier selection and order allocation problem based on the waste management and resilience dimensions: A data-driven approach
Engineering Applications of Artificial Intelligence,
Journal Year:
2025,
Volume and Issue:
153, P. 110692 - 110692
Published: April 19, 2025
Language: Английский
A novel stochastic machine learning approach for resilient-leagile supplier selection: a circular supply chain in the era of industry 4.0
Bahar Javan Molaei,
No information about this author
Mohssen Ghanavati-Nejad,
No information about this author
AmirReza Tajally
No information about this author
et al.
Soft Computing,
Journal Year:
2025,
Volume and Issue:
unknown
Published: April 28, 2025
Language: Английский
Creating reliable and resilient logistic systems—A new conceptual approach
Elsevier eBooks,
Journal Year:
2025,
Volume and Issue:
unknown, P. 125 - 249
Published: Jan. 1, 2025
Language: Английский
A Hybrid Machine Learning Approach to Evaluate and Select Agile-Resilient-Sustainable Suppliers Considering Supply Chain 4.0: A Real Case Study
Mahyar Abbasian,
No information about this author
Amin Jamili
No information about this author
Process Integration and Optimization for Sustainability,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Jan. 14, 2025
Language: Английский
A Hybrid Machine Learning-Based Model for Evaluating the Performance of Agile-Sustainable Supply Chains in the Context of Industry 4.0: A Case Study
Aboozar Ghorbani,
No information about this author
Mehdi Fadaei,
No information about this author
Mansour Soufi
No information about this author
et al.
RAIRO - Operations Research,
Journal Year:
2024,
Volume and Issue:
58(5), P. 4681 - 4700
Published: Aug. 16, 2024
In
today’s
world,
businesses
and,
in
general,
supply
chains
have
undergone
extensive
transformations,
and
relying
solely
on
traditional
metrics
such
as
cost
quality
cannot
provide
a
comprehensive
complete
evaluation
of
companies
active
various
sections
chains.
One
the
main
concerns
chain
managers
is
to
create
an
integrated
structure
for
evaluating
performance
branches.
this
context,
study
presents
that,
by
simultaneously
considering
agility
sustainability
within
context
industry
4.0,
which
has
brought
about
fundamental
changes
environment
recent
years,
aims
evaluate
branches
dairy
product
chain.
On
other
hand,
increase
volume
data
produced
development
applications
machine
learning
algorithms
fields,
offer
better
compared
intuitive
approaches,
led
use
hybrid
data-driven
are
combination
expert-based
methods
documented
organizational
data,
Therefore,
innovative
terms
approach
developed.
first
step,
appropriate
dimensions
agility,
sustainability,
Industry
general
were
identified,
then
fuzzy
best-worth
method
(FBWM)
was
used
weight
metrics.
According
findings,
data-driven,
marketing,
overhead
costs,
delivery
timeframe,
selected
most
important
Subsequently,
using
developed
artificial
neural
network
algorithm,
calculates
input
weights
FBWM
method,
model
presented,
findings
show
that
performs
than
problem
with
more
92%
accuracy.
Language: Английский