The
rapid
advancement
of
technology
has
led
to
increased
electronic
consumption
and
a
corresponding
rise
in
e-waste
generation.
To
address
this
issue,
study
develops
low-carbon
e-commerce
closed-loop
supply
chain
(LCE-CLSC)
system
with
remanufacturer,
platform,
the
government
as
key
stakeholders.
An
evolutionary
game
approach
is
employed
investigate
system's
dynamics,
analyzing
impact
stakeholder
strategies
on
equilibrium.
Numerical
simulations
reveal
important
findings:
Firstly,
initiatives
promoting
recycling,
carbon
taxes
traditional
products,
can
incentivize
remanufacturers
produce
items
encourage
platform
investment
recycling
services.
Secondly,
higher
sales
revenues
motivate
products.
Thirdly,
consumer
sensitivity
services
strengthens
waste
recovery
when
platforms
increase
investments
these
services,
leading
choosing
production.
Lastly,
subsidies
may
become
ineffective
if
cost
remanufacturers'
reduction
platforms'
exceeds
their
affordability.
These
findings
offer
insights
for
sustainable
development
obsolete
product
systems.
Abstract
Québec
is
transitioning
to
a
circular
economy
(CE)
by
promoting
the
implementation
of
CE
strategies,
such
as
remanufacturing.
However,
adoption
remanufacturing
practices
achieve
sustainable
in
an
enterprise
demanding
and
highly
challenging.
It
requires
balancing
economic,
environmental,
social
dimensions,
guaranteeing
products’
remanufacturability
system
circularity
along
closed-loop
supply
chains
(CLSC).
Key
performance
indicators
(KPI)
emerge
decision-support
tools
for
decision-makers
control
enhance
performance.
Nevertheless,
multidimensional
nature
makes
it
challenging
determine
suitable
KPIs
employ.
Therefore,
this
study
performs
systematic
literature
review
identify
main
its
scope
CLSC.
A
total
100
documents
from
Scopus
database
were
analyzed
reveal
most
frequently
used
42
key
(KPI),
categorized
25
14
3
social-related
indicators.
The
distributed
among
different
CLSC
actors,
providing
insights
on
selection
useful
consider
each
actor.
Journal of Green Economy and Low-Carbon Development,
Journal Year:
2023,
Volume and Issue:
2(3), P. 122 - 136
Published: Sept. 30, 2023
In
the
face
of
escalating
resource
scarcity
driven
by
consumption
non-renewable
resources,
industrial
circular
economy
(ICE)
emerges
as
a
vital
paradigm
shift,
pivotal
for
fostering
resource-efficient
societies
and
ensuring
national
security.
This
integrative
review
aims
to
critically
assess
evolution
challenges
inherent
within
ICE
over
recent
years,
with
specific
focus
on
burgeoning
role
machine
learning
(ML)
in
this
domain.
By
synthesizing
extant
literature,
examination
reveals
several
key
findings.
Firstly,
significantly
contributes
cost
reduction
through
enhanced
recycling
secondary
utilization,
underscoring
its
environmental
stewardship.
Secondly,
it
is
evident
that
ML
exhibits
substantial
promise
manufacturing
sector,
not
only
augmenting
production
processes
but
also
elevating
product
precision,
reducing
defect
rates,
minimizing
likelihood
mishaps.
Most
crucially,
application
identified
potent
catalyst,
driving
advancements
across
various
facets
-
data
analysis,
model
development,
technological
innovation,
equipment
refinement.
analysis
further
elucidates
intrinsic
value
waste
management,
yielding
improvements
rates
methodologies,
which
turn
curtails
costs
amplifies
output
efficiency.
Despite
strides
made
replacing
traditional
models
more
sustainable
practices,
persist,
particularly
regarding
suboptimal
levels
continued
generation
waste.
The
integration
frameworks
posited
transformative
approach,
offering
capabilities
superior
quality
trajectory
future
development.
study,
therefore,
growing
discourse
synergistic
potential
revolutionizing
ICE,
thereby
aligning
broader
objectives
economic
Computers & Industrial Engineering,
Journal Year:
2024,
Volume and Issue:
194, P. 110368 - 110368
Published: July 6, 2024
With
the
pressing
need
to
shift
towards
sustainable
sociotechnical
systems,
high-tech
equipment
manufacturers
are
facing
an
increasing
demand
for
accountability
in
reverse
logistics.
Implementation
of
logistics
introduces
complexities,
particularly
handling
unpredictability
product
returns,
coordination
network,
and
communication
among
different
stakeholders
along
chain.
To
navigate
these
complexities
facilitate
decision-making,
objective
this
paper
is
design
a
reference
architecture
tailored
manufacturing
sector.
The
proposed
encompasses
variety
activities,
ranging
from
installed
base
management
recovery
processes,
achieve
supply
chain
circularity,
couples
witha
data-driven
decision-making
processes
enhance
efficiency
reliability
throughout
instantiated
context
real
company
case
published
literature
evaluated
by
domain
experts
practitioners
target
industry.
Evaluation
results
questionnaire
analysis
indicate
very
positive
responses
our
solution
terms
completeness,
flexibility,
integration,
simplicity,
understandability,
usability.
Overall,
supports
integrated
organisational
operational
levels.
Sustainability,
Journal Year:
2024,
Volume and Issue:
16(7), P. 2727 - 2727
Published: March 26, 2024
As
consumers
and
governments
prioritize
cost-effectiveness
ecological
sustainability,
the
limitations
of
traditional
manufacturing
paradigms
become
apparent
in
context
constrained
resources.
The
adverse
effects
these
on
environment
society
hinder
achievement
a
sustainable
product
life
cycle.
Intelligent
processes
offer
solution
by
efficiently
gathering
meaningful
data,
such
as
usage
recycling
information,
from
previous
generations
to
enhance
design
subsequent
(SMPs).
Modular
family
architecture
(PFA)
holds
promise
promoting
sustainability
addressing
diverse
consumer
needs.
PFA
SMPs
are
inherently
interconnected
within
intelligent
frameworks.
This
paper
aims
integrate
decision-making
underlying
with
SMPs.
We
model
integrated
SMP
decisions
Stackelberg
game,
which
involves
hierarchical
joint
optimization
(HJO)
for
assessing
modularity
fulfillment.
develop
bilevel
0–1
integer
nonlinear
programming
represent
HJO
process
propose
nested
genetic
algorithm
(NGA)
solve
problem.
A
case
study
laptop
is
conducted
validate
feasibility
potential
proposed
problems
E3S Web of Conferences,
Journal Year:
2024,
Volume and Issue:
581, P. 01009 - 01009
Published: Jan. 1, 2024
In
order
to
assess
the
energy
efficiency
of
building
activities
in
real-time,
this
research
offers
a
data-driven
methodology.
Efficiently
managing
usage
while
minimizing
negative
effects
on
environment
is
focus
study.
Using
large
dataset
that
includes
ratings
obtained
from
sophisticated
analytics
and
continuous
monitoring,
as
well
specific
consumption
(SEC)
measurements,
our
study
reveals
intricate
patterns
use.
Reducing
by
15%
during
peak
hours
possible
with
use
predictive
modeling
tools,
which
show
possibility
proactive
actions.
With
dynamic
modifications
resulting
20%
reduction
total
use,
there
are
substantial
benefits
implementing
adaptive
techniques
based
real-time
data.
The
method’s
dependability
confirmed
comparing
it
industry-standard
standards,
highlights
how
strong
evaluation
system
is.
Building
managers
may
benefit
greatly
research’s
findings
efficiency,
will
help
create
more
sustainable
financially
feasible
systems.
