clicOH Boosts Last-Mile Delivery Efficiency 20x with NVIDIA cuOpt

Driven
by
shifts
in
consumer
behavior
and
the
pandemic,
e-commerce
continues
its
explosive
growth
and
transformation.
As
a
result,
logistics
and
transportation
firms
find
themselves
at
the
forefront
of
a
parcel
delivery
revolution.
This
new
reality
is
especially
evident
in
last-mile
delivery,
which
is
now
the
most
expensive
element
of
supply
chain
logistics,
representing
more
than
41%
of
total
supply
chain
costs
across
industries,
from
retail
to
manufacturing,
according
to

NVIDIA
Technical
Blog.

Transforming
Routing
Services

These
challenges
are
compounded
by
the
vehicle
routing
problem
(VRP),
a
generalization
of
the
traveling
salesman
problem
that
asks,
“What
is
the
optimal
set
of
routes
that
a
fleet
of
vehicles
should
undertake
to
make
deliveries
to
a
specific
set
of
customers?”
With
just
10
delivery
destinations,
over
3
million
permutations
and
combinations
of
trips
are
possible.
With
15
destinations,
the
number
of
possible
routes
can
exceed
1
trillion,
surpassing
the
capabilities
of
even
the
fastest
supercomputers.
This
does
not
account
for
operational
constraints
like
fleet
availability,
navigation
capabilities,
and
access
limitations.

clicOH,
a
member
of
the
NVIDIA
Inception
program
for
startups,
has
developed
a
proprietary
routing
model
to
address
these
challenges.
Leveraging
NVIDIA’s
cutting-edge
technologies,
including
heuristic
and
metaheuristic
optimization
algorithms,
machine
learning,
and
AI,
clicOH’s
solution
adapts
to
different
requirements
in
package
distribution
density,
cost
efficiency,
and
delivery
time
optimization
for
last-mile
delivery.

Optimizing
Last-Mile
Delivery
Costs

To
tackle
routing
challenges,
clicOH
adopted

NVIDIA
cuOpt
to
support
its
work
related
to
the
traveling
salesman
problem
and
to
determine
optimal
delivery
routes.
The
cuOpt
library
works
with
GPUs
and
other
NVIDIA
libraries
like
RAPIDS
and
CUDA
to
generate
faster
and
more
accurate
delivery
routes.

RAPIDS
enables
clicOH
to
implement
unsupervised
machine
learning
algorithms
without
modifying
code,
resulting
in
more
efficient
data
analyses.
These
algorithms
cluster
high-demand
zip
codes
for
more
efficient
delivery
and
identify
hard-to-reach
areas.
Combined
with
NVIDIA
cuOpt,
these
algorithms
can
process
thousands
of
routings
in
minutes
or
even
seconds,
optimizing
delivery
times
while
accounting
for
local
routing
constraints,
ultimately
reducing
delivery
costs.

Using
NVIDIA
GPUs
on
AWS
development
environments,
clicOH
analyzed
thousands
of
pre-existing
routes
across
multiple
cities
to
map
routing
inefficiencies.
This
analysis
streamlined
the
development
of
its
logistics
solution
and
enhanced
the
application’s
adaptive
capabilities.

clicOH
has
also
developed
a
deep
learning
model
to
optimize
delivery
times,
maximize
fleet
utilization,
and
identify
zip
codes
with
delivery
challenges
due
to
scheduling
constraints.
By
optimizing
its
AI
models
with
NVIDIA
accelerated
computing,
clicOH
achieved
a
20x
speedup
in
cluster
route
planning
and
a
15%
reduction
in
overall
operating
costs.

For
more
insights
into
clicOH’s
accelerated
logistics
solutions,
visit
the

NVIDIA
Technical
Blog.

Image
source:
Shutterstock