Supply chain logistics : vehicle routing and scheduling

Vehicle Routing and
Scheduling
Martin Savelsbergh
The Logistics Institute
Georgia Institute of Technology

Vehicle Routing and
Scheduling
Part I:
Basic Models and Algorithms

Introduction
Freight routing
routing of shipments
Service routing
dispatching of repair technicians
Passenger routing
transportation of elderly

Freight Routing (LTL)
pickup and delivery to and from end-of-line
terminal
shuttle from end-of-line terminal to regional
distribution center
transportation between distribution centers
rail
sleeper teams
single driver

LTL Linehaul Network
EOLs
DCs
Customers

Origin-Destination Route
Origin
EOL
DC
Destination
Linehaul
scheduling
Pickup and
delivery
Shuttle
scheduling

Routing and Scheduling
Routing and scheduling does not follow a
single “one-size-fits-all” formula. Routing
and scheduling software must usually be
customized to reflect the operating
environment and the customer needs and
characteristics

Models
Traveling Salesman Problem (TSP)
Vehicle Routing Problem (VRP)
Vehicle Routing Problem with Time
Windows (VRPTW)
Pickup and Delivery Problem with Time
Windows (PDPTW)

Traveling Salesman Problem
In the TSP the objective is to find the shortest
tour through a set of cities, visiting each city
exactly once and returning to the starting city.
Type of decisions:
routing

Vehicle Routing Problem
In the VRP a number of vehicles located at a
central depot has to serve a set of
geographically dispersed customers. Each
vehicle has a given capacity and each customer
has a given demand. The objective is to
minimize the total distance traveled.
Type of decisions:
assigning
routing

with Time Windows
In the VRPTW a number of vehicles is located at
a central depot and has to serve a set of
geographically dispersed customers. Each
vehicle has a given capacity. Each customer
has a given demand and has to be served within
a given time window.
Type of decisions:
assigning
routing
scheduling

with Time Windows

Pickup and Delivery Problem
with Time Windows
In the PDPTW a number of vehicles has to serve a
number of transportation requests. Each vehicle
has a given capacity. Each transportation request
specifies the size of the load to be transported,
the location where it is to be picked up plus a
pickup time window, and the location where it is
to be delivered plus a delivery time window.
Type of decisions:
assigning
routing
scheduling

Pickup and Delivery Problem
with Time Windows
Delivery
Pickup

Objectives
minimize vehicles
minimize miles
minimize labor
satisfy service requirements
maximize orders
maximize volume delivered per mile

Practical considerations
Single vs. multiple depots
Vehicle capacity
homogenous vs. hetrogenous
volume vs. weight
Driver availability
Fixed vs. variable start times
DoT regulations (10/1, 15/1, 70/8)

Practical considerations (cont.)
Delivery windows
hard vs. soft
single vs. multiple
periodic schedules
Service requirements
Maximum ride time
Maximum wait time

Practical considerations (cont.)
Fixed and variable delivery times
Fixed vs. variable regions/route

Recent Variants
Dynamic routing and scheduling problems
More and more important due to availability
of GPS and wireless communication
Information available to design a set of
routes and schedules is revealed dynamically
to the decision maker
Order information (e.g., pickups)
Vehicle status information (e.g., delays)
Exception handling (e.g., vehicle breakdown)

Recent Variants
Stochastic routing and scheduling
problems
Size of demand
Travel times

Algorithms
Construction algorithms
Improvement algorithms
Set covering based algorithms

Construction Heuristics
Savings heuristic
Insertion heuristics

Savings
Initial
i j
Intermediate Final
Savings s(i,j) = c(i,0) + c(0,j) – c(i,j)

Savings Heuristic (Parallel)
Step 1. Compute savings s(i,j) for all
pairs of customers. Sort savings.
Create out-and-back routes for all
customers.
Step 2. Starting from the top of the
savings list, determine whether there exist
two routes, one containing (i,0) and the
other containing (0,j). If so, merge the
routes if the combined demand is less
than the vehicle capacity.

Savings Heuristics (Sequential)
Step 1. Compute savings s(i,j) for all
pairs of customers. Sort savings. Create
out-and-back routes for all customers.
Step 2. Consider route (0,i,…,j,0) and
determine the best savings s(k,i) and s(j,l)
with routes containing (k,0) and (0,l).
Implement the best of the two. If no
more savings exist for this route move to
the next.

Savings Heuristic -
Enhancement
Route shape parameter
s(i,j) = c(i,0) + c(0,j) - λ c(i,j)
The larger λ, the more emphasis is placed
on the distance between customers being
connected

Insertion
Initial
i
j
k
Intermediate Final

Insertion Heuristics
Start with a set of
unrouted stops
Select an unrouted stop
Are there any
unrouted stops?
Insert selected stop in
current set of routes
Yes
Done
No

Nearest addition
Selection:
If partial tour T does not include all cities,
find cities k and j, j on the tour and k not, for
which c(j,k) is minimized.
Insertion:
Let {i,j} be either one of the two edges
involving j in T, and replace it by {i,k} and
{k,j} to obtain a new tour including k.

Nearest Insertion
Selection:
which c(j,k) is minimized.
Insertion:
Let {i,j} be the edge of T which minimizes
c(i,k) + c(k,j) - c(i,j), and replace it by {i,k}
and {k,j} to obtain a new tour including k.

Farthest Insertion
Selection:
which c(j,k) is maximized.
Insertion:
Let {i,j} be the edge of T which minimizes
c(i,k) + c(k,j) - c(i,j), and replace it by {i,k}
and {k,j} to obtain a new tour including k.

Cheapest Insertion
Selection:
find for each k not on T the edge {i,j} of T
which minimizes c(T,k) = c(i,k) + c(k,j) -
c(i,j). Select city k for which c(T,k) is
minimized.
Insertion:
Let {i,j} be the edge of T for which c(T,k) is
minimized, and replace it by {i,k} and {k,j} to
obtain a new tour including k.

Worst case results
Nearest addition: 2
Nearest insertion: 2
Cheapest insertion: 2
Farthest insertion:
> 2.43 (Euclidean)
> 6.5 (Triangle inequality)

Implementation
Priority Queue
insert(value, key)
getTop(value, key)
setTop(value, key)
k-d Tree
deletePt(point)
nearest(point)

Implementation
Tree->deletePt(StartPt)
NNOut[StartPt] := Tree->nearest(StartPt)
PQ->insert(Dist(StartPt, NNOut(StartPt)), StartPt)
loop n-1 time
loop
PQ->getTop(ThisDist, x)
y := NNOut[x]
If y not in tour, then break
NNOut[x] = Tree->nearest(x)
PQ->setTop(Dist(x, NNOut[x]), x)
Add point y to tour; x is nearest neighbor in tour
Tree->deletePt(y)
NNOut[y] = Tree->nearest(y)
PQ->insert(Dist(y, NNOut[y]), y)
Delayed update
Find nearest point
Update

Improvement Algorithms
Start with a
feasible solution x
x
y
z
Is there an
improving neighbor
y in N(x)?
Define neighborhood N(x).
Replace x by y
Yes
N(x)
x is locally optimal
No

1-Relocate
O(n2) possibilities

2-Relocate
O(n2) possibilities

Vehicle Routing and
Scheduling

Vehicle Routing and
Scheduling
1-relocate

Set covering based
algorithms
Assignment decisions are often the most
important
Assignment decisions are often the most
difficult

Vehicle routing and scheduling
Possible route

Set partitioning formulation
c1 c2 c3 c4 …
Cust 1 1 1 0 1 … = 1
Cust 2 1 0 1 0 … = 1
Cust 3 0 1 1 0 … = 1
…
Cust n 0 1 0 0 … = 1
y/n y/n y/n y/n …

Set covering based
algorithms
Advantage
very flexible
heuristics for route generation
complicating constraints in route generation
Disadvantage
small to medium size instances

Supply chain logistics : vehicle routing and scheduling

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