TRAP
Tactical Route Assignment Planning
Tim Jonker Data Scientist
Fleur Doorman Data Scientist
Daan Hiemstra Data Scientist
Robin Hunteler Engineer
Elise van Dam MLOps
Mitch Vonk MLOps
September 12 2023

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Tim Jonker
Data Scientist
@ PostNL

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Visit every address every day, no storage
> 1 million parcels per day

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Potential savings > 5 million/year
By using TRAP
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How does TRAP save money?
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Initial assignment of areas to routes
1 2 3 4 5 6 7 8 9 10 11 12
Months
Parcel volume
• Anticipates on fluctuating parcel volume
• Automates the planning process
• Minimizes operational changes

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Algorithm 1
A geographic data
processing step
required for route
planning
Algorithm 3
An algorithm that
sorts the delivery
areas within a
route in delivery
order
TRAP
Algorithm 2
An optimization
algorithm for the
assignment of areas
to routes

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TRAP’s goals and restrictions
Algorithm 2
An optimization
algorithm for the
assignment of areas
to routes
Three ordered goals:
1. Minimize the number of routes
2. Maximize compactness of each route
3. Fair share of work for each route
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✓
Restrictions:
• Complete assignment
• Capacity
• Consistency
• Capabilities
• Non-crossing

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Algorithm description
Restrictions:
• Each area should be assigned to a route
• Maximum amount of work hours, parcels, stops, volume
• Routes maximally deviate x% between iterations
• Routes possess the required capabilities (Electric, Permission, Knowledge)
• Routes can traverse another route for maximally y minutes
Three ordered goals:
1. Minimize the number of routes
2. Maximize compactness of each route
3. Fair share of work for each route
✓
5

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Available data
Internal data:
• Area characteristics
• Route characteristics
Open source data:
• Travel times
• Area shapes

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Areas are adjacent if there is a road between them that does not cross another area
Transfer problem into two graphs
TravelTimeGraph: Full
connected graph with
travel time as edge
weights (OSRM)
a
b
c
d
f
e
Adjacency graph: Full
connected graph with travel
time as edge weights (OSRM)
Except for adjacent areas: 0
0
b
0
d
0
0

Generic term Consequence Graphical
representation
Capacitated restriction Maximum worktime,
volume etc.
Minimum Spanning Tree Promote adjacent areas
Minimum ball Promote compact routes
Spanning Forest Multiple routes
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Decomposition of the problem definition
Capacitated Minimum Ball Shaped Spanning Forest
Combined

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Large Neighbourhood heuristic
1. Start with current assignment
2. Execute operations within a randomly selected neighbourhood
3. Terminate at stop criterion
4. Remove a route if solution is feasible or add a route if it is infeasible
5. Repeat 2-5 until no improvement

Large Neighbourhood heuristic
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Iteratively search for neighbouring solutions that improve on the
incumbent solution
Neighbourhoods:
• Insertion
• Greedy
• K-regret
• Assignment
• Swap
• Edge removal
• Shotgun removal

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Insert an unassigned area into a route
Insertion
Greedy Regret

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Assign one area to a different route
Assign

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Swap two areas from different adjacent routes
Swap

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Unassign areas at the edge of 2 adjacent routes
Edge area removal

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Unassign areas at that are shot
Shotgun removal

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Work in progress
Results
• TRAP requires fewer
routes than currently used
• TRAP results in more
compact routes

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Algorithm 1
A geographic data
processing step
required for route
planning
Algorithm 3
An algorithm that
sorts the delivery
areas within a
route in delivery
order
TRAP
TRAP: Potential savings > 5 million/year
Algorithm 2
An optimization
algorithm for the
assignment of
areas to routes
• Anticipates on fluctuating parcel volume
• Automates the planning process
• Minimizes operational changes

Questions?
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