PostNL

Transcript

1. 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
   

   View Slide

2. 2
   Tim Jonker
   Data Scientist
   @ PostNL
   

   View Slide

3. 3
   Visit every address every day, no storage
   > 1 million parcels per day
   

   View Slide

4. View Slide

5. View Slide

6. Potential savings > 5 million/year
   By using TRAP
   6
   

   View Slide

7. How does TRAP save money?
   7
   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
   

   View Slide

8. 8
   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
   

   View Slide

9. 9
   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
   5
   ✓
   Restrictions:
   • Complete assignment
   • Capacity
   • Consistency
   • Capabilities
   • Non-crossing
   

   View Slide

10. 10
    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
    

    View Slide

11. 11
    Available data
    Internal data:
    • Area characteristics
    • Route characteristics
    Open source data:
    • Travel times
    • Area shapes
    

    View Slide

12. 12
    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
    

    View Slide

13. 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
    13
    Decomposition of the problem definition
    Capacitated Minimum Ball Shaped Spanning Forest
    Combined
    

    View Slide

14. 14
    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
    

    View Slide

15. Large Neighbourhood heuristic
    15
    Iteratively search for neighbouring solutions that improve on the
    incumbent solution
    Neighbourhoods:
    • Insertion
    • Greedy
    • K-regret
    • Assignment
    • Swap
    • Edge removal
    • Shotgun removal
    

    View Slide

16. 16
    Insert an unassigned area into a route
    Insertion
    Greedy Regret
    

    View Slide

17. 17
    Assign one area to a different route
    Assign
    

    View Slide

18. 18
    Swap two areas from different adjacent routes
    Swap
    

    View Slide

19. 19
    Unassign areas at the edge of 2 adjacent routes
    Edge area removal
    

    View Slide

20. 20
    Unassign areas at that are shot
    Shotgun removal
    

    View Slide

21. 21
    Work in progress
    Results
    • TRAP requires fewer
    routes than currently used
    • TRAP results in more
    compact routes
    

    View Slide

22. 22
    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
    

    View Slide

23. Questions?
    23
    

    View Slide