TKI Dinalog en de Topsector Logistiek investeren in projecten die leiden tot robuuster plannen binnen de logistiek. Presentatie met interessante resultaten uit deze projecten die beter inzicht bieden op de mogelijkheden en de voordelen voor bedrijven. Ook nog veel werk te doen voor alle stakeholders.

1. Robuust Plannen
11 april 2018 – Logistiek Manager van het Jaar

2. Today’s Agenda
 Introductie / Aanleiding
 ITSLOG / SAILOR project
 DAIPEX
 Hoe ziet u de toepassing?

3. Introductie
Grensoverschrijdend
Vandaag de dag is grensoverschrijdend de norm: in techniek, in organisatie en
mens.
De moeilijkheid zit in het bewaren van een evenwichtige balans tussen deze drie
grootheden. Dikwijls werken modernistische systemen niet doordat mens en
organisatie onvoldoende toegerust zijn om hier effectief mee om te gaan. Of
worden goedbedoelde organisatiewijzingen een fiasco vanwege gebrek aan
ondersteunende techniek en participerende mens.
De mens is vaak een doorslaggevende factor, maar zeker in deze
grensoverschrijdende wereld komt die factor mens onder druk te staan.
Nieuwe rollen vragen om nieuwe kennis, een andere houding en ander gedrag. En
dat is zeker binnen het logistieke domein niet eenvoudig te realiseren.
Flexibiliteit, aanpassingsvermogen, veranderingsgezindheid, omgaan met nieuwe
normen en waarden, en ethische kaders

4. Robuust Plannen
 Gedeeld maatschappelijk en bedrijfseconomisch belang
 Slack uit het netwerk halen
 Uiteindelijk slim gebruik maken van capaciteit (infra en transport)
 Vergt slimme toepassing van technologie
 Maar ook samenwerking
 En de mens die het doet

5. Robuust Plannen
 Projecten in Topsector Logistiek gericht op de technologie
 Slim gebruik maken meerdere databronnen
 DAIPEX / SAILOR / ITSLOG / SDFI / …..
 Maar meer en meer ook de verandering van organisaties en
de rol van de mens
 Planning and human decision-making for the unpredictable
 Increasing the usability, adoption, and acceptance of
advanced planning and scheduling systems

6. Stakeholders
Issues
Solutions
Dutch research on ITS in citylogistics
ITSLOG and SAILOR

7. Local government
LSP’s and shippers
Receivers
IT service providers/OEM’s
Stakeholders

8. Local government
LSP’s and shippers
Receivers
IT service providers/OEM’s
Current Dutch research

9. 80% of truck drivers time today is within
city boundaries
60% of truck drivers time is in walking
and delivery in cities
Current planning systems lead to 40 to
50% slack in city logistics networks
One out of two cars on Amsterdam’s
main roads are delayed because of
unloading on the streets
Inefficient use of loading and unloading
zones (and buffer locations)
Congestion and road unsafety

11. Local government
Inefficient use of loading and unloading
zones
Many tools are developed to support
local government in planning
(but no standard approach yet)
Tools needed for tactical data analysis,
intelligent access and enforcement
Tools needed for improving local traffic
data accuracy

12. ITSLOG project
SAILOR AMSTERDAM
SPARKS ROTTERDAM
TENDERS AMSTERDAM
Progress April 2018
Technology is reading
Test partner DHL
Cost of solution is too high
New technology necessary
Start at Overtoom Amsterdam
after tender phase

13. Retail control tower and traffic data

14. ITSLOG project

16. SAILOR

17. ITSLOG project Progress March 2018
1. Analysis of Ahold planning’s methods (CWI)
2. Data analysis Ahold planning (TU Delft)
3. Software and platform development – tested and working
4. Testing functionality (not in truck yet)
5. Buffering not necessary based on trucks in Simacan CT
6. Human factor: too many apps, too much information
7. ITS/TMS not available of the shelf

18. ITSLOG project Progress March 2018
1. Technical complexities: linking data, real time data and frequencies
2. Local government role in finding buffer locations
3. Further role out only feasible if all trucks are in Simacan CT
(potentially for more retailers using buffer zones)
4. More information necessary on illegal parking on unloading zones
(enforcement)
5. Human factor (in car) is critical
6. Application can be used for other distribution networks with
loading/unloading docks and bays
7. Discussion with TLN and EVOfenedex (also about governance)

19. Need for address intelligence for operational,
tactical and strategic planning
Current planning systems lead to 40 to
50% slack in city logistics networks
LSP’s and shippers
Planning of driving, walking and delivery
(time!)
How can data support operational,
tactical and strategic planning
Planning from minutes to seconds
Use actual traffic data: speed, road blocks,
unloading zones, iVRIs
Human factor and in car technology

20. Need for address intelligence for operational,
tactical and strategic planning
Planning of driving, walking and delivery
(time!)
Planning from minutes to seconds
Use actual traffic data: speed, road blocks,
unloading zones, iVRIs
Human factor and in car technology

21. IT service
Providers
and OEM’s
Need for address intelligence for operational,
tactical and strategic planning
Planning of driving, walking and delivery
(time!)
Planning from minutes to seconds
Use actual traffic data: speed, road blocks,
unloading zones, iVRIs
Human factor and in car technology
Local ITS systems
(Connekt)
European standards
NLIP/Security/Blockchain

22. DAIPEX

23. Roadmap – main themes
Meer optimale routeplanning (project scope)
 Betere voorspelling op basis van interne en externe databronnen
 Uitwisseling stops op basis van real-time status ritten
 Matchen lading en voertuigen die in verschillende ketens zijn
 Matchen verschillende condities lading in meerdere ketens
Algoritmes ontwikkeld om veelheid aan mogelijkheden snel door te rekenen
en meest optimale keuze te presenteren aan de planner

24. 24© 2016 Quintiq
Goal was to develop information aggregation and
optimization algorithms to generate
Robust and efficient transportation plans for
realistic transportation planning problems including
uncertain travel and service times
Information aggregation provides information on travel and
service times
Optimization algorithms exploit that information
DAIPEX: Information aggregation and optimization

25. 25© 2016 Quintiq
Challenges are on
required modeling detail and size of instances
Examples of required modeling detail:
• Certain shipments can enter another chain and others not
• Compatibility of shipments and trucks across different
chains
• Compatibility of shipments from different chains
• More heterogeneous fleet
We are solving problems where the size of the search space
grows exponentially. This means increasing the size of the
instances puts a serious extra burden on the algorithms.
Challenges a 4C environment defines

26. 26© 2016 Quintiq
Benefits in a 4C environment
Scenario #Vehicles Total distance Cost
Partner A 5 1683 2531
Partner B 5 1916 2716
Work separately 10 3599 5247
Cross Chain Collaboration 9 2649 4308
Improvement -10.0% -26.4% -17.9%

27. 27© 2016 Quintiq
Towards Robust Planning
Step by step …
 Often route planning software is based on AB relation with fixed travel times
 From Tilburg to Den Bosch is 31 minutes, Always!
 Usually not taken into account
 Moment of driving - traffic
 Uncertainty

28. 28© 2016 Quintiq
Real-life example
357 orders
One depot
Actual orders, distances, costs,
capacities, time windows
First tests assume that being
late is very expensive: €100
per occurrence.

29. 29© 2016 Quintiq
Real-life example: normal plan
The normal optimizer plans routes that hit all time windows.
Average reliability is 90.5% however, leading to a lateness penalty.

30. 30© 2016 Quintiq
Real-life example: robust plan
Robust optimizer finds plan of lower expected cost (€14978 vs. €16380)
Higher reliability (96.8% vs. 90.5%) outweighs higher cost (€13840 vs.
€12990)

31. 31© 2016 Quintiq
Real-life example: less costly lateness
This plan results when being late costs €35 per occurrence.
Both direct cost (€12439 vs. €13840) and reliability (90.9% vs.
96.8%) decrease.

32. 32© 2016 Quintiq
Real-life example: even less costly lateness
This plan results when being late costs €10 per occurrence.
Direct cost (€11042 vs. €12439) and reliability (73.9% vs. 90.9%)
decrease further, showing the different potential trade-offs.

33. 33© 2016 Quintiq
Summary real-life example
Results where being late is estimated to cost €35
Cost + Lateness
Penalty
Cost
Average
reliability
Distance #Vehicles
Normal € 14,176 € 12,990 90.5% 6716 23
Robust € 13,570 € 12,439 90.9% 6190 27
-4.3% -4.24%
Results where being late is estimated to cost €10
Cost + Lateness
Penalty
Cost
Average
reliability
Distance #Vehicles
Normal € 13,329 € 12,990 90.5% 6716 23
Robust € 11,975 € 11,042 73.9% 5207 21
-10.2% -15.0%
Results where being late is estimated to cost €100
Cost + Lateness
Penalty
Cost
Average
reliability
Distance #Vehicles
Normal € 16,380 € 12,990 90.5% 6716 23
Robust € 14,978 € 13,840 96.8% 7118 29
-8.6% 6.5% -66.4%

34. 34© 2016 Quintiq
Having travel time probability distribution data and
a robust optimizer proactively taking that uncertainty data into
account gives up to a
10% cost reduction and up to a
65% reduction in missed time windows
Recommendation
Especially if you are faced with considerable uncertainty in travel
and service times and your time windows are tight, we
recommend using this technology in addition to having a great
reactive optimizer
Summary

35. Hoe ziet u de toepassing?
 Welk probleem zijn we aan het oplossen?
 Wie heeft het probleem eigenlijk?
 Welke toepassingen zijn denkbaar?
 Wat betekent het voor uw organisatie en uw mensen?
 Bent u bereid te investeren?
Vraagsturing!

36. 36© 2016 Quintiq
Robuust Plannen
11 april 2018 – Logistiek Manager van het Jaar