Operational Transport Planning with Incidents Experiments with Traplas

1. Operational Transport Planning with IncidentsExperiments with Traplas Jonne Zutt and Cees WitteveenFaculty of EEMCS

2. Operational Transport Planning with Incidents Contents Problem Methods Experiments Future work • Operational Planning for the Pickup and Delivery Transportation problem • Approach • Research questions • Operational Planning Methods • Experiments • Future work

3. Problem description Contents Problem Methods Experiments Future work • Pickup and Delivery Transportation Problem: Freight has to be transported from a source to a destination location respecting specified timeintervals on a transport network with limitedcapacities and speeds. • Limited capacities lead to conflicts.

4. Approach Contents Problem Methods Experiments Future work • Multi-agent system, where the agents are vehicle planners, crossroad agents, customers, auctioneers, etc. • TRAPLAS: based on Pamela’s RTL, a discrete event simulation kernel [vanGemund]. • Linking experimental results to theory and use results to discover new relations.

5. Research questions Contents Problem Methods Experiments Future work • What information is necessary to obtain efficient planning methods (collaboration vs competition)? • What happens when varying workload, number of agents (scalability), incident level (normal to extreme circumstances, robustness)? • What is the relation between performance and characteristics of the transport network?

6. Application Contents Problem Methods Experiments Future work • AGV terminals (ECT) • Underground logistic system (OLS) • Inland shipping • Taxiway routing

7. Planning methods Contents Problem Methods Experiments Future work • Uninformed: not aware of plans of other agents. • Informed: planning around reservations of other agents. • Revising priority: reconsidering precedences on crossroads. • Revising routes: reconsidering routes.

8. Planning methods T1 Contents Problem Methods Experiments Future work T2 r0 r1 r2 r3 r4 T3 r5 r6 r7 r8 r9 r10 r11 r12 r13 r14

9. Uninformed planning Contents Problem Methods Experiments Future work r0 r1 r2 r3 r4 r5 r6 r7 r8 r9 r10 r11 r12 r13 r14 A1 r0 r1 r2 r3 r4 5 r4 r3 r2 r1 r0 5 A2 r4 r3 r2 r1 r0 5 A3 Σ 15 Time 

10. Informed planning Contents Problem Methods Experiments Future work r0 r1 r2 r3 r4 r5 r6 r7 r8 r9 r10 r11 r12 r13 r14 A1 r0 r1 r2 r3 r4 5 r4 r9 r8 r7 r6 r5 r0 7 A2 r4 r9 r8 r7 r6 8 r5 r0 A3 Σ 20 Time 

11. Revising priorities Contents Problem Methods Experiments Future work r0 r1 r2 r3 r4 r5 r6 r7 r8 r9 r10 r11 r12 r13 r14 A1 r0 9 r1 r2 r3 r4 r4 r3 r2 r1 r0 5 A2 r4 r3 r2 r1 r0 6 A3 Σ 20 Time 

12. Revising routes Contents Problem Methods Experiments Future work r0 r1 r2 r3 r4 r5 r6 r7 r8 r9 r10 r11 r12 r13 r14 A1 r0 7 r5 r6 r7 r8 r9 r4 r4 r3 r2 r1 r0 5 A2 r4 r3 r2 r1 r0 6 A3 Σ 18 Time 

13. Experiments Contents Problem Methods Experiments Future work • 8x8 grid networks • 32 transport agents • Workload varies from 160 to approx. 1000 transportation orders • Incident level varies from normal (no incidents) to severe (failure probability 0.1) circumstances

14. Increasing workload Contents Problem Methods Experiments Future work

15. 800 600 400 200 0.3 0.2 0.1 0.0 -0.1 -0.2 -0.3 Groups of agents Contents Problem Methods Experiments Future work Topology: grid Informed Uninformed Revising routes (order profits) W$lfar$_Group_1024 Workload (numb$r of transportation ord$rs)

16. Increasing level of incidents Contents Problem Methods Experiments Future work

17. Future work Contents Problem Methods Experiments Future work • Verifying theoretical results on collaboration and congestion games • Add planning methods (variants) • Experimenting with transport network topologies • Inland shipping

18. Questions? Contents Problem Methods Experiments Future work ?

19. Modeling conflicts Contents Problem Methods Experiments Future work