1. www.rsTRAIL.nl Jonne Zutt Delft University of Technology Information Technology and Systems Collective Agent Based Systems Group Fault detection and recovery in multi-modal transportation networks with autonomous mobile actors TRAIL/TNO Project 16 Supervisors Dr. C. Witteveen Dr. ir. Z. Papp Dr. ir. A.J.C. van Gemund

2. www.rsTRAIL.nl Content 1.Multi-agent Transport Planning 2.Algorithms 3.TP Simulator (demo) 4.Experiments 5.Coordination

3. www.rsTRAIL.nl Transport Planning - Overview Infrastructure Orders Incidents Agents (Re)Planning Execution & monitoring Statistics

4. www.rsTRAIL.nl TP - Orders Infrastructure Orders Incidents Agents O = (rt, f, v, s, T s, d, T d, l, u, p) rtrelease time, f, v freight / volume, s, d source / destination location, T s, T d source / delivery time-window, l, u loading / unloading costs, p penalty function. Statistics

5. www.rsTRAIL.nl TP - Agents Infrastructure Orders Incidents Agents A = T x C x I T transportation agent: algorithms, transportation resource: capacity, max. speed, C customer agent: algorithms, Iinfrastructure agent: algorithms. Statistics

6. www.rsTRAIL.nl TP - Infrastructure Infrastructure Orders Incidents Agents I = (R i,E,K,C,S) R i infrastructure resources, E direct connectivity relation, K capacity function, Cdistance function, Smax. speed function. Statistics

7. www.rsTRAIL.nl TP - Incidents Infrastructure Orders Incidents Agents J = (rt,t, ,T,f) rt release time, ttype,  infrastr./transport resource, T effective time-window f severity [0..1]. Statistics

8. www.rsTRAIL.nl TP - Statistics Infrastructure Orders Incidents Agents #/min/max/sum/avg/var/skw/kur P A final agent plans, UR t transport res. utilization, UR i infrastructure res. utilization, C agent communication, P, D pick-up / delivery penalties, … many more. Statistics

9. www.rsTRAIL.nl Agent plan A route Rt = [  1,  2,  3, …,  n ], A schedule Sd = [  1,  2,  3, …,  n ], where Sd[i] is the time at which resource Rt[i] is claimed, A sequence of sets of orders to load L = [{o 1,o 2 }, {}, {o 3 }, …, L n ], A sequence of sets of orders to unload U = [{}, {}, {o 1 }, …, U n ],

10. www.rsTRAIL.nl Performance criteria Infrastructure resource (vehicle load over time) and transportation resource (drive / (un)load / wait / idle) utilization, Sum of order penalties over all agents, Sum of delays for an agent, Make-span, when is the last agent done, Scalability: cpu-consumption and communication load.

11. www.rsTRAIL.nl Algorithms for routing and scheduling Arbiter (local heuristic) Summed delays Deadlines, (  -C)/C Plan length Hatzack & Nebel Look ahead Scheduling order Extend with rerouting Stentz D(ynamic A)* Multiple agents Time-windows

12. www.rsTRAIL.nl Example AB CD E AC AE AB BE BD CE CD DE AB C E D cap:  dist: 0 cap: 1 dist: 100 cap:  dist: 0 roads have dist: 10, cap: 1 5 identical agents in A, 5 in B, 10 orders from A to D in [0,100], 10 orders from B to C in [0,100], no incidents

13. www.rsTRAIL.nl Goals of the experiments Testing performance and robustness of routing/scheduling algorithms in normal conditions varying order densities / agents / infrastructure properties. Testing performance and robustness with different incident rates.

14. www.rsTRAIL.nl Coordination Formation of coalitions: static:agreed in advance, dynamic:formed by e.g. overlapping routes. Particular examples of coordination: Platooning increases capacity / throughput by decreasing the vehicle separation distance, (Re)assignment of orders, Transshipment to avoid empty rides.

15. www.rsTRAIL.nl Thesis outline Introduction Multi-agent systems and transportation Model for multi-agent transport planning Application of the model Agent algorithms –Routing and scheduling –Coordination Experiments Conclusions

16. www.rsTRAIL.nl No scheduling algorithm used

17. www.rsTRAIL.nl H&N/with rerouting, sov-function=delay

18. www.rsTRAIL.nl H&N/rerouting, sov-function=deadlines