Jonne Zutt Delft University of Technology Information Technology and Systems

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

2. Content • Outline of the project • Problem setting:Transport Planning Problem • Set-up of experiments • Preliminary results of experiments • Achievements / Future plans

3. Project Characteristics “Fault detection and recovery in multi-modal transportation networks with autonomous mobile actors” • Planning, fault detection and recovery • Multi-agent approach • Multi-layered approach for distributed planning • Operational aspect of multi-modal transportation

4. Applications • Autonomous Guided Vehicle (AGV) terminal, • FTAM-5/6 (Davinci) • Simple infrastructures with capacity restrictions and many conflicts • Taxi-cab companies, • SMM-6 (M.M. de Weerdt) • Medium infrastructure sizes, few capacity restrictions • Freight transportation, distribution centers • FTAM-1 (L.D. Aronson) • Large infrastructures without capacity restrictions • Multi-Agent diagnosis • STW: Distributed Model-Based Diagnosis and Repair • Fault detection and recovery

5. Transportation orders Infrastructure resources Transport resources Agents Transport Planning Problem

6. TPP – Orders Transport Planning Problem: • Transportation orders • Infrastructure resources • Transport resources • Agents O = (f, v, s, Ts, d, Td, l, u, p) f, v freight identifier / volume,s, d source / destination location,Ts, Td source / delivery time-window,l, u loading / unloading costs,p penalty.

7. TPP – Infrastructure • Transportation orders • Infrastructure resources • Transport resources • Agents

8. TPP – Infrastructure model • Transportation orders • Infrastructure resources • Transport resources • Agents

9. TPP – Transport resources • Transportation orders • Infrastructure resources • Transport resources • Agents

10. TPP – Agent architecture TAC CUS OPR CRA Transportation orders Transport resources Infrastructure resources

11. Incident Management What are incidents? • Any event from outside the planning system that cannot be anticipated with certainty. • new orders, changes in orders • road blocks, traffic jams • malfunctional vehicles What is incident management? • Ensuring the correct operation of a system under the events of incidents • Detection, repair and notification of problems

12. Contents • Outline of the project • Problem setting:Transport Planning Problem • Set-up of experiments • Preliminary results of experiments • Achievements / Future plans

13. Generating infrastructures • Locations:Retrieve a list of related postal codes, convert to latitude / longitude,then to (x, y) coordinates, • Arcs:Paul Bourke’s efficient triangulation algorithm (for terrain modeling) • As equilateral as possible (avoiding wedge shaped triangles), • Fast  O(n·log(n)).

14. Generating transportation orders • Generate a (possibly infeasible) set of transportation orders using several statistical functions, • Generate a feasible set • Create random plans for the transport agents by just letting them drive around, • Extract a set of orders they could have been executing (using a density parameter),

15. Contents • Outline of the project • Problem setting:Transport Planning Problem • Set-up of experiments • Preliminary results of experiments • Achievements / Future plans

16. Distributed operational planning • Job-shop Scheduling with BlockingHatzack & Nebel (ECP 2001) • JS scheduling: find an optimal allocation of a set R of scarce resources to a set of activities (jobs) J over time • Blocking means that a resource is claimed by a job until it claims the next resource • Agent plan: ((IR1, 0-2), (IR2, 5-7), (IR3, 8-9) …)

17. Experiments • Used 3 different infrastructures, • 20 transport agents each execute one order, • Randomly chosen source-, destination location and fixed time-window, • H&N algorithm with rerouting.

18. Results (averaged over 100 problem instances) Delay  { aA (Ca – Ma) / Ca } / |A| Tardiness aA Ca - a if Ca< a Average % of delay Tardiness Number of alternatives Number of alternatives

19. Achievements • AGV Terminal Demonstrator (delayed, Mar’03) • D**: dynamic (re)routing algorithm for AGVs in a terminal [FTAM02 / TRAIL02] • Distributed operational planning using Hatzack & Nebel’s approach [BNAIC02] • Development Uptime tool for multi-agent based diagnosis [SPIE02]

20. Future Plans • Complete problem instances for the experiments, • Survey on routing and conflict resolution algorithms, • Build Incident Generator, • Redo experiment, this time influenced by incidents, • Delivering an efficient and robust demonstrator.

21. The End

22. Example infrastructure (1)

23. Example infrastructure (2)

24. Need for complex experiments • AGV terminals usually have very simple infrastructures. • This is to keep things easy, not efficient, • As terminals become larger, the problem will return.

25. Properties of these infrastructures • Many routes from a source location to a destination location, • The arcs (and their cost values) are reasonably realistic.

26. Tactical Level (1) • Responsible for finding plans and keeping those in line with reality • Customers may add and remove orders, as well as change them • Problems that cannot be handled by the operational planners must be dealt with at the tactical level • Output is a list of order assignments foreach operational agent

27. Tactical Level (2) • Planning is done by means of a heuristic function, which tells which agent should execute an order. If no agent can be found, a reordering is necessary • Replanning is done by removing the affected orders and offer them to the planner again

28. Operational Level (1) • Responsible for performing the tasks they have been assigned • Tactical layer may add and remove orders as well as change them • In the event of incidents, the operational planner should detect these, and try to fix within the bounds set by the tactical layer. Incidents that cannot be dealt with, are reported to the higher level

29. Operational Level (2) • Adaptive route planning, adapting to usage levels of the roads. Agents will take another route if they see a road is congested • Traffic control at crossroads to ensure that only one agent can make use of a crossroad. Traffic flow is being maximized

30. TPP – Transport resources • Transportation orders • Infrastructure resources • Transport resources • Agents

31. TPP - Agents • Transportation orders • Infrastructure resources • Transport resources • Agents Planners Customers

32. TPP – Agent architecture customer agentsinterface,auction mechanismtactical agentsoperational agents /cross road agentsinfrastructure/ transport resources orders