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Fault detection and recovery in multi-modal transportation networks with autonomous mobile actors

TRAIL/TNO Project 16. Fault detection and recovery in multi-modal transportation networks with autonomous mobile actors. Jonne Zutt j.zutt@its.tudelft.nl Delft University of Technology Information Technology and Systems Collective Agent Based Systems Group. Supervisors Dr. C. Witteveen

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Fault detection and recovery in multi-modal transportation networks with autonomous mobile actors

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  1. TRAIL/TNO Project 16 Fault detection and recovery in multi-modaltransportation networks with autonomous mobile actors Jonne Zutt j.zutt@its.tudelft.nl 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 • Project Characteristics • Problem setting:Transport Planning Problem • Scheduling Example • Preliminary Results • 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. Transport Planning Problem – Orders • 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.

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

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

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

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

  9. 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

  10. 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) …)

  11. Algorithm • Schedule(Agent a, Route Rta) ≡ • consider the head of route Rta, • t is the first time at which resource  is not claimed by other agents, • increment t and schedule  at t until the tail of route Rta is (recursively) scheduled successfully.

  12. Algorithm (2) • Process(Agent a, Order o) ≡ • negotiate until agent a is allowed to schedule, • Agent a makes a schedule, • if agent a violates order o’s time-window: negotiate until agent a is allowed to reroute, • after each reroute (of any agent), the above steps are repeated

  13. Deadline Time A B C D E F B G E F B G H C I J H C I J Example E J F I A B C D G H

  14. EX: Determine Scheduling Order (6 – 4) / 4 = 0.5 2 (7 – 4) / 4 = 0.75 5 (6 – 4) / 4 = 0.5 3 (6 – 4) / 4 = 0.5 4 (5 – 4) / 4 = 0.25 1 Deadline Time  (o– Mo) / Mo A B C D E F B G E F B G H C I J H C I J

  15. EX: Compute Schedules A B C D E F B E F B G H C I J H C I J Deadline Deadline Time  Time  2 A B C D 5 E F B G 3 E F B G 4 H C I J 1 H C I J

  16. EX: Compute Schedules Deadline Deadline Time  Time  2 A B C D A B C D 5 E F B G E F B G 3 E F B G E F B G 4 H C I J H C I J 1 H C I J H C I J

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

  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. Future Plans • Generate realistic problem instances, • Repeat experiments with more different routing and conflict resolution algorithms, • Repeat experiments under influence of incidents.

  20. TRAIL/TNO Project 16 Fault detection and recovery in multi-modaltransportation networks with autonomous mobile actors Jonne Zutt j.zutt@its.tudelft.nl 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

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