fault detection and recovery in multi-modal transportation networks with autonomous mobile actors
DESCRIPTION
TRAIL/TNO Project 16. Fault detection and recovery in multi-modal transportation networks with autonomous mobile actors. Jonne Zutt [email protected] Delft University of Technology Information Technology and Systems Collective Agent Based Systems Group. Supervisors Dr. C. Witteveen - PowerPoint PPT PresentationTRANSCRIPT
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Fault detection and recovery in multi-modaltransportation networks with autonomous mobile actors
TRAIL/TNO Project 16
Supervisors
Dr. C. Witteveen
Dr. ir. Z. Papp
Dr. ir. A.J.C. van Gemund
Jonne Zutt
Delft University of Technology
Information Technology and Systems
Collective Agent Based Systems Group
www.rsTRAIL.nl
Content
1. Project Characteristics
2. Problem setting:Transport Planning Problem
3. Scheduling Example
4. Preliminary Results
5. Future plans
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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
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Transport Planning Problem – Orders
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.
• Transportation orders
• Infrastructure resources
• Transport resources
• Agents
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TPP – Infrastructure• Transportation orders
• Infrastructure resources
• Transport resources
• Agents
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TPP – Infrastructure model• Transportation orders
• Infrastructure resources
• Transport resources
• Agents
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TPP – Transport resources• Transportation orders
• Infrastructure resources
• Transport resources
• Agents
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TPP – Agent architecture
Infrastructureresources
Transportresources
Transportationorders
CUS
TAC
OPR CRA
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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
Incident Management
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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) …)
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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.
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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
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H C I J
H C I J
E F B G
E F B G
A B C D
Time Deadline
EX: Determine Scheduling Order
(o – Mo) / Mo
(6 – 4) / 4 = 0.5
(7 – 4) / 4 = 0.75
(5 – 4) / 4 = 0.25
(6 – 4) / 4 = 0.5
(6 – 4) / 4 = 0.5
2
5
3
4
1
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EX: Compute Schedules
H C I J
H C I J
E F B G
E F B G
A B C D
Time Deadline
2
5
3
4
1 H C I J
H C I J
E F B G
E F B
A B C D
Time Deadline
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EX: Compute Schedules
H C I J
H C I J
E F B G
E F B G
A B C D
Time Deadline
2
5
3
4
1 H C I J
H C I J
E F B G
E F B G
A B C D
Time Deadline
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Experiments
• Used 3 different infrastructures,
• 20 transport agents each execute one order,
• Randomly chosen source-, destination location and fixed time-window.
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Results (averaged over 100 problem instances)
Number of alternatives
Ave
rage
% o
f de
lay
Number of alternatives
Tar
dine
ss
Tardiness aA Ca - a if Ca< aDelay { aA (Ca – Ma) / Ca } / |A|
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Future Plans
• Generate realistic problem instances,
• Repeat experiments with more different routing and conflict resolution algorithms,
• Repeat experiments under influence of incidents.
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Jonne Zutt
Delft University of Technology
Information Technology and Systems
Collective Agent Based Systems Group
Fault detection and recovery in multi-modaltransportation networks with autonomous mobile actors
TRAIL/TNO Project 16
Supervisors
Dr. C. Witteveen
Dr. ir. Z. Papp
Dr. ir. A.J.C. van Gemund