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www.rsTRAIL.nl
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
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Content
1. Multi-agent Transport Planning
2. Algorithms
3. TP Simulator (demo)
4. Experiments
5. Coordination
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Transport Planning - Overview
Infrastructure
Orders
Incidents
Agents(Re)Planning
Execution &monitoring
Statistics
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TP - Orders
Infrastructure
Orders
Incidents
Agents
O = (rt, f, v, s, Ts, d, Td, l, u, p)rt release time, f, v freight / volume,s, d source / destination location,Ts, Td source / delivery time-window,l, u loading / unloading costs,p penalty function.
Statistics
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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,I infrastructure agent: algorithms.
Statistics
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TP - Infrastructure
Infrastructure
Orders
Incidents
Agents
I = (Ri,E,K,C,S)
Ri infrastructure resources,E direct connectivity relation,K capacity function,C distance function,S max. speed function.
Statistics
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TP - Incidents
Infrastructure
Orders
Incidents
Agents
J = (rt,t,,T,f)
rt release time,t type, infrastr./transport resource,T effective time-windowf severity [0..1].
Statistics
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TP - Statistics
Infrastructure
Orders
Incidents
Agents
#/min/max/sum/avg/var/skw/kurPA final agent plans,URt transport res. utilization,URi infrastructure res. utilization,C agent communication,P, D pick-up / delivery penalties,… many more.
Statistics
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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 loadL = [{o1,o2}, {}, {o3}, …, Ln],
• A sequence of sets of orders to unloadU = [{}, {}, {o1}, …, Un],
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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.
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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
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Example
A B
C D
E
AC
AE
AB
BE
BD
CE
CD
DE
A B
C
E
D
cap: dist: 0
cap: 1dist: 100
cap: dist: 0
roads have dist: 10, cap: 15 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
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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.
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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.
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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
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No scheduling algorithm used
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H&N/with rerouting, sov-function=delay
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H&N/rerouting, sov-function=deadlines
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