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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

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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