TRAIL/TNO Project 16
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TRAIL/TNO Project 16. Fault detection and recovery in multi-modal transportation 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

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Jonne zutt delft university of technology information technology and systems

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

12/9/04 – Review TNO/TRAIL project #16


Contents

Contents

  • Transportation planning

  • Problem description

  • Progress

  • Methods and hypotheses

  • Experiments

12/9/04 – Review TNO/TRAIL project #16


Issues in design and control of mhs

Issues in design and control of MHS

  • Guide-path design

  • Estimating optimal number of vehicles

  • Vehicle maintenance

  • Order allocation

  • Idle-vehicle positioning

  • Vehicle routing

  • Conflict-resolution

12/9/04 – Review TNO/TRAIL project #16


Layers

Layers

  • Guide-path design

  • Estimating optimal number of vehicles

  • Vehicle maintenance

  • Order allocation

  • Idle-vehicle positioning

  • Vehicle routing

  • Conflict-resolution

Strategic

months

Tactic

hours

Operational

minutes

12/9/04 – Review TNO/TRAIL project #16


Problem description

Problem description

  • Design a model for operational transport planning,

  • Develop multi-agent routing and scheduling methods that can take into account incidents,

  • Search suitable performance indicators to be used in experiments for comparing the quality of different methods taking into account properties of the environment.

12/9/04 – Review TNO/TRAIL project #16


Progress previous years

Progress – previous years

  • Model for operational transport planning

  • Methods for operational transport planning taking into account incidents

  • Transport planning simulator

12/9/04 – Review TNO/TRAIL project #16


Progress last year

Progress – last year

  • Test set

  • Performance indicators

  • Experimental results

  • Thesis structure

  • Approximately two chapters written

12/9/04 – Review TNO/TRAIL project #16


Progress future work

Progress – future work

  • Complete single-agent experiments [December’04]

  • Coordination experiments [February’05]

  • Writing [June’05]

12/9/04 – Review TNO/TRAIL project #16


Overview methods

Overview methods

LPA*

HNZ

rerouting

hi bj rk

loose commitments/ decommitments

HNZ-0

HN

hi bj

fixed routing

Arb-ci

strictcommitments

no planning

look-ahead

12/9/04 – Review TNO/TRAIL project #16


Conflicts

Conflicts

1.Resources have limited capacity

A

B

C

D

B

A

Time

2.Instantaneous exchange

A

B

C

B

A

D

Time

12/9/04 – Review TNO/TRAIL project #16


About cycles and deadlocks

About cycles and deadlocks

K(A)=1

A

A

B

C

B

History: F,E,D,CCurrent: B,A

P(K_sema_C)V(K_sema_B)

12/9/04 – Review TNO/TRAIL project #16


Methods simple plan based arbiter policies

Methods – Simple/plan-based arbiter policies

  • First-In-First-Out

  • Agent priority

  • Longest-Queue-First

  • Longest-Queue-First-Inc

  • Longest-Plan-First

  • Most-Urgent-Deadline-First

  • Max-Reward-Decrease-First

  • Max-Reward-Decrease-Queue-First

Hypothesis:No/very smalldifference

Hypothesis:Plan-based policies outperform the simple policies

12/9/04 – Review TNO/TRAIL project #16


Methods hnz

Methods – HNZ

  • Wait for a change in plan(s)

  • While agents are not ready

    • Compute traffic-aware shortest path

    • Agent compete who schedules first (P1)

    • Winner schedules n resources (P2)

  • If current order rewards are below threshold, agent tries to reroute (P3)

Hypothesis: Much better than no planning

Hypothesis:Rerouting most important par

12/9/04 – Review TNO/TRAIL project #16


Method agent selection functions p1

Method: agent selection functions (P1)

  • RandomProvides a baseline for the others

  • DelaysAgent with maximum wait time first

  • DeadlinesAgent with most strict deadlines first

  • PenaltiesAgent with lowest planned reward first

Hypo: All agent selection functions will outperform random

12/9/04 – Review TNO/TRAIL project #16


Method resource block size p2

Method: resource block-size (P2)

  • How many resources (fraction of route) are scheduled after the agent is selected by the agent selection function?

Hypothesis:A smaller block-size slightly increasesperformance but also increases computation time

12/9/04 – Review TNO/TRAIL project #16


Number of reroute opportunities

Tardiness

Average % of delay

Number of alternatives

Number of alternatives

Number of reroute opportunities

Delay  { aA (Ca – Ma) / Ca } / |A|

Tardiness aA Ca - a if Ca> a

12/9/04 – Review TNO/TRAIL project #16


Agent selection

Agent selection

  • Random

  • Delays

  • Deadlines

  • Penalties

0 500 1000 1500 2000 2500 3000 3500

Average sum of delivery penalties

0 reroutes

1 reroute

0 reroutes

1 reroute

0 reroutes

1 reroute

No incidents

Pfail = 0.1

Pfail = 0.2

12/9/04 – Review TNO/TRAIL project #16


Block size

Block size

  • max. number of reroutes

  • block size

Average sum of delivery penalties

0 1000 2000 3000

0

0

1

1

1

1

0

0

1

1

1

1

0

0

1

1

1

1

2

2

4

6

2

2

4

6

2

2

4

6

No incidents

Pfail = 0.1

Pfail = 0.2

12/9/04 – Review TNO/TRAIL project #16


Time for different block sizes

Time for different block sizes

  • max. number of reroutes

  • block size

0 1 2 3 4 5 6 7

Average cpu time

0

0

1

1

1

1

0

0

1

1

1

1

0

0

1

1

1

1

2

2

4

6

2

2

4

6

2

2

4

6

No incidents

Pfail = 0.1

Pfail = 0.2

12/9/04 – Review TNO/TRAIL project #16


Coordination coalition formation

Coordination – Coalition Formation

  • Static

    • Different companies

  • Dynamic

    • Based on current position

    • Based on source/destination locations, or plan distance function

    • Grouped orders

Hypothesis:Dynamic coalitions are preferable, though staticcoalitions already improve the coalition’s welfare

12/9/04 – Review TNO/TRAIL project #16


Coordination how to improve welfare

Coordination – How to improve welfare?

  • Exchange orders with coalition members (cf. simulated trading)

  • Conflict-resolution:In case of a conflict, determine Δ(C) instead of Δ(A) to determine who wins.

12/9/04 – Review TNO/TRAIL project #16


Questions

Questions

  • CABS project:http://cabs.ewi.tudelft.nl

  • My homepage: http://dutiih.twi.tudelft.nl/~jonne

  • My email: [email protected]

12/9/04 – Review TNO/TRAIL project #16


Thesis

Introduction

Challenges in transportation

Problem description

Approach

Research contributions

Overview

A model and formalism for multi-agent transport planning

Introduction

Building blocks

Correctness criteria

Performance criteria

Single-agent methods for transport planning

Order allocation

Operational planning

Route planning

Simple arbiter policies

Revising priorities

Revising route

Lifelong Planning A*

Experiments on single-agent methods

Experimental setting

Description of the test set

Experimental results

Multi-agent methods for transport planning

Introduction

Coalition formation

Exchanging transportation orders

Conflict solving

Experiments on multi-agent methods

Experimental setting

Experimental results

Conclusions

Mathematical preliminaries

Complexity of transport planning

Thesis

12/9/04 – Review TNO/TRAIL project #16


Model

Model

Customeragent

Auctioneeragent

max. speed

capacity

distance

Transportagent

Transportagent

Transportagent

cooperative

competitive

Transportresource

Transportresource

Transportresource

speed

capacity

12/9/04 – Review TNO/TRAIL project #16


Model incidents

Model: incidents

  • Events that disrupt regular plan execution and generally require re-planning

  • Examples: customers that change or retract transportation orders, unpredictable congestion, vehicle break-down, communication failure

  • Incidents are generated proportional to the resources. Pfail = 0.x means each resources is expected to fail x·10% of the time.

12/9/04 – Review TNO/TRAIL project #16


Method traffic aware shortest path

Method: traffic-aware shortest path

  • Agents know which time-windows are in use by other agents per resource

  • Run an A* algorithm: store routes on open list, check for conflict when appending to candidate route

  • Process is guaranteed to terminate and find the traffic-aware shortest path

12/9/04 – Review TNO/TRAIL project #16


Experiments

Experiments

  • 10 transport networks with 25 resources, ‘random’ topology.

  • 10 sets of transportation orders with 250 random orders each

  • 2 different sets of agents with 25 randomly located agents each

  • Incidents with failure probability 0, 0.1, …, 1.0 and impact 0.1.

12/9/04 – Review TNO/TRAIL project #16


Blocktime

Blocktime

12/9/04 – Review TNO/TRAIL project #16


Simple arbiter policies

Simple arbiter policies

12/9/04 – Review TNO/TRAIL project #16


Hnz 0 1 150 orders

HNZ-0/1150 orders

12/9/04 – Review TNO/TRAIL project #16


Hnz 0 1 250 orders

HNZ-0/1250 orders

12/9/04 – Review TNO/TRAIL project #16


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