october 21 – bnaic 2004 jonne zutt and cees witteveen multi-agent transport planning delft...

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October 21 – BNAIC 2004 Jonne Zutt and Cees Witteveen Multi-Agent Transport Planning Delft University of Technology

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Page 1: October 21 – BNAIC 2004 Jonne Zutt and Cees Witteveen Multi-Agent Transport Planning Delft University of Technology

October 21 – BNAIC 2004

Jonne Zutt and Cees Witteveen

Multi-Agent Transport Planning

Delft University of Technology

Page 2: October 21 – BNAIC 2004 Jonne Zutt and Cees Witteveen Multi-Agent Transport Planning Delft University of Technology

October 21 – BNAIC 2004

Contents

• Transportation planning

• Model

• Problem description

• Methods

• Results

• Future work

Page 3: October 21 – BNAIC 2004 Jonne Zutt and Cees Witteveen Multi-Agent Transport Planning Delft University of Technology

October 21 – BNAIC 2004

Transportation planning

• Guide-path design• Estimating optimal

number of vehicles• Vehicle maintenance• Order allocation• Idle-vehicle positioning• Vehicle routing• Conflict-resolution

Page 4: October 21 – BNAIC 2004 Jonne Zutt and Cees Witteveen Multi-Agent Transport Planning Delft University of Technology

October 21 – BNAIC 2004

Transportation planning

• Guide-path design• Estimating optimal

number of vehicles• Vehicle maintenance• Order allocation• Idle-vehicle positioning• Vehicle routing• Conflict-resolution

Strategic

Tactic

Operationalminutes

hours

months

Page 5: October 21 – BNAIC 2004 Jonne Zutt and Cees Witteveen Multi-Agent Transport Planning Delft University of Technology

October 21 – BNAIC 2004

Model

Auctioneeragent

Transportagent

Transportagent

Transportagent

Customeragent

Transportresource

Transportresource

Transportresource

speedcapacity

max. speedcapacitydistance

cooperativecompetitive

Page 6: October 21 – BNAIC 2004 Jonne Zutt and Cees Witteveen Multi-Agent Transport Planning Delft University of Technology

October 21 – BNAIC 2004

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.

Page 7: October 21 – BNAIC 2004 Jonne Zutt and Cees Witteveen Multi-Agent Transport Planning Delft University of Technology

October 21 – BNAIC 2004

Problem description

• Find conflict-free routes for the operational agents such as to execute all orders, to maximize rewards and to minimize costs

• Maintain feasible plans even when incidents occur

Page 8: October 21 – BNAIC 2004 Jonne Zutt and Cees Witteveen Multi-Agent Transport Planning Delft University of Technology

October 21 – BNAIC 2004

Conflicts

1. Resources have limited capacity

A B C

2. Instantaneous exchange

ABD

Time

A B C

AB

Time

D

Page 9: October 21 – BNAIC 2004 Jonne Zutt and Cees Witteveen Multi-Agent Transport Planning Delft University of Technology

October 21 – BNAIC 2004

Method

• Do (partial) order assignment

• 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)

Page 10: October 21 – BNAIC 2004 Jonne Zutt and Cees Witteveen Multi-Agent Transport Planning Delft University of Technology

October 21 – BNAIC 2004

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

Page 11: October 21 – BNAIC 2004 Jonne Zutt and Cees Witteveen Multi-Agent Transport Planning Delft University of Technology

October 21 – BNAIC 2004

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

Page 12: October 21 – BNAIC 2004 Jonne Zutt and Cees Witteveen Multi-Agent Transport Planning Delft University of Technology

October 21 – BNAIC 2004

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 small block-size slightly increases performance but also increases computation time

Page 13: October 21 – BNAIC 2004 Jonne Zutt and Cees Witteveen Multi-Agent Transport Planning Delft University of Technology

October 21 – BNAIC 2004

Experiments

• 10 transport networks with 25 resources

• 10 sets of transportation orders with 75 random orders each

• 2 different sets of agents with 30 randomly located agents each

• Incidents with failure probability 0, 0.1 and 0.2.

Page 14: October 21 – BNAIC 2004 Jonne Zutt and Cees Witteveen Multi-Agent Transport Planning Delft University of Technology

October 21 – BNAIC 2004

Agent selectionA

vera

ge s

um

of

del

iver

y p

enal

ties

No incidents Pfail = 0.1 Pfail = 0.2

0 reroutes 1 reroute 0 reroutes 1 reroute 0 reroutes 1 reroute

1. Random2. Delays3. Deadlines4. Penalties

0

500

1

000

150

0 2

000

250

0 3

000

350

0

Page 15: October 21 – BNAIC 2004 Jonne Zutt and Cees Witteveen Multi-Agent Transport Planning Delft University of Technology

October 21 – BNAIC 2004

Block size

No incidents Pfail = 0.1 Pfail = 0.2

0 1 1 1 1 1 10 0 0 0 01 1 1 1 1 1

Ave

rage

su

m o

f d

eliv

ery

pen

alti

es

2 2 4 6 ∞ 2 4∞ 2 ∞ 2 ∞6 ∞ 2 4 6 ∞

1. max. number of reroutes2. block size

0

100

0

2

000

300

0

Page 16: October 21 – BNAIC 2004 Jonne Zutt and Cees Witteveen Multi-Agent Transport Planning Delft University of Technology

October 21 – BNAIC 2004

Time for different block sizes

No incidents Pfail = 0.1 Pfail = 0.2

2 2 4 6 ∞ 2 4∞ 2 ∞ 2 ∞6 ∞ 2 4 6 ∞0 1 1 1 1 1 10 0 0 0 01 1 1 1 1 1

Ave

rage

cp

u t

ime

0

1

2

3

4

5

6

71. max. number of reroutes2. block size

Page 17: October 21 – BNAIC 2004 Jonne Zutt and Cees Witteveen Multi-Agent Transport Planning Delft University of Technology

October 21 – BNAIC 2004

Conclusions

• Rerouting instead of static routing improves performance

• Prioritizing agents according to the order’s deadlines seems to work well (on random network topologies)

• Smaller block size is better, at the cost of some extra computation time

Page 18: October 21 – BNAIC 2004 Jonne Zutt and Cees Witteveen Multi-Agent Transport Planning Delft University of Technology

October 21 – BNAIC 2004

Future work

• Experiments for different network topologies

• (Market-based) distributed version

• Effect of coordination strategies

Page 19: October 21 – BNAIC 2004 Jonne Zutt and Cees Witteveen Multi-Agent Transport Planning Delft University of Technology

October 21 – BNAIC 2004

Questions

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

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

• My email: [email protected]