A Multi-Agent System Architecture for Coordination of Just-In-Time Production and Distribution

A Multi-Agent System Architecture for Coordination of Just-In-TimeProduction and Distribution

Paul Davidsson and Fredrik WernstedtDepartment of Software Engineering and Computer Science

Blekinge Institute of Technology

SWEDEN

Overview

Characterization of problem MAS architecture Case study: District Heating Systems Simulation experiments Conclusions

The Problem: Just-In-Time Production and Distribution

A set of producers of resources (P1,…, Pn)

A set of consumers of resources (C1,…, Cm)

P1

P2P3

C1

C6

C4

C5

C7

C10C8 C9

C2

C3


We can control how much resources are produced We cannot control the demands of the consumers We do not know future consumer demands We can monitor the actual consumption

P1

P2P3

C1

C6

C4

C5

C7

C10C8 C9

C2

C3


The Production time (PT) and/or the Distribution time (DT) is relatively long

Resources must be consumed relatively soon– limited storage capacity, or– quality of resources degrades quickly, etc

P1

P2P3

C1

C6

C4

C5

C7

C10C8 C9

DTC2

C3


It is possible to redistribute resources between consumers that are close in proximity relatively cheap and fast

P1

P2P3

C1

C6

C2

C3

C4

C5

C7

C10C8 C9


There is a single “owner” of the producers, i.e., no competition between the producers

There is a long term “contract” between producers and consumers (about the prize of resources etc.)

P1

P2P3

C1

C6

C2

C3

C4

C5

C7

C10C8 C9


Examples:

– car production (the retailers are the consumers)

– iron and steel production

– district heating

P1

P2P3

C1

C6

C4

C5

C7

C10C8 C9

C2

C3


Sub-problem 1: produce the right amount of resources at the right time

Sub-problem 2: distribute these resources to the right consumers

P1

P2P3

C1

C6

C4

C5

C7

C10C8 C9

C2

C3


Conflicting goals!1. Produce as little resources as possible

2. Satisfy the demands of all consumers

P1

P2P3

C1

C6

C4

C5

C7

C10C8 C9

C2

C3

Solution: Just-In-TimeProduction and Distribution

Increase the knowledge about the current and the future states of the system (i.e., a decision support system at the producer side)

Redistribution of resources between consumers

P1

P2P3

C1

C6

C4

C5

C7

C10C8 C9

C2

C3

Producer agent

Redistribution agents

Consumer agents

MAS architecture

Each consumer has a consumer agent Consumers that are ”close” forms a cluster and

each cluster has a redistribution agent One producer agent (interacts with all plants)

Consumer agents

MAS architecture

Make predictions of future demands Monitor the actual consumption Communicate this to the redistribution agent Perform received redistribution instructions

Redistribution agents

MAS architecture

Make predictions for the whole cluster Monitor the actual consumption of the cluster Communicate this to the producer agent Compute and send redistribution instructions

Producer agent

MAS architecture

Interacts with production operators Compiles predictions for the whole system Compiles consumption for the whole system Informs redistributor if demands cannot be met

Case study: District heating

Production plants heat water (cheaply) Distribute hot water to consumer substations Substations exchange heat to secondary flows

within buildings (both radiator and tap water) Cold water is returned to plant in separate pipes Long distribution time, up to 24 hours!

Substation

Radiator water

Cold water

Hot tap water

Control unit

Outdoor temperature sensor

Hot water, in

Return water

New type of substation is being developed by Cetetherm that programmable and supports two-way communication

Example: the total consumption in a network serving 500 households

Time [h]

Total consumption [kW]

0

200

400

600

800

1000

1200

0 6 12 18 24

P

R

C

C

C

C

C

C

R

Multi-Agent System

Redistribution is done by issuing “restrictions” (upper limits for consumption)

Tap water has higher priority than radiator water

P

R

C

C

C

C

C

C

R

Multi-Agent System

Predictions are made for each 10 min interval

– Each C computes the average consumption for the corresponding interval over the last 5 days

SimulatorSIMULATOR

MASCG

PG PC

C

C

R

C

C

C

R CG

CG

CG

CG

CG

Consumption generated using a statistical model Both MAS and simulator implemented in JADE

Experiment I: Quality of Service vs. Surplus production

0

20

40

60

80

100

120

140

0 1 2 3 4Surplus production (%)

Num

ber

of R

estr

ictio

ns(o

ne m

inut

e at

one

sub

stat

ion)

5

radiator water

tap water

Cluster has 10 substations (5*40 and 5*60 households) Reference: 7% surplus needed to get 0 restrictions

Experiment II: Quality of Service vs. Size of cluster

0

50

100

150

200

250

300

2 4 8 16Cluster size

Num

ber

of r

estr

ictio

ns

Note: the cluster size is often limited by factors beyond our control, e.g., proximity of consumers

Conclusions

Suggested MAS approach makes it possible to control the trade-off between Quality of Service and the degree of surplus production

Possible to reduce the amount of production while maintaining the same Quality of Service

The larger the cluster size, the higher is the Quality of Service that can be achieved

– However, cluster size is often limited by factors beyond our control, e.g., proximity of consumers

Future work

Improve the prediction mechanism Improve the simulation environment Extend experiments to several producers Perform actual field tests Evaluate the generality of the result in other

just-in-time domains Test other restriction policies than fairness,

e.g., based on priorities between consumers

Software architecture

Different approaches possible

– centralized, semi-distributed and distributed

– agent-based and traditional approaches We have chosen a semi-distributed agent-based

approach…

Why a semi-distributed approach? District heating systems are distributed per se

– at least sensor reading and heat exchanger control must be distributed

Possible to centralize all computation, but– communication bottleneck at the central computer– computational bottleneck at the central computer

(e.g., for computing the forecasts)– complex (many different types of substations etc)– private information should be kept locally

Possible to distribute all computation, but– increase the number of messages sent

Why an agent-based approach? District heating systems have all the character-

istics of the ”perfect” agent application [Parunak]:

– modular

– decentralized

– changeble

– ill-structured

– complex More general arguments include increased:

– robustness, efficiency, flexibilty, openness, scalability, and economy

TP = production time TD = distribution time

t0 = the start time of the actual consumption interval

during each “prediction interval” the consumption is reported n times

t0-(TP+TD)

Consumer Producer agent Producer

total predicted demand

production

total consumtion

t0-TD

t0+1

t0+n

Consumer agent Redistribution agent

future restriction future restriction

consumption

redistribute

consumption

redistribute

predicted demandpredicted

cluster demand

cluster consumption

production

t0+2

Interaction protocol

total consumtionconsumption

redistribute

consumption

redistribute

cluster consumption

Reference production

0

200

400

600

800

1000

1200

0 4 8 12 16 20

time [h]

consumption [kW]

24

7% surplus production needed to get 0 restrictions

Documents

A Multi-Agent System Architecture for Coordination of Just-In-Time Production and Distribution