metrics for evaluating distributed manufacturing control systems

Metrics for evaluating distributed manufacturingcontrol systems

Robert W. Brennan*, Douglas H. NorrieDepartment of Mechanical and Manufacturing Engineering, University of Calgary,

2500 University Dr. N.W., Calgary, Alberta, Canada T2N 1N4

Abstract

In this paper, two classes of metrics are proposed that can be used to evaluate alternative manufacturing control architectures:

those dealing with the controlled system (i.e. the manufacturing system) and those dealing with the control system. In order to

illustrate the use of these metrics, the problem of scheduling a simple manufacturing cell is tackled using two test control

architectures and the performance of these alternative approaches is evaluated using a modular discrete-event simulation model.

The results of the paper suggest that it is worthwhile to use both manufacturing and control system performance measure to

evaluate alternative control architectures, especially when considering dynamic architectures such as a Holonic Manufacturing

System (HMS; i.e. architectures that adapt to changes in the manufacturing environment).

# 2003 Elsevier Science B.V. All rights reserved.

Keywords: Distributed control systems; Performance metrics

1. Introduction

To meet the requirements of agile manufacturing,

research in the area of manufacturing systems control

has moved away from traditional centralised appro-

aches and has focused on the development of a spectrum

of distributed control architectures that range from

hierarchical structures to non-hierarchical (NH) or

heterarchical structures [1].

The motivation for this work is based on the

requirement for manufacturing systems that are cap-

able of adapting to internal (e.g. machine breakdown)

as well as external (e.g. changes in demands or product

specifications) disturbances. In these systems, the

removal and introduction of new manufacturing equip-

ment and the introduction of new products should be

easily achievable, whereby the manufacturing system

and their control should be easily re-configurable.

Since the environment the manufacturing systems will

be operating in is continually changing they must as

well posses the ability to change dynamically. This

concerns the control architecture, the physical system

and the tuning of certain control parameters, thus

making the systems self-adapting and self-tuning.

Even though some of the suggested architectures

seem promising, none has been fully capable of

coping with the demands that influence the manufac-

turing system and its performance. In order for the

manufacturing systems to fully cope with these

demands current research is heading towards manu-

facturing control architectures that are hybrids of the

Computers in Industry 51 (2003) 225–235

* Corresponding author. Tel.: þ1-403-220-5798;

fax: þ1-403-282-8406.

E-mail addresses: [email protected] (R.W. Brennan),

[email protected] (D.H. Norrie).

0166-3615/03/$ – see front matter # 2003 Elsevier Science B.V. All rights reserved.

doi:10.1016/S0166-3615(03)00038-1

hierarchical and heterarchical architectures. The

purpose is to combine the predictability of the cen-

tralised and hierarchical control architectures with the

agility and robustness against disturbances and high

degree of adaptability of the heterarchical control

architectures.

Holonic Manufacturing Systems (HMS) has been

suggested as a concept for these future manufacturing

systems [2]. The concepts of HMS originate from the

Hungarian philosopher Arthur Koestler, who (based

on his study on living organisms and social organisa-

tions) stated that wholes and parts in the absolute sense

do not exists anywhere [3]. Instead, everything is

made up of dynamical hierarchies. Koestler coined

the term holon as the basic element of these systems.

The holon posses the basic characteristics of auton-

omy and co-operation, whereby it is capable of plan-

ning and executing for itself. Furthermore, the co-

operation capability enables it to co-operate with

other holons in order to achieve a common goal or

objective. Holons are aggregated in to holarchies. A

holarchy is a holon within itself. The holarchies

describe the dynamic hierarchies of a Holonic System

and throughout the life cycle of a Holonic System

these dynamical hierarchies will be created and

deleted on a regular basis as a response to the current

situation.

Multi-Agent Systems (MAS), a technology which

has emerged through research within Distributed Arti-

ficial Intelligence (DAI), is a promising enabler for

HMS [4,5]. Since the holon encompasses in most

cases both a software part and a physical part, software

agents play an important role in implementing Holo-

nic Systems.

An important question that follows from research in

this area is, given that Holonic Systems are dynamic in

nature, what is the most appropriate architecture at any

given time? This decision (i.e. should the Holonic/

Multi-Agent System should be run as a hierarchy,

heterarchy or a hybrid?) corresponds to defining the

degree of autonomy of the holons/agent within the

manufacturing system. This paper raises these ques-

tions and suggests a number of metrics that can be

used to evaluate manufacturing control architectures.

In order to provide an objective comparison of

alternative control architectures, two test control archi-

tectures are identified: a non-hierarchical or ‘‘heter-

archical’’ control architecture, and a unconstrained

hierarchical (UH) architecture. These two architec-

tures, as well as a simple emulated test environment

are described in Section 2.

In Section 3, two classes of metric will be defined,

the first dealing with the performance of the manu-

facturing system being controlled and the second

dealing with the performance of the control architec-

tures themselves. Next, we evaluate the relative per-

formance of these architectures using these measures

in Section 4. Finally, we provide our conclusions

based on our experimental work with the two test

control architectures in Section 5.

2. The test environment

2.1. The test control architectures

Fig. 1 provides a top-level view of the two control

architectures investigated in this paper. The key rela-

tionship that is being investigated in the experiments

that follow is how increases in planning horizon affect

the performance of an initially reactive control archi-

tecture. With this in mind, the unconstrained hierarch-

ical architecture can be viewed as a hybrid of the

characteristically reactive non-hierarchical architec-

ture: i.e. the UH architecture combines the planning

capabilities of hierarchical architectures with the reac-

tiveness of heterarchical architectures.

The NH architecture consists of two physical

agents: Part Agents and Machine Agents that are

associated with individual parts and workstations,

respectively, in the emulated manufacturing system.

Part Agents encapsulate the information necessary to

manage the processing of individual parts such as the

part’s process plan, machining requirements, proces-

sing status, and due date; Machine Agents are con-

cerned with managing the individual workstations in

the manufacturing system and contain information

concerning the workstations’ machining capabilities

and status.

The two types of agents in the NH architecture co-

ordinate their actions using the contract net approach

[6]: in this case, the Part Agents serve as managers

and the Machine Agents serve as contractors. A part-

oriented bidding-based approach is used to allocate

part processing to machines where Part Agents

broadcast requests to have available part operations

226 R.W. Brennan, D.H. Norrie / Computers in Industry 51 (2003) 225–235

processed on workstations, while Machine Agents

respond with an estimate of how many operations

they can process and the time it will take to complete

this processing. Part Agents then collect proposals

and choose the best one based on how likely the

proposal is to help them satisfy their due date com-

mitments. Each Part Agent repeats this process until

all of its operations are completed. More details of

the Part Agent and Machine Agent interactions can

be found in [7].

Various researchers have used this same approach to

resource allocation. Most notably, Duffie and Piper [8]

showed on a similar test-bed to the one used here that

this approach leads to natural load balancing, a reli-

able mechanism for failure recovery, and allows

agents to be introduced and removed dynamically.

As noted previously, when Part Agents broadcast

their call-for-proposals they request bids for all avail-

able operations. Since there are typically precedence

relationships between operations, not all operations

can be assigned to machines in one bidding cycle. For

the parts that are processed in our emulated system,

the experimental results have shown that Part Agents

award an average of 1.88 operations (out of a total of

eight operations) per bidding cycle, or in other words,

the NH architecture plans approximately two opera-

tions ahead. The UH architecture has been designed to

allow this horizon to be varied.

Like the NH architecture, the UH architecture Part

Agents broadcast a call-for-proposals for processing,

but unlike the NH architecture they specify the max-

imum number of operations (N) that they would like to

see completed per bidding cycle (i.e. the planning

horizon). This message is broadcasted to a middle agent

called a Bargainer Agent that serves as a facilitator to a

partial hierarchy of related agents as is illustrated in

Fig. 1 (in this case, each Bargainer Agent is assigned to

a manufacturing cell consisting of a milling station

Machine Agent and a turning station Machine Agent).

Like the NH architecture, the UH architecture uses

the contract net for resource assignment. Part Agents

request bids from Bargainer Agents and Bargainer

Agents request bids from Machine Agents. As noted

previously, the UH architecture can be viewed as a

hybrid version of the NH architecture that allows Part

Agents to plan ahead. The Bargainer Agents are

responsible for managing these plans in the form of

a schedule. A more detailed description of the Part

Agent, Bargainer Agent and Machine Agent interac-

tions can be found in [7].

Fig. 1. The test control architectures.

R.W. Brennan, D.H. Norrie / Computers in Industry 51 (2003) 225–235 227

2.2. The experimental test-bed

For the experiments conducted for this research the

two test control architectures are evaluated using a

modular experimental test-bed developed for this

research [9]. This test-bed allows various control

architectures to be developed in an object-oriented

environment (Cþþ) and linked to a discrete-event

simulation model (Arena) of the test manufacturing

system.

Since the majority of the control architecture work

described in the literature is concerned with part

scheduling in flexible manufacturing systems (FMS)

[10,11] and cellular manufacturing systems [12,13],

the system that is investigated for this research is

concerned with the scheduling of parts through a

simple manufacturing cell. For the experiments

reported in this paper, a simulation model of a simple

manufacturing cell was used that is similar in structure

to the manufacturing cells investigated by others in

this area [8,14]. This model consists of four automatic

machines connected by a material-handling robot.

Each of the machines in the system is capable of

performing various operations depending upon its tool

set-up, which is limited by the number of tools that can

be held at the machine’s local tool storage area. As

well, each machine in the investigated system is prone

to failure. Details of this test environment can be found

in [9].

3. Metrics used to evaluate the test controlarchitectures

The main purpose of this section is to introduce the

metrics that are used to evaluate the test control

architectures described in the previous section. Two

classes of metric will be defined, the first dealing with

the performance of the manufacturing system being

controlled and the second dealing with the perfor-

mance of the control architectures themselves. A

second purpose is to further characterise these control

architectures using a number of structural parameters

that are defined later in this section.

Although the primary focus of the research pre-

sented in this paper is concerned with gaining insights

into alternative manufacturing control architectures, it

is important to note at this point that the main purpose

of any research of this nature is improved manufactur-

ing system performance. With this in mind, this sec-

tion will start by introducing the manufacturing

system performance measures in Section 3.1. Next,

in Section 3.2, metrics will be described that relate

directly to the test control architectures. First, we

discuss the notion of control system performances

measures. To illustrate this concept, a specific mea-

sure, inter-agent coupling, is described that is later

evaluated for both test control architectures in Section

4. Next, the notion of control system structural para-

meters that are intended to describe the characteristics

or features of different types of control architectures,

is described. Again, a single metric is developed to

illustrate this concept: decision-making flexibility.

3.1. Manufacturing system performance measures

Ultimately, control system design is concerned with

the efficient operation of the manufacturing system. In

order to evaluate the performance of the manufactur-

ing system for various control architectures it is

important to define a number of manufacturing system

performance measures that will be evaluated by

experimentation.

Examples of the types of performance criteria [15]

that can be investigated follow:

(i) criteria based on job completion times (e.g.

average job flowtime),

(ii) criteria based on due date performance (e.g.

average lateness, average tardiness, proportion

of tardy jobs),

(iii) criteria based on inventory costs or resource

utilisations (e.g. number of parts waiting,

number of finished goods, number of parts in

process, machine idle time).

The three sets of criteria described above relate to

the primary objectives that apply to scheduling pro-

blems [16]. First, we are concerned with minimising

the amount of time that jobs spend in the manufactur-

ing system (criteria based on completion time and

criteria based on inventory levels). Next, a typical goal

is to reduce late job completion (criteria based on due

dates). Finally, an important consideration for manu-

facturing systems, consisting of expensive equipment

and personnel, is to fully utilise their limited capacity

(criteria based on utilisation).


Unfortunately, these three scheduling objectives are

in conflict with each other in typical manufacturing

systems. For example, as Vollmann et al. [16] point

out, due date performance can be improved if more

resource capacity is provided but this will cause

resources to be less intensively utilised. Similarly,

flow time performance can be improved (i.e. jobs

can be processed in less time due to reduced conges-

tion) under the same conditions.

The relative performance of the test control archi-

tectures have been evaluated using the three basic

scheduling objectives described above. In the experi-

ments that are reported in [9], the following manu-

facturing system performance criteria were evaluated:

average flow time, average tardiness, and average

work-in-process. In Section 4, we evaluate the NH

and UH control architectures’ average flow time per-

formance relative to the control system metrics

described in the next section.

3.2. Control system metrics

As noted previously, we have found it convenient to

evaluate alternative control architectures in terms of

two classes of control system metrics: control system

performance measures and control system structural

parameters. In this subsection, we look at each of these

metrics.

3.2.1. Control system performance measures

The control system performance measure described

in this paper is one of several measures investigated in

[17] that are primarily based on message traffic in the

control architecture (coupling, listening coefficient)

and how likely individual agents are to change during

operation (volatility). In this paper, the focus will be

on inter-agent coupling.

Coupling is a term that is often used in the literature

to describe the strength or weakness of the relation-

ships between agents in a control system. In order to

achieve overall control system objectives, agents must

somehow co-ordinate their behaviour through their

interactions with each other. These interactions may

lead to a system that can be described as either

‘‘tightly’’ or ‘‘loosely’’ coupled, or somewhere in

between. For example, Davis and Smith [18] define

a distributed problem solving system as a system that

achieves the ‘‘co-operative solution of problems by a

decentralised, loosely coupled collection of problem

solvers’’. In this case, the definition of a loosely

coupled system is one where agents spend most of

their time in computation rather than in communica-

tion [18]. Although it is stated in the literature that the

degree of coupling between agents plays an important

role in distributed systems, definitions such as Davis

and Smith do not lead to a relative measure of coupling

between alternative control architectures.

In the experiments presented in Section 4, the

coupling, cij, from agent i to agent j is defined as:

cij ¼number of messages sent from i to j

number of messages sent from i to all others(1)

In other words, coupling between agent i and agent j is

a measure of the proportion of agent i’s transmitted

messages that are sent to agent j. Clearly, with this

definition, cij can be different from cji so the coupling

between them needs to be considered from the per-

spective of each of the agents. From this definition, cij

can be thought of as the transmit coupling between

agent i and agent j. Similarly, an equation can be used

to determine the receive coupling, cij0, between agent i

and agent j (i.e. the proportion of the messages

received from agent j to those received by all other

agents).

An example of the use of the inter-agent coupling

metric is to provide insights into the relationships

between the various agents involved in the tasks

carried out by the architecture. Since holonic archi-

tectures are intended to by dynamic and metamorphic

in nature, it is conceivable that an outside observer

may not be able to clearly describe the relationships

between holons at any given time. Inter-agent cou-

pling could be used by a meta-controller to evaluate

the performance of the control system and advise

when and where changes should be made [19]. Deci-

sions of this type will require the meta-controller to be

capable of characterising the relationships between

agents (e.g. whether a given group of holons are

characteristically ‘‘hierarchical’’ or ‘‘heterarchical’’).

3.2.2. Control system structural parameters

Unlike performance measures, control system struc-

tural parameters are intended to help define each

individual architecture with greater precision and to

provide a basis for comparison between each of the

approaches. It is through the analysis of these para-


meters that the inherent characteristics of various

decision-making architectures can be evaluated. One

structural parameter that is commonly used in the

literature is planning horizon [20,21]: i.e. the full time

span that an agent considers. Parunak [22] also pro-

posed using decision-maker specialisation to charac-

terise the different capabilities of agents in a control

architecture. In this paper, we define a parameter that

allows control architectures to be compared on the

basis of how much decision-making flexibility each

architecture offers that is based on the computer

science theory of formal languages [23].

The flexibility referred to here is related to the

number of possible ways that agents in the control

system can generate a solution to the control problem.

In a very flexible system, a variety of agents will co-

operate to generate a solution (e.g. complete a part

process plan) that reflects the current state of the system.

Decision-making flexibility is necessary to handle dis-

ruptions such as machine failures and fluctuations in

demand; this will require agents with the capability of

altering their behaviour subject to these disruptions.

In order to quantify the decision-making flexibility

of alternative control architectures, a method is

required that can be used to describe the different

types of decisions that are available to agents. The

method that has been chosen involves describing the

structure of the messages (i.e. the rules for construct-

ing messages) that are passed between agents in a

control architecture using formal language techniques.

Since the motivation for formal language techniques is

to provide a precise characterisation of the structure of

languages, these techniques seem appropriate for the

type of analysis that is required for the control archi-

tecture message structures. The approach used in this

paper involves two main steps:

(i) defining the types of messages that are passed

between agents, and

(ii) defining the structure that message strings can

take given the decision-making options available

to agents in the control architecture.

The formal language technique provides informa-

tion concerning the number of possible alternatives

that agents in a given test control architecture have

when making a decision. When there is more than one

option for an agent, a method is required that gives a

relative indication of this decision-making flexibility.

In order to quantify decision-making flexibility a

flexibility function is developed that is an extension

of the routing flexibility function defined by Upton [24].

This, in combination with a formal language descrip-

tion of inter-agent coupling is used to obtain a measure

of the relative decision-making flexibility for each of

the test control architectures. For example, when an NH

architecture Part Agent requests bids from the Machine

Agents the following set of messages is generated by the

control system: [rqb][1m_sb][2m_sb][3m_sb][4m_sb].

In other words, a bid request from the Part Agent [rqb],

results in each of the four machines responding with

specific bids [m_sb].

At this point, the Part Agent can respond with one of

four different messages, depending upon which

machine it selects for processing. For example, if

the Part Agent decides to award the part’s processing

to machine #1, the following string will occur:

[award_1m][1m_er]. In other words, the Part Agent

awards the processing to machine #1, [award_1m],

then the Machine Agent evaluates the Part Agent’s

request for processing [1m_er]. The full phrase gram-

mar for the NH control architecture is shown in Fig. 2.

Using this phrase grammar, we can explicitly define

decision-making options that are available to the NH

control architecture. This same process is performed

for the UH control architecture. The phrase grammar

for this architecture is shown in Fig. 3.

This formal language analysis can be further devel-

oped to show how Upton’s [24] measure of routing

flexibility in an FMS can be extended to an entropic

measure of ‘‘decision-making flexibility’’ for a given

control architecture. Upton [24] defines ‘‘routing flex-

ibility’’, pi, in an FMS where, pij, is the cost advantage

of routing a part from machine j to machine i.

xi ¼ �Xn

j¼1

pij logðpijÞ (2)

This ‘‘cost advantage’’ is directly related to processing

time in this definition. It is proposed that the same

measure of flexibility be used here with ‘‘cost advan-

tage’’ in this definition relating to the ‘‘advantage’’ or

probability of a given decision being made. This will

require the grammars for the control structures to be

written as stochastic grammars. The productions will

be of the following form:

v)p

w


where p represents the probability of the production

(v;w) occurring.

The representation of the control system in terms of

a stochastic grammar follows logically from the per-

vious definitions. For example, for the NH control

architecture, the productions resulting from the non-

terminal symbol ‘‘R’’ (i.e. response to bids) each have

an equal probability of occurring:

R)0:25

½award 1m½1m erE

R)0:25

½award 2m½2m erE

R)0:25½award 3m½3m erE

R)0:25

½award 4m½4m erE

i.e. the part may select any of the four machines for its

processing.

In order to extend the definition of decision-making

flexibility to the control language, the entropy for the

stochastic productions resulting from a non-terminal

symbol w 2 VN can be determined [24]. First, the

decision-making flexibility resulting from each of

the productions can be calculated using a modified

form of Eq. (2):

x ¼ �Xn

i¼1

pi log2ðpiÞ (3)

where i ¼ 1; 2; . . . ; n represents the number of alter-

native decisions that the production allows. For

example, we can calculate the decision-making flex-

ibility for the NH architecture example discussed

above:

x ¼ �Xn

i¼1

pi log2ðpiÞ ¼ �4 log2ð0:25Þ ¼ 2

To determine the overall decision-making flexibility

for a control architecture, all of the productions for the

control architecture must be considered. The technique

Fig. 2. Phrase grammar for the NH control architecture.


that is used to determine the overall decision-making

flexibility, F, is described by the following equation:

F ¼ xþ Fnext (4)

where Fnext is the overall decision-making flexibility

of the next (i.e. subsequent) production.

In the next section, we evaluate the decision-making

flexibility of the NH and UH control architectures

using this technique.

4. Experiments with the control architectures

As noted in Section 2, the two test control archi-

tectures were evaluated controlling a simple emulated

manufacturing cell [9]. In this section, we report on the

manufacturing and control system performance result-

ing from these experiments.

In a stochastic environment, one would expect that

the UH architecture’s alternating between planning

and reacting would occur very frequently, resulting in

an architecture that is, in extreme cases, purely reac-

tive. As well, one would expect this effect to be

particularly prevalent when the planning horizon is

long. As a result, we chose to evaluate the two test

architectures for various planning horizons to observe

how the UH architecture’s ability to plan ahead impacts

the manufacturing system performance as well as the

behaviour of its agents. By varying the maximum

length of time that UH architecture scheduling agents

(i.e. ‘‘Bargainer Agents’’) can plan into the future, the

impact of uncertainty on a planning-based scheduling

approach can be observed and compared to the more

reactive-based scheduling approached used by the UH

architecture.

For both architectures, Machine Agent receive cou-

pling and average flow time were collected during the

simulation runs. These results are shown in Fig. 4; the

figure illustrates the concept of ‘‘Machine Agent receive

coupling’’. In this figure, we only plot UH architecture

receive coupling and average flow time since, as noted

previously, only the UH architecture allows us to alter

Fig. 3. Phrase grammar for the UH control architecture.


its planning horizon. The NH architecture receive cou-

pling and average flow time results are indicated by

arrows on the left and right of Fig. 4, respectively.

Rather thanplottingMachineAgentcouplingdirectly

in this figure, a ratio of supremal agent (i.e. Part or

Bargainer Agent) over peer agent (i.e. Machine Agent)

coupling is used. By presenting inter-agent coupling in

this way, the relative coupling between the agents con-

cerned with the task of part scheduling in each archi-

tecture can be evaluated: i.e. ‘‘loosely’’ coupled agents

would have a supremal/peer ratio close to 1 while

‘‘tightly’’ coupled agents would have a supremal/peer

coupling that diverges from 1 (greater than 1 would

indicate a supremal/infimal or a ‘‘master/slave’’ rela-

tionship characteristic of hierarchical architectures).

Given that the UH architecture has the ability to

plan or react, one would expect that, as a result of

disturbances such as machine failures and processing

delays, the UH architecture would start to behave like

the NH architecture. As one can see in Fig. 4, the

simulation results show a trend in this direction: i.e. an

increase in UH architecture Machine Agent coupling

agrees with the prediction that the combination of

uncertainty and long planning horizons would result in

frequent re-planning.

These results indicate that as the UH architecture

planning horizon increases, its Bargainer and Machine

Agents start to behave more reactively as a result of re-

scheduling (i.e. as planning horizon is increased,

Bargainer and Machine Agents coupling is higher

or ‘‘tighter’’ than the Machine Agent peer-to-peer

coupling). This relatively tighter coupling between

these agents results in a clear supremal/infimal rela-

tionship characteristic of hierarchical architectures.

Fig. 4. Machine Agent receive coupling.


As well, the receive coupling results also indicate

that there is a degree of hierarchy that exists in non-

hierarchical or heterarchical control architectures: i.e.

Machine Agent receive coupling for the NH architec-

ture is greater than 1, indicating a supremal/infimal or

‘‘master/slave’’ relationship characteristic of hierarch-

ical architectures.

When we evaluate the overall decision-making

flexibility of the two architectures we see that the

UH control architecture displays a higher decision-

making flexibility than the NH control architecture:

FNH ¼ 2:5

FUH ¼ 3:5

This is a result of the UH architecture’s Bargainer

Agents’ ability to choose to either use their own

machines for part processing or request help from

another Bargainer Agent. If this option is not available

to the UH control architecture’s Bargainer Agents, the

UH control architecture’s decision-making flexibility

reduces to that of the NH architecture (i.e. FUH ¼2:5). The relative decision-making flexibility results

between the UH and NH control architectures seem to

indicate that the UH control architecture’s use of a

schedule does not inhibit the flexibility of its agents. In

fact, the average flow time results shown in Fig. 4

indicate that this increased flexibility results in better

overall manufacturing system performance (i.e. aver-

age flow time performance).

5. Conclusions

In this paper, we have looked at two basic classes of

performance metrics (manufacturing system and con-

trol system) that can be used to evaluate the relative

performance of alternative control architectures for

manufacturing and have provided examples of each.

These metrics were then used to compare two basic

control architectures, a non-hierarchical or ‘‘heter-

archical’’ control architecture (NH) and a hybrid

control architecture (UH) on the same experimental

test-bed.

As a result of this work, we feel that benefits can be

gained from going beyond traditional manufacturing

system performance measures such as average flow

time, to the definition and use of control system

performance measures and structural parameters.

For example, quantitative results using measures such

as inter-agent coupling can be used to gauge the

interactions between agents in a dynamic control

architecture such as a Holonic Manufacturing System.

When these metrics are combined with other metrics,

they can potentially be used by system designers (or

high-level meta-agents) to learn the characteristic

interactions between agents for given performance

requirements. In other words, the control system

can use performance feedback (i.e. control and man-

ufacturing system performance measures) based on its

current and past configurations (i.e. its current and past

structural parameter values) to tune itself to meet the

system’s performance objectives. These performance

objectives are typically stated in terms of manufactur-

ing system performance measures.

For example, the experiments reported in this paper

have shown that, although the UH architecture agents

start to behave in a similar fashion to the NH architec-

ture agents as planning horizon is increased (i.e. the two

architectures’ inter-agent coupling start to converge),

the added flexibility of the agents in the UH architecture

give it an added advantage over purely reactive

approaches: i.e. because of the highly structured nature

of shop floor control, some degree of planning is useful,

even in an unpredictable environment.

Acknowledgements

The authors wish to thank Natural Sciences and

Engineering Research Council for their generous sup-

port of this research under grant OGP-019-7339.

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Robert W. Brennan is an associate

professor of mechanical and manufac-

turing engineering and director of the

manufacturing program at the Univer-

sity of Calgary, Alberta, Canada. His

research interests include distributed

real-time control of manufacturing sys-

tems, modelling and analysis of manu-

facturing systems, and manufacturing

control architectures. He holds BSc and

PhD degrees from the University of

Calgary. He has over 7 years of industrial experience in project

management and control systems and is a professional engineer and

a member of SME, IIE, IEEE, and INFORMS.

Douglas H. Norrie is adjunct professor

of mechanical and manufacturing en-

gineering at the University of Calgary,

Alberta, Canada. Formerly, he was head

of the Department of Mechanical En-

gineering at the University of Calgary,

and more recently head of the division

of manufacturing engineering. He is also

formerly an adjunct professor of com-

puter science at the same institution. He

has been involved in two major interna-

tional Research Consortia in Intelligent Manufacturing Systems,

being a founder member in the Holonic Manufacturing Systems

and the Gnosis Knowledge systematisation consortia. He is author

or co-author of numerous publications, including seven books. His

research interests are in Intelligent Systems and, in particular, in

Multi-Agent Systems.


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