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A methodology for evaluating the manufacturing abilityof equipment in virtual enterprisesH. H. Wang a; Z. D. Zhou a; Y. P. Chen a; S. K. Ong b; A. Y. C. Nee ba School of Mechanical Science and Engineering, Huazhong University of Scienceand Technology, Wuhan, P R Chinab Department of Mechanical Engineering, Faculty of Engineering, National Universityof Singapore, Singapore

Online Publication Date: 01 June 2006To cite this Article: Wang, H. H., Zhou, Z. D., Chen, Y. P., Ong, S. K. and Nee, A.Y. C. (2006) 'A methodology for evaluating the manufacturing ability of equipment in

virtual enterprises', International Journal of Computer Integrated Manufacturing, 19:4, 339 - 349To link to this article: DOI: 10.1080/09511920500064714URL: http://dx.doi.org/10.1080/09511920500064714

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A methodology for evaluating the manufacturing abilityof equipment in virtual enterprises

H. H. WANGy, Z. D. ZHOUy, Y. P. CHENy, S. K. ONGz and A. Y. C. NEE*z

ySchool of Mechanical Science and Engineering,Huazhong University of Science and Technology, Wuhan, 430074, P R China

zDepartment of Mechanical Engineering, Faculty of Engineering,National University of Singapore, Singapore 117576

This paper proposes a methodology for evaluating the manufacturing ability of equipment

in a multi-agent-based virtual manufacturing enterprise by enveloping the manufacturing

information of the various equipment as equipment agents. The manufacturing ability of

equipment consists of two classes: the processing ability and the processing capability of

the equipment. The processing ability of equipment indicates the tasks and operations

that the equipment can perform, and is peculiar to the equipment. The processing

capability of equipment shows the capability of the equipment to undertake and

accomplish a specified task under some constraints, such as time and cost, and it depends

on both the equipment and the task. It varies with different equipment and tasks, and at

different times. The concept of an operation spectrum of equipment is introduced to

verify the processing ability of the equipment, and methods are formulated to calculate

the current capability of the equipment and estimate the cost for bidding for new tasks.

The proposed methodology on the manufacturing ability of the equipment in a virtual

enterprise can be implemented easily using equipment agents. This methodology can

support the equipment agents to bid for new tasks as real independent agents in virtual

manufacturing enterprises or multi-site enterprises.

Keywords: Manufacturing ability; Equipment agent; Multi-agent system; Virtual

enterprise

Notation

AGVs Automatic Guided Vehicles

CDPS Cooperative Distributed Problem-Solving

DA Drilling Machine Agent

DAI Distributed Artificial Intelligence

JAM Job Announcement Messages

KQML Knowledge Query and Manipulation Language

MaA Manager Agent

Mai ith Milling machine Agents

MAS Multi-Agent-based System

RBM Resource Bidding Message

STEP Standard for the Exchange of Product model data

TCP/IP Transmission Control Protocol / Internet Protocol

1. Background

There have been new emerging trends in manufacturing

systems in recent decades. Factors such as demand

variability and unpredictability, shorter product life cycles,

market globalization and increased competition have

notably increased the physical and organizational complex-

ity of manufacturing processes. Thus, manufacturing sys-

tems need to be able to respond to the dynamic nature of

demands (Sousa and Ramos 1999). In fact, in dynamic

scheduling, solutions need to be achieved in the shortest

time possible. Scheduling in manufacturing systems is a

distribution problem involving physical resources such as

*Corresponding author. Email: mpeneeyc@nus.edu.sg

International Journal of Computer Integrated Manufacturing, Vol. 19, No. 4, June 2006, 339–349

International Journal of Computer Integrated ManufacturingISSN 0951-192X print/ISSN 1362-3052 online ª 2006 Taylor & Francis

http://www.tandf.co.uk/journalsDOI: 10.1080/09511920500064714

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NC machines, robots, AGVs, conveyors, etc., where several

tasks can be carried out at the same time. Thus, frameworks

based on distributed artificial intelligence (DAI) and agent-

based systems for dynamic scheduling of industrial tasks

have been proposed (Sousa and Ramos 1999, Rabelo et al.

1999, Ferber 1999, Macchiaroli and Riemma 2002).

The use of intelligent agent-based systems has become a

common approach in the research on DAI and distributed

problem solving. In the DAI research, there are studies on

the cooperative distributed problem-solving (CDPS) ap-

proaches (Miyashita 1998). Planning and scheduling acti-

vities in a manufacturing enterprise can be easily modelled

using the CDPS approach. Various research efforts of using

a multi-agent-based system (MAS) approach to scheduling

have been reported (Roy and Anciaux 2001, Macchiaroli

and Riemma 2002, Wang et al. 2002a, b, Miao et al. 2002,

Jia et al. 2002). In the scheduling problem, agents, which

are responsible for different aspects of the problem, work

cooperatively to attain the common goal of formulating a

most profitable global schedule. At the same time, each

agent acts in a distributive way to solve its own local sub-

problem of maximizing its own objectives. However, in the

multi-agent-based scheduling problem, a single decision by

an agent can create unpredictable rippling effects in the

whole system.

2. Introduction

There are many situations in which an equipment agent has

to make decisions, such as bidding for new jobs, actions to

be taken when an event occurs, etc. In order to make these

decisions, an equipment agent should not only have the

corresponding algorithms but should also be aware of the

manufacturing abilities of the equipment. There are many

reported researches on the decision-making systems ofMAS

applications in manufacturing enterprises. Miao et al.

(2002) proposed a computational agent-reasoning model

for constructing intelligent agents that have the ability to

model, reason, and make decisions on behalf of human

beings.Wang et al. (2002a) discussed the way to build intelli-

gent negotiation agents and used an object-oriented con-

straint language formodelling constraint agents. In addition,

the newly emerging constraint agent technology provides a

promising solution for developing negotiation agents. These

negotiation agents are able to interact and negotiate with

users and with each other. Jia et al. (2002) reported a web-

based system for production scheduling in a distributed

manufacturing environment using the internet technology.

A scheduling agent was built and a genetic algorithm, involv-

ing one gene crossover and two gene mutations, was adopted

as the scheduling kernel to achieve distributed scheduling

optimization. Using this system, manufacturing participants

are no longer isolated production elements and information

among the participants can be shared and exchanged in real

time. Many researchers have discussed the manufacturing

ability of equipment in an enterprise. Wen discussed the

concept of ‘ability management and ability requirement

planning’ (Wen 1994). Kloshel (2000) put forward a

resource information model. In this model, resource

information is the basis for connecting the process planning

systems in the different departments in an enterprise.

Presently, the research on the manufacturing ability of

equipment is mainly in the balancing of the working load of

the equipment, and cannot fully express the manufacturing

ability, manufacturing capability, and the status of the

equipment. Hence, it cannot be applied to MAS systems.

Wang et al. (2002b) developed a multi-agent-based

scheduling system for a virtual manufacturing environ-

ment, and proposed an effective hybrid hierarchical model

for agile job scheduling in a virtual workshop environment.

In this system, the most important task of an equipment

agent is bidding for new jobs. Before an equipment agent

can bid for a new job, it should be aware of what the

equipment can do and whether it can schedule the job in its

agenda. An equipment agent that is not conversant with

the ability of the equipment cannot bid for new jobs

intelligently. Therefore, the ways to define the manufactur-

ing ability of equipment and evaluate the capability of the

equipment for receiving new jobs are the essential elements

in implementing equipment agents. However, there has

been little reported research on this issue. Hence, in this

paper, the main objective is to define and verify the

manufacturing ability of equipment in an equipment agent

and formulate systematic evaluation algorithms for under-

taking new jobs.

3. Manufacturing ability of equipment

3.1. Concept

The requests for the manufacturing ability of equipment

are different in different decision-making layers and under

different situations. At present, there are many different

definitions of the manufacturing ability of equipment

that can be used for different occasions. Wen (1994) used

the output rate of equipment in a certain period of time

to define the manufacturing ability. This type of manu-

facturing ability is the output rating of equipment.

Manufacturing ability can also be defined by meeting

particular workpiece specifications, such as surface finish

and tolerance. This is also called the processing ability of

equipment. Manufacturing ability can sometimes be

defined as the metal removal rate. These definitions can

only describe certain aspects of the manufacturing ability of

equipment, and can only be applied to specific situations.

They cannot satisfy the requirements for defining the

manufacturing ability of equipment in a multi-agent-based

manufacturing system. In a multi-agent-based manufactur-

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ing system, an equipment agent determines its bidding

strategy for new in-coming production assignments by

evaluating the manufacturing ability of the equipment.

Thus, the function of evaluating the manufacturing ability

of equipment is important in equipment agent.

3.2. Definition

The manufacturing ability of equipment is its ability to

accept and accomplish operations under certain constraints

and technical specifications. It consists of two classes:

processing ability and processing capability, as shown in

table 1. Processing ability indicates the operations and

tasks that equipment can perform, and is peculiar to a

specific piece of equipment. The processing capability

shows the capability of the equipment to undertake and

accomplish a specific task with some constraints, such as

time and economy. It varies with different equipment, tasks

and times. Hence, when calculating the processing cap-

ability and the processing ability of an equipment, the new

task and tasks that the equipment is currently undertaking

should be considered.

4. Evaluating the manufacturing ability of equipment

As discussed above, there are many parameters related to

the manufacturing ability of equipment that should be

encapsulated in an equipment agent. Some of the para-

meters are quantitative, while others are qualitative.

Certain information is closely related to the tasks and the

equipment, while others are only related to the equipment.

Among this information are hard- and soft-constraints that

must be satisfied or pre-requisites that must be fulfilled

before an equipment agent can bid for new tasks. Thus, it is

necessary to formulate an evaluation methodology based

on such information.

4.1. Specifications

4.1.1. Processing ability. The processing ability can be

expressed using the process code shown in figure 1. Each

piece of equipment will have a set of process codes corres-

ponding to the operations that it can perform. Each code

consists of nine characters. C1 represents the operation

type that the equipment can perform. C1 is generally the

first character of an operation type, such as ‘B’ means the

‘Boring’ operation. ‘C2C3’ is a double digit that represents

the shape to be achieved using the operation type. ‘C4’ and

‘C5’ represent the lower and upper dimensional tolerance

classes of the operation respectively. Generally, dimen-

sional tolerances of machining processes are classified into

ten grades—from ISO IT4 to IT13 (refer to table 2), which

can be depicted by the numbers ‘0’ – ‘9’. A smaller number

depicts a smaller range of the dimensional tolerance.

Similarly, ‘C6’ and ‘C7’ represent the lower and upper

geometric tolerance classes of an operation respectively.

‘C8’ and ‘C9’ depict the lower and the upper grades of the

surface roughness of an operation. Roughness values (from

Ra=25 mm to Ra=0.025 mm) are also classified into ten

grades similar to dimensional tolerances. Table 3 shows

some examples of these process codes. Therefore, the

processing ability of an equipment agent can be expressed

as a set of all the process codes of the equipment. The

equipment agent can obtain the processing ability of

equipment by searching through its process codes.

4.1.2. Achievable performance. Performance parameters

are important data of equipment and the cutting tools that

Table 1. Manufacturing ability of equipment.

Classification Description

ABILITY

Processing ability (ability to meet

a work-piece specification)

It consists of the following four features, viz., types of operations, machining

features, tolerance and surface roughness of the operations. It can be

expressed using the process codes shown in figure 1.

1. Types of operations (milling, drilling, turning, etc.)

2. Machining features (slot, step, special features, etc.)

3. Tolerance (dimension and form tolerance)

4. Surface finish (depends on machine/tool combination)

Achievable performance Refer to table 3, the achievable performance comprises the machine and tool

specifications.

1. Equipment parameters, such as the worktable size, maximum work-piece size,

power and rotation speed of the principal axis, etc.

2. Cutting tool information, such as the category, model, material, etc.

CAPABILITY

Available capacity This indicates the time available in the working schedule of equipment before the

deadline of a task. The available capacity is the total time available of

equipment in which the task can be performed, considering the tasks that the

equipment is currently undertaking in its working schedule.

Status parameter This indicates the running status of equipment and the cutting tools. The status

can be divided into five classes: very good, good, average, poor and faulty.

Evaluating manufacturing ability of equipment in virtual enterprises 341

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should be encapsulated in an equipment agent. Different

equipment have different tools and performance. For

example, in a milling machine agent, the data shown in

table 4 should be preset.

4.1.3. Available capacity. The available capacity of

equipment indicates the time available in the working

schedule of the equipment. Before the deadline of a

particular task, the total available capacity is the total time

available of equipment in which a task can be performed,

considering the tasks that the equipment is currently

undertaking in its working schedule.

To calculate the available capacity, the time T1, which

the machine needs for performing the new task, should first

be calculated using equation (1).

T1 ¼ tm þ tc þ ts þ ta ð1Þ

where tm is the machining time for material removal; tc is

the tool changing time; ts is the set-up time of the machine;

and ta is the time of other accessory operations.

Figure 1. Structure of process code.

Table 2. An excerpt of values of standard tolerance grades.

IT

Nominal size (mm)

Above – 3 6 10 18 30 50 80 120 180 250 315 400

Up to 3 6 10 18 30 50 80 120 180 250 315 400 500

Values of standard tolerance (mm)

4 3 4 4 5 6 7 8 10 12 14 15 18 20

5 4 5 6 8 9 11 13 15 18 20 23 25 27

6 6 8 9 11 13 16 19 22 25 29 32 36 40

7 10 12 15 18 21 25 30 35 40 46 52 57 63

8 14 18 22 27 33 39 46 54 63 72 81 89 97

9 25 30 36 43 52 62 74 87 100 115 130 140 155

10 40 48 58 70 84 100 120 140 160 185 210 230 250

11 60 75 90 110 130 160 190 220 250 290 320 360 400

12 100 120 150 180 210 250 300 350 400 460 520 570 630

13 140 180 220 270 330 390 460 540 630 720 810 890 970

342 H. H. Wang et al.

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Next, all the available time intervals {T2} in the working

schedule of a machine that are greater or equal to T1 and

within the deadline (or due date) of the new task are

searched for. {T2}={T21, T22, . . . . . ., T2k}, where k is the

number of time intervals as shown in figure 2. Finally, the

available capacity of an equipment can be represented as

equation (2).

fjg ¼ fj1; j2; . . . . . . ;jkg; ð2Þ

where k is the number of time intervals and j1= (T21 –T1)/

T1, j2= (T22 – T1)/T1, . . ., jk=(T2k – T1)/T1.

The total available capacity of an equipment is calculated

using equation (3).

jT ¼Xk

i¼1ji ð3Þ

If k=0, the equipment does not have any free time-slot

to perform the new task and jT is set as zero; if k5 1, the

equipment has the capability to perform the new task; and

jT is calculated using equation (3). The larger the value of

jT, the larger is the capacity available.

4.2. Evaluation process

When an equipment agent bids for a new task, it has to put

forward its bidding information, Resource BiddingMessage

(RBM), defined as follows: (Equipment, Operation-ID,

{Time-Interval, Free-Time-Till-End}, Economic-Budget)

(Wang et al. 2002b). That is to say, to bid for a new job,

an equipment agent has to disclose to the manager agent its

manufacturing capability with respect to the new job, the

proposed time-slot for the new job, the length of free time

it will have from the completion date to the due date of

the job, and the economic budget to do the job. With this

information of all the equipment agents, the manager agent

can determine the most suitable equipment agent to do the

new job.

To build an RBM message, the three stages shown in

figure 3 should be performed step-by-step by the equipment

agents. First, the processing ability of equipment will be

verified for performing the new task. If the processing

ability verification process does not show any error, the

available capacity is calculated; otherwise the process is

stopped. Finally, if the total available capacity jT4 0, a

time interval in {T2} in the schedule of this equipment is

searched for, and a cost is estimated when the equipment

performs this new task. If the total available capability

jT=0, the process is stopped.

Table 3. Examples of process codes.

Processing ability descriptions (Nominal size=18*30mm)

Process codes

Machining

Method Surface type L-D-T* (mm) U-D-T* (mm) L-G-T* (mm) U-G-T* (mm) L-R* (mm) U-R-H* (mm)

M03140323 Milling Plane + 0.009 + 0.033 + 0.006 + 0.021 0.1 0.4

D00697868 Drilling Cylinder + 0.084 + 0.33 + 0.13 + 0.21 1.6 12.5

T02475735 Turning Step + 0.033 + 0.13 + 0.052 + 0.13 0.2 1.6

*L-D-T: lower value of dimensional tolerance U-D-T: upper value of dimensional tolerance

L-G-T: lower value of geometric tolerance U-G-T: upper value of geometric tolerance

L-R-H: lower value of roughness value U-R-H: upper value of roughness value

Figure 2. Available time intervals {T2}.

Table 4. Performance parameters of a milling machine.

Milling machine Cutting tool information

Feed_range Speed_range Material

Min Position Tolerance Axis (number) Hardness

Max Work Piece Length MaxPower Form

Max Work Piece Width MaxForce Cuttingtool diameter

Max Work Piece Height RatingCost Required Cutter Type

Rigidity (deflection error) Tool Changer Capacity (Pockets)

Travel (maximum stroke, etc.) Max Tool Shank Diameter

Accuracy (machine slide movement) Tool Change Time

Rapid Traverse Rate

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4.2.1. Verification of processing ability. An efficient way

of verifying the processing ability is to search the historical

records for operations that the equipment has performed

before. There are two kinds of records: good records and

bad records. Good records are those jobs that have been

successfully completed by the equipment. If the processed

surfaces of a workpiece have been processed on the

equipment, fully according to the technical specifications,

such as the surface finish, dimensions, geometric shapes and

tolerances, the processing of this job can be regarded as a

good record of the equipment; otherwise, it is classified as a

bad record. Hence, all the historical records, including both

good and bad records, should be stored in the historical

database of the equipment. A new concept termed as

operation spectrum is proposed in this research for easy

processing ability verification.

An operation spectrum is defined as a distribution of the

historical records of equipment arranged in the order of

the operation name or the occurrence times. It shows the

names of the operations that have been performed by the

equipment, as well as the good and bad records. Figure 4 is

an example of an operation spectrum of a milling machine

where the ‘Op’ axis represents the operations that the

milling machine has performed, and the ‘Num’ axis

represents the numbers of good and bad records of these

operations.

Using an operation spectrum, an equipment agent can

easily determine whether a task has been undertaken before

by the equipment, as well as the number of good and bad

records of this operation. The proportion of good records

in the total number of records of this operation represents

the processing ability of the equipment to perform this task.

For example, in figure 4, the milling machine has 380 good

records and 10 bad records for the M0 task in its operation

spectrum. Hence, the proportion z (z=[0, 1]) can be

calculated to be 0.97 and the equipment has a 0.97 ability to

perform this task.

For tasks that cannot be found in the operation spectrum

or the ability database, the users will be prompted to

determine the processing abilities of the equipment for

these tasks. These calculated values as well as user-defined

ability data, will be stored in the ability database of the

equipment. The complete processing ability verification

process is shown in figure 5.

4.2.2. Formation of bidding messages. In amulti-agent-

based manufacturing system, equipment agents bid for new

tasks by sending bidding messages to a manager agent

for negotiations (Wang et al. 2002b). There could be many

equipment agents bidding for the same tasks.Basedonbidding

messages received from the equipment agents, the manager

agent selects which equipment to assign the task to. Hence, the

bidding messages should include two important parameters:

job schedule and economic budget (EB).

The first parameter shows how soon the equipment can

undertake the new task. It can be expressed in the following

format: {Time-Interval, Free-Time-Till-End}, where Time-

Interval represents the operation period, and Free-Time-

Till-End shows the total idling time of the equipment

before the deadline of the new task, which can be obtained

using the algorithm introduced in an earlier research (Wang

et al. 2002b).

The second parameter EB defines the processing costs

required by the equipment to perform the new task.

Typically in industrial accounting, this required cost is set

as a cost per unit time for each equipment resource j,

namely Cj. This cost is set as the standard cost per unit time

for resource j, and it is normally computed as the sum of all

the direct and indirect costs. For a job i, flexible process

plans are usually formed and available, i.e. manufactur-

ing operations might occur on different machines, with

different processing times and cost. Hence, the required

processing cost Cij is determined as follows using

equation (4).

Figure 3. Formation of bidding message.

Figure 4. Operation spectrum of a milling machine.

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Cij ¼ pij � Cj ð4Þ

where pij is the processing time of job i on machine j.

However, in any job-shop manufacturing facility, jobs

entering the shopfloor are assigned priorities that reflect

their status, which are generally a function of several

parameters, such as the due date, the penalty for delays, etc.

Thus, the values of these parameters would indicate the

possible increase in the initial budget if the jobs are not

completed on time. The increase is computed as a function

of these parameters, i.e. due date (l1) and penalty (l2),given as follows:

l1 ¼T1i

DDið5Þ

l2 ¼ Peni ð6Þ

where T1i is the processing time of operation i, and DDi and

Peni are the due date and the penalty for delay of the task

respectively.

The complete expression for the economic budget EBi for

an operation i is:

EBi ¼ Cij � ð1þ l1 � l2Þ � y ð7Þwhere y is a coefficient for moderating the budget based on

heuristics and past experience. The coefficient y can range

from 0.5 to 2.0 depending on the situations, such as the

current economic situation, workpiece complexity, trans-

portation modes of goods delivery, etc.

After receiving all RBMs, the manager agent selects an

equipment agent to handle each operation. This selection is

based on heuristic rules (e.g. equipment with more idle time

before the deadline; or equipment that is the cheapest to

operate) according to the objectives.

5. Multi-agent-based virtual enterprise

With the information of the manufacturing ability of the

equipment and other relevant data encapsulated in the

equipment agents, the architecture of a multi-agent-based

virtual enterprise is formed. The two aspects of this

architecture are the structure of the equipment agents and

the manufacturing information architecture.

5.1. Equipment agent

In order actively to react to new in-coming jobs, different

functional equipment agents should be built for all the

equipment resources such as NC machine tools, AGVs,

manipulators, etc., in a manufacturing system. It is impera-

tive to build equipment agents that can act as intelligent

nodes in a virtual manufacturing system.

Intelligent equipment agents can communicate and co-

operate with other types of agents, such as manager agents,

to negotiate for new in-coming jobs, obtain manufacturing

information for machine tools that they are representing,

monitor the operating conditions of these machine tools,

and broadcast the abilities of these machine tools to the

agents’ community.

Equipment resources are wrapped as equipment agents to

allow these equipment to function autonomously. The agent-

wrapping processes determine the state set, ability set, method

set and rule set of each virtual manufacturing resource. These

can be described as a hexahedral group as follows:

Equip Agenti ¼ ðAgentIdi, Statei, Abilityi,Methodi, Rulei, ConstraintiÞ

where ‘AgentIdi’ is an exclusive agent identifier; ‘Statei’ is

the state set; ‘Abilityi’ is the ability set that consists of all the

functions executed by ‘Equip_Agenti; Methodi’ is the

method set that comprises all the service processes; ‘Rulei’

is the knowledge base of ‘Equip_Agenti’, and ‘Constrainti’

contains the constraints related to ith equipment. These

variables can be expressed as follows:

Statei¼ðData1i;Data2i; ... ... ;DataniÞAbilityi¼ðAbility1i;Ability2i; ... ... ;AbilitymiÞMethodi¼ðaction1i; action2i; ... ... ; actionkiÞRulei¼ðrule1i; rule2i; ... ... ; rulejiÞConstrainti¼ðConstraint1i; Constraint2i; ... ... ; ConstraintliÞ

where n, m, k, j, and l are the numbers of Statei, Abilityi,

Methodi, Rulei, and Constrainti respectively.

An agent-wrapping process has to be formulated to

implement these equipment agents. As different manufac-

turing resources have different behaviours and character-

istics, each manufacturing resource agent has to be defined

individually. The wrapping process involves two steps: (1)

forming an adaptive layer around the existing controllers of

the equipment to transform them into generic servers; and

(2) building a manager agent with functionalities similar to

that of the resource being wrapped. Figure 6 shows the

structure of a virtual equipment resource agent. The server,

acting as a wrapped machine controller, controls the tasks’

Figure 5. Processing ability verification.

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execution and monitors the status of the equipment. The

manager handles the agenda and broadcasts the capabilities

of the server to the agent community. While the manager

continues to negotiate with the agent community for new

tasks, the server continues to execute its assigned tasks.

Thus, this architecture can speed-up the entire scheduling

process.

An equipment agent, such as an agent of a milling

machine, can be implemented using the following

pseudo-codes:

Agent 5milling-machine4Private data1, data2, data3, . . . . . .

Knowledge-base rule1, rule2, rule3, . . . . . .

Process 5process-name14On 5event14 Do 5action14 at Priority

5priority14Process 5process-name24

On 5event24 Do 5action24 at Priority

5priority24. . . . . . . . . . .

Action 5action14Action 5action24. . . . . . . . . .

Processor 5processor-address4

End

5.2. Manufacturing information architecture

The physical implementation of the manufacturing infor-

mation architecture can be achieved in several ways, e.g.

using centralized or decentralized (distributed/federated)

databases. Figure 7 shows the centralized manufacturing

information architecture; it also describes the manufactur-

ing information in an enterprise. However, it is not suitable

for an agent-based virtual manufacturing enterprise,

because, unlike a normal enterprise, a virtual enterprise is

formed temporally from many different enterprises. The

main disadvantage of this type of manufacturing informa-

tion architecture is its vulnerability owing to the centraliza-

tion of all the data at one node. This data centralization

becomes a technical and organizational bottleneck in a

virtual enterprise in which a group of enterprises are

involved. Another disadvantage is the inefficiency involved

in data management in a big enterprise that has several

production sites. In this case, large amounts of data from

several physically distributed sources need to be updated

constantly and integrated into the manufacturing informa-

tion database, and the architecture has to manage a large

number of information access and requests from these

distributed locations.

The proposedmethodology of encapsulating all themanu-

facturing information of the equipment in their respective

equipment agents can efficiently solve these problems. Since

useful manufacturing information of equipment is stored in

its equipment agent, this information is shared when the

agent joins a virtual enterprise. The manufacturing infor-

mation architecture of a multi-agent-based system is shown

in figure 8.

In this architecture, manufacturing information is distri-

buted among the agents in a virtual enterprise. When

certain information is needed, a mobile agent is launched to

collect this information for the manager agent and other

agents. This architecture has several advantages as com-

pared with the centralized architecture.

1. High reliability. By distributing the manufacturing

information that is associated with different equip-

ment to the independent equipment agents, the dis-

tributed architecture avoids a complete system

breakdown when error occurs in the database.

Figure 6. Virtual equipment resource agent.

Figure 7. Architecture of centralized manufacturing in-

formation system.

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2. Support for virtual enterprise. The distributed archi-

tecture solves the communication bottleneck and can

handle non-cooperative agents as well as unauthor-

ized agents. In a virtual enterprise, some member

enterprises do not have the authority to access all the

manufacturing information. Only equipment that is

contracted to the virtual enterprise can be seen in the

agent community.

3. Ease of maintenance. The operators of the equip-

ment, who know these equipment very well, can

easily maintain the manufacturing information that

is associated with these equipment in real time

according to the status of these equipment.

6. Case study

JATLite is a useful tool for building agents (Jia et al. 2002).

It comprises a set of Java packages that facilitate agent

framework development using the Java language, and

provides basic communication tools and templates based

on TCP/IP. A platform for building equipment agents and

manager agents has been developed using Java based on

JATLite. There are four agents in this simple case study,

namely, a Manager Agent (MaA), a Drilling machine Agent

(DA), and two Milling machine Agents (MA1, MA2).

Table 5 shows the main information of each equipment

agent.

The MaA manages jobs and member agents. After

obtaining a new job (in general, jobs are described in the

ISO-10303-21 STEP format), it abstracts the job’s main

features that are described in figure 1, and announces this

job in the equipment agent community using KQML

messages. In this case study, there are two new jobs. The

Job-Announcement-Messages (JAM) that are sent to the

equipment agents are shown as follows:

JAM message of Job 1:

language: KQML

performative: publish

receiver: MA1, MA2, DA

content: (#jobname W9-Op12 #machining Milling

#surface Slot #roughness 23.0 #size_precision 33.0

#shape_precision 22.0 #earliest_start_time Thu Sep

26 12:28:00 CST 2002 #due_date Thu Sep 26

14:28:00 CST 2002)

sender: manager

JAM message of Job 2:

language: KQML

performative: publish

receiver: MA1, MA2, DA

content: (#Jobname W9-Op13 #machining Drilling

#surface Cylinder #roughness 88.0 #size_precision

50.0 #shape_precision 50.0 #earliest_start_time Thu

Sep 26 12:28:00 CST 2002 #due_date Thu Sep 26

14:28:00 CST 2002)

sender: manager

An equipment agent, such as the MA, has four groups

of functions besides the basic functions of an agent:

(1) monitoring the status and parameters of the machine

tool; (2) evaluating the processing ability and capability;

(3) searching for work agendas and historical works records

in local databases; and (4) generating bidding information

to bid for new coming jobs.

After receiving the JAM messages, the equipment agent

would begin to search its processing ability and operation

spectrum databases to match its manufacturing ability.

Table 6 shows the results of processing ability verification.

Next, MA1 and DA would download the respective

STEP (Standard for the Exchange of Product model data)

formatted specification files of W9-Op12 and W9-Op13, via

the FTP or email service from the MaA, to obtain the

necessary information. MA2 would quit negotiation since it

cannot meet the specifications. The job schedules of MA1

and DA are searched for to calculate the processing

capability, and the economic budgets are estimated. The

cost is shown in table 7.

Next, the RBM messages are formed and sent to the

manager agent. The RBM messages that can be seen in the

message window of MaA are as follows:

RBM message from MA1:

language: KQML

performative: Bid

receiver: MaA

content: (Job_name W9-Op12

Start_time Thu Sep 26 12:28:00 CST 2002

End_time Thu Sep 26 14:28:00 CST 2002

Cost 14)

sender: MA1

RBM message from DA:

language: KQML

Figure 8. Architecture of multi-agent-based manufacturing

information system.

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performative: Bid

receiver: MaA

content: (Job_name W9-Op13

Start_time Thu Sep 26 12:28:00 CST 2002

End_time Thu Sep 26 14:28:00 CST 2002

Cost 1.8)

sender: DA

7. Conclusions and future research

This paper proposes a methodology for evaluating the

manufacturing ability of equipment in a multi-agent-based

virtual manufacturing enterprise. The concept and defini-

tion of the manufacturing ability of equipment are

explained in detail. A new concept, namely the operation

spectrum, is proposed for verifying the process ability of

equipment, and calculating the available capability and cost

to build a bidding message. The proposed methodology for

evaluating the manufacturing ability of an equipment can

be easily implemented in equipment agents. This metho-

dology supports the equipment agents to formulate bidding

messages in virtual manufacturing enterprises or multi-site

enterprises.

The main contribution of this paper is the concept of

encapsulating all the manufacturing information of an

equipment in its equipment agent. Since the contents and

formats of the manufacturing information are different for

different types of equipment, it is impractical to compress

the many different types of information into one centralized

database. Hence, it is more efficient and practical to store

the manufacturing information of the equipment in their

respective agents.

Future work in this research will see the establishment of

an equipment agent platform for easy generation of various

types of equipment agents, e.g. DA, lathe agent, horizontal

milling machine agent, etc. The algorithms, which would

enable the equipment agents to make intelligent decisions

when there are more tasks to be assigned at any one time,

as well as the algorithms with which the manager agent can

calculate and select the best equipment to undertake the

tasks, will be studied. Another research direction will be to

construct a multi-agent-based manufacturing information

architecture to share and exchange information within a

multi-site enterprise as well as across different enterprises

uniformly.

Acknowledgements

The authors would like to acknowledge the support of

funding from A*STAR, Agency for Science, Technology

Table 5. Information of equipment agents.

Operation spectrum Performance data Processing ability Jobs in schedule

MA1

W1-Op1 (100, 8)y MaxShapeHeight: 200mm M02241423 W99-Op7 (10.5, 10.8)zW9-Op12 (78, 3) MaxShapeLength: 1200mm M03140323

W88-Op6 (324, 10) Tool change time (tc): 0.5min M04140324 W99-Op9 (12, 12.2)

W17-Op41 (12, 0) Rating cost: $28/h M05140324

MA2

W12-Op1 (10, 0)y Tool change time (tc): 0.5min M02462434 W99-Op8 (11, 11.8)

W19-Op2 (178, 13) Rating cost: $15/h M03452423 W28-Op23 (15, 15.2)

W28-Op16 (24, 1) : M04452423

DA

W1-Op8 (100, 0) Tool change time (tc): 0.5min D00565756

W9-Op7 (78, 0) Rating cost: $10/h D01464646

yW1-Op1: job ID; 100: good record; 8: bad record.

zW99-Op7: job ID; 10.5: start time (h); 10.8: end time (h), default date is current day.

Table 6. Verifying the processing ability.

Equipment

agent name

Result of processing ability verification

W9-Op12 W9-Op13

MA1 Pass No

MA2 No No

DA No Pass

Table 7. Calculation of available capability.

Items

Calculation

MA1 DA

Available capability T1=20+0.5+

5+5=30.5 min

�T=2.54

T1=0.5+0.5+

5+5=11 min

�T=7.05

Economic budget $14 $1.8

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and Research for the China – Singapore Joint Research

Programme between China and Singapore and the funding

(grant: 5991076861) from the National Science Fund

Committee (NSFC) of China, as well as the contributions

from all the collaborators of the mentioned projects. They

would also like to thank Huazhong University of Science

and Technology of China and the National University of

Singapore for supporting this joint work.

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