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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: [email protected]
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.
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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.
References
Ferber, J., Multi-Agent Systems: An Introduction to Distributed Artificial
Intelligence, 1999, (Addison-Wesley: Harlow).
Jia, H.Z., Fuh, J.Y.H., Nee, A.Y.C. and Zhang, Y.F., Web-based multi-
functional scheduling system for a distributed manufacturing environ-
ment. Concurrent Eng. Res. Appl., 2002, 10, 27–39.
Kloshel, S., Virtual Production Developing Technology, 2002 (Mechanical
Industry Publishing Company, China). In Chinese.
Macchiaroli, R. and Riemma, S., A negotiation scheme for autonomous
agents in job shop scheduling. Int. J. Comput. Integ. Manuf., 2002, 15,
222–232.
Miao, C.Y., Goh, A., Miao, Y. and Yang, Z.H., Agent that models,
reasons and makes decisions. Knowledge-Based Syst., 2002, 15,
203–211.
Miyashita, K., CAMPS: a constraint-based architecture for multiagent
planning and scheduling. J. Intell. Manuf., 1998, 9, 147–154.
Rabelo, R.J., Camarinha-Matos, L.M. and Afsarmanesh, H., Multi-
agent-based agile scheduling. Robot Auton. Syst., 1999, 27, 15–28.
Roy, D. and Anciaux, D., Shop-floor control: a multi-agents approach.
Int. J. Comput. Integ. Manuf., 2001, 14, 535–544.
Sousa, P. and Ramos, C., A distributed architecture and negotiation
protocol for scheduling in manufacturing systems. Comp. Ind., 1999, 38,
103–113.
Wang, H.Q., Liao, S. and Liao, L.J., Modelling constraint-based
negotiating agents. Decis. Support Syst., 2002a, 33, 201–217.
Wang, H.H., Zhou, Z.D., Chen, Y.P. and Ai, W., 2002b, Research of
multi-agent agile scheduling model in virtual manufacturing unit.
Computer Engineering and Applications, 2002b, 38, 74–76, 95. In Chinese.
Wen, Y.T., Manufacturing Resource Planning System, 1994 (Mechanical
Industry Publishing Company, China). In Chinese.
Evaluating manufacturing ability of equipment in virtual enterprises 349
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