Directory UMM :Data Elmu:jurnal:I:International Journal of Production Economics:Vol65.Issue1.Apr2000:
Int. J. Production Economics 65 (2000) 73}84
The development of intelligent decision support tools to aid the
design of #exible manufacturing systems
Felix T.S. Chan!,*, Bing Jiang!, Nelson K.H. Tang"
!Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong
"Leicester University Management Centre (LUMC), University of Leicester, Leicester LE1 7RH, UK
Abstract
The design of #exible manufacturing systems (FMSs) is an essential but costly process. Although FMS design appears
to be an excellent area for applying arti"cial intelligence (AI) and computer simulation techniques, to date there have
been limited investigations on integrating AI with the modular simulation software available for FMS design. In this
paper an integrated approach for the automatic design of FMS is reported, which uses simulation and multi-criteria
decision-making techniques. The design process consists of the construction and testing of alternative designs using
simulation methods. The selection of the most suitable design (based on the multi-criteria decision-making technique, the
analytic hierarchy process (AHP)) is employed to analyze the output from the FMS simulation models. Intelligent tools
(such as expert systems, fuzzy systems and neural networks), are developed for supporting the FMS design process. Active
X technique is used for the actual integration of the FMS automatic design process and the intelligent decision support
process. ( 2000 Elsevier Science B.V. All rights reserved.
Keywords: FMS design; Systems simulation; Multi-criteria decision support; AI; Integration
1. Introduction
Flexible manufacting system (FMS) design is
a very complex task due to two important characteristics: (a) The wide variety of alternative system
control strategies and con"gurations available to
the designer [1]; (b) FMS design is a task in which
a variety of selection criteria are involved, many of
which are di$cult to quantify. Additionally, some
criteria have to be balanced against each other
* Corresponding author. Tel.: 00852-2859-7059; fax: 008522858-6535.
E-mail address: [email protected] (F.T.S. Chan)
while taking into account the preferences of managers of the "rm [2,3].
Modeling techniques have been devised to model
and evaluate FMSs prior to their installation.
Modeling is advantageous since it is costly to
evaluate the performance of an FMS after installation. Today, physical models, analytical models,
discrete simulation models, and, more recently,
knowledge-based simulation systems, have been
used for this purpose. However, a major problem
exists as current modeling techniques are unable to
capture all the FMS design dimensions, i.e. they are
not able to solve the FMS design problem as
a whole. This is a consequence of local, myopic, and
isolated approaches to FMS design [4]. Therefore,
a new approach combining operational research
0925-5273/00/$ - see front matter ( 2000 Elsevier Science B.V. All rights reserved.
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F.T.S. Chan et al. / Int. J. Production Economics 65 (2000) 73}84
Fig. 1. Outline of the intelligent decision support system for the FMS design.
and intelligent decision-making process is needed
and a user-friendly interface can be considered as
being an essential requirement.
The approach introduced in this paper integrates
initial FMS design, systems analysis, decision-making support and arti"cial intelligence (AI) techniques and methodologies into one system. Fig. 1
shows the outline of this integrative approach for
FMS design. As Fig. 1 indicates, FMS design models are built based on the objectives obtained from
engineers. The multi-criteria decision support technique, the analytic hierarchy process (AHP), is then
used to choose the best design. AI techniques (expert system, fuzzy sets and neural network) are used
for the FMS design initialization, analysis, and
evaluation. In other words, the ongoing research
project by the present authors tries to integrate the
FMS simulation models, AI tools and the decision
support system into a uni"ed system. Thus, developing an integrative intelligent decision support
system for the design of FMS is the core activity of
this research.
The expert system tool (AI-1, Fig. 1) is developed
to (i) analyze output from an FMS simulation
model, (ii) determine whether speci"ed design objectives are met, (iii) identify design de"ciencies or
opportunities for improvement and (iv) propose
designs which overcome identi"ed de"ciencies or
which exploit improvement opportunities. In order
to establish the FMS models and AI-1, three di!erent sources of expertise have been consulted. One
source is an industrial engineering group in a Hong
F.T.S. Chan et al. / Int. J. Production Economics 65 (2000) 73}84
Kong manufacturing company. Another source of
expertise is from one of the research project directors who has had over 20 years of experience in the
use of simulation techniques in process design. The
third source is from the literature.
In this research, the AHP technique has been
employed to develop the decision-making support
tool for FMS design. AHP applications in the FMS
area have been proved to be e!ective by our colleagues [5]. However, applications of AHP still
need human judgement and this relies on experienced technical operators. Fuzzy sets and neural
network intelligent techniques are also implemented for assisting the development work. Fig. 1
shows the fuzzy sets tool (AI-2) and the neural
network tool (AI-3) which have been built to support the evaluating of systems performance
measures.
Integration of FMS design system is also a very
important task. In this research, there are tools for
FMS design, simulation and decision-making support. All these tools are integrated in a unique
environment. A user-friendly interface is needed for
the whole system. The general programming languages such as Visual Basic and C/C#
#are the
preferred media among these tools. Active X technique is employed for integration. The Active
X technique is developed by Microsoft Company
for application integration. This technique allows
Windows applications to control each other and
themselves via a programming interface.
This paper "rst reviews the current literature
concerning FMS design. Secondly, the simulation
of generic FMS models and the expert systems tool
for initial FMS model building are presented.
Thirdly, the AHP process and the intelligent tools
(fuzzy sets and neural networks) for decision support are described. Finally, the integration of the
intelligent decision support tools and the design
procedure using Active X technique is discussed.
2. Literature review
Many researchers have suggested various
approaches for FMS design and analysis [6].
Engineering economics and operational research/
management science methodologies and techniques
75
have been applied with the object of obtaining
performance data (e.g. lead time, productivity, cost,
#exibility, product quality, etc.) from di!erent con"gurations [7,8].
Simulation modeling has attracted much attention recently [9]. The simulation technique is mainly a computerized procedure utilizing numerical
techniques [10}12]. For FMS, simulation models
represent the facilities, the layout, and the interconnections. Running simulation models shows basic
operations and input and output of FMS. In FMS
design, a simulation model is developed using computer software; the developed model is then executed and the designer analyses the output to
determine whether or not design objectives can be
achieved [13]. For FMS simulation models, the
performance measures are often the total production, the average waiting time in a queue, the maximum time waiting in queue, the maximum number
of parts that were at any time waiting in the queue,
the average and maximum #ow-time of parts, and
the utilization of machines, etc. A model described
by Aly and Subramaniam [2] is one such example.
In addition to the simulation modeling method,
the FMS design problem also appears to be a very
good application for expert system technique [14].
An expert system uses a number of heuristics in
much the same way as a human designer would
approach the problem. Mellichamp and Wahab
[15] proposed an expert system to design FMSs,
and they have demonstrated that development of
expert systems for FMS design is within the realms
of possibility. Recently, researchers have paid much
attention to the integration of simulation with expert systems. El Maraghy and Ravi [16] have reviewed some applications of knowledge-based
(expert system) simulation systems in the domain of
FMSs. They have also discussed the potential of
knowledge-based simulation systems towards the
development of new, powerful and intelligent simulation environments for modeling and evaluating
FMSs.
Regarding the modeling of FMS, analysis and
evaluation of the possible alternatives would be the
most important process in the design. In fact, the
FMS design process is a very complex task due to
the wide variety of alternative systems [1] and the
variety of selection criteria that are involved [2,3].
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F.T.S. Chan et al. / Int. J. Production Economics 65 (2000) 73}84
Basically, the problem in FMS design is a matter of
selecting one of the preferred alternatives in the
light of a variety of criteria, including tangible and
intangible criteria. A company may often have several alternative plans when it prepares to implement an FMS [5]. Therefore, analysis and
evaluation of the FMS design becomes very important, with the ideal objective being that of selecting
the most suitable plan for implementation.
Analysis and evaluation of the possible alternatives require su$cient knowledge to make comparisons among them [17}19]. In fact, this process
is called system performance measurement
[20}23]. There are many approaches suggested by
researchers [24}26]. Multi-criteria decision support approaches, such as AHP, are appropriate and
provide a useful method because FMS design is an
evolutionary decision-making process [27,28]. The
multi-criteria decision-making technique is a methodology that provides the ability to incorporate
both qualitative and quantitative factors in the
decision-making process. For example, the AHP
uses a hierarchical model (not to be confused with
a simulation model) comprising a goal, criteria, and
perhaps several levels of sub-criteria and alternatives for each problem or decision [29]. The objective of AHP is to choose the best alternative.
The use of AI, such as expert system, fuzzy logic
and neural network, to support the decisionmaking in FMS design has attracted much attention in recent years [14,30,31]. When analyzing the
output of the FMS design model, AI techniques are
implemented by many researchers. Fuzzy logic and
neural network are two popular techniques [5,31].
When making decisions in comparative problems,
the fuzzy logic technique is quite useful for the
measurement of preferences [5,32]. Unsupervised
and supervised neural networks combined with
decision science are e!ective to handle complex
multivariate relationships and nonlinear problems
in manufacturing system planning and scheduling
[30,33,34]. However, studies in this regard are just
beginning and it is expected that more research will
be conducted in this area in the next few years.
However, the main problem in FMS design (including analysis and evaluation) is the isolated use
of design techniques and methods [9]. Development of fully integrated, automated intelligent tools
for the design of FMS is desired. As described by
Spano et al. [6], the e!ective performance of these
tools depends primarily on the proper design speci"cation of the various components, and also on the
operation of these components as an integrated
system. Thus, an integration of design and analysis
tools is useful and practical; this is the approach
which is described in the present paper.
3. Expert system tool (AI-1) for initial FMSs design
The expert system tool (AI-1) is built to support
the initial FMSs design, i.e. the simulation models
in this research project. The aim of the AI-1 is to
ensure that the design objectives can be met. The
procedure (logic) of the initial design is shown in
Fig. 2; this is a schematic of the initial design
procedure. As the diagram indicates, the AI-1 requires design objectives such as production output,
investment, and operating conditions. For initially
building the simulation models, the equipment features of the FMS are speci"ed by selecting individual machines, robots, conveyors and automated
guided vehicles.
In attempting to determine simulation models
that are e$cient with respect to FMS design objectives, the AI-1 analyses the results of the simulation
model, and uses a number of heuristics in much the
same way a human designer would approach the
problem. These are identi"ed as: operational heuristics, which are used to assess production level
considerations; economic heuristics, which consider the "nancial options, and social and human
heuristics, which are used to access the number of
operators and maintenance technicians and workload of workers, etc.
In case the operational objectives are not met,
the AI-1 searches for ways of modifying the simulation models to make it more e$cient. This is accomplished by identifying bottlenecks in the
simulation models } these are usually indicated by
very high or low utilization and excessive queue
lengths. There are three causes of bottlenecks:
f inadequate loading/unloading or transporting
devices,
f over-utilized machines,
F.T.S. Chan et al. / Int. J. Production Economics 65 (2000) 73}84
77
Fig. 2. Schematic of the initial design procedure.
f machines having one or more excessively long
processing times (with respect to that machine or
other machines in the model).
The AI-1 "rst determines the speci"c machine
which is on the operational routing and for which
the part production objectives are not being met.
Utilization and queue length statistics for that machine are examined. If the statistics do not indicate
a potential problem, the system will consider the
machine on the next operational routing for which
the objectives are not met; if the statistics do indicate a potential problem, the AI-1 takes a global
view to track down the causes of the problem. This
global view considers the machine itself, machines
which precede the problem machine, and materials
handling devices which serve the problem machine.
When a bottleneck is identi"ed and the underlying
cause is isolated, the AI-1 attempts to locate (from
a list of equipment alternatives) a more e$cient
replacement for the problem machine or materials
handling device.
The structure of the expert system tool is a basic
scheme of a knowledge base and an inference engine. The knowledge base includes the machine
knowledge, the material handling facilities knowledge, the computing equipment knowledge, etc.
Rules in the knowledge base are composed of design objective rules and operational analysis rules.
The inference engine is a standard match}act cycle,
in which the action part of the rule is invoked when
the premises of the rule are matched by the facts
stored in the knowledge base. The match}act cycle
is continued until a recommendation is made.
In the current con"guration, the simulation
models have been integrated with the AI-1. The
simulation models are executed and selected outputs (i.e. production rates, equipment utilization
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F.T.S. Chan et al. / Int. J. Production Economics 65 (2000) 73}84
statistics, and queue lengths) are input to the AI-1.
Once the AI-1 has made a recommendation, the
simulation models are adjusted appropriately and
re-run. This process continues until an acceptable
design is obtained. The interface between the simulation models and the AI-1, especially for the
alteration of a simulation model to re#ect changes,
is a complex task. Our approach uses Active X
technology.
As the system currently exists, simulation models
are developed in ARENA program [35] and executed on an IBM PC. The AI-1 is written in
CAPPA [36], which is an expert system development tool, and run on an IBM PC machine. To
illustrate the capability of the FMS design expert
system tool, a case study is presented in this paper.
A clock manufacturing company would like to
do a feasibility study for FMS implementation. The
system shown in Fig. 3 represents the FMS operations for the production of two di!erent sealed
electronic units. Part A and Part B arrive with
pre-determined inter-arrival times to the Part A
Preparation area and the Part B Preparation area,
respectively. After being delayed by the corresponding processing time, the two parts are transferred to the sealer. At the sealing operation, the
electronic components are inserted, the case is assembled and sealed, and "nally the sealed unit is
tested. According to the companys statistics, 91%
of the parts pass the inspection and will be transferred directly to the shipping department. The remaining parts are transferred to the rework area
where the parts are disassembled, repaired, cleaned,
assembled, and re-tested. Eighty percent of the
parts here are salvaged and transferred to the shipping department as reworked parts. The remaining
parts are transferred to the scrap area. We assume
all transfer times are 2 minutes and two automatic
guided vehicles (AGVs) are used. We collect statistics in each area on resource utilization, number in
queue, time in queue, and the cycle time (total
process time) by shipped parts, salvaged parts, or
scrapped parts.
There are several objectives when building FMS
models, such as studying the e!ect of various dispatching rules at the workstation, di!erent machine
selection rules for alternative operations, balancing
the workload between the machines, and maximizing the routing #exibility. In this paper, 10 models
are developed according to di!erent machine types
and dispatching rules. The FMS models are developed in the environment of a simulation program. During the simulation, animation of the
FMS and the simulation time can be displayed on
the screen. Thus, the simulation process can easily
be observed and inspected by the user. After simulation, the report of the stations utility, the queues,
the operations, etc. can be generated. An example
of such a report is shown in Fig. 4.
In most of the current research work on the
design of FMS, the simulation models and the
expert system tools are separated. The output (i.e.
production time, machine utilities, and queue
length, etc.) produced by the running of the simulation models is input to the expert system tool. The
recommendations made by the expert system tool
Fig. 3. An example model.
F.T.S. Chan et al. / Int. J. Production Economics 65 (2000) 73}84
79
Fig. 4. An example of simulation result.
are then shown to the operators. Then, the
parameters of the simulation models are modi"ed
manually. This iterative process continues until the
design is accepted.
In this research, an automatic interface (although
not fully automatic) between the simulation models
and the expert system tool has been built. The
simulation models and the expert system tools automatically write and read "les. The expert system
tool also automatically supports adjustment of
parameters in the simulation models. Currently, the
simulation models are developed in ARENA [35]
and run on an IBM PC. The expert system tools are
written in KAPPA [36], which is an expert system
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F.T.S. Chan et al. / Int. J. Production Economics 65 (2000) 73}84
building tool. The integration computing environment is Visual Basic using Active X technology.
4. Fuzzy and neural network decision support
tools (AI-2 and AI-3)
As described in Section 3, it is probable that
more than one design is available when a company
designs its FMS. The evaluation of the di!erent
designs then becomes a problem for managers and
engineers. Many researchers have considered this
problem and several techniques are available, the
multi-criteria decision support technique being one
of them.
The AHP is a multi-criteria decision support
technique, which has been employed e!ectively by
our colleagues in the area of CIM and FMS [5].
AHP is a methodology that provides the ability to
incorporate both qualitative and quantitative factors in the decision-making process. The AHP uses
a hierarchical model (not to be confused with
a simulation model) comprised of a goal, criteria,
perhaps several levels of sub-criteria and alternatives for each problem or decision [29]. In each
level, pairwise comparisons between each criterion
are used to make judgements on the relative importance of criteria. The alternatives are also compared pairwise according to their importance with
respect to each criterion. Finally, a composite importance weight for each of the alternatives is calculated, resulting in a "nal ranking of the
alternatives.
The evaluation of FMS simulation models is
conducted by AHP methodology in this research
project and Fig. 5 shows the hierarchical structure
used. The goal (top of the hierarchical structure) of
the AHP model is to choose the best (the most
suitable) FMS for a company. First-level criteria
are "nance, productivity, #exibility, building time
and risk. The sub-criteria of "nance are the facilities
and installation cost, and the operational cost. The
sub-criteria of productivity are production rate,
total production, lead time, inventory and machine
utility. The sub-criteria of #exibility are #exibility of
part type, machine, process, product, routing, expansion, operation and transfer. The sub-criteria of
building time are planning time and implementing
time. The sub-criteria of risk are technical risk and
operational risk. Thus, the AHP approach decomposes a problem into the elements. Then, the elements (i.e. criteria) are assessed by pairwise
comparisons. The lowest level is the alternatives.
Pairwise comparison means that one element is
compared against another with respect to the level
above. For example, when choosing the best FMS
design, we may look at "nance against productivity
with respect to the goal (upper level). The judgement comes from operators. In order to support the
judgement (decision-making), a fuzzy set decision
support tool (AI-2), and a neural network decision
support tool (AI-3) are proposed in this research.
In AHP, pairwise comparisons use verbal comparisons, and could be considered fuzzy, in the
sense that decision makers (operators) need to express their preference in an approximate way by
verbal judgement or by stating a single number
taken from the 1}9 comparison scale. A fuzzy set
(logic) tool (AI-2) which supports the pairwise comparison is proposed.
A fuzzy set is an excellent tool with which one
can represent the satisfaction of alternatives to criteria. In fuzzy logic, the truth of any statement is
a matter of degree. Assume we have a set of alternatives in a decision:
X"[X , X ,2, X ].
(1)
1 2
n
If we have a particular criterion A, we can associate, with each value in X, a number A[X ] in the
i
interval [0, 1], indicative of how well X satis"es
i
criterion A, which of course then speci"es A as
a fuzzy set of X.
In this paper, it was de"ned that alternatives (X)
are 10 di!erent feasible FMS models, and criteria
(A) are 19 criteria. To determine how an alternative
satis"es a criterion, a membership function has to
be de"ned.
For those criteria that are to be maximized (like
total production), the following membership function is used:
measured value!minimum value
.
maximum value!minimum value
For those criteria that are to be minimized (like
lead time), the following membership function is
F.T.S. Chan et al. / Int. J. Production Economics 65 (2000) 73}84
Fig. 5. Hierarchical structure of FMS design criteria.
81
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F.T.S. Chan et al. / Int. J. Production Economics 65 (2000) 73}84
used:
maximum value!measured value
.
maximum value!minimum value
A decision function, say D, is needed to determine
the desirable x. Function D is de"ned as follows:
D"Max(Min Aai(x)), i"1, 2,2, p,
(2)
i
where p is the number of criteria, Aai(x)"(A (x))ai
i
i
and a is the power of importance of each criterion.
i
The power of importance of each criterion is derived by the AI-3.
In AHP, calculating the preference ratings establishes the power of importance of each element in
the reciprocal comparison matrix (the reciprocal
comparison matrix comes from the pairwise comparisons, see [28]). In AI-3, we use a feed-forward
neural network proposed by Stam et al. [33] to
approximate the mapping from the reciprocal comparison matrix in the AHP to the associated preference ratings. The information that is provided by
the n]n pairwise comparison matrix serves as input to the feed-forward neural network. Thus, the
neural network will have n "n(n!1)/2 input
i
nodes. The desired output vector consists of the
n-dimensional true preference rating vector r, so
that the number of output nodes is n "n. The
o
compound vector (aT, rT) represents one pattern in
the training set.
The software NEUFrame [37] is used to train
separate neural nets for each matrix size. For the
FMS problem discussed in this paper, networks
with two hidden layers provided good results. The
network con"gurations yielded tolerable errors and
converged within a reasonable time. A feed-forward
neural network for the 3]3 pairwise comparison
matrices has 10 nodes in the "rst hidden layer and
6 in the second. The result of AI-3 is the assigned
weight for performance measures.
5. Automation and integration of design process and
decision support tools
One of our objectives in this research was to
automate the FMS design and evaluation processes; also we aimed to integrate the intelligent
tools into one system. The automation and integration of the intelligent tools included the following
tasks:
f A template panel object has been built. The
panel includes four templates, Arrive, Sealer}t,
Rework}t, and Depart. The templates are special
modules in simulation models and have their
own logic and functions. Through the Active
X technology, the templates is integrated with the
utilities in AI-1. Thus, modules could be created
from the recommendations made by AI-1.
f The results of running the FMS models are saved
as text only "les. The developed intelligent tools,
AI-2 and AI-3, read the text "les as the input.
The output data of the AI-2 and the AI-3 are also
saved as the text "les.
f AHP models read the text "les generated by the
AI-2 and the AI-3 to produce the "nal ranking of
each alternative. Wizards (i.e. guides to the operators) are created in cooperation with AHP
models for generating specialized graphs and reports, loading data, etc.
f A user-friendly environment has been built using
the Visual Basic Forms and the C## objects
(for the programs that are associated with C##
language). Active X technology has been used for
the automation of di!erent functions.
Fig. 6 shows the integration methods used in
FMS design and intelligent decision support tools.
6. Conclusion
The research work described in this paper demonstrates that development of intelligent decision
support tools for the design of FMS is now within
the realms of possibility. The design of FMS is
employed with a simulation approach supported
by an expert system tool. Though more than one
design is probable, a multi-criteria decision support
method, AHP, is able to suggest the most suitable
design, which is then supported by the tools of
fuzzy sets and neural networks. The integration of
simulation and multi-criteria decision support
methods is tested in this research, and work to
date indicates that it is likely to be a usable and
promising methodology in FMS design.
F.T.S. Chan et al. / Int. J. Production Economics 65 (2000) 73}84
Fig. 6. Integration of FMS design tools.
A certain degree of automation in FMS design is
realized in this research by using the Active X
technology. It is important that future research
e!ort should be directed toward fully automating
the interface between the simulation models and
the intelligent tools, thus enhancing the intelligence
of the design system and enlarging its knowledge
base.
To summarise, the current research has demonstrated that the use of an integrated methodology
of system modeling and AI tools for FMS design
has enabled system designers to improve the e$ciency of the design task. The present research
work will continue to test the system in practical
situations. Enlarging the knowledge base is the
main task so that the system would have the capability to design a suitable FMS system for various
industrial sectors.
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The development of intelligent decision support tools to aid the
design of #exible manufacturing systems
Felix T.S. Chan!,*, Bing Jiang!, Nelson K.H. Tang"
!Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong
"Leicester University Management Centre (LUMC), University of Leicester, Leicester LE1 7RH, UK
Abstract
The design of #exible manufacturing systems (FMSs) is an essential but costly process. Although FMS design appears
to be an excellent area for applying arti"cial intelligence (AI) and computer simulation techniques, to date there have
been limited investigations on integrating AI with the modular simulation software available for FMS design. In this
paper an integrated approach for the automatic design of FMS is reported, which uses simulation and multi-criteria
decision-making techniques. The design process consists of the construction and testing of alternative designs using
simulation methods. The selection of the most suitable design (based on the multi-criteria decision-making technique, the
analytic hierarchy process (AHP)) is employed to analyze the output from the FMS simulation models. Intelligent tools
(such as expert systems, fuzzy systems and neural networks), are developed for supporting the FMS design process. Active
X technique is used for the actual integration of the FMS automatic design process and the intelligent decision support
process. ( 2000 Elsevier Science B.V. All rights reserved.
Keywords: FMS design; Systems simulation; Multi-criteria decision support; AI; Integration
1. Introduction
Flexible manufacting system (FMS) design is
a very complex task due to two important characteristics: (a) The wide variety of alternative system
control strategies and con"gurations available to
the designer [1]; (b) FMS design is a task in which
a variety of selection criteria are involved, many of
which are di$cult to quantify. Additionally, some
criteria have to be balanced against each other
* Corresponding author. Tel.: 00852-2859-7059; fax: 008522858-6535.
E-mail address: [email protected] (F.T.S. Chan)
while taking into account the preferences of managers of the "rm [2,3].
Modeling techniques have been devised to model
and evaluate FMSs prior to their installation.
Modeling is advantageous since it is costly to
evaluate the performance of an FMS after installation. Today, physical models, analytical models,
discrete simulation models, and, more recently,
knowledge-based simulation systems, have been
used for this purpose. However, a major problem
exists as current modeling techniques are unable to
capture all the FMS design dimensions, i.e. they are
not able to solve the FMS design problem as
a whole. This is a consequence of local, myopic, and
isolated approaches to FMS design [4]. Therefore,
a new approach combining operational research
0925-5273/00/$ - see front matter ( 2000 Elsevier Science B.V. All rights reserved.
PII: S 0 9 2 5 - 5 2 7 3 ( 9 9 ) 0 0 0 9 1 - 2
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F.T.S. Chan et al. / Int. J. Production Economics 65 (2000) 73}84
Fig. 1. Outline of the intelligent decision support system for the FMS design.
and intelligent decision-making process is needed
and a user-friendly interface can be considered as
being an essential requirement.
The approach introduced in this paper integrates
initial FMS design, systems analysis, decision-making support and arti"cial intelligence (AI) techniques and methodologies into one system. Fig. 1
shows the outline of this integrative approach for
FMS design. As Fig. 1 indicates, FMS design models are built based on the objectives obtained from
engineers. The multi-criteria decision support technique, the analytic hierarchy process (AHP), is then
used to choose the best design. AI techniques (expert system, fuzzy sets and neural network) are used
for the FMS design initialization, analysis, and
evaluation. In other words, the ongoing research
project by the present authors tries to integrate the
FMS simulation models, AI tools and the decision
support system into a uni"ed system. Thus, developing an integrative intelligent decision support
system for the design of FMS is the core activity of
this research.
The expert system tool (AI-1, Fig. 1) is developed
to (i) analyze output from an FMS simulation
model, (ii) determine whether speci"ed design objectives are met, (iii) identify design de"ciencies or
opportunities for improvement and (iv) propose
designs which overcome identi"ed de"ciencies or
which exploit improvement opportunities. In order
to establish the FMS models and AI-1, three di!erent sources of expertise have been consulted. One
source is an industrial engineering group in a Hong
F.T.S. Chan et al. / Int. J. Production Economics 65 (2000) 73}84
Kong manufacturing company. Another source of
expertise is from one of the research project directors who has had over 20 years of experience in the
use of simulation techniques in process design. The
third source is from the literature.
In this research, the AHP technique has been
employed to develop the decision-making support
tool for FMS design. AHP applications in the FMS
area have been proved to be e!ective by our colleagues [5]. However, applications of AHP still
need human judgement and this relies on experienced technical operators. Fuzzy sets and neural
network intelligent techniques are also implemented for assisting the development work. Fig. 1
shows the fuzzy sets tool (AI-2) and the neural
network tool (AI-3) which have been built to support the evaluating of systems performance
measures.
Integration of FMS design system is also a very
important task. In this research, there are tools for
FMS design, simulation and decision-making support. All these tools are integrated in a unique
environment. A user-friendly interface is needed for
the whole system. The general programming languages such as Visual Basic and C/C#
#are the
preferred media among these tools. Active X technique is employed for integration. The Active
X technique is developed by Microsoft Company
for application integration. This technique allows
Windows applications to control each other and
themselves via a programming interface.
This paper "rst reviews the current literature
concerning FMS design. Secondly, the simulation
of generic FMS models and the expert systems tool
for initial FMS model building are presented.
Thirdly, the AHP process and the intelligent tools
(fuzzy sets and neural networks) for decision support are described. Finally, the integration of the
intelligent decision support tools and the design
procedure using Active X technique is discussed.
2. Literature review
Many researchers have suggested various
approaches for FMS design and analysis [6].
Engineering economics and operational research/
management science methodologies and techniques
75
have been applied with the object of obtaining
performance data (e.g. lead time, productivity, cost,
#exibility, product quality, etc.) from di!erent con"gurations [7,8].
Simulation modeling has attracted much attention recently [9]. The simulation technique is mainly a computerized procedure utilizing numerical
techniques [10}12]. For FMS, simulation models
represent the facilities, the layout, and the interconnections. Running simulation models shows basic
operations and input and output of FMS. In FMS
design, a simulation model is developed using computer software; the developed model is then executed and the designer analyses the output to
determine whether or not design objectives can be
achieved [13]. For FMS simulation models, the
performance measures are often the total production, the average waiting time in a queue, the maximum time waiting in queue, the maximum number
of parts that were at any time waiting in the queue,
the average and maximum #ow-time of parts, and
the utilization of machines, etc. A model described
by Aly and Subramaniam [2] is one such example.
In addition to the simulation modeling method,
the FMS design problem also appears to be a very
good application for expert system technique [14].
An expert system uses a number of heuristics in
much the same way as a human designer would
approach the problem. Mellichamp and Wahab
[15] proposed an expert system to design FMSs,
and they have demonstrated that development of
expert systems for FMS design is within the realms
of possibility. Recently, researchers have paid much
attention to the integration of simulation with expert systems. El Maraghy and Ravi [16] have reviewed some applications of knowledge-based
(expert system) simulation systems in the domain of
FMSs. They have also discussed the potential of
knowledge-based simulation systems towards the
development of new, powerful and intelligent simulation environments for modeling and evaluating
FMSs.
Regarding the modeling of FMS, analysis and
evaluation of the possible alternatives would be the
most important process in the design. In fact, the
FMS design process is a very complex task due to
the wide variety of alternative systems [1] and the
variety of selection criteria that are involved [2,3].
76
F.T.S. Chan et al. / Int. J. Production Economics 65 (2000) 73}84
Basically, the problem in FMS design is a matter of
selecting one of the preferred alternatives in the
light of a variety of criteria, including tangible and
intangible criteria. A company may often have several alternative plans when it prepares to implement an FMS [5]. Therefore, analysis and
evaluation of the FMS design becomes very important, with the ideal objective being that of selecting
the most suitable plan for implementation.
Analysis and evaluation of the possible alternatives require su$cient knowledge to make comparisons among them [17}19]. In fact, this process
is called system performance measurement
[20}23]. There are many approaches suggested by
researchers [24}26]. Multi-criteria decision support approaches, such as AHP, are appropriate and
provide a useful method because FMS design is an
evolutionary decision-making process [27,28]. The
multi-criteria decision-making technique is a methodology that provides the ability to incorporate
both qualitative and quantitative factors in the
decision-making process. For example, the AHP
uses a hierarchical model (not to be confused with
a simulation model) comprising a goal, criteria, and
perhaps several levels of sub-criteria and alternatives for each problem or decision [29]. The objective of AHP is to choose the best alternative.
The use of AI, such as expert system, fuzzy logic
and neural network, to support the decisionmaking in FMS design has attracted much attention in recent years [14,30,31]. When analyzing the
output of the FMS design model, AI techniques are
implemented by many researchers. Fuzzy logic and
neural network are two popular techniques [5,31].
When making decisions in comparative problems,
the fuzzy logic technique is quite useful for the
measurement of preferences [5,32]. Unsupervised
and supervised neural networks combined with
decision science are e!ective to handle complex
multivariate relationships and nonlinear problems
in manufacturing system planning and scheduling
[30,33,34]. However, studies in this regard are just
beginning and it is expected that more research will
be conducted in this area in the next few years.
However, the main problem in FMS design (including analysis and evaluation) is the isolated use
of design techniques and methods [9]. Development of fully integrated, automated intelligent tools
for the design of FMS is desired. As described by
Spano et al. [6], the e!ective performance of these
tools depends primarily on the proper design speci"cation of the various components, and also on the
operation of these components as an integrated
system. Thus, an integration of design and analysis
tools is useful and practical; this is the approach
which is described in the present paper.
3. Expert system tool (AI-1) for initial FMSs design
The expert system tool (AI-1) is built to support
the initial FMSs design, i.e. the simulation models
in this research project. The aim of the AI-1 is to
ensure that the design objectives can be met. The
procedure (logic) of the initial design is shown in
Fig. 2; this is a schematic of the initial design
procedure. As the diagram indicates, the AI-1 requires design objectives such as production output,
investment, and operating conditions. For initially
building the simulation models, the equipment features of the FMS are speci"ed by selecting individual machines, robots, conveyors and automated
guided vehicles.
In attempting to determine simulation models
that are e$cient with respect to FMS design objectives, the AI-1 analyses the results of the simulation
model, and uses a number of heuristics in much the
same way a human designer would approach the
problem. These are identi"ed as: operational heuristics, which are used to assess production level
considerations; economic heuristics, which consider the "nancial options, and social and human
heuristics, which are used to access the number of
operators and maintenance technicians and workload of workers, etc.
In case the operational objectives are not met,
the AI-1 searches for ways of modifying the simulation models to make it more e$cient. This is accomplished by identifying bottlenecks in the
simulation models } these are usually indicated by
very high or low utilization and excessive queue
lengths. There are three causes of bottlenecks:
f inadequate loading/unloading or transporting
devices,
f over-utilized machines,
F.T.S. Chan et al. / Int. J. Production Economics 65 (2000) 73}84
77
Fig. 2. Schematic of the initial design procedure.
f machines having one or more excessively long
processing times (with respect to that machine or
other machines in the model).
The AI-1 "rst determines the speci"c machine
which is on the operational routing and for which
the part production objectives are not being met.
Utilization and queue length statistics for that machine are examined. If the statistics do not indicate
a potential problem, the system will consider the
machine on the next operational routing for which
the objectives are not met; if the statistics do indicate a potential problem, the AI-1 takes a global
view to track down the causes of the problem. This
global view considers the machine itself, machines
which precede the problem machine, and materials
handling devices which serve the problem machine.
When a bottleneck is identi"ed and the underlying
cause is isolated, the AI-1 attempts to locate (from
a list of equipment alternatives) a more e$cient
replacement for the problem machine or materials
handling device.
The structure of the expert system tool is a basic
scheme of a knowledge base and an inference engine. The knowledge base includes the machine
knowledge, the material handling facilities knowledge, the computing equipment knowledge, etc.
Rules in the knowledge base are composed of design objective rules and operational analysis rules.
The inference engine is a standard match}act cycle,
in which the action part of the rule is invoked when
the premises of the rule are matched by the facts
stored in the knowledge base. The match}act cycle
is continued until a recommendation is made.
In the current con"guration, the simulation
models have been integrated with the AI-1. The
simulation models are executed and selected outputs (i.e. production rates, equipment utilization
78
F.T.S. Chan et al. / Int. J. Production Economics 65 (2000) 73}84
statistics, and queue lengths) are input to the AI-1.
Once the AI-1 has made a recommendation, the
simulation models are adjusted appropriately and
re-run. This process continues until an acceptable
design is obtained. The interface between the simulation models and the AI-1, especially for the
alteration of a simulation model to re#ect changes,
is a complex task. Our approach uses Active X
technology.
As the system currently exists, simulation models
are developed in ARENA program [35] and executed on an IBM PC. The AI-1 is written in
CAPPA [36], which is an expert system development tool, and run on an IBM PC machine. To
illustrate the capability of the FMS design expert
system tool, a case study is presented in this paper.
A clock manufacturing company would like to
do a feasibility study for FMS implementation. The
system shown in Fig. 3 represents the FMS operations for the production of two di!erent sealed
electronic units. Part A and Part B arrive with
pre-determined inter-arrival times to the Part A
Preparation area and the Part B Preparation area,
respectively. After being delayed by the corresponding processing time, the two parts are transferred to the sealer. At the sealing operation, the
electronic components are inserted, the case is assembled and sealed, and "nally the sealed unit is
tested. According to the companys statistics, 91%
of the parts pass the inspection and will be transferred directly to the shipping department. The remaining parts are transferred to the rework area
where the parts are disassembled, repaired, cleaned,
assembled, and re-tested. Eighty percent of the
parts here are salvaged and transferred to the shipping department as reworked parts. The remaining
parts are transferred to the scrap area. We assume
all transfer times are 2 minutes and two automatic
guided vehicles (AGVs) are used. We collect statistics in each area on resource utilization, number in
queue, time in queue, and the cycle time (total
process time) by shipped parts, salvaged parts, or
scrapped parts.
There are several objectives when building FMS
models, such as studying the e!ect of various dispatching rules at the workstation, di!erent machine
selection rules for alternative operations, balancing
the workload between the machines, and maximizing the routing #exibility. In this paper, 10 models
are developed according to di!erent machine types
and dispatching rules. The FMS models are developed in the environment of a simulation program. During the simulation, animation of the
FMS and the simulation time can be displayed on
the screen. Thus, the simulation process can easily
be observed and inspected by the user. After simulation, the report of the stations utility, the queues,
the operations, etc. can be generated. An example
of such a report is shown in Fig. 4.
In most of the current research work on the
design of FMS, the simulation models and the
expert system tools are separated. The output (i.e.
production time, machine utilities, and queue
length, etc.) produced by the running of the simulation models is input to the expert system tool. The
recommendations made by the expert system tool
Fig. 3. An example model.
F.T.S. Chan et al. / Int. J. Production Economics 65 (2000) 73}84
79
Fig. 4. An example of simulation result.
are then shown to the operators. Then, the
parameters of the simulation models are modi"ed
manually. This iterative process continues until the
design is accepted.
In this research, an automatic interface (although
not fully automatic) between the simulation models
and the expert system tool has been built. The
simulation models and the expert system tools automatically write and read "les. The expert system
tool also automatically supports adjustment of
parameters in the simulation models. Currently, the
simulation models are developed in ARENA [35]
and run on an IBM PC. The expert system tools are
written in KAPPA [36], which is an expert system
80
F.T.S. Chan et al. / Int. J. Production Economics 65 (2000) 73}84
building tool. The integration computing environment is Visual Basic using Active X technology.
4. Fuzzy and neural network decision support
tools (AI-2 and AI-3)
As described in Section 3, it is probable that
more than one design is available when a company
designs its FMS. The evaluation of the di!erent
designs then becomes a problem for managers and
engineers. Many researchers have considered this
problem and several techniques are available, the
multi-criteria decision support technique being one
of them.
The AHP is a multi-criteria decision support
technique, which has been employed e!ectively by
our colleagues in the area of CIM and FMS [5].
AHP is a methodology that provides the ability to
incorporate both qualitative and quantitative factors in the decision-making process. The AHP uses
a hierarchical model (not to be confused with
a simulation model) comprised of a goal, criteria,
perhaps several levels of sub-criteria and alternatives for each problem or decision [29]. In each
level, pairwise comparisons between each criterion
are used to make judgements on the relative importance of criteria. The alternatives are also compared pairwise according to their importance with
respect to each criterion. Finally, a composite importance weight for each of the alternatives is calculated, resulting in a "nal ranking of the
alternatives.
The evaluation of FMS simulation models is
conducted by AHP methodology in this research
project and Fig. 5 shows the hierarchical structure
used. The goal (top of the hierarchical structure) of
the AHP model is to choose the best (the most
suitable) FMS for a company. First-level criteria
are "nance, productivity, #exibility, building time
and risk. The sub-criteria of "nance are the facilities
and installation cost, and the operational cost. The
sub-criteria of productivity are production rate,
total production, lead time, inventory and machine
utility. The sub-criteria of #exibility are #exibility of
part type, machine, process, product, routing, expansion, operation and transfer. The sub-criteria of
building time are planning time and implementing
time. The sub-criteria of risk are technical risk and
operational risk. Thus, the AHP approach decomposes a problem into the elements. Then, the elements (i.e. criteria) are assessed by pairwise
comparisons. The lowest level is the alternatives.
Pairwise comparison means that one element is
compared against another with respect to the level
above. For example, when choosing the best FMS
design, we may look at "nance against productivity
with respect to the goal (upper level). The judgement comes from operators. In order to support the
judgement (decision-making), a fuzzy set decision
support tool (AI-2), and a neural network decision
support tool (AI-3) are proposed in this research.
In AHP, pairwise comparisons use verbal comparisons, and could be considered fuzzy, in the
sense that decision makers (operators) need to express their preference in an approximate way by
verbal judgement or by stating a single number
taken from the 1}9 comparison scale. A fuzzy set
(logic) tool (AI-2) which supports the pairwise comparison is proposed.
A fuzzy set is an excellent tool with which one
can represent the satisfaction of alternatives to criteria. In fuzzy logic, the truth of any statement is
a matter of degree. Assume we have a set of alternatives in a decision:
X"[X , X ,2, X ].
(1)
1 2
n
If we have a particular criterion A, we can associate, with each value in X, a number A[X ] in the
i
interval [0, 1], indicative of how well X satis"es
i
criterion A, which of course then speci"es A as
a fuzzy set of X.
In this paper, it was de"ned that alternatives (X)
are 10 di!erent feasible FMS models, and criteria
(A) are 19 criteria. To determine how an alternative
satis"es a criterion, a membership function has to
be de"ned.
For those criteria that are to be maximized (like
total production), the following membership function is used:
measured value!minimum value
.
maximum value!minimum value
For those criteria that are to be minimized (like
lead time), the following membership function is
F.T.S. Chan et al. / Int. J. Production Economics 65 (2000) 73}84
Fig. 5. Hierarchical structure of FMS design criteria.
81
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F.T.S. Chan et al. / Int. J. Production Economics 65 (2000) 73}84
used:
maximum value!measured value
.
maximum value!minimum value
A decision function, say D, is needed to determine
the desirable x. Function D is de"ned as follows:
D"Max(Min Aai(x)), i"1, 2,2, p,
(2)
i
where p is the number of criteria, Aai(x)"(A (x))ai
i
i
and a is the power of importance of each criterion.
i
The power of importance of each criterion is derived by the AI-3.
In AHP, calculating the preference ratings establishes the power of importance of each element in
the reciprocal comparison matrix (the reciprocal
comparison matrix comes from the pairwise comparisons, see [28]). In AI-3, we use a feed-forward
neural network proposed by Stam et al. [33] to
approximate the mapping from the reciprocal comparison matrix in the AHP to the associated preference ratings. The information that is provided by
the n]n pairwise comparison matrix serves as input to the feed-forward neural network. Thus, the
neural network will have n "n(n!1)/2 input
i
nodes. The desired output vector consists of the
n-dimensional true preference rating vector r, so
that the number of output nodes is n "n. The
o
compound vector (aT, rT) represents one pattern in
the training set.
The software NEUFrame [37] is used to train
separate neural nets for each matrix size. For the
FMS problem discussed in this paper, networks
with two hidden layers provided good results. The
network con"gurations yielded tolerable errors and
converged within a reasonable time. A feed-forward
neural network for the 3]3 pairwise comparison
matrices has 10 nodes in the "rst hidden layer and
6 in the second. The result of AI-3 is the assigned
weight for performance measures.
5. Automation and integration of design process and
decision support tools
One of our objectives in this research was to
automate the FMS design and evaluation processes; also we aimed to integrate the intelligent
tools into one system. The automation and integration of the intelligent tools included the following
tasks:
f A template panel object has been built. The
panel includes four templates, Arrive, Sealer}t,
Rework}t, and Depart. The templates are special
modules in simulation models and have their
own logic and functions. Through the Active
X technology, the templates is integrated with the
utilities in AI-1. Thus, modules could be created
from the recommendations made by AI-1.
f The results of running the FMS models are saved
as text only "les. The developed intelligent tools,
AI-2 and AI-3, read the text "les as the input.
The output data of the AI-2 and the AI-3 are also
saved as the text "les.
f AHP models read the text "les generated by the
AI-2 and the AI-3 to produce the "nal ranking of
each alternative. Wizards (i.e. guides to the operators) are created in cooperation with AHP
models for generating specialized graphs and reports, loading data, etc.
f A user-friendly environment has been built using
the Visual Basic Forms and the C## objects
(for the programs that are associated with C##
language). Active X technology has been used for
the automation of di!erent functions.
Fig. 6 shows the integration methods used in
FMS design and intelligent decision support tools.
6. Conclusion
The research work described in this paper demonstrates that development of intelligent decision
support tools for the design of FMS is now within
the realms of possibility. The design of FMS is
employed with a simulation approach supported
by an expert system tool. Though more than one
design is probable, a multi-criteria decision support
method, AHP, is able to suggest the most suitable
design, which is then supported by the tools of
fuzzy sets and neural networks. The integration of
simulation and multi-criteria decision support
methods is tested in this research, and work to
date indicates that it is likely to be a usable and
promising methodology in FMS design.
F.T.S. Chan et al. / Int. J. Production Economics 65 (2000) 73}84
Fig. 6. Integration of FMS design tools.
A certain degree of automation in FMS design is
realized in this research by using the Active X
technology. It is important that future research
e!ort should be directed toward fully automating
the interface between the simulation models and
the intelligent tools, thus enhancing the intelligence
of the design system and enlarging its knowledge
base.
To summarise, the current research has demonstrated that the use of an integrated methodology
of system modeling and AI tools for FMS design
has enabled system designers to improve the e$ciency of the design task. The present research
work will continue to test the system in practical
situations. Enlarging the knowledge base is the
main task so that the system would have the capability to design a suitable FMS system for various
industrial sectors.
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