Knowledge-based support for simulation analysis of manufacturing cells

Knowledge-based support for simulation analysis ofmanufacturing cells

Shi-Jie (Gary) Chena, Li-Chieh Chenb, Li Linc,*

aDepartment of Industrial Engineering, National Taipei University of Technology, Taipei, TaiwanbDepartment of Industrial Design, Tatung University, Taipei, Taiwan

cDepartment of Industrial Engineering, State University of New York at Buffalo, Buffalo, NY 14260, USA

Received 28 September 1998; received in revised form 29 January 2000; accepted 11 August 2000

Abstract

Simulation is a widely used approach for assisting design and improvement of manufacturing systems. It is a complex

activity and needs a great deal of human expertise. Since the knowledge of analyzing simulation output for decision making is

not inherently captured in the simulation modeling methodology, a framework that integrates simulation and knowledge-based

decision analysis is needed. In this paper, we develop a knowledge-based system that cooperates with simulation for

improving the performance of manufacturing cells. Using Axiomatic Design as a guideline, a hierarchical knowledge base

structure that corresponds to the decision process is built. Our proposed knowledge-based system consists of a set of facts and

three levels of rules in a hierarchy that is consistent with the manufacturing cell system con®guration. The system

demonstrates the effectiveness of utilizing Axiomatic Design concept when developing a knowledge-based system. The

results of an industrial study show that our method contributes to improving the performance of manufacturing cells.

# 2001 Elsevier Science B.V. All rights reserved.

Keywords: Axiomatic Design; Simulation; Knowledge-based system; Manufacturing cells

1. Introduction

A manufacturing cell is a cluster of machines or

processes in close proximity and dedicated to the

manufacturing of certain identi®ed part families that

share similar manufacturing requirements. To

improve design and performance of manufacturing

cells, simulation has become an effective method for

its versatility in modeling complex and dynamic

operations. Nevertheless, improving the performance

of a manufacturing cell is still a complex activity that

not only is time consuming but also demands much

human expertise in its decision making. In addition,

the skills required to conduct simulation studies cor-

rectly and accurately are not widespread [13]. By

using knowledge-based system techniques, these

skills and knowledge for the simulation analysis pro-

cess can be captured in a computer. This calls for the

need of a framework that integrates simulation and

knowledge-based decision analysis. According to the

simulation outcome, the knowledge-based system will

assist the decision process for the improvement of the

manufacturing cell performance. However, since

human experts typically do not express their knowl-

edge in a well-structured manner during system devel-

opment, knowledge-based systems often suffer from

Computers in Industry 44 (2001) 33±49

* Corresponding author. Tel.: �1-716-645-2357/ext. 2119;

fax: �1-716-645-3302.

E-mail address: [email protected] (L. Lin).

0166-3615/01/$ ± see front matter # 2001 Elsevier Science B.V. All rights reserved.

PII: S 0 1 6 6 - 3 6 1 5 ( 0 0 ) 0 0 0 7 1 - 3

the problems of poor structure, redundancy, and dif®-

culty in maintenance [6,8,16]. To develop such a

decision support system, a well-organized knowledge

base structure that re¯ects how the human experts

solve problems is essential.

To meet this critical need, our research aims at the

following objectives:

1. To develop a knowledge-based system that

cooperates with simulation to support decision

making for manufacturing cell performance im-

provement.

2. To construct a knowledge base structure in

assisting the systematic development of our

proposed knowledge-based decision support sys-

tem.

3. To demonstrate the effectiveness of the knowledge

base for decision support of manufacturing cell

performance improvement.

The research focuses on ¯ow-line type manufacturing

cells where parts travel from upstream to downstream

workstations sequentially in a ®xed route. Every

workstation consists of machines, loaders (i.e. opera-

tors or robots), and a conveyor. The proposed knowl-

edge-based system analyzes outputs from a simulation

model of a manufacturing cell, determines whether the

speci®ed objectives are achieved, and identi®es oppor-

tunities for improvement.

2. Related literature review

2.1. Simulation and knowledge-based systems

An effective approach for improving manufacturing

cell performance is to develop a simulation model for

testing and selecting the con®guration that meets the

desired objectives [2,18]. The primary objective faced

by engineers is to obtain a superior solution by

analyzing manufacturing cell simulation outputs that

include throughput, utilization, time/number in queue,

and time/number in system [9]. Based on this analysis,

engineers would improve the initial system by chan-

ging certain parameters, such as number of machines,

speed of robots or conveyers. This process repeats

until satisfactory results are obtained. However, even

the procedure of analyzing simulation results could

rely on various guidelines and rules, the decision

making still requires signi®cant human expertise

and computer resources. To use simulation ef®ciently

in the decision process, the integration of knowledge-

based systems (also termed as expert systems) with

simulation has been emphasized [4,9,10,13].

O'Keefe developed a taxonomy for combining

simulation models and knowledge-based systems

[10]:

1. Embedded model: The simulation may be em-

bedded within a knowledge-based system, or vice

versa. A knowledge-based system sometimes

needs to run a simulation to obtain results for

the users. On the other hand, a simulation model

may need heuristics for choosing parameters

during the execution.

2. Intelligent-front-end model: A knowledge-based

system functions as an intelligent interface

between the user and a simulation package. It

generates necessary instructions, executes the

simulation, and interprets the results to the user.

3. Parallel model: The simulation and the knowl-

edge-based system are designed, developed, and

implemented as separate software in parallel.

Additional links are built for their communica-

tions.

4. Cooperative model: The simulation and the

knowledge-based system cooperate in performing

the task and sharing the data. The user is able to

access both the simulation and the knowledge-

based system sequentially or concurrently.

In the ®rst three models (embedded, intelligent-front-

end, and parallel models), the user interacts with only

one tool (either simulation model or knowledge-based

system). For instance, Ford and Schroer [4] developed

a system that combines a knowledge-based system

with a commercial simulation language for simulating

an electronics manufacturing plant. Their efforts

focused on providing a natural language interface

so that decision-makers do not have to learn the

simulation language. However, natural language inter-

face is not necessary for the engineers if they could

acquire knowledge and skills in simulation.

In the cooperative model, the user could interact

with both simulation and the knowledge-based sys-

tem. Sagi and Chen [11] proposed a framework that

integrates simulation, neural networks, and knowl-

edge-based system tools for manufacturing cell

34 S.-J. Chen et al. / Computers in Industry 44 (2001) 33±49

design. Simulation is used to estimate performance

measures based on input parameters and given cell

con®gurations. Neural networks are applied to predict

the cell design con®guration and the corresponding

complexities of control functions. Training of neural

networks is performed with both forward and back-

ward methods by using the same pair of data sets for

inputs and outputs, such as performance measures,

cell con®gurations, and cell control function ratings.

The rule-based system is employed to store the expert

knowledge regarding the relation between cell control

functions, cost of controls, performance measures and

cell con®guration. Increasingly, the manufacturing

cell designer or engineer possesses some knowledge

of simulation. In cooperative systems, since the

knowledge-based system does not connect with the

simulation directly, engineers will have a better oppor-

tunity to monitor the procedure of decision analysis as

well as to justify the system's results, if necessary.

This gives the cooperative system more ¯exibility for

making decisions.

2.2. Knowledge base structure

Even though combining simulation and knowledge-

based systems is an effective approach to improving

the performance of manufacturing cells, capturing the

domain knowledge still remains a challenge to the

knowledge-based system developers. It has been

emphasized that the most important issue in develop-

ing a knowledge-based system that cooperates with

simulation is capturing of manufacturing experts'

domain knowledge [13]. This is vital to a good under-

standing on the interactions of manufacturing system

components. However, without an explicit and well-

organized knowledge base structure, the knowledge

rules for decision analysis will be dif®cult to de®ne

and develop. Here, the authors would like to empha-

size the importance of a good structure of a knowl-

edge-based system, which is often overlooked in the

design stage.

A knowledge-based system with well-structured

rules is easy to understand, verify, validate, and hence

to maintain [6,8,16]. Higa [6] emphasized that a rule

base is dif®cult to maintain usually due to its complex

rule structure. As the number of rules increases, the

amount of possible interactions among rules increases

rapidly. This would considerably reduce the ef®ciency

of the system if too many unsubstantiated relation-

ships between rules exist. The integrity of the system

even suffers if invalid or inconsistent results cannot be

prevented. In order to remedy the problems and there-

fore improve the performance of the knowledge-based

system, various methods or algorithms have been

proposed to simplify the rule bases. Higa [6] proposed

a rule simpli®cation procedure to eliminate potential

inconsistency in a rule base and thus improve its

maintainability. His procedure contains four condi-

tions to detect and simplify the structures of the rule

bases: (1) no duplicated rules; (2) no rule that sub-

sumes other rules; (3) no overlapping rules; and (4) no

rule that has the same consequent value and adjacent

antecedent value as any other rules. The procedure

reduces the complexity of the knowledge base,

because the structure of rules and the relationships

between them are simpli®ed to manageable units.

Likewise, Vanthienen and Dries [16] developed a

procedure to restructure and simplify rule bases by

transforming rules into an ef®cient rule base using

decision tables. Their procedure reduces redundancy,

con¯ict and incompleteness of rule bases. Other than

the rule simpli®cation procedure, Mehrotra and Wild

[8] developed a multiviewpoint clustering analysis

method to reveal signi®cant structures within the rule

base and partition the rule-based system into a number

of meaningful units. The method is able to enhance the

comprehensibility, maintainability, and reliability of

knowledge-based systems.

In the aforementioned research, the authors

intended to capture both the explicit and implicit

knowledge by clarifying the existing rule sets in the

knowledge base and then simplifying the rule struc-

ture. For instance, a complete knowledge-based sys-

tem is developed in front, then a series of subsequent

actions are followed in restructuring, simplifying, and

optimizing the rule sets of the built system. We argue

that to develop a knowledge-based system more effec-

tively and ef®ciently, a well-organized knowledge

base structure should be established at the very begin-

ning and be followed throughout the system develop-

ment stages. This will signi®cantly reduce time and

cost in modifying the built rule bases. Hence, it will

increase the quality of the knowledge-based system.

Clearly, there is a strong need of a systematic method

that can guide the development of such a structured

knowledge base.

S.-J. Chen et al. / Computers in Industry 44 (2001) 33±49 35

2.3. The Axiomatic Design method

In the real world, engineers tend to tackle a complex

problem by decomposing it into sub-problems and

attempting to maintain independent solutions for these

smaller problems. This calls for an effective method

that provides guidelines for the decomposition of

complex problems and independent mappings

between problems and solutions. Axiomatic Design

(AD) [15] developed by Suh offers such a good

decomposition mechanism. Two axioms, the founda-

tion of the Axiomatic Design, are formally de®ned as

follows:

Axiom 1. The Independence Axiom

Maintain the independence of the Functional

Requirements (FRs). In an acceptable design, the

mapping between Functional Requirements (FRs)

and Design Parameters (DPs) is such that each

requirement can be satis®ed without affecting any

other requirements.

The mapping between FRs and DPs can be

expressed by the following equation

FR1

FR2

..

.

FRn

8>><>>:9>>=>>; �

A11 A12 � � � A1n

A21 A22 � � � A2n

..

. ...

} ...

An1 An2 � � � Ann

2666437775

DP1

DP2

..

.

DPn

8>><>>:9>>=>>;

Where {FRs} is an n� 1 column matrix (or a vector);

{DPs} is an n� 1 column matrix; and [A] is an n� n

matrix with its component, Aij � @FRi=@DPj, indicat-

ing the relation between FRi and DPj. To satisfy the

Independence Axiom, the design matrix [A] must be

either diagonal or triangular so that the relationships

among FRs and DPs can be either uncoupled or

decoupled which are claimed as good or acceptable

design in AD. An uncoupled design matrix is in the

following form. Design solutions can be performed

concurrently or in any order without affecting each

other in an uncoupled design matrix.

Diagonal Design Matrix

�A� �A11 0 � � � 0

0 A22 � � � 0

..

. ...

} ...

0 0 � � � Ann

2666437775 �Uncoupled�

A decoupled design has the following design matrix.

Tasks can be accomplished sequentially in a

Decoupled Design Matrix.

Triangular Design Matrix

�A� �A11 0 � � � 0

A21 A22 � � � 0

..

. ...

} ...

An1 An2 � � � Ann

2666437775 �Decoupled�

A coupled design matrix, which has non-zero

elements in both upper and lower triangles of the

design matrix, is not recommended by AD because

much iteration will be involved in the design

process.

Axiom 2. The Information Axiom

Minimize the information content of the design.

Among all proposed solutions that satisfy Indepen-

dence Axiom, the best design has the minimum

information content.

The axiomatic approach to design consists of the

following key concepts [5]:

1. The design world consists of distinct domains,

such as the `̀ consumer,'' `̀ functional,'' `̀ physi-

cal,'' and `̀ process'' domains.

2. The design process involves mapping between the

domains.

3. Each domain is de®ned (or characterized) by a

characteristic vector, which can be decomposed

by zig-zagging between functional domain and

physical domain. The physical solutions (i.e. DPs)

should be found before decomposing the corre-

sponding FRs at the same level in the hierarchy.

That is, the entire FR hierarchy cannot be

constructed without referring to the DP hierarchy

at each corresponding level.

4. The mapping process involves creative concep-

tualization, which must satisfy the design axioms,

i.e. the Independence Axiom (Axiom 1) and the

Information Axiom (Axiom 2).

The ®rst axiom facilitates concurrent design without

interactions. The second axiom is a variation of the old

adage `̀ keep it simple.'' They represent two quality

characteristics of the design [3].

Due to its usefulness of basic principles for ana-

lyzing, comparing, and selecting solutions, AD has


been applied in various design ®elds such as

manufacturing, materials, software, organization

and systems since 1990 [14]. In a software system

design [7], FRs are the outputs of a software and DPs

are the key inputs to the software which can char-

acterize or control the FRs. The process variables

(PVs) in the process domains are in the form of

subroutines, operating systems, and compilers. In

addition, Axiomatic Design provides the decision-

making criteria to evaluate different designs. Even

though, in the context of knowledge-based system

design, FRs and DPs may be domain-dependent,

the Independence Axiom and Information Axiom

are general guidelines that are applicable for different

domains.

Other applications of Axiomatic Design include

manufacturing system process improvement [15],

arti®cial skin design [5], software system design

[7], design of paper handling mechanisms of an

ATM (Automatic Teller Machine) [12], structural

design in civil engineering structures [1], and envir-

onmental problem solving [17]. These studies have

convincingly shown the applicability and bene®ts

of AD in solving industrial problems. In addition,

since AD provides the independent mapping bet-

ween each set of FR and its corresponding DP, it

would help relieve the burden of system development

processes.

2.4. Summary of literature review

The research reviewed has shown that a cooperative

model of combining the simulation and knowledge-

based systems is an ef®cient approach. However, the

developed rules are dif®cult to manage due to the

complexity of manufacturing processes. Without a

well-organized knowledge base structure, the true

root causes (e.g. the bottleneck workstations or

machines) may not be clearly exposed by these

rules. Therefore, we need a tool to guide the devel-

opment of a knowledge base systematically. AD is

a useful tool for assisting the system design and

development, in which the framework, criteria, and

methodologies are well established. Numerous

applications have demonstrated that Axiomatic

Design is applicable to solving various engineering

design problems. Using AD as a guideline, the

domain knowledge of simulation and manufacturing

cell can be well structured in the development of

knowledge-based systems.

3. Methodology

3.1. Description of manufacturing cells

In this research, the manufacturing cells consist of

several workstations where parts travel in a ®xed

sequence. A workstation may be a dial-type index

machining center or a stand-alone machine. These

machining resources are linked by a conveyor of a

certain type as part transport means, such as a hook

conveyor, a belt conveyor, or a pallet conveyor. Opera-

tors are assigned to workstations to serve as a part

loader as well as an inspector. Some workstations may

use a robot to automate part loading and unloading.

Hence, each workstation in our case study will incor-

porate machines, loaders (e.g. operators or robots),

and a conveyor.

3.2. System development procedure

Following O'Keefe's taxonomy [10], a cooperative

model of combining simulation and knowledge-based

decision support system is developed to improve the

performance of manufacturing cells. An iterative sys-

tem development procedure shown in Fig. 1 illustrates

how our proposed knowledge-based system coop-

erates with simulation.

First, the current con®guration of the manufacturing

cell is used as the input data to build a simulation base

model. The simulation run of the base model will

produce the current cell performance, such as resource

utilization, blocking percentage, and throughput. They

will be the input to the knowledge-based decision

process. If the performance target is not achieved, the

knowledge-based system will recommend how to

modify the simulation model by varying the number

of machines, the number of operators, conveyor speed,

etc. The iteration continues until a satisfying cell

con®guration is reached, i.e. the performance target

of the cell is met.

In order to provide decision support for identifying

and improving bottlenecks, the working procedure of

our knowledge-based system contains four steps that

identify the bottlenecks from the workstation level to


the resource level. These steps in the knowledge-based

system are as follows:

Step 1 to collect sets of facts at the initial step

including cell configurations, simulation

output, as well as performance criteria,

Step 2 to identify the bottleneck workstations one

by one,

Step 3 to identify the bottleneck resources (i.e.

machine, operator, robot, or conveyor) within

each identified bottleneck workstation,

Step 4 to provide recommendations for improving

the bottleneck resources.

Once the knowledge-based system has made the

recommendations, the simulation model is adjusted

accordingly and rerun. The simulation component and

knowledge-based system component are cooperating

with each other until the objective (target throughput)

is achieved.

Fig. 1. System development procedure.


3.3. Structure of the knowledge base

To assist the development of the proposed knowl-

edge-based system, we introduce a systematic struc-

ture with a four-level knowledge base. The Axiomatic

Design approach helps the design of this four-level

knowledge base structure. There are several advan-

tages for developing the knowledge base structure

using AD as design guidance.

1. Tasks are decomposed top-down so that they can

be achieved with smaller scale.

2. Root causes of the problems at each level are

narrowed down and clearly identi®ed so that

engineers can focus on solving sub-problems one

by one.

3. Tasks are completed systematically with the guide

of Independence Axiom at each level to ensure

ef®ciency of the solution procedure.

AD decomposes a design problem by the mappings

between Functional Requirements (FRs/problems)

and their corresponding design parameters (DPs/solu-

tions). The Independence Axiom maintains the inde-

pendence of Functional Requirements. That is, in the

idea case of uncoupled design, each DP can satisfy

only one FR without affecting any other FRs in the

mapping processes. It is the essence of AD in which a

complex problem is decomposed into sub-problems

(FRs at each level) and maintaining the independent

solutions (one DP for one corresponding FR) for these

smaller problems. Using this `̀ independence'' philo-

sophy in AD, a four-level knowledge base structure is

built and shown in Fig. 2.

The four levels in the hierarchy are formed in terms

of the mapping between FRs and the corresponding

DPs. We intend to improve the performance of man-

ufacturing cell by the cooperation of simulation and

the knowledge base system. The problem sources

(FRs) are those physical components in the manufac-

turing cell that need to be improved. The feasible

solutions (DPs) are the facts and rules in the knowl-

edge base to make the improvement for the cell

performance. These four levels of facts and rules

are systematically applied in the knowledge-based

decision process.

3.3.1. Base level (facts to declare cell con®guration,

simulation output and performance criteria)

Since the primary objective in our study is to

improve the throughput of a manufacturing cell, a

simulation analysis assisted by the knowledge-based

decision process is carried out. A fact list that supports

Fig. 2. Structure of the knowledge base.


all the required information for decision making

before executing the knowledge-based system needs

to be developed ®rst. Details of the three types of facts

needed for the proposed knowledge-based system are

as follows:

1. The ®rst type of facts represents the manufactur-

ing cell con®guration of the simulation model,

including:

1.1. total number of workstations in the cell;

1.2. number of machines within each workstation;

1.3. number of loaders and their types (e.g. robot

or operator); and

1.4. maximum number of machines and operators

allowed due to the space constraint.

2. The second type of facts correspond to the

simulation outputs, containing:

2.1. machine and loader utilization;

2.2. time between part departure at each of the

workstations;

2.3. blocking percentage of each workstation; and

2.4. throughput of the entire cell.

3. The third type of facts indicates the performance

criteria for evaluating the system, including:

3.1. target throughput;

3.2. target average time between departure; and

3.3. maximum utilization and minimum utiliza-

tion limitations.

At this initial level, FR is a manufacturing cell that

needs to increase its throughput to the required target.

The corresponding DP is the facts that collect the cell

con®guration, simulation output, and target criteria.

The FR and the corresponding DP are as follows:

Base level:

FR to improve manufacturing cell throughput,

DP provide sufficient information regarding

the manufacturing cell configuration, simu-

lation output, and performance criteria in a

fact list.

FR� � � A� � DP� �Element A in the design matrix represents the mapping

between FR and DP. Since this level has only one FR

and one DP, the design matrix [A] has only one

element A and thus is uncoupled. The Independence

Axiom is automatically satis®ed.

The design matrix [A], showing the mapping rela-

tionship between FRs and DPs, is an indicator that

determines whether the current level maintains the

independent solutions and satis®es the Independence

Axiom in AD. An uncoupled design matrix [A] with

only non-zero elements on diagonal represents the

best design matrix in AD in which each FR can be

satis®ed by its corresponding DP independently with-

out affecting or being affected by other FRs or DPs. A

decoupled design matrix [A] with non-zero elements

on upper or lower triangle is an acceptable design

because DPs can be performed sequentially. A

coupled design matrix [A] with non-zero elements

on both upper and lower triangles is a bad design

and not acceptable due to many iterations.

3.3.2. Level 1 (rules to identify the bottleneck

workstation)

In order to improve the manufacturing cell through-

put, the bottleneck workstations should be identi®ed

®rst. Thereby the cell improvement processes (i.e. at

Level 2 and Level 3) can be initiated from the recog-

nized bottleneck workstations. However, improving

one bottleneck workstation will affect both upstream

and downstream workstations in a ¯ow-line cell. If

one identi®ed bottleneck workstation is alleviated, the

improved throughput of this workstation may change

some other workstations into the next bottleneck. This

will require iterations in solving the bottleneck pro-

blems. Therefore, all the true bottleneck workstations

have to be identi®ed from the outset. By doing so, the

improving processes can be carried out effectively.

A manufacturing cell with various workstations

linked by certain types of conveyors is a complex

scenario. Commonly, simulation output consists of the

performance measures for each workstation, such as

utilization and blocking percentage of resources, and

throughput, etc. The information that needs to identify

the bottleneck workstation is not provided directly.

Therefore, it is dif®cult to distinguish the key bottle-

neck on the outset without the help of a systematic

analysis. So, an effective approach to correctly iden-

tify the key bottleneck workstation is needed. Usually,

machine or workstation utilization may be suitable to

indicate the ef®ciency of the cell. However, it is not

appropriate for bottleneck identi®cation. A highly

utilized workstation may not be the bottleneck in

the cell. Instead, it may be merely in¯uenced


(blocked) by its succeeding workstations that are the

true bottleneck workstations. For example, a highly

utilized workstation may contain a large portion of

waiting time during which the part has to stay at the

current workstation and cannot be delivered to the

next workstation due to the bottleneck of its succeed-

ing workstations with limited buffer capacity.

In AD, each Functional Requirement (FR) in the

problem domain is satis®ed by a corresponding

Design Parameter (DP) in the solution domain, inde-

pendently. For example, `̀ Determine whether work-

station Wi is the bottleneck?'' represents an FRi in the

problem domain and `̀ developing a rule/rules to

answer FRi'' serves as a DPi in the solution domain.

It is our purpose to develop such rule(s) that enable us

to identify any bottleneck workstation without the

in¯uence from other workstation(s), which is the

essence of independence in AD. At this level, we

decompose FR from the base level into n FRs (from

FR1 to FRn) because there are n workstations in the

manufacturing cell. Each FRi (i � 1; . . . ; n) represents

a workstation that needs to be examined for bottle-

neck. The corresponding DPi is the rules that detect

the bottleneck workstations. Two related issues on

criteria of identifying bottlenecks are developed and

shown in Fig. 3 (1) examine the ®rst workstation for

bottleneck (rule 1±1); and (2) check all other work-

stations for bottleneck (rule 1±2). The advantage of

doing so is that the problems (bottleneck worksta-

tions) can be focused and solved step by step without

the in¯uence from other workstations.

Since parts need to pass every workstation in the

cell, the target throughput has to be maintained at each

workstation. That is, the average time between two

consecutive parts leaving a workstation should also be

maintained for each workstation. This is de®ned as

time between departure (TBD). In addition, any inef-

®cient workstation, except for the ®rst workstation,

could block their upstream workstations that signi®-

cantly affect the throughput of their upstream work-

stations. This is termed as the blocking percentage (B),

which is the proportion of machine blocking time of

the total simulation time. These two factors, TBD and

B, will be used to identify the bottleneck workstations

in the knowledge rules.

1. Examine the ®rst workstation for bottleneck (rule

1±1): Since the ®rst workstation W1 has no

preceding workstation and no upstream work-

station can be blocked by W1, the rule does not

need to consider the blocking percentage (B)

caused by W1. The bottleneck of W1 is checked by

Fig. 3. Rules to identify the bottleneck workstation.


comparing the target TBD (dividing simulation

time by target throughput) with TBD1. If the target

TBD is less than TBD1 that means the workstation

W1 cannot reach the target throughput, then W1 is

a bottleneck workstation. Here, we assume that

there is an adequate source of work for the ®rst

workstation.

2. Check all other workstations for bottleneck (rule

1±2): The bottleneck workstation not only in-

creases TBD, but also blocks its preceding

workstation. Hence, two conditions have to be

checked in this rule. One is TBD, the other is

blocking percentage B. If TBDi of workstation Wi

is greater than TBDiÿ1 of its preceding work-

station, then Wi would have less throughput than

its preceding workstation. Therefore, Wi may be a

bottleneck workstation. In addition, if the block-

ing percentage, Bi, of workstation Wi is less than

Biÿ1 of its preceding workstation, then Wi could

also be a bottleneck workstation because Wi

causes a high value of Biÿ1. That is, Wi causes

more blocking to its preceding workstation Wiÿ1

but suffers less blocking resulted from the next

workstation Wi�1. Therefore, Wi is less ef®cient

than Wiÿ1 and Wi�1. Consequently, the bottleneck

workstation is identi®ed if both situations are

present.

The number of FRs and DPs at this level depends on

how many workstations the cell has. The decomposed

FRs and the corresponding DPs are as follows:

Level 1:

FR1 to identify bottleneck of W1

FRi to identify bottleneck of Wi

FRn to identify bottleneck of Wn

DP1 use rule 1±1 to identify bottleneck W1

DPi use rule 1±2 to identify bottleneck Wi

DPn use rule 1±2 to identify bottleneck Wn

where, W1 is the ®rst workstation in the cell, Wi the ith

workstation in the cell (i � 1; . . . ; n) and Wn is the last

workstation in the cell.

FR1

..

.

FRi

..

.

FRn

2666664

3777775 �A11 0 0 0 0

0 } 0 0 0

0 0 Aii 0 0

0 0 0 } 0

0 0 0 0 Ann

266664377775

DP1

..

.

DPi

..

.

DPn

2666664

3777775

Elements A11 to Ann in the design matrix represent the

mapping relationships between FRs (from FR1 to FRn)

and DPs (from DP1 to DPn), respectively. The design

matrix [A] is uncoupled so that Independence Axiom

is satis®ed.

3.3.3. Level 2 (rules to identify the bottleneck

resource within the bottleneck workstation)

Now we have identi®ed the bottleneck workstation,

the next step is to examine each resource in the

workstation to pinpoint the root cause of not meeting

the target throughput. This is accomplished by using

the knowledge-based decision support system and the

established rules at Level 2. Within each bottleneck

workstation i at this level, we decompose FRi

(i � 1; . . . ; n) from Level 1 into four FRs (from

FRi1 to FRi4) because there are four types of resources

(machine M, operator O, robot R, and conveyor C) in

each workstation. FRi1 to FRi4 represent each resource

that needs to be examined for bottleneck. The corre-

sponding DPs from DPi1 to DPi4 are the rules that

detect the bottleneck resources.

Four groups of rules (rules 2±1, 2±2, 2±3, and 2±4)

regarding checking machine, conveyer, robot, and

operator, accordingly, are developed and shown in

Fig. 4. At this level, the desired TBD of parts and

utilization of machine, robot and operator are the

gauges to identify the bottleneck resources.

If the processing time of a machine, a robot, or an

operator is greater than the target TBD times the

speci®ed minimal utilization level, it implies that

the part stays at the workstation longer than the target

TBD when the resources are at their minimal utiliza-

tion. Therefore, the bottleneck resource is the

machine, robot, or operator, respectively (i.e. rule

2±1, rule 2±3.2, and rule 2±4.2). The multiplication

of the minimal utilization is to guarantee the resource

is able to deliver the target throughput as required. For

example, if a machine's processing time is equal to the

target TBD, the machine cannot produce the target

throughputs without 100% of utilization. A full utili-

zation of resources is not recommended in industrial

practice due to the extreme demand on maintenance of

the resources, as well as the dif®culty of production

planning and scheduling of manufacturing cells.

The utilization of machine, robot, or operator has to

be checked (i.e. rule 2±2, rule 2±3.1, and rule 2±4.1) if

their processing time is less than or equal to the target


TBD times the speci®ed minimal utilization level. In

rule 2±2, if the utilization of machine and robot (or

operator) is lower than the minimal level, it implies

that they are always waiting for the part. Therefore,

the conveyer is the bottleneck. In rules 2±3.1 and 2±

4.1, on the other hand, if the utilization level of

machine is lower than the minimal level and the loader

(either a robot or an operator) has higher utilization

than the speci®ed maximal level, the loader is too busy

to handle the incoming parts. In this case, the loader is

the bottleneck.

For each bottleneck workstation Wi, the four FRs

and their corresponding DPs are as follows:

Level 2:

FRi1 to identify bottleneck resource M (ma-

chine) at workstation i

FRi2 to identify bottleneck resource C (con-

veyor) at workstation i

FRi3 to identify bottleneck resource R (robot) at

workstation i

FRi4 to identify bottleneck resource O (operator)

at workstation i

DPi1 use rule 2±1 to identify bottleneck resource

M at workstation i

DPi2 use rule 2±2 to identify bottleneck resource

C at workstation i.

DPi3 use rule 2±3.1 and rule 2±3.2 to identify

bottleneck resource R at workstation i

DPi4 use rule 2±4.1 and rule 2±4.2 to identify

bottleneck resource O at workstation i

FRi1

FRi2

FRi3

FRi4

2666437775�

Ai1ÿi1 0 0 0

0 Ai2ÿi2 0 0

0 0 Ai3ÿi3 0

0 0 0 Ai4ÿi4

2666437775

DPi1

DPi2

DPi3

DPi4

2666437775

Elements Ai1ÿi1 to Ai4ÿi4 in the design matrix repre-

sent the mapping relationships between FRs (from

FRi1 to FRi4) and DPs (from DPi1 to DPi4), respec-

tively. The design matrix [A] is uncoupled so that

Independence Axiom is satis®ed.

3.3.4. Level 3 (rules to improve the throughput)

After the bottleneck resources within the worksta-

tion are identi®ed, the improvement processes can

then be started. Within each bottleneck workstation

i at this level, we decompose each of the FRi1, FRi2,

Fig. 4. Rules to identify the bottleneck resource.


FRi3, and FRi4 from Level 2 into the four correspond-

ing FRs (from FRi11 to FRi44) because each identi®ed

bottleneck resource needs to improve accordingly.

The FRs from FRi11 to FRi44 represent each kind of

resources that needs to be improved if it is the bottle-

neck resource. The corresponding DPs from DPi11 to

DPi44 are the rules that improve the bottleneck

resources.

Four groups of rules are developed as guidelines for

solving the key bottleneck resources and thus improv-

ing the throughput (see Fig. 5). Due to limited space

capacity, the number of machines in the workstation

needs to be constrained by a maximum allowable

number. If a machine is the bottleneck and the space

is still enough for accommodating extra machines, the

rules will suggest adding one machine (rule 3±1.1). On

the other hand, if there is no space for additional

machine, the rules will suggest replacing the current

machine (rule 3±1.2). If the bottleneck is a conveyor or

a robot, it needs to adjust its speed (rules 3±2 and 3±3).

Otherwise, if an operator is the bottleneck, one addi-

tional operator will be suggested (rule 3±4).

Four FRs and four corresponding DPs to improve

the bottleneck resources are as follows:

Level 3:

FRi11 to improve bottleneck resource M (ma-

chine) at workstation i

FRi22 to improve bottleneck resource C (con-

veyor) at workstation i

FRi33 to improve bottleneck resource R (robot) at

workstation i

FRi44 to improve bottleneck resource O (opera-

tor) at workstation i

DPi11 use rule 3±1.1 and rule 3±1.2 to improve

bottleneck resource M at workstation i

DPi22 use rule 3±2 to improve bottleneck resource

C at workstation i


R at workstation i


O at workstation i.

FRi11

FRi22

FRi33

FRi44

2666437775 �

Ai11ÿi11 0 0 0

0 Ai22ÿi22 0 0

0 0 Ai33ÿi33 0

0 0 0 Ai44ÿi44

2666437775

DPi11

DPi22

DPi33

DPi44

2666437775

Elements Ai11ÿi11 to Ai44ÿi44 in the design matrix

represent the mapping relationships between FRs

(from FRi11 to FRi44) and DPs (from DPi11 to

DPi44), respectively. The design matrix [A] is

uncoupled so that Independence Axiom is satis®ed.

4. An illustrative example

To validate the proposed knowledge-based system,

an example that uses a real manufacturing cell in the

Fig. 5. Rules to improve the throughput.


industry with eight workstations is implemented and

tested. The simulation model is built based on actual

data from this manufacturing cell shown in Fig. 6.

Fig. 7 shows the simulation model generated by the

discrete event simulation software system ProModel.

The simulation time is based on one standard shift,

which equals to 7.33 h (8 h with 40 min break time

from labor contract). The performance criteria for this

example are speci®ed in the following:

1. Target throughput � 400 parts/shift;

2. Target TBD � 7:33h/400 parts � 66 s;

3. Minimal utilization � 60%; and

4. Maximal utilization � 80%.

The required con®guration data are shown in Table 1.

In addition, it is important to note that many of the

tasks in Fig. 6 are stochastic in nature, i.e. the machine

processing times and loader cycle times are various in

Fig. 6. The con®guration of manufacturing cell example.

Table 1

Con®guration of the example

Workstation 1 2 3 4 5 6 7 8

No. of machine 1 0 1 2 1 1 2 0

Loader type Operator Robot Operator Operator Operator Operator Operator Operator

No. of operator 1 0 1 1 1 1 1 1

Max no. of machine 1 0 2 3 1 2 3 0

Max no. of operator 2 0 2 2 2 2 2 2


a range. Table 2 de®nes the necessary stochastic

elements in the simulation, such as processing times

of workstations or machines, cycle times of loaders

(operators or robots), and speeds of conveyors. From

the analysis of collected data, the inter-arrival rate of

incoming parts at workstation W1 is best represented

as a triangular distribution with values of minimum,

mean, and maximum equal to 10, 60, and 1200 s,

respectively.

Simulation run of the current cell indicates that the

throughput is 249. The performances are shown in

Table 3.

Fig. 7. The simulation model of manufacturing cell example.

Table 2

Stochastic elements in the simulation

Workstation no./loader no. Machine processing time (s) Loader cycle time (s) (min., mean, max.)

(a) The eight workstations:

W1/operator 1 43.2 � 0.1 (28.0, 33.5, 42.0)

W2/robot ± (8.6, 11.4, 16.8)

W3/operator 2 1.0 � 0.2 (19.0, 24.9, 33.0)

W4/operator 3 30.0 � 0.2 (38.0, 46.7, 60.0)

W5/operator 4 44.5 � 0.3 (35.0, 43.6, 56.0)

W6/operator 5 43.4 � 0.6 (18.0, 22.9, 30.0)

W7/operator 6 17.4 � 0.2 (19.0, 23.9, 30.0)

W8/operator 7 2.7 � 0.1 (15.0, 20.3, 30.0)

Conveyor no. Type Linked workstations Distance (ft) Capacity (pcs) Speed (ft/min)

(b) The seven conveyors:

Conveyor 1 Pallet belt W1 & W2 15 9 32.0

Conveyor 2 Furnace W2 & W3 26.5 72 7.3

Conveyor 3 Circle belt W3 & W4 128 24 32.0

Conveyor 4 Hook W4 & W5 90.7 34 16.8

Conveyor 5 Belt W5 & W6 16 24 32.0

Conveyor 6 Belt W6 & W7 13.7 32 27.5

Conveyor 7 Belt W7 & W8 7 20 23.8


Since the throughput is less than the target, analysis

of the simulation output is necessary for possible

improvement. However, it is dif®cult to know how

to improve the current manufacturing cell by simply

inspecting the output data. For example, from Table 3,

the resource utilization of workstations W1 (95% for

machine) and W8 (100% for loader) are very high so

that W1 and W8 could be the bottlenecks. Additionally,

TBDs of all workstations are greater than the target

TBD (66 s), thus, all workstations in the cell may be

the bottleneck. If only examining the face values of

these outcomes, the true bottleneck workstations can-

not be identi®ed. With the help of our knowledge-

based system, the problems are easily revealed and the

recommendations are provided.

First, the simulation output data shown in Table 3

are formulated as the facts for the knowledge-based

system. With these facts, the rules at three levels are

®red one after another as shown in the following

demonstration.

4.1. From Level 1 (rules to identify the bottleneck

workstation)

For the ®rst workstation W1, since its average TBD1

of parts is longer than the target (TBDtarget � 66

< TBD1 � 83:75), Rule 1±1 is ®red and W1 is iden-

ti®ed as a bottleneck workstation.

Rule 1±2 is used to identify whether the subsequent

workstations are the bottleneck or not. For instance,

the average TBD of workstation W5 is longer than that

of its upstream workstation W4 (TBD5 � 90:59 >TBD4 � 88:47). Also, the average blocking percen-

tage is less than that of W4 (B5 � 50:94 < B4 �66:26). This means workstation W5 not only takes

longer to process a part but also contributes to the

blocking percentage of W4. Therefore, it is identi®ed

as a bottleneck workstation by Rule 1±2. Similarly,

workstations W6 and W8 are identi®ed as bottleneck

workstations by Rule 1±2.

4.2. From Level 2 (rules to identify the bottleneck

resource within the bottleneck workstation)

For bottleneck workstations W1, W5, W6, and W8,

the bottleneck resources are identi®ed as follows:

At workstations W1 and W6, the machine processing

times (MPT1 � 40, MPT2 � 68) are longer than the

target time between departure times the minimal

utilization (TBDtarget � 60% � 39:6). Rule 2±1 is

®red, and the bottleneck is identi®ed at the machine.

At work station W5, although the machine proces-

sing time (MPT5 � 22:5) is less than the target time

between departure times the minimal utilization, the

utilization of machine and operator is less than 60%.

Therefore, Rule 2±2 is ®red, and the conveyer is a

bottleneck resource.

Although there is no machine at workstation W8, the

operator processing time (OPT8 � 40:6) is longer

than the target time between departure times the

minimal utilization. Rule 2±4.2 is ®red, and the

operator is a bottleneck.

4.3. From Level 3 (rules to improve the throughput)

For bottleneck resources found, the recommenda-

tions for throughput improvement are generated as

follows:

For the bottleneck machine at workstation W1, since

the number of machine is already equal to the maximal

value. The only way to reduce processing time is to

replace it with another machine that has shorter

processing time (Rule 3±1.2). For the bottleneck

machine at workstation W6, since the number of

machine is still less than the maximal value. The

processing time could be reduced by adding one

Table 3

Simulation output of the example Ð the current cell

Workstation 1 2 3 4 5 6 7 8

Machine utilization 0.95 N/A 0.20 0.34 0.49 0.74 0.30 N/A

Loader utilization 0.50 0.29 0.29 0.61 0.57 0.31 0.43 1.00

TBD (s) 83.75 83.72 85.25 88.47 90.59 98.09 104.85 105.86

Blocking (%) 4.75 0.00 6.56 66.26 50.94 20.39 53.89 0.00


machine (Rule 3±1.1). For the bottleneck conveyer at

workstation W5, a faster speed is needed to increase

the utilization of the machine and the operator (Rule

3±2). For the bottleneck operator at workstation W8,

since the number of operators is still less than the

maximal value, the processing time could be reduced

by adding one operator (Rule 3±4).

All the recommendations from the proposed knowl-

edge-based system are listed in Fig. 8.

The con®gurations of the initial simulation model

are then modi®ed according to the recommendations.

After the next simulation run, the throughput equals

to 416 for the modi®ed cell model. The target is now

achieved. The throughput has improved by 67% from

249 to 416 parts per shift. The engineers can use the

new set of con®gurations to improve the real manu-

facturing cell.

5. Conclusions

A knowledge-based system that cooperates with

simulation has been developed. They compensate

each other in assisting the decision making for man-

ufacturing cell improvement. With our knowledge

base decision support, the key bottleneck workstations

as well as bottleneck resources are clearly identi®ed.

Hence, the improvement processes can be carried out

precisely. In solving this decision process, a hierarch-

ical structure of knowledge-based system is con-

structed. The Independence Axiom in Axiomatic

Design has been followed during the establishment

of the knowledge base structure. Unlike existing

research that only attempt to simplify the structure

of a knowledge base after it is built, our work empha-

sizes the development of a good knowledge base

structure even before it is built. Such a sound structure

will help build the knowledge base systematically

with good solution ef®ciency and consistency. To

demonstrate the effectiveness of our proposed knowl-

edge-based system, a real industry case is used. The

simulation results show that the suggestions provided

contribute to increasing the throughput. In conclusion,

the engineer can improve the manufacturing cell with

the help of the knowledge-based system and the

simulation. It reduces the burden of engineers by

revealing the problem sources and providing recom-

mendations for solving the problems. Moreover, the

study demonstrates the applicability and usefulness of

AD in the design of knowledge-based decision support

system.

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Fig. 8. Output recommendations from the knowledge-based

system.


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Shi-Jie (Gary) Chen is an Assistant

Professor of Industrial Engineering at

the National Taipei University of Tech-

nology, Taipei, Taiwan. He received his

BS degree in Automatic Control Engi-

neering (1989) from Feng-Chia Univer-

sity, Taiwan, and completed his MS

degree in Mechanical Engineering

(1995) from State University of New

York at Buffalo. He completed his Ph.D.

in Industrial Engineering (1999) from

State University of New York at Buffalo. His research interests

include concurrent engineering, project management, simulation,

knowledge-based systems and CAD/CAM/CIM.

Li-Chieh Chen is an Assistant Professor

in the Department of Industrial Design at

Tatung University, Taipei, Taiwan. He

received his BS degree in Mechanical

Engineering (1990) from Feng-Chia Uni-

versity and completed his MS degree in

Mechanical Engineering (1992) from

Tatung Institute of Technology, Taiwan.

He completed his Ph.D. in Industrial Engi-

neering (1999) from the State University

of New York at Buffalo. His research

interests include product design methodologies, concurrent engi-

neering, computer-integrated manufacturing and arti®cial intelli-

gence applications in design.

Li Lin is an Associate Professor of Indus-

trial Engineering at the State University

of New York, Buffalo. He received a BS

in Mechanical Engineering (1982), an

MTech (1984) in Graphic Communica-

tions and an MSE in Industrial Engineer-

ing (1986). After his Ph.D. in Industrial

and Management Systems Engineering

from Arizona State University in 1989,

Dr. Lin has been a faculty member at

SUNYat Buffalo. His research interests are in modeling and control

of ¯exible manufacturing systems, product life-cycle design in

concurrent engineering and computer simulation. Dr. Lin's research

has been funded by the National Science Foundation and a number

of industrial companies, including American Axle and Manufactur-

ing, Delphi Harrison Thermal Systems, DuPont, Leica and Praxair.

He is a senior member of the Institute of Industrial Engineers (IIE).


Documents

Knowledge-based support for simulation analysis of manufacturing cells