J. Software Engineering & Applications, 2010, 3, 661-673
doi:10.4236/jsea.2010.37076 Published Online July 2010 (http://www.SciRP.org/journal/jsea)
661
Development of a Simulation-Based Intelligent
Decision Support System for the Adaptive
Real-Time Control of Flexible Manufacturing
Systems
Babak Shirazi1, Iraj Mahdavi1, Maghsud Solimanpur2
1Mazandaran University of Science and Technology, Babol, Iran; 2Urmia University, Urmia, Iran.
Received April 25th, 2010; revised May 19th, 2010; accepted May 27th, 2010.
ABSTRACT
This paper describes a simulation-based intelligent decision support system (IDSS) for real time control of a flexible
manufacturing system (FMS) with machine and tool flexibility. The manufacturing processes involved in FMS are com-
plicated since each operation may be done by several machining centers. The system design approach is built around
the theory of dynamic supervisory control based on a rule-based expert system. The paper considers flexibility in op-
eration assignment and scheduling of multi-purpose machining centers which have different tools with their own effi-
ciency. The architecture of the proposed controller consists of a simulator module coordinated with an IDSS via a real
time event handler for implementing inter-process synchronization. The controller’s performance is validated by
benchmark test problem.
Keywords: Intelligent Decision Support System, Real Time Control, Flexible Manufacturing System, Multi-Purpose
Machining Centers
1. Introduction
A flexible manufacturing system (FMS) architecture can
be characterized as a set of multi-purpose machine tools
connected by automatic material handling and tool
transportation devices. The material handling system has
a mechanism to transport parts between machining cen-
ters. Automatic tool transportation devices can also
transfer tools among tool magazines and the central tool
storage area [1,2]. Any material handling system has a
mechanism to transport parts and tools automatically.
These systems can transfer tools among tool magazines
and the central tool storage area while the system is in
operation [3,4]. FMSs are essentially more flexible than
the conventional manufacturing systems, mainly because
of utilizing versatile manufacturing lines, redundant and
reconfigurable machines, alternate routings, and flexibil-
ity in operation sequencing [5,6].
Due to different operations on a product and machine
requirements to process each step of production, it is so
hard to control different events that might happened at
different cells to achieve best practice of performance
criteria [7]. Regarding these considerations, control of
these environments plays an essential role at manufac-
turing systems. Control framework has been studied on
FMSs in the literature and there are different methods for
selecting the most appropriate control policies at each
decision point [8-15]. These strategies deal with the al-
location of jobs to multi-purpose machining centers
which have to be made in a flexible way. Most of these
studies focus on reactive strategies that enable the FMS
to better deal with randomness and variability. It means
that most of these FMS controllers usually use fixed and
offline policies to operate the system. However, these
methods do not consider many realistic constraints and
dynamic changes such as tool magazine capacity, opera-
tive efficiency changes and availability of tools in the
part selection and operation assignment problems. These
offline methods are mainly categorized into two forms:
priori reactive control and the posteriori reactive control
methods. The control is planned according to the struc-
tural information, forecasts, orders, management rules

Development of a Simulation-Based Intelligent Decision Support System for the Adaptive
Real-Time Control of Flexible Manufacturing Systems
662
and objectives [16]. The online posteriori control adapted
directly to the system for preventive deviations by con-
trolling occurrence of events.
Improving the performance of an FMS supervised by
an effective controller is still a complex task that not only
is time consuming but also needs much human expertise
in decision making [17]. In order to implement an adap-
tive controller, DSS have become an effective method for
their adaptability in controlling complex and dynamic
operations [18]. There have been limited investigations
on IDSS for controlling such systems as a unified ap-
proach. There is a need to construct a framework in
which a knowledge-based decision analysis will assist
the decision process to improve the FMS control pa-
rameters.
An effective approach for reinforcement of IDSS per-
formance is to develop an embedded simulation model
that meets the desired objectives of the system [19-22].
Discrete-event simulation is a very powerful tool that can
be used to evaluate alternative control policies in the
manufacturing system [23-26]. Although the procedure
of analyzing simulation results could rely on various
guidelines and rules, decision-making still requires sig-
nificant human expertise and computer resources. To
efficiently use simulation in the decision process, inte-
gration of IDSS with simulation has been emphasized
[27-30]. However, there have been limited investigations
on integrating IDSS with the modular simulation lan-
guages as a unified approach for controlling manufactur-
ing systems. So FMS control appears to be an excellent
area for applying adaptive IDSS simulation-based con-
troller.
This research focuses on developing a simulation-
based intelligent expert system with dynamic rules con-
templating tool and machine flexibility control. For im-
plementing inter-process synchronization in real-time
control of FMS, the proposed IDSS receives online re-
sults from simulation module and different scenarios of
control parameters with simulation replication action.
The outline of the paper is as follows. Section 2 describes
adaptive flexibility control on FMS shop floor. Section 3
deals with FMS adaptive controller architecture to build
IDSS. Sections 4 present experimental study to validate
the effectiveness of the proposed system. Finally, con-
clusions are made in Section 5.
2. Adaptive Flexibility Control on FMS Shop
Floor
2.1 Adaptive Control Mechanism
Adaptive supervisory implies selection of an appropriate
control policy based on the current state of the workcell.
Regarding dynamic control of manufacturing systems,
jobs are dispatched to machining centers using dispatch-
ing rules at the specific moment based on the available
information. Afterwards, appropriate tool is mounted in
machining center according to the tooling strategy [31].
Because of the flexible characteristics of FMSs, control
decisions should be applied as soon as possible based on
the real time state of the system. An FMS adaptive con-
troller has to deal with the dynamic environment in
which the system operates to seize online machines and
tools redundancy capabilities, alternative routing and
hazard control remedy.
2.2 FMS Shop Floor Flexibility Control
Functions
The most commonly accepted definition of flexibility is
the ability to take up different positions or alternatively
the ability to adopt a range of states [32]. Many different
authors have defined many different types of flexibilities
(machine, process, product, operation, routing, volume,
production and expansion flexibility) in the literature
[33-37]. Here we consider the flexibility control function
as machine flexibility and tool flexibility. Browne et al.
[38] defined machine flexibility as the ease of change to
process a given set of part types. Buzacott [39] clarifies
machine flexibility as the ability of the system to cope
with changes. There are three technical constraints re-
lated to a machining center: number and capacity of ma-
chine-tools, local input/output buffer (LIB/LOB) size and
operative efficiency. Das and Nagendra [40] define ma-
chine flexibility of a machining center as the ability of
performing more than one type of processing operation
efficiently. Therefore, machine flexibility is measured by
the number of operations that a workstation processes
and the time needed to switch from one operation to an-
other. The more operations a workstation processes and
the less time switching takes, the higher the machine
flexibility becomes [37]. Figure 1 shows the proposed
adaptive flexibility control functions of the FMS shop
floor.
As illustrated in Figure 1, tool flexibility can be de-
fined as getting the right tool, to the right place at the
right time [34,41]. The need for tooling strategies origi-
nates from the high variety and number of cutting tools
that are typically found in automated manufacturing sys-
tems. The adoption of appropriate tool management poli-
cies that consider alternative tools allows the desired part
mix and quantities to be manufactured efficiently while
achieving improved performance [42]. At machine tool
level, there are two technical constraints related to tool
allocation: tool magazine capacity and tool life. Due to
tool magazine capacity, there is a restriction on the num-
ber of operations that can be processed in a single tool
setup. On the other hand, if tools can be loaded and un-
loaded while the machine is running, the capacity of the
tool magazine can be assumed to be unlimited [32,43].

Development of a Simulation-Based Intelligent Decision Support System for the Adaptive
Real-Time Control of Flexible Manufacturing Systems
663
FMS Host
Co-Simulator
C.N.C
Tool Magazine
Machine
Tool
LIB/LOB
C.N.C
Tool Magazine
Machine
Tool
LIB/LOB
P.L.C
Measuring
Machine
P.L.C
Part Preparation
Load/Unload
P.L.C
Tool Preparation
Machining Center Flexibility Function
Machine Setup
Machining center identification*
No. of machine tools
Information on tool magazine
Machine tool capacity
Machining Process
Machine state (Processing-Idle-Failed)
Machining rules (RAN-SQL-LULIB)
Load part from LIB (Local Input Buffer)
Unload part to LOB (Local Output Buffer)
Load/Unload parts from/to Central Buffer
Remain process time
Operative efficiency
Part Control Function
Part Setup
Part identification*
Inter arrival time , Due date
Earliness/Tardiness penalty
Information on fixturing & Palletizing
Part Routing
NO. Of operation, Tools required
Alternative machining center
Operation time, Shared operation
Sequence selection option
Part Process Planning
Part routing
Machining operation
Tool Flexibility Control Function
Tool Setup
Tool identification*
Information on fixturing
Tool magazine capacity
Tool life, Tool slot, Kit ID
Tool Routing
Part identification
NO. of operation , Operation time
Alternative machining center
Tools required, Sequence selection
ToolingStrategy (
SPT-SOT-FCFS-FNOP-LTL )
Tool Replacement
Tool routing, Tool Changeover
Machining operation
Alte
r
native
tools
I
ndustrial FieldBus
Figure 1. FMS shop floor flexibility control functions
The tool magazine capacity is an influential factor in
determining the flexibility of the system. A proper tool
management is needed to control processing of parts and
enhance the flexibility to variety of parts. It is important
to design a tool management control function so that the
proper tools are available at the right machine at the de-
sired time for processing of scheduled parts.
The work-order processing and part control system
essentially drives other control functions. This module
concerns the determination of a subset of part types from
a set of part types for processing.
A number of criteria can be used for selecting a set of
part types for processing (i.e. due date, inter arrival time,
requirement of tools, operation time, shared operation,
operations sequence).
3. FMS Adaptive Controller Architecture
3.1 FMS Configuration Parameters
The following notations and criteria are utilized in devel-
oping the rule-based model of the FMS controller ad-
dressed in this research. Table 1 represents the notations.
These parameters are defined in such a way that con-
tains information about the previous control functions on
a platform of multi-purpose machines. In other words,
definition of these parameters considers machine and tool

Development of a Simulation-Based Intelligent Decision Support System for the Adaptive
Real-Time Control of Flexible Manufacturing Systems
664
Table 1. FMS configuration parameters and performance criteria
FMS configuration parameters
Notation Definition Notation Definition
Pi i-th production order, pi 1 OEijkl Operative efficiency of Oij on Mkl
Oij j-th operation of order Pi, i
nj 1 DDPi Due date of Pi
M/Ck k-th machining centers, mk 1 Tij Time for processing operation Oij 
Mkl l-th machine of M/Ck, k
Ll1 αi Penalty weight for Pi when ACTi is less than DDPi
IATi inter arrival time between Pi and Pi-1 βi Penalty weight for Pi when ACTi is greater than DDPi
TMCkl Tool magazine capacity of M
kl ni The number of Pi operations.
TLhkl Tool life of h-th tool of Mkl (time based) REi The number of operation remain to complete Pi
TMhkl h-th tool of Mkl tool magazine MinU Minimum utilization
MSij Set of machines which can handle Oij MaxU Maximum utilization
LIBkl Local input buffer size of Mkl STij Standard time of Oij with 100% operative efficiency
LOBkl Local output buffer size of Mkl TP Throughput
PTH Duration of planning time horizon RTOij Number of required Tool for Oij
t Current time ETTij Elapsed time between Oij and its latter operation
Simulator outputs performance criteria
Notation Definition Notation Definition
TBDkl Time between departures on Mkl TITkl Total idle time of Mkl
ACTi Actual cycle time of order Pi TWTi Total waiting time of Pi
Zi Total penalty of Pi MUkl Machine Mkl utilization
OSi(t) Set of operations of Pi processed until tOSkl(t) Set of operations processed on Mkl until t
QMkl Queue size of Mkl CTkl Completion time in Mkl
TUhkl Tool usage of h-th tool of Mkl (time based) BUkl Buffer usage of Mkl
Table 2. Binary control flags
Variable Definition Variable Definition
OAijkl Equal to 1 if Oij is assigned to Mkl, other-
wise it is equal to 0 MIUkl Equal to 1 if machine M
kl is in use; otherwise it is
equal to 0
TMLhijkl Equal to 1 if TMhkl load to perform Oij
on Mkl and equal to 0 if unloads RTMh BSAkl
Equal to 1 if buffer space of Mkl is available; other-
wise it is equal to 0
PCi Equal to 1 if Pi complete otherwise it is
equal to 0 MBkl Equal to 1 if machine Mkl is bottleneck; otherwise it is equal to 0
λi Equal to 1 if ACTi is less than DDPi,
otherwise it is equal to 0 ODijkl Equal to 1 if Oij is done on Mkl, and depart it; other-
wise it is equal to 0
APOi Equal to 1 if Pi should be scheduled next
otherwise it is equal to 0 PAi Equal to 1 if Pi arrive otherwise it is equal to 0
OWij
Equal to 1 if Oij is waiting for process;
otherwise it is equal to 0
flexibility characteristics of an FMS. Table 2 shows the
binary control flags (BCF’s).
3.2 Simulation-Based Intelligent Decision
Support System
Figure 2 shows the combination between simulation and
intelligent decision support system as for FMS adaptive
control. The figure shows the cooperation between IDSS
and the simulator module. The current configuration pa-
rameters of the FMS are read by user interface and are
used as the input data to build conceptual model. The
simulation model will evaluate the current shop per-
formance, such as actual cycle time, tool and buffer
utilization. This process continues until a satisfying and
controllable shop floor configuration is reached.
The system presents details of the architecture, com-
ponents and functions of a FMS decision-making con-
troller. The proposed controller consists of a simulator
model coordinate rule based IDSS with a real time me-
chanism. The simulation output data are fed to the
knowledge-based system as input data. The rule-based
IDSS analyzes output of simulation model to control the
real-time status of FMS. Once the IDSS makes recom-
mendations, the simulation model is adjusted accordingly
and the process is repeated. The simulation and IDSS
components cooperate with each other until the control
goals are achieved. Since the primary objective is to im-
prove the throughput of the shop floor, a simulation
analysis assisted by decision process is carried out. The
status of the cell, machines, part orders, the availability

Development of a Simulation-Based Intelligent Decision Support System for the Adaptive
Real-Time Control of Flexible Manufacturing Systems
665
Intelligent Decision support
system
Multi-
p
erformance Simulation
Optimizer Block
Simulator
Block
Sim.Data.eXchange
Sim.O
p
timization
FMS.Conceptual Model
RTCSim
Evaluation
Mechanism
User Interfaces
Experiment.Parameters
Figure 2. The structure of simulation-based IDSS for FMS adaptive control
of operators and system control flags are recorded in
separate databases. Sequence of jobs is used to control
the flow of parts through the system. The first step to
estimate the performance criteria is assigning the opera-
tions to machines and scheduling the operations on each
machine.
The above posteriori adaptive control mechanism em-
ploys a simulator block to predict different performance
criteria of the FMS conceptual model. The simulator
contains the discrete event simulation model and is able to
measure several FMS performance criteria depending on
the different inputs. The simulation results are then for-
warded between external interfaces belonging to different
external models. On the other hand, these interfaces han-
dle the necessary communications with the simulation and
coordinate IDSS control signal transformations into the
simulator.
Sequence of jobs is used to control the flow of parts
through the system. The first step to estimate the per-
formance criteria of FMS is assigning the operations Oij to
machining centers and extracting the set OSMkl(t) includes
operations processed on M/Ck until t. The real time adap-
tive control framework is based on affiliating all current
events and expected future event to a time tag for process
synchronization. The following pseudo-code shows the
initialization phase of the simulation in order to configure
the FMS conceptual model.
The initialization phase should be run in execution
mode using the function RealTimeInitialize(t) to syn-
chronize simulation logic with an external process of FMS
controller system. The module RTCSim(t) represents FMS
events simulation to handle machine and tool flexibility.
The simulation clock is set to the real-time clock of the
operating FMS system and all other simulation processes
are initiated by InitProcess(Oij). Because of the random-
ness of processing times in each replication, the expecta-
tions of system outputs are estimated by sample means.
The function (, ,)
i hklkl
TAVG ACTTUBU records the
values of system outputs throughout each replication and
finally estimates the expectation of these statistics
FMSConfigParam( )
Read Number of parts, machining centers and planning horizon (p,m,PTH);
For i: = 1 to p Read Number of parts operations and due date (ni, DDPi, IATi, αi, βi);
For k: = 1 to m Read Number of machines at each machining centers (Lk);
For k: = 1 to m initialize Machining Centers Resources M/Ck, Capacity;
For k: = 1 to m
For l: = 1 to Lk initialize Machine Tools, Tool magazine and buffers (MTkl, TMCkl, LIBkl, LOBkl);
For i: = 1 to p
For j: = 1 to ni
Initialize queue used to hold part operations (Oij (Process.Queue));
Read (processing time of each operation (Oij , Tij , STij , MSij , ETTij , RTOij);
InitTime(t); (Initialize simulation current time)
RealTimeInitialize(t); (Initialize inter-process synchronization)

Development of a Simulation-Based Intelligent Decision Support System for the Adaptive
Real-Time Control of Flexible Manufacturing Systems
666
R
TCSim(t):
RealTimeRecieve(t);
(Receive real-time actions from the DSS and passes them to simulator)
Let NREP:= 0;(simulation optimization level)
Let REPNum:= 0;(replications per simulation counter)
While
R
EPNum MaxREP
; (Maximum simulation replications)
For i:= 1 to p
Create (P
i
) ; (parts entry in the simulation model)
Set APO
i
= 1, OS
i
(t)= ф ; (P
i
should be processed next)
For j:= 1 to n
i
 OS
i
(t) (for remaining operations)
Set OW
ij
= 1; (operation O
ij
is waiting for process)
For k:= 1 to m
//end While
ShutdownIPS; (terminate the simulation replication)
DAVG
ˆˆˆ
([],[ ],[]);
ii i
E
ACTEZE TWT
(Return the average of time-persistent statistics throughout all replications)
InitProcess(O
ij
):
While OW
ij
= 1 do (O
ij
is waiting for process)
{WriteIPSQueue(O
ij
);
Return Flags (OA
ijkl
,PC
i
, TML
hijkl
) };
//end While
For h:= 1 to TMC
kl
Read TL
hkl
;
(Tool life of h-th tool of MT
kl
)
TS. Select(t);
(Tooling strategy from DSS )
Load TM
hkl
;
(h-th tool of MT
kl
tool magazine)
Assign O
ij
;
(Process.NumberIn) ;
Assign O
ij
(Process. LIB
kl
) ;
Seize O
ij
(Proces s. Qu eue);
Delay τ
ij
(Time (kl ));
Set OA
ijkl
= 1, MIU
kl
= 1;
Dispose (P
i
. LOB
kl
);
Release M/C
kl
;
Set OD
ijkl
= 1, APO
i
= 0, OW
ij
= 0;
Update OS
i
(t),RE
i
, ACT
i
;
Tool flexibility
For l: = 1 to L
k
Read OE
ijkl
; (operative efficiency of O
ij
on MT
kl
)
DR.Select (t) ; (select dispatching rule from DSS)
RVG (T(O
ijkl
)); (random value generator of processing time)
InitProce ss(O
ij
) (beginning of the simulation replication)
TAVG (CT
kl
,TBD
kl
,IT
kl
, QM
kl
,BU
kl
,MU
kl
,TIT
kl
)
(records the tally variable throughout this replication)
Return TF IN; (final simulation time)
REPNum:= REPNum + 1; (increment replication number)
}; //end InitProcess
M
achine
f
lexibilit
y
through the average functionˆ
([ ],
i
DAVG E ACTˆ[],
hkl
ETU
ˆ[])
kl
EBU over MaxREP simulation replications. The
number of replications per simulation (MaxREP) should
be set to the minimum number necessary to obtain a re-
liable estimate of performance criteria.
Based on the results obtained at each level of optimi-
zation (NREP) and exchanging them with IDSS, addi-
tional number of replications may be re-simulated for each
design. The expected value of FMS performance criteria
are extracted under design,
ijkl ijkl
OA 


. Theˆ[|
i
EACT
,],
ijkl ijkl
OA 

ˆ[|,],
hkl ijklijkl
ETU OA 

ˆ[|,]
kl ijklijkl
EBU OA 


represent the stochastic effects of system output by sam-
ple mean. The ultimate goal is to find the solution that
optimizes the value of these performance criteria. The
optimization procedure uses the outputs from the simula-
tion model of previous NREP to construct a response
surface at each simulation optimization level of
,|
ijklijklNREP r
OA 


and to extract the next level of
,
ijkl ijkl
OA 

as an input to the model.
To control the external processes of FMS, the simulator
block and IDSS are synchronized via simulation data
exchange Sim.Data.eXchange(IDSS). The IDSS ana-
lyzes outputs of simulation model to control the real-time
status of FMS after receiving these results by Real-
TimeSend() function. The IDSS then sends appropriate
control signals of beginning operation to the correspond-
ing entities when an event is occurred. Proposing the
adaptive controller with this structure allows modeling of
synchronization mechanism between FMS entities and
transmission times for messages exchanged between the
IDSS and simulator.
Figure 3 schematically describes the inter-process
synchronization between different components of co-
simulator. The approach for adaptive controller designing
is built around the theory of supervisory control based on
exchanging simulation outputs with an event-condi-
tion-action real time system. The proposed system uses a
posteriori adaptive control mechanism that also is an
online control method acting after the event occurs versus
such popular reactive control method.
The simulator can trigger the rule-based IDSS to gen-
erate the appropriate control policy. The simulator block
sends messages to the external rule-based system to in-
dicate simulated results from FMS by RealTimeSend().
The rule-based IDSS interprets these results and sends
appropriate action messages back to the simulator and
user to indicate the instructions to be done.
3.3 Rule Production for FMS Real Time
Simulation-Based Controller
The IDSS collect the facts into appropriate data base using
CollectFact(), which is then used for inference by simu-
lation outputs in feed forwarding reasoning. The control
framework is implemented by integration of the adaptive
control rules and real time simulator for enforcing dy-
namic strategies of FMS shop floor control. In order to
strengthen the expert system reasoning, knowledge-elicit-

Development of a Simulation-Based Intelligent Decision Support System for the Adaptive
Real-Time Control of Flexible Manufacturing Systems
667
‹‹Event Handler››
♀ CEL,FEL
♀ InitTime()
♀ Create()
♀ InitProcess()
♀ Assign()
♀ Set()
♀ Update()
♀ TAVG()
‹‹RealTimeModule››
♀ RealTimeInitialize()
♀ UpdateTime()
♀ RealTimeRecive()
♀ RealTimeTerminate()
♀ ReadIPSQueue()
♀ WriteIPSQueue()
♀ ShutdownIPS ()
‹‹UserInterface››
♀ FMSConfigParam()
♀ SpecifyCritera()
♀ ExperimentParam()
♀ DefineDataBase()
‹‹RuleBaseEngine
♀ CollectFact()
♀ FireRule()
♀ EventCheck()
♀ UpdateDB()
♀ ExecuteAction()
♀ DSSReSim()
‹‹SimDataeXchange››
♀ Extract ()
♀ ReturnFlag ()
♀ RealTimeSend()
♀RealTimeTerminate()
Simulator Block
Intelligent Decision
support system
‹‹SimOptimization››
♀ DAVG ()
♀ Estimate()
♀ DesignExperiment()
♀ ExperimentExe()
♀ RespEstimate()
♀ EvaluteCriteria()
IPS
‹‹RESOURCES››
♀ Seize()
♀ Delay()
♀ Release()
♀ Dispose()
‹‹VARIABLES››
♀ TNOW
♀ TFIN
♀ MREP
♀
NREP
User Interface
Tier
IDSS Tier Simulator Tier
Multi-Performance
Simulator Optimizer
IPS
Optimizer Tier
Initialization
Figure 3. Real time simulation data exchange via inter-process synchronization
tation techniques are used for preventing ineffective re-
dundancy at concurrent firing of rules and high degree of
parallelism. This knowledge-based IDSS is aimed at pro-
viding a powerful control on different operations of FMS.
It acts as a cell manager which may work alongside the
operating cell-oriented part and tool management system.
These sections describe the knowledge representation
through a set of control rules. Design of IDSS controller
focuses on the development of appropriate Event-Condi-
tion-Action (ECA) rules for tuning control parameters.
These rules are formulated by the techniques of data
gathering and knowledge elicitation to construct IDSS.
The IDSS is able to obtain feedback results from the
on-line system of simulator. These results are very sig-
nificant and let the expert system to re-simulate if the
performance criteria are not desirable.
The rules applied in this paper are structured in the
following form and consist of three segments: event type,
condition and action:
When ‹Event1 , Event2 ,Event3 , … ›
If ‹Condition 1 , Condition2 , Condition3 , … ›
Then ‹Action1 , Action2 , Action3 ,… ›
Event type: This tag specifies that analysis of condi-
tion should be done once the events take place.
Condition: This segment of ECA rules specifies a list
of conditions. In order to trigger an action rule, all condi-
tions should be satisfied. These conditions refer to a log-
ical assertion of the FMS states extracted by the simula-
tor module RTCSim(t).
Action: This segment specifies actions which may
consist of a list of operations. Whenever an action rule is
triggered by an event, the operations being in its action
list will be initiated sequentially. The proposed rule-
based system for manufacturing execution system pro-
vides the parts sequence list to the multi-purpose ma-
chines available and then the operation assignment and
task proportions of parts on related machines. The output
can be manipulated by changing the rules and strategies
entered at the expert system query stage. Table 3 illus-
trates MES control function about dispatching rules.
For each part Pi the slack index is defined as:
1
,
i
n
iPi ij
j
SlackDDSTti



. The function Sort(array)
finds the maximum or minimum value in the array and
the binary flag APOi specifies the next scheduled part.
Table 4 illustrates MES control function for machining
rules in the FMS.
The binary flag OAijkl specifies the assignment of op-
eration Oij to machine Mkl. Table 5 illustrates MES con-
trol function for tooling strategy in the FMS.
Operative efficiency of doing operation O
ij on Mkl is
defined as OEijkl and thus tool usage can be considered as
(1);, ,
hkl ijijkl
TUTOEh kl

 . For each executable
operation Oij, the proportion of O
ij performed on M kl is
denoted as )(t
ijkl

, 01
ijkl


. IDSS monitors all
events and states transition of FMS by considering ρijkl to

Development of a Simulation-Based Intelligent Decision Support System for the Adaptive
Real-Time Control of Flexible Manufacturing Systems
668
Table 3. MES control function (dispatching rules)
MES Control Function: Dispatching Rules
Dispatching Rule When [Event] If (Condition) Action
Shortest Processing Time 0t
RealTimeRecieve() .()
D
R SelecttSPT

() ;
ij
Sort STij 1;
i
APO 
First Come First Serve 0t
RealTimeRecieve() .()
D
R SelecttFCFS

();
i
Sort IATi1;
i
APO 
Operation with Least
Slack
0t
RealTimeRecieve()
.()
D
R SelecttSLACK

0&& 0
ii
PC Slack ();
i
Sort Slacki1;
i
APO 
Slack Per Remaining
Work
0t
RealTimeRecieve()
.()/
D
R SelecttSRMOP

0&& 0
ii
PC Slack ();
i
i
Slack
Sorti j
RE  1;
i
APO 
Slack Per Remaining
Work
0t
RealTimeRecieve()
.()/
D
R SelecttSRMWK

0&& 0
ii
PC Slack
();
i
ij
j
Slack
Sorti j
ST

1;
i
APO 
Earliest Due Date 0t
RealTimeRecieve() .()
D
R SelecttEDD

();
Pi
Sort DDi1;
i
APO 
Table 4. MES control function (machining rules)
MES Control Function: Machining Rules
Machining Rule When [Event] If (Condition) Action
Random Selection 0t
RealTimeRecieve()
.()
M
R SelecttRAN

1
i
APO

();
kl
SortRand Mkl 1;
ijkl
OA 
Shortest Queue Length 0t
RealTimeRecieve()
.()
M
R SelecttSQL

1
i
APO

() ;
kl
Sort QMkl 1;
ijkl
OA 
Lowest Utilized Buffers 0t
RealTimeRecieve()
.()
M
R SelecttLUB

1
i
APO

() ;
kl
Sort BUkl 1;
ijkl
OA 
Table 5. MES control function (tooling strategy)
MES Control Function: Tooling Strategy
Tooling Strategy =TS When [Event] If
(Condition) Action
Shortest Operation
Time
0t
RealTimeRecieve()
.()TS SelecttSOT

1
ijkl
OA

() ;
ij
Sort STij

(,);
hkl ij
A
ssignTool TMOij
();
kl
UpdateToolMag M
Shortest Processing
Time
0t
RealTimeRecieve()
.()TSSelecttSPT

1
ijkl
OA

();
Pi
Sort DDi

(,);
hkl i
A
ssignTool TMPi

();
kl
UpdateToolMag M
First Come First Serve
0t
RealTimeRecieve()
.()TSSelecttFDFS

1
ijkl
OA

();
i
Sort IATi

(,);
hkl i
A
ssignTool TMPij
();
kl
UpdateToolMag M
dynamically rebuild new configuration and replicate si-
mulation module RTCSim(t). Table 6 contains the rules
for control of transition of different states in FMS, bot-
tleneck detection and resolving, assigning operation to a
non-bottleneck machining centers.
For each part Pi actual cycle time is defined
as: 










kl
OSj ijkl
ijijkl
iji OE
ST
ETTACT

and the penalty is
defined as:




p
i
i
Piiii
i
Piii DDACTACTDDZ
1
))(1()( 
.
4. Experimental Study
The problem presented has been adopted in this paper to
validate the proposed method by Sarin and Chen [43].
The model presents machine loading and tool allocation
problem in FMS with tool life and magazine capacity.
The FMS model includes tool and machine alternatives.
The experiment was done on a FMS with four machining
centers. Tables 7 and 8 show tool-operation and machine
-tool compatibility.
Table 9 represents the machining time of operations
on alternative tools.

Development of a Simulation-Based Intelligent Decision Support System for the Adaptive
Real-Time Control of Flexible Manufacturing Systems
669
Table 6. MES control function
MES Control Function: States, Bottleneck, Assigning
When [Event] If (Condition) Action
0t
iPAi ;1
0;
kl
M
IUk l (.);
I
nitializationinitialconfigparameters
();();( );
D
efineDBSpecifyCriteriaRTCSim t
();UpdateTime t();SimDataeXchange IDSS
0t
ReadIPSQueue (Oij)
0;!,
kl
M
Bkl , 1
i
PA

RealTimeRecieve((),(),(),);
kli ijkl
OSt OS ttBCF 
0t
ReadIPSQueue (Oij)
1; ,0;
kl kl
MIUk lBSA 0
i
PC

RealTimeRecieve((),(),( ),);
kli ijkl
OSt OS ttBCF 
();UpdateTime t
0t
ReadIPSQueue (Oij)
0; ,1
kl kl
MIUk lBSA 
1;();
ijkl i
OAjOS t 0
i
PC

1;
kl
MIU

RealTimeRecieve((),(),( ),);
kli ijkl
OSt OS ttBCF 
();UpdateTime t
0t
ReadIPSQueue (Oij)
0; ,0;
kl kl
MIUk lBSA0
i
PC

RealTimeRecieve((),(),(),);
kli ijkl
OSt OS ttBCF 
();UpdateTime t
States transition control rules
0t
RealTimeRecieve() 1
[()]()/;,
p
kli K
i
nOStnLk l



 1
kl
MB

0t
RealTimeRecieve()

1; kPTH
TBD k
MinU 
 , 1
kl
MIU

1
kl
MB

0t
RealTimeRecieve()
(1) (1)
[()& &()]
klklklk l
TBD TBDU U


1
kl
MIU 
1
kl
MB

0t
RealTimeRecieve()
;,
kl
M
UMinUkl 1
kl
MIU

1
kl
MB

Bottleneck detection
0t
RealTimeRecieve()
0&&0; ,
kl kl
M
BMIUkl
1;()
ijkl i
OAjOS t
1;
kl
MIU

RealTimeSend((),( ),(),);
kli ijkl
OSt OS ttBCF 
();UpdateTime t();SimDataeXchange IDSS
0t
RealTimeRecieve()
0;
i
PC i
1
kl
MB 
'1; ' ijkl
OAl l


RealTimeSend((),( ),(),);
kli ijkl
OSt OS ttBCF 
();UpdateTime t();SimDataeX changeIDSS
0t
RealTimeRecieve()
''
(;,',,')||();
klklkl
M
UMUkkllMUMinU
'' 0;,', ,';0 klk li
MUMUkkl lPC

 
1;();1
ijkli kl
OAjOS tMIU


RealTimeSend((),( ),(),);
kli ijkl
OSt OS ttBCF 
();UpdateTime t();SimDataeXchange IDSS
Assigning operation to
non-bottleneck
Table 7. Tool-operation compatibility
Part/Tool 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
O11 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
O12 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
O13 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
P1
O14 0 0 0 0 0 0 00 0 1 0 0 1 0 0 0 0 0 0 0
O21 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
O22 0 0 0 0 0 0 01 0 0 0 0 0 0 0 1 0 0 0 0
O23 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0
P2
O24 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
O31 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0
O32 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0
O33 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0
P3
O34 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
O41 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
O42 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
O43 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0
P4
O44 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0
Tool life 25 21 25 20 22 25 252220 2518 20 21 25 17 20 20 21 22 24

Development of a Simulation-Based Intelligent Decision Support System for the Adaptive
Real-Time Control of Flexible Manufacturing Systems
670
Table 8. Machine-tool compatibility
Machine/Tool 1 2 3 4 5 6 7 8 9 10 1112 1314 1516 17 18 19 20
M11 1 0 1 0 1 0 1 001 0 1 0 0 0 1 0 0 0 0
M21 1 0 1 1 0 1 1 001 1 0 0 1 0 0 0 0 1 1
M31 0 1 0 1 0 1 0 110 0 1 0 0 0 0 1 0 0 0
M41 0 0 0 0 1 0 0 010 1 0 1 0 1 0 0 1 0 0
Table 9. Machining time on alternative tools (Tij) for parts
Part No P1 P
2
Operation No O11 O
12 O 13 O 14 O 21 O 22 O 23 O 24
Tool No 1 2 4 7 6 10 13 1 3 8 16 10 17 4 12
M11 104 68 84 114 114 25 106 96
M21 110 120 130 110 76 126 98 66 116
M31 101 106 118 29 112 84
M41 100 119
Standard Time 95 98 105 60 104 72 95 112 91 115 18 60 20 107 82
Part No P3 P
4
Operation No O31 O
32 O 33 O 34 O 31 O 32 O 33 O 34
Tool No 12 15 9 18 11 193 14 2 4 5 20 13 14 7 8
M11 67 82 137 68
M21 1174785 110 114 38 115 53
M31 102 90
49140 87
M41 134 120 40 132 118 120
Standard Time 60 127 84 30 1154050 10042109 113 35 115 115 53 85
Table 10. Machining efficiency for parts on alternative tools
Order No P1 P
2
Operation No O11 O
12 O 13 O14 O 21 O 22 O 23 O 24
Tool No 1 2 4 7 6 10131 3 8 1610 17 4 12
OE11(%) 91 88 86 98 80 70 56 85
OE21(%) 86 88 46 95 95 89 93 91 91
OE31(%) 9799 88 69 95 98
OE41(%) 95 96
Standard Time 95 98105 60 104 72 95 112 91 115 18 60 20 107 82
Order No P3 P
4
Operation No O31 O
32 O 33 O 34 O 41 O 42 O 43 O 44
Tool No 12 15 9 18 11193 14 2 4 5 2013 14 7 8
OE11(%) 90 61 82 78
OE21(%) 98855991
96 92 100 100
OE31(%) 59 93 8678 98
OE41(%) 95 70 75 87 96 96
Standard Time 60 12784 30 11540 50 10042109 11335115 115 53 85
Table 11. Operation assignment and task proportion (ijkl  ) and tool load
Part/Machine M11 M
21 M 31 M 41
O11 0.27 (1) 0.73 (2)
O12 0.81 (7) 0.19 (4)
O13 0.78 (6) 0.22 (6)
P1
O14 0.12 (10) 0.88 (10)
O21 0.89 (1) 0.11 (3)
O22 0.79 (16) 0.21 (8)
O23 0.75 (10) 0.25 (17)
P2
O24 0.22 (12) 0.78 (12)
O31 0.91 (12) 0.09 (12)
O32 0.79 (9) 0.21 (18)
O33 1 (19)
P3
O34 0.35 (3) 0.65 (14)
O41 0.66 (4) 0.34 (2)
O42 0.48 (20) 0.52 (5)
O43 1 (12)
P4
O44 0.45 (7) 0.33 (7) 0.22 (8)

Development of a Simulation-Based Intelligent Decision Support System for the Adaptive 671
Real-Time Control of Flexible Manufacturing Systems
Table 12. Difference between the proposed method and the heuristic method of [43]
Total Actual
Cycle Time
Total Idle
Time
Total Time between
Departure
Total Waiting
Time
Penalty
Proposed system 2703 441 35 127 48.5
(Earliness)
Classis mathematical
method
3108 731 63 463 154
(Tardiness)
Table 13. Statistical analysis of difference between the proposed and mathematical method
Actual Cycle
Time
Idle Time Time between
Departure
Waiting
Time
Mean 2705.68 442.936 34.360 128.226
StDev 16.68 9.932 2.029 10.436
SEMean 0.85 0.507 0.104 0.533
T-Value –472.54 –568.36 –276.55 –628.64
Sample
Size = 384
P-Value 0.000 0.000 0.000 0.000
Table 10 represents machining efficiency of operation
allocation on alternative tools.
It is assumed that due date (DD = 2800), αi = βi = 0.5,
and LIB kl = LOBkl = 15. Tool magazine capacity and tool
life are considered 20 and 100, respectively. Manufac-
turing execution system also includes dispatching rules
(SPT), tooling strategies (FCFS) and machining rules
(SQL). Table 11 shows the operation assignment and
task proportion according to the rules of the proposed
method.
Table 12 represents the difference of total actual cycle
time, total idle time, total time between departures and
total waiting time between the proposed rule-based sys-
tem and the mathematical method.
The solution obtained from proposed method creates a
balanced and controlled actual cycle time on machining
centers. The proposed approach outperforms the heuristic
method in terms of the total actual cycle time, total idle
time, total time between departures and total waiting time.
The proposed system presents 48.5 units of earliness pe-
nalty despite the 154 unit of tardiness penalty of mathe-
matical method. To show the effects of difference be-
tween the proposed method outputs and ِclassic mathe-
matical method, statistical analysis is given as shown in
Table 13.
The aforementioned results verify and validate the
FMS shop floor links to the supervisory control of ma-
chine and tool flexibility. Different scenarios of per-
formance criteria levels demonstrate effectiveness of the
proposed method for the system control. The proposed
method is also efficient in terms of the computation time
which is highly important for the real time control of a
manufacturing system. The proposed real-time simula-
tion-based intelligent decision support system provides a
real time control mechanism for improving performance
of a flexible manufacturing shop floor.
5. Conclusions
This paper presents an intelligent decision support sys-
tem to tackle the production control of a FMS. Develop-
ment of the present knowledge-based system is aimed at
integrating an ECA rule-based system and a simulator
module to ease the cell adaptive supervisory control. A
novel architecture of this intelligent adaptive controller
prototype which is based on a real-time simulator core
has been developed and presented to validate the pro-
posed approach.
The FMS shop floor data are gathered and stored into
the appropriate databases over time. The adaptive control
mechanism employs a real time discrete event simulator
to predict performance of the given system during the
remaining time of planning horizon. The current state of
the FMS performance criteria from the simulator is then
stored on the appropriate databases. The proposed me-
thod provides an applicable and efficient framework for
real-time control of the shop floor in flexible manufactur-
ing system. The criteria considered to measure perform-
ance of the system shows that the proposed approach is
effective and efficient in controlling shop floor. The main
contributions of this paper can be summarized as follows.
1) Designing real time ECA rules according to feed
forward reasoning with the high degree of granularity.
2) Reinforcement of the expert system reasoning tech-
nique using data mining and knowledge-elicitation tech-
niques.
3) Proposed method constitutes the framework of
adaptive controller supporting the co-ordination and co-
operation relations by integrating a real time simulator
and an IDSS for implementing dynamic strategies.
4) Avoiding ineffective redundancy at concurrent fir-
ing of rules and high degree of parallelism
5) The simulation based IDSS uses a posteriori adap-

Development of a Simulation-Based Intelligent Decision Support System for the Adaptive
Real-Time Control of Flexible Manufacturing Systems
672
tive control mechanism that also is an online control me-
thod acting after the event occurs versus such popular
reactive control method.
As a result, the proposed system is suitable for differ-
ent control frameworks on an existing flexible manufac-
turing system considering the physical constraints and
the production objectives. Furthermore, the system illus-
trates the potential of using the intelligent rule-based
DSS for adaptive control of modern industrial plants.
Future researches may concentrate on the application of
other types of flexibility in shop floors using simulation-
based predictive controllers.
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