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Expert post-processor for simulation output analysis
Computers ind. En~n~ Vol.15, Nos 1-4, pp.98-I03, 1988 Printed in Great Britain.
All rights reserved
0360-8352/88 $3.00+0.00 Copyright c 1988 Pergamon Press plc
EXPERT POST-PROCESSOR FOR SIMULATION OUTPUT ANALYSIS
V. R a m a c h a n d r a n ,
D.L.
Kimbler*
, and
G. N a a d i m u t h u
D e p a r t m e n t of Industrial E n g i n e e r i n g Fairleigh D i c k i n s o n University, Teaneck, NJ 07666 * D e p a r t m e n t of Industrial E n g i n e e r i n g C l e m s o n University, Clemaon, SC 29634
ABSTRACT This paper d i s c u s s e s the c o n c e p t u a l framework and c a p a b i l i t i e s of an expert P o s t - P r o c e s s o r to aid in the analysis of s i m u l a t i o n output data. It is proposed to handle both t e r m i n a t i n g and s t e a d y state systems and for a n a l y z i n g a single s y s t e m or for s t a t i s t i c a l l y c o m p a r i n g two or more systems. The v a r i o u s modules in it are d e s i g n e d to handle d e t e r m i n a t i o n of the initial t r u n c a t i o n point, sample size or run length, variance r e d u c t i o n t e c h n i q u e s and also for p e r f o r m i n g s e n s i t i v i t y analysis of the system. The framework p r e s e n t e d here is still in the conceptual stages and is d e s i g n e d for inclusion in the Intelligent S i m u l a t i o n G e n e r a t o r (ISG), work on w h i c h is c u r r e n t l y being done. This is d e s i g n e d for use in c o n j u n c t i o n with a n y simulator, i r r e s p e c t i v e of the language it uses.
INTRODUCTION C o m p u t e r s i m u l a t i o n as a tool for d e c i s i o n making is g a i n i n g wide a c c e p t a n c e in industry due to the rapid d e v e l o p m e n t of c o m p u t e r t e c h n o l o g y and a u t o m a t e d m a n u f a c t u r i n g . The c o m p l e x nature of m a n u f a c t u r i n g systems does not lend itself to m a t h e m a t i c a l m o d e l i n g and hence s i m u l a t i o n becomes a useful tool. Recent years h a v e seen an e m e r g e n c e of s i g n i f i c a n t l i t e r a t u r e in the areas of s i m u l a t i o n a p p l i c a t i o n s and also in the form of p r o g r a m g e n e r a t o r s or simulators. The increasing number and s o p h i s t i c a t i o n of s i m u l a t i o n languages like SIMAN, SLAM, GASP, GPSS, and S I M S C R I P T have c o n t r i b u t e d to the above m e n t i o n e d work. U n f o r t u n a t e l y , more time and effort is spent on d e v e l o p i n g the s i m u l a t i o n models and p r o g r a m m i n g and less time on model validation. Due to the r a n d o m n e s s involved in simulation, the results of one run will not provide the user with the a c c u r a t e r e s u l t s n e c e s s a r y for d e c i s i o n making. The user has to p e r f o r m the n e c e s s a r y s t a t i s t i c a l analysis which would help in m i n i m i z i n g the error b e t w e e n the model and the actual s y s t e m behavior.
This Expert P o s t - p r o c e s s o r for S i m u l a t i o n Output A n a l y s i s (EPSOAN) c o n s i s t i n g of two main m o d u l e s i.e. s t a t i s t i c a l and s e n s i t i v i t y analysis, will aid the user in v a l i d a t i n g the model and also in a n a l y z i n g it. While the former helps in r e d u c i n g the variance b e t w e e n the model output and the actual s y s t e m output, the latter is n e c e s s a r y in o p t i m i z i n g the output generated.
98
Ramachandran et al. : Simulation output analysis
SYSTEM
SIMULATOR
99
AND OUTPUT A N A L Y S I S
A simulator is defined by Mathewson as "an interactive software tool that translates the logic of a model described in a relatively general s y m b o l i s m into the code of a simulation language and so enables a computer to mimic model behaviour." The purpose of this as mentioned in the definition is to remove the task of programming from the user and to involve him/her only in the input stages. Some of these simulators do contain certain elements of output analysis like initial system status, number of replications etc. but leaves the task of a comprehensive output analysis to be performed by the user. So the ideal system would be one which performs even this task of validating the model. This enables a person with little knowledge of statistics and simulation to perform complex simulation studies. Figure 1 shown below illustrates a common skeletal structure of a simulator which does not perform statistical analysis of the output data. Thus the output provided to the user is not validated and might result in improper usage of simulation by a user not possessing the knowledge, time or resources to conduct proper statistical tests.
USER
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ISIMULATION
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MODEL USING A N ~ ANGUAGE J ~INITI~L ~OUTPUT STATISTICS
I SYSTEM
FIGURE
I. EXISTING
SIMULATOR
WITHOUT
OUTPUT
ANALYSIS
I I I I I I I I I I
i00
Proceedings of the lOth Annual Conference on Computers & Industrial Engineering
Figure 2 shows the E P S O A N e m b e d d e d w i t h i n a simulator. As seen in the figure it comprises of two main modules: I) s t a t i s t i c a l analysis module and 2) s e n s i t i v i t y analysis module. Each one performs a d i s t i n c t type of analysis which is d i s c u s s e d in the following sections. Figures 1 and 2 imitate a g e n e r i c simulator i.e. the s i m u l a t i o n model is d e v e l o p e d using a n y special purpose s i m u l a t i o n language or high level language such as FORTRAN, PASCAL, C etc. The menu is structured in a manner that e n a b l e s the user to proceed to the EPSOAN module or to exit the system. The n e c e s s a r y s t a t i s t i c a l a n a l y s i s is performed by c a l c u l a t i n g the parameters' run length, number of replications, etc., and then going back to the p r o g r a m w r i t i n g stage. When the user is p r o v i d e d w i t h a v a l i d set of output data he/she can p e r f o r m s e n s i t i v i t y a n a l y s i s on the v a l i d a t e d model or accept the e x i s t i n g output.
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SIMULATOR
WITH OUTPUT
ANALYSIS
1
Ramachandran et al.
STATISTICAL
: Simulation
output
analysis
101
ANALYSIS MODULE
AS shown in Figure 3 this module is s u b d i v i d e d into several s u b m o d u l e s each one p e r f o r m i n g a d i f f e r e n t type of analysis. Since s i m u l a t i o n is used to a n a l y z e an e x i s t i n g or p r o p o s e d s y s t e m or to evaluate a m o n g v a r i o u s a l t e r n a t i v e systems, E P S O A N is d e s i g n e d to handle both types of study. While a n a l y z i n g the output data it is imperative to c l a s s i f y the s y s t e m into either a t e r m i n a t i n g or s t e a d y state type. The s t a t i s t i c a l tests for both types m a y differ and hence the n e c e s s i t y for separate classification.
STATISTICAL 1 ANALYSIS MODEL
i
[
[
I SIMULATING A SINGLE SYSTEM
ISIMULATION OF MULTIPLE SYSTEMS
I
r
f STEADY STATE
TERMINATING
L I M I N A T I O N OF NITIAL BIAS SUBMODULE
ANALYSIS OF EACH SYSTEM
[
I
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I
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EVALUATE DIFFERENT SYSTEMS
ELIMINATION OF INITIAL BIAS
#1
SUBMODULE
#i
SUBMODULE
l
RUN LENGTH OR SAMPLE SIZE SUBMODULE
#2
i CONFIDENCE INTERVAL
CONFIDENCE INTERVAL SUBMODULE
SUBMODULE
#3
SUBMODULE
VARIANCE REDUCTION TECHNIQUE SUBMODULE
#4
FIGURE
C~I~5: I/4-S
#5
I
1 VARIANCE REDUCTION TECHNIQUE
3.
STATISTICAL
l
#4
ANALYSIS
MODULE
#6
102
P r o c e e d i n g s of t h e 10th Annual C o n f e r e n c e on Computers b I n d u s t r i a l
Engineering
The s u b m o d u l e s d e a l i n g with e l i m i n a t i o n of initial bias and variance reduction t e c h n i q u e s are shared by both the s t e a d y state and t e r m i n a t i n g s y s t e m s since they have the same methods. The information flow from each module and the generator may not be identical and further, the user may have to go from one module to a n o t h e r before going back to the generator to rewrite the m o d i f i e d model. The s u b m o d u l e s m a y also be c o n s i d e r e d as a knowledge base c o m b i n e d with an inference engine. The user might want to remove the transient response from the s y s t e m and hence is connected to submodule #I where many d i f f e r e n t methods could be stored. He can opt to use a n y of the m e t h o d s and go to the next module after d e l e t i n g the transient response. Two of the common methods to be s t o r e d in this s u b m o d u l e m a y be 1) t r u n c a t i n g data collected up to the t r a n s i t i o n point, and 2) d e t e r m i n i n g each r e p l i c a t i o n In this state. If the user decides to use the first method he m a y decide to start data c o l l e c t i o n after s t e a d y state is reached. The user m a y resort to submodule #2 for d e t e r m i n i n g the run length, if necessary. The t r a n s i e n t s are m o s t l y due to the handling of r a n d o m numbers w h i c h c o n t r i b u t e to the d e v i a t i o n of the variance from the true v a r i a n c e and hence r e m o v i n g the transients enables us to reduce the error in the sample variance. But this by itself does not validate the model and hence the user m a y have to use the other modules. The user might then be taken back to g e n e r a t o r and after m a k i n g one more run might decide to use s u b m o d u l e #4 to reduce the variance using a n y of the methods a v a i l a b l e there like I) c o m m o n r a n d o m numbers, 2) a n t i t h e t i c variates, and 3) control varlates. Thus d e p e n d i n g on the nature of output a n a l y s i s required the user is taken to some or all of the submodules. While s i m u l a t i n g more than one system, the user performs above a n a l y s i s for each s y s t e m and using the output from the v a l i d a t e d run m a y evaluate t h e m using a n y of the available m e t h o d s in submodule #6.
SENSITIVITY
ARAL¥SIS
the
MODULE
P e r f o r m i n g the required s t a t i s t i c a l a n a l y s i s on the output data has a i d e d the user in v a l i d a t i n g the model and m i n i m i z i n g the influence of r a n d o m n e s s of the variates. This by itself m a y not be s u f f i c i e n t in most instances as the user has to interpret the data and make m o d i f i c a t i o n to the s y s t e m components to optimize the system's efficiency. Incluslon of this module in EPSOAN will enable the computer to be an intelligent s y s t e m by p e r f o r m i n g s e n s i t i v i t y a n a l y s i s on the various s y s t e m components. Some of the areas of interest to a user are i) service time, 2) queue c h a r a c t e r i s t i c s , 3) i n t e r - a r r i v a l time, and 4) bottlenecks. These areas are s e n s i t i v e to the changes in the number of machines, m a t e r i a l h a n d l i n g system, storage buffer capacity, and manpower. F r o m the v a l i d a t e d output data, EPSOAN s h o u l d s t u d y the a v e r a g e w a i t i n g time for a part at a p a r t i c u l a r machine, the number of d i f f e r e n t parts that are processed there, a n d m a c h i n e v i s i t a t i o n s e q u e n c e of the parts. Then it can increase the number of machines in a cell, create the c o d i n g for the m o d i f i e d s y s t e m and run the s i m u l a t i o n again. After p e r f o r m i n g the n e c e s s a r y s t a t i s t i c a l a n a l y s i s for this result it should go to the next c o m p o n e n t or m o d i f y the same c o m p o n e n t again. This iterative procedure would enable the user to get an o p t i m u m s o l u t i o n of hls/her model. Though time c o n s u m i n g when taking into c o n s i d e r a t i o n the time saved in model development, p r o g r a m m i n g and d e b u g g i n g the results wlll J u s t i f y It. B o t t l e n e c k s can be studied by having an a l g o r i t h m within a model for identifying machine blocking. The EPSOAN should be built in a manner that would indicate the machine which is the b o t t l e n e c k and the reasons for it.
Ramachandran et al. : Simulation output analysis
CONCLUSION Statistical v a l i d a t i o n of s i m u l a t i o n output though v e r y vital is being ignored in most of the a p p l i c a t i o n s . The reason for this can be a t t r i b u t e d to the c o m p l e x i t y of statistics for industrial users and the time c o n s u m i n g nature of an e f f e c t i v e analysis. This paper provides a basis for interactive systems a n a l y s i s and d e s i g n through an expert p o s t - p r o c e s s o r . The p r o p o s e d s t a t i s t i c a l expert module will aid the user in obtaining a validated output, while the s e n s i t i v i t y expert module will enable him/her in e x p l o r i n g a l t e r n a t i v e s y s t e m c o n f i g u r a t i o n s for m a k i n g intelligent decisions.
REFERENCES
I) Haddock, J., "Simulation Generator for Flexible M a n u f a c t u r i n g S y s t e m Design and Control", W o r k S n q Paper, C l e m s o n University, 1985. 2) KlelJnen, J.P.C., S t a t i s t i c a l Tool~ for S i m u l a t i o n Practitioners, Marcel Dekker, Inc., 1986. 3) Law, A.M. and Kelton W.D., S i m u l a t i o n M o d e l l n u and Analvsls, M c G r a w - H i l l Book Company, 1982. 4} Mathewson, S.C., "The A p p l i c a t i o n of P r o g r a m Generator Software and Its E x t e n s i o n to Discrete Event S i m u l a t i o n Modeling", IIE T r a n s a c t i o n s 16(1), pp 3-18, 1984. 5) P e g d e n C.D., Corporation,
Introductions 1985.
to SIMAN,
Systems M o d e l i n g
6) R a m a c h a n d r a n , V., P r o a r a m Generator for S i m u l a t i o n of M a n u f a c t u r i n g Systems, Master's Thesis, U n i v e r s i t y of South Florida, 1987.
BIOGRAPHICAL
SKETCH
V. R a m a c h a n d r a n holds a B.S. in M e c h a n i c a l E n g i n e e r i n g from U n i v e r s i t y of Madras, India, and a M.S. degree in Industrlal E n g i n e e r i n g from the U n i v e r s i t y of South Florida. He is a full-tlme lecturer at F a l r l e l g h D i c k i n s o n University. His r e s e a r c h interests are in simulation, and m a n u f a c t u r i n g s y s t e m and has a u t h o r e d papers on S i m u l a t i o n and R o b o t i c Safety. He is a member of Alpha Pl Mu, ASEE and a senior member of lIE.
103
All rights reserved
0360-8352/88 $3.00+0.00 Copyright c 1988 Pergamon Press plc
EXPERT POST-PROCESSOR FOR SIMULATION OUTPUT ANALYSIS
V. R a m a c h a n d r a n ,
D.L.
Kimbler*
, and
G. N a a d i m u t h u
D e p a r t m e n t of Industrial E n g i n e e r i n g Fairleigh D i c k i n s o n University, Teaneck, NJ 07666 * D e p a r t m e n t of Industrial E n g i n e e r i n g C l e m s o n University, Clemaon, SC 29634
ABSTRACT This paper d i s c u s s e s the c o n c e p t u a l framework and c a p a b i l i t i e s of an expert P o s t - P r o c e s s o r to aid in the analysis of s i m u l a t i o n output data. It is proposed to handle both t e r m i n a t i n g and s t e a d y state systems and for a n a l y z i n g a single s y s t e m or for s t a t i s t i c a l l y c o m p a r i n g two or more systems. The v a r i o u s modules in it are d e s i g n e d to handle d e t e r m i n a t i o n of the initial t r u n c a t i o n point, sample size or run length, variance r e d u c t i o n t e c h n i q u e s and also for p e r f o r m i n g s e n s i t i v i t y analysis of the system. The framework p r e s e n t e d here is still in the conceptual stages and is d e s i g n e d for inclusion in the Intelligent S i m u l a t i o n G e n e r a t o r (ISG), work on w h i c h is c u r r e n t l y being done. This is d e s i g n e d for use in c o n j u n c t i o n with a n y simulator, i r r e s p e c t i v e of the language it uses.
INTRODUCTION C o m p u t e r s i m u l a t i o n as a tool for d e c i s i o n making is g a i n i n g wide a c c e p t a n c e in industry due to the rapid d e v e l o p m e n t of c o m p u t e r t e c h n o l o g y and a u t o m a t e d m a n u f a c t u r i n g . The c o m p l e x nature of m a n u f a c t u r i n g systems does not lend itself to m a t h e m a t i c a l m o d e l i n g and hence s i m u l a t i o n becomes a useful tool. Recent years h a v e seen an e m e r g e n c e of s i g n i f i c a n t l i t e r a t u r e in the areas of s i m u l a t i o n a p p l i c a t i o n s and also in the form of p r o g r a m g e n e r a t o r s or simulators. The increasing number and s o p h i s t i c a t i o n of s i m u l a t i o n languages like SIMAN, SLAM, GASP, GPSS, and S I M S C R I P T have c o n t r i b u t e d to the above m e n t i o n e d work. U n f o r t u n a t e l y , more time and effort is spent on d e v e l o p i n g the s i m u l a t i o n models and p r o g r a m m i n g and less time on model validation. Due to the r a n d o m n e s s involved in simulation, the results of one run will not provide the user with the a c c u r a t e r e s u l t s n e c e s s a r y for d e c i s i o n making. The user has to p e r f o r m the n e c e s s a r y s t a t i s t i c a l analysis which would help in m i n i m i z i n g the error b e t w e e n the model and the actual s y s t e m behavior.
This Expert P o s t - p r o c e s s o r for S i m u l a t i o n Output A n a l y s i s (EPSOAN) c o n s i s t i n g of two main m o d u l e s i.e. s t a t i s t i c a l and s e n s i t i v i t y analysis, will aid the user in v a l i d a t i n g the model and also in a n a l y z i n g it. While the former helps in r e d u c i n g the variance b e t w e e n the model output and the actual s y s t e m output, the latter is n e c e s s a r y in o p t i m i z i n g the output generated.
98
Ramachandran et al. : Simulation output analysis
SYSTEM
SIMULATOR
99
AND OUTPUT A N A L Y S I S
A simulator is defined by Mathewson as "an interactive software tool that translates the logic of a model described in a relatively general s y m b o l i s m into the code of a simulation language and so enables a computer to mimic model behaviour." The purpose of this as mentioned in the definition is to remove the task of programming from the user and to involve him/her only in the input stages. Some of these simulators do contain certain elements of output analysis like initial system status, number of replications etc. but leaves the task of a comprehensive output analysis to be performed by the user. So the ideal system would be one which performs even this task of validating the model. This enables a person with little knowledge of statistics and simulation to perform complex simulation studies. Figure 1 shown below illustrates a common skeletal structure of a simulator which does not perform statistical analysis of the output data. Thus the output provided to the user is not validated and might result in improper usage of simulation by a user not possessing the knowledge, time or resources to conduct proper statistical tests.
USER
C
N
m
-
]
IIN UT I I
| EXISTING [MODEL
! MODEL
[
~
THE
! MODEL
I
7 I
j READ El DATA FIL
I I
lCREAE ! DATA F I L I ~
I MODEL I C~ARA=TE~ISTICSl FOR ~RRORS i ANALYZE
ISIMULATION
os ,s,
I
i
MODEL USING A N ~ ANGUAGE J ~INITI~L ~OUTPUT STATISTICS
I SYSTEM
FIGURE
I. EXISTING
SIMULATOR
WITHOUT
OUTPUT
ANALYSIS
I I I I I I I I I I
i00
Proceedings of the lOth Annual Conference on Computers & Industrial Engineering
Figure 2 shows the E P S O A N e m b e d d e d w i t h i n a simulator. As seen in the figure it comprises of two main modules: I) s t a t i s t i c a l analysis module and 2) s e n s i t i v i t y analysis module. Each one performs a d i s t i n c t type of analysis which is d i s c u s s e d in the following sections. Figures 1 and 2 imitate a g e n e r i c simulator i.e. the s i m u l a t i o n model is d e v e l o p e d using a n y special purpose s i m u l a t i o n language or high level language such as FORTRAN, PASCAL, C etc. The menu is structured in a manner that e n a b l e s the user to proceed to the EPSOAN module or to exit the system. The n e c e s s a r y s t a t i s t i c a l a n a l y s i s is performed by c a l c u l a t i n g the parameters' run length, number of replications, etc., and then going back to the p r o g r a m w r i t i n g stage. When the user is p r o v i d e d w i t h a v a l i d set of output data he/she can p e r f o r m s e n s i t i v i t y a n a l y s i s on the v a l i d a t e d model or accept the e x i s t i n g output.
USIR
V
m m
m
~
,-,~
,~.
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. . . . . . . .
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,
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~P~O~ZSSOR FOR ~4~OUTPUT-ANALYSZS~
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I VAlidAteD
I
OUTPUT
I
SUMMARY
i
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+i
SENSITIVITY ANALy$:$
I SYSTEM FIGURE
2.
SIMULATOR
WITH OUTPUT
ANALYSIS
1
Ramachandran et al.
STATISTICAL
: Simulation
output
analysis
101
ANALYSIS MODULE
AS shown in Figure 3 this module is s u b d i v i d e d into several s u b m o d u l e s each one p e r f o r m i n g a d i f f e r e n t type of analysis. Since s i m u l a t i o n is used to a n a l y z e an e x i s t i n g or p r o p o s e d s y s t e m or to evaluate a m o n g v a r i o u s a l t e r n a t i v e systems, E P S O A N is d e s i g n e d to handle both types of study. While a n a l y z i n g the output data it is imperative to c l a s s i f y the s y s t e m into either a t e r m i n a t i n g or s t e a d y state type. The s t a t i s t i c a l tests for both types m a y differ and hence the n e c e s s i t y for separate classification.
STATISTICAL 1 ANALYSIS MODEL
i
[
[
I SIMULATING A SINGLE SYSTEM
ISIMULATION OF MULTIPLE SYSTEMS
I
r
f STEADY STATE
TERMINATING
L I M I N A T I O N OF NITIAL BIAS SUBMODULE
ANALYSIS OF EACH SYSTEM
[
I
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I
]
EVALUATE DIFFERENT SYSTEMS
ELIMINATION OF INITIAL BIAS
#1
SUBMODULE
#i
SUBMODULE
l
RUN LENGTH OR SAMPLE SIZE SUBMODULE
#2
i CONFIDENCE INTERVAL
CONFIDENCE INTERVAL SUBMODULE
SUBMODULE
#3
SUBMODULE
VARIANCE REDUCTION TECHNIQUE SUBMODULE
#4
FIGURE
C~I~5: I/4-S
#5
I
1 VARIANCE REDUCTION TECHNIQUE
3.
STATISTICAL
l
#4
ANALYSIS
MODULE
#6
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P r o c e e d i n g s of t h e 10th Annual C o n f e r e n c e on Computers b I n d u s t r i a l
Engineering
The s u b m o d u l e s d e a l i n g with e l i m i n a t i o n of initial bias and variance reduction t e c h n i q u e s are shared by both the s t e a d y state and t e r m i n a t i n g s y s t e m s since they have the same methods. The information flow from each module and the generator may not be identical and further, the user may have to go from one module to a n o t h e r before going back to the generator to rewrite the m o d i f i e d model. The s u b m o d u l e s m a y also be c o n s i d e r e d as a knowledge base c o m b i n e d with an inference engine. The user might want to remove the transient response from the s y s t e m and hence is connected to submodule #I where many d i f f e r e n t methods could be stored. He can opt to use a n y of the m e t h o d s and go to the next module after d e l e t i n g the transient response. Two of the common methods to be s t o r e d in this s u b m o d u l e m a y be 1) t r u n c a t i n g data collected up to the t r a n s i t i o n point, and 2) d e t e r m i n i n g each r e p l i c a t i o n In this state. If the user decides to use the first method he m a y decide to start data c o l l e c t i o n after s t e a d y state is reached. The user m a y resort to submodule #2 for d e t e r m i n i n g the run length, if necessary. The t r a n s i e n t s are m o s t l y due to the handling of r a n d o m numbers w h i c h c o n t r i b u t e to the d e v i a t i o n of the variance from the true v a r i a n c e and hence r e m o v i n g the transients enables us to reduce the error in the sample variance. But this by itself does not validate the model and hence the user m a y have to use the other modules. The user might then be taken back to g e n e r a t o r and after m a k i n g one more run might decide to use s u b m o d u l e #4 to reduce the variance using a n y of the methods a v a i l a b l e there like I) c o m m o n r a n d o m numbers, 2) a n t i t h e t i c variates, and 3) control varlates. Thus d e p e n d i n g on the nature of output a n a l y s i s required the user is taken to some or all of the submodules. While s i m u l a t i n g more than one system, the user performs above a n a l y s i s for each s y s t e m and using the output from the v a l i d a t e d run m a y evaluate t h e m using a n y of the available m e t h o d s in submodule #6.
SENSITIVITY
ARAL¥SIS
the
MODULE
P e r f o r m i n g the required s t a t i s t i c a l a n a l y s i s on the output data has a i d e d the user in v a l i d a t i n g the model and m i n i m i z i n g the influence of r a n d o m n e s s of the variates. This by itself m a y not be s u f f i c i e n t in most instances as the user has to interpret the data and make m o d i f i c a t i o n to the s y s t e m components to optimize the system's efficiency. Incluslon of this module in EPSOAN will enable the computer to be an intelligent s y s t e m by p e r f o r m i n g s e n s i t i v i t y a n a l y s i s on the various s y s t e m components. Some of the areas of interest to a user are i) service time, 2) queue c h a r a c t e r i s t i c s , 3) i n t e r - a r r i v a l time, and 4) bottlenecks. These areas are s e n s i t i v e to the changes in the number of machines, m a t e r i a l h a n d l i n g system, storage buffer capacity, and manpower. F r o m the v a l i d a t e d output data, EPSOAN s h o u l d s t u d y the a v e r a g e w a i t i n g time for a part at a p a r t i c u l a r machine, the number of d i f f e r e n t parts that are processed there, a n d m a c h i n e v i s i t a t i o n s e q u e n c e of the parts. Then it can increase the number of machines in a cell, create the c o d i n g for the m o d i f i e d s y s t e m and run the s i m u l a t i o n again. After p e r f o r m i n g the n e c e s s a r y s t a t i s t i c a l a n a l y s i s for this result it should go to the next c o m p o n e n t or m o d i f y the same c o m p o n e n t again. This iterative procedure would enable the user to get an o p t i m u m s o l u t i o n of hls/her model. Though time c o n s u m i n g when taking into c o n s i d e r a t i o n the time saved in model development, p r o g r a m m i n g and d e b u g g i n g the results wlll J u s t i f y It. B o t t l e n e c k s can be studied by having an a l g o r i t h m within a model for identifying machine blocking. The EPSOAN should be built in a manner that would indicate the machine which is the b o t t l e n e c k and the reasons for it.
Ramachandran et al. : Simulation output analysis
CONCLUSION Statistical v a l i d a t i o n of s i m u l a t i o n output though v e r y vital is being ignored in most of the a p p l i c a t i o n s . The reason for this can be a t t r i b u t e d to the c o m p l e x i t y of statistics for industrial users and the time c o n s u m i n g nature of an e f f e c t i v e analysis. This paper provides a basis for interactive systems a n a l y s i s and d e s i g n through an expert p o s t - p r o c e s s o r . The p r o p o s e d s t a t i s t i c a l expert module will aid the user in obtaining a validated output, while the s e n s i t i v i t y expert module will enable him/her in e x p l o r i n g a l t e r n a t i v e s y s t e m c o n f i g u r a t i o n s for m a k i n g intelligent decisions.
REFERENCES
I) Haddock, J., "Simulation Generator for Flexible M a n u f a c t u r i n g S y s t e m Design and Control", W o r k S n q Paper, C l e m s o n University, 1985. 2) KlelJnen, J.P.C., S t a t i s t i c a l Tool~ for S i m u l a t i o n Practitioners, Marcel Dekker, Inc., 1986. 3) Law, A.M. and Kelton W.D., S i m u l a t i o n M o d e l l n u and Analvsls, M c G r a w - H i l l Book Company, 1982. 4} Mathewson, S.C., "The A p p l i c a t i o n of P r o g r a m Generator Software and Its E x t e n s i o n to Discrete Event S i m u l a t i o n Modeling", IIE T r a n s a c t i o n s 16(1), pp 3-18, 1984. 5) P e g d e n C.D., Corporation,
Introductions 1985.
to SIMAN,
Systems M o d e l i n g
6) R a m a c h a n d r a n , V., P r o a r a m Generator for S i m u l a t i o n of M a n u f a c t u r i n g Systems, Master's Thesis, U n i v e r s i t y of South Florida, 1987.
BIOGRAPHICAL
SKETCH
V. R a m a c h a n d r a n holds a B.S. in M e c h a n i c a l E n g i n e e r i n g from U n i v e r s i t y of Madras, India, and a M.S. degree in Industrlal E n g i n e e r i n g from the U n i v e r s i t y of South Florida. He is a full-tlme lecturer at F a l r l e l g h D i c k i n s o n University. His r e s e a r c h interests are in simulation, and m a n u f a c t u r i n g s y s t e m and has a u t h o r e d papers on S i m u l a t i o n and R o b o t i c Safety. He is a member of Alpha Pl Mu, ASEE and a senior member of lIE.
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