The development of a simulation assistant for modeling discrete event processes is presented. Included are an overview of the system, a description of the simulation generators, and a sample process generated using the simulation assistant

Dwan, Wen-Shing

Schroer, Bernard J.

Tseng, Fan T.

English

NASA Technical Reports Server

N88-16403
LISP Based Simulation Generators
for Modeling Complex Space Processes
Fan T. Tseng
Bernard J. Schroer
Wen-Shing Dwan
University of Alabama in Huntsville
Huntsville, AL 35899
(205) 895-6510
ABSTRACT
This paper presents the development of a simulation assistant for
modeling discrete event processes. Included in this paper are an overview
of the system, a description of the simulation generators, and a sample
process generated using the simulation assistant.
INTRODUCTION
Numerous simulation languages exist for modeling discrete event
processes and have now been ported to microcomputers (Pegden 1985 and
Minuteman 1986}. Graphic and animation capabilities have been added to many
of these languages to assist the users build models and evaluate the
simulation results.
However, with all these languages and added features, the user is still
plagued with learning the simulation language which can be rather time
consuming, especially if a complex system is being modeled. Furthermore,
the time to construct and then to validate the simulation model is always
greater than originally anticipated.
One approach to minimize the time requirement is to use pre-defined
macros that describe various common processes or operations in a system.
The literature recently has contained several approaches to developing and
using these macros or simulation generators. For example, a simulation
generator has been developed that translates the characteristics of a
Flexible Manufacturing System (FMS) into a simulation model using SIMAN
(Haddock 1987).
Another example is a ruled based expert system that assists the modeler
in constructing simulation models (Khoshnevis 1986}. The expert system
automatically generates the corresponding SLAM simulation code. The icons
used in this system are very similar to those in GPSS and SIMAN.
A Natural Language Interface (NLI) for an electronics assembly domain
has also been developed that automatically generates SIMAN code from user
defined text (Ford 1986}. This NLI uses a Symbolics 3670 processor and
presently has a limited dictionary.
A set of simulation generators has also been developed for simulating
manufacturing systems (Schroer 1987). These generators are written in
GPSS/PC and can be linked together through a GPSS main program.
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SYSTEMOVERVIEW
Several simulation generators have been developed for modeling
manufacturing processes (Tseng 1987). These simulators have greatly
expedited the modeling process. The next step is to develop a more user
friendly interface, or simulation assistant, between the modeler and the
actual simulation language. Such a simulation assistant should reduce the
user's need to know the details of the simulation language and reduce the
time to construct and validate the model. The end result should be
increased user productivity.
Figure 1 outlines a system schematic for the simulation assistant
currently under development. The user sits at a Symbolics 3620 processor
and, based on the prompts from the simulation assistant, defines the process
or the model. The simulation assistant is written in Symbolics' Common
Lisp.
Oncethe user has defined the process to be simulated, the simulation
assistant automatically generates the corresponding GPSSsimulation code of
the process. The simulation assistant also calls on the library of
simulation generators in generating the GPSScode.
The Symbolics 3620 is interfaced to a 3670 file server which is
interfaced, via Ethernet, to a VAX785. Resident of the VAX is the GPSS
simulation system. The user then inputs the necessary run statements, the
model is executed and the results printed. Currently, the VAXinterface is
not operational. Instead, the GPSScode that is generated on the Symbolics
3620 is re-entered into an IBMAT which has resident the GPSSsystem. The
user then inputs the run commandson the AT to execute the model.
Userdefines I
problemprompte(l___J
by simulation -I
assistant I,
Stmuletlon
essiste_t
GP,S,S
simulation
generators
OPS.S
simulation
language
___ Automatically
generated
OP&Scode
Symbollcs
3620 Vex785
Model
results
t1_ User inputs
run parameters
Figure I. System overview
244
SIMULATIONGENERATORS
Three simulation generators have been developed:
generator, a manufacturing cell generator, and
generator. Each generator consists of a GPSS macro
1987).
an assembly station
an inventory transfer
or subroutine (Tseng
Figure 2 is a typical assembly station. The generator operates as
follows. Items wait in a queue until the station becomes available. The
item then seizes the station and waits until item A is available at stock
point A. An amount of time is then simulated while item A is assembled to
item B resulting in item C. If stock point A is empty, a signal is sent to
the inventory transfer generator to move the empty cart to the manufacturing
cell. The manufacturing cell then makes the required items for the cart.
Another signal is sent to the inventory transfer generator to return the
full cart back to the corresponding assembly station. This method of
inventory control is commonly called the pull mode. If the assembly station
is operated in the push inventory control mode, the items at stock point A
are replenished at predefined intervals.
item A at
stock point A
station 1
tlme to assemble
Item A to item B
Item C
Figure 2. Typical assembly station
Figure 3 is a typical manufacturing cell. The generator operates as
follows. Item D, or raw material D, is used to manufacture item E.
Likewise, item F is used to make item G. An amount of time is then
simulated to make an item. The finished items are stored in stock points E
and G. Each stock point contains a defined inventory or carts with a fixed
cart capacity. This output can go to either another manufacturing cell or
an assembly station. The inventory at the stock points D and F can be raw
material or items from another manufacturing cell.
Item D at Item E at
stock poln_ _oint E
"_ IManufacturing I
--- Icell 2
Item F at _'_ __tem G at
stock point F times to make items E and G stock point G
from Items D and F
Figure 3 Typical manufacturing cell
245
SIMULATION ASSISTANT
An example of a typical process, which could translate to a space
manufacturing task, is given in Figure 4 and consists of two assembly
stations and one manufacturing cell. The following constraints are placed
on the process: one inventory transfer generator for all item movement; a
pull inventory control is imposed for items B and D; and a push inventory
control is imposed for items A and C.
Item X
Assemblystation I
C
stock
point A
item Y
Assemblystation 2
stockpoint B stockpoint D
itemJ
v
stock
point B
stock
stock pointD
point C
Figure 4 Typical manufacturing process
Figure 5 is a listing from the Symbolics 3620 screen showing a porLion
of the prompts from the simulation assistant and the corresponding user
response. The screen consists of two windows. The right window contains
various subwindows and options that the user selects via the mouse. Once
the selection is made, the left window prompts the user for specific data
that are used to construct a model of the process and to provide data for
the simulation generators.
Several of the various subwindows are given in Figure 6. The first
subwindow allows the user to select the type of distribution for the
assembly time at a station. The current options are either exponential or
normal. After the user selects the appropriate distribution, the user
enters the mean time and standard deviation in the left window. The second
subwindow allows the user to select the method of inventory, either push or
pull. Once the method is selected, the user enters the amount of inventory
at the various stock points.
CONCLUSIONS
In summary, a simulation assistant is an effective method of rapidly
developing discrete event simulation models with minimum knowledge of the
simulation language. An expanded set of simulation generators are necessary
for modeling more complex processes. However, it is difficult to define
these general purpose generators and to then link these generators to the
simulation assistant. Considerable more work is required to make the
simulation assistant more robust.
246
Oomand: (mumple-processl
Numeof line: _IN
Numberof stations: 2
Nean arrival time: ](_
Nameof inventory part required at station l: B
Heax assembly time: 100
Hale of inventory part required at station 2: D
Neax assembly time: lO0
Any mre lines to create? (Y or N) No.
Maximumpart g at assembly line [default lOOh 2
Haxinu cart B at assembly line [default lOOh 2
Maximumpart B at nanufacturi_ cell [default lOOh 2
Naxinum cart B at manufacturing cell [default 100]: 2
)lean manufacturing Line: 10
Waneof inventory transfer: _I01
Mean inventory transfer time: 2
Nameof manufacturing cell: CBLLI
Num]_erof types of input items: 2
Nameof input item: k
Nameof input item: C
Waxlnum part D at aasembly line [default lOOh 2
Naxieum cart D at assembly line Idefault lOOh 2
Maximumpart D at nanufacLurinq cell [default lO01:2
Maximumcart D at nanufacturinq cell Idefault 1001:2
Weanmanufacturing time: 10
Nameof inventory transfer: _IGI
Nean inventorytransfertime: 2
Nameof manufacturinq cell: CELLI
Number of types of input items: 2
Nameof input itum: A
Nameof input itum: C
Naxtmumpart k capacity at assumbly line: [default 5000h lO0
Maximumpart C capacity at assembly line: Idefault 50001:]00
Figure5. Simulationassistantdisplay
ORIGINAl] PA_G]_ 18
OF POOR QUALI'I_
DistributionofstationI
I Exponential I
Normal
Inventory control of part B
Push
i I
Flgure 6, Various subwlndows
REFERENCES
Ford, D. R. and B. J. Schroer. 1987. "An Expert Manufacturing Simulation
System." Simulation, vol 48, no. 5.
Haddock, J. and Robert P. Davis. 1985. "Building a Simulation Generator
for Manufacturing Cell Design and Control." Proceedings 1985 lIE Sprin 9
Conference, Los Angeles, CA.
Khoshnevis, B. and A. P. Chen. 1986. "An Expert Simulation Model
Builder." Intelligent Simulation Environments, Society for Computer
Simulation, vol 17, no. 1.
Mathewson, S. C. 1984. "The Application of Program Generator Software and
Its extensions to Discrete Event Simulation Modeling." lIE Transactions,
vol 16, no. 1.
Pegden, C. Dennis. 1985. Introduction to SIMAN. Systems Modeling Corp.
Schroern B. J. and F. T.
Systems Using Simulation."
December 1987, Atlanta, GA.
Tseng. 1987. "Modeling Complex Manufacturing
Proceedings 1987 Winter Simulation Conference,
Tseng, F. T. and B. J.
Modeling Manufacturing
Conference SESC 87,
Huntsvillen AL.
Schroer. 1987. "Use of Simulation Generators in
Systems." Proceedings Southeastern Simulation.
Society for Computer Simulation, October 1987,
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LISP based simulation generators for modeling complex space processes

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