DSES6620 Simulation Modeling and Analysis
Professor: Ernesto Gutierrez-Miravete
Term Project Report: Rapid Prototyping Cell
Simulation
Hanspeter Bayer
Telephone: 203-492-8051
Email: peter.bayer@ussurg.com
21 December 2000
DSES6620 Simulation Modeling and Analysis - Term Project Report: Rapid Prototyping Cell
Simulation
Table of Contents
TABLE OF CONTENTS .................................................................................................................. 2
ABSTRACT ..................................................................................................................................... 4
INTRODUCTION ............................................................................................................................. 5
GOALS ............................................................................................................................................ 5
SCOPE ............................................................................................................................................ 5
REQUIREMENTS ............................................................................................................................ 5
THE SYSTEM .................................................................................................................................. 5
THE MODEL.................................................................................................................................... 6
ENTITIES ........................................................................................................................................ 7
LOCATIONS, ROUTINGS AND PROCESSES ........................................................................................ 7
RESOURCES .................................................................................................................................. 7
SIMPLIFICATIONS AND ASSUMPTIONS ..................................................................................... 7
NO REJECTED WORK ORDERS ......................................................................................................... 7
TRANSIT TIMES ARE NEGLIGIBLE ...................................................................................................... 7
MODEL MAKER UTILIZATION IS THE SAME AS W ORKSTATION UTILIZATION.......................................... 7
NUMBER OF PARTS REQUESTED PER W ORK ORDER IS NOT HANDLED EXPLICITLY............................. 7
SLA MACHINES CAN HANDLE TWO W ORK ORDERS SIMULTANEOUSLY ............................................. 8
NO DOWNTIME ............................................................................................................................... 8
SLA SERVICE TIME = IN-PROCESS TIME – MODEL MAKER SERVICE TIME ........................................ 8
MODEL MAKERS DO NOT LEAVE AN SLA JOB TO W ORK ON ANOTHER JOB....................................... 8
DATA ............................................................................................................................................... 8
Table 1: Relevant Model Shop Work Order Fields................................................................... 8
INPUT ANALYSIS ........................................................................................................................... 9
PERFORMANCE ANALYSIS ....................................................................................................... 10
VERIFICATION, VALIDATION AND RESULTING REFINEMENTS ........................................... 12
SHIFTS......................................................................................................................................... 12
GATE ........................................................................................................................................... 12
DISK DRIVES ................................................................................................................................ 12
UV OVEN ..................................................................................................................................... 12
VALIDATION METRICS ............................................................................................................... 12
Table 2: Validation Metrics ..................................................................................................... 13
VARIATIONS................................................................................................................................. 13
ADDITION OF A THIRD W ORKSTATION (AND MODEL MAKER) ........................................................... 13
FORCED BATCHING ...................................................................................................................... 13
ADDITION OF A THIRD SLA MACHINE ............................................................................................. 13
RESULTS ...................................................................................................................................... 13
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COMPARISON OF SYSTEMS ........................................................................................................... 14
CONCLUSIONS ............................................................................................................................ 14
REFERENCES .............................................................................................................................. 14
APPENDICES ............................................................................................................................... 15
APPENDIX 1: TEXT PRINTOUT OF THE ORIGINAL MODEL ................................................................. 15
APPENDIX 2: OUTPUT RESULTS (AVERAGED) FROM THE ORIGINAL MODEL SIMULATION................... 18
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Abstract
This paper presents the modeling and simulation of a stereolithography-based rapid prototyping
work cell. Model development, assumptions, input data, verification and validation are discussed.
Mean time in system is identified as the major performance metric. The results of the simulation
are presented, as are the results of three variations that were modeled in an attempt to reduce
mean time in system. Conclusions are drawn based on the results.
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DSES6620 Simulation Modeling and Analysis - Term Project Report: Rapid Prototyping Cell
Simulation
Introduction
The system being modeled and simulated is a set of two stereolithography apparatuses and the
equipment and people associated with them. This system of components can be considered as a
rapid prototyping cell in which prototype parts are “grown” out of a plastic resin. The cell is part of
a larger model shop in a large, manufacturing company.
Work for the cell is sent to the shop by design engineers via the creation of an electronic work
order. These work orders are often referred to as “jobs.” The work order is accompanied by a 3D
parametric solid model, which the engineer has created and which can be read directly by the
rapid prototyping software. The work order typically requests between one and ten two samples
be made of a part, and may include several different parts. There is a perception among the
design engineers who feed work orders into this system that the system cycle time is too long.
Each SLA machine is dedicated to producing parts using a certain resin. The first resin, referred
to as “5170”, is very stiff and somewhat brittle. The second resin, referred to as “SOMOS” is
more pliable. The resin is chosen by the design engineer, depending on the characteristics
required by the design.
Goals
The goals from the original project proposal were simplified and reduced to:
 Develop a model of the rapid prototyping cell
 Validate the model; that is show that the model is accurate
 Make several changes to the model and compare their resulting performance to that of the
original model to see if any of the changes predict a reduction in time in system.
Scope
The scope of the project is confined to the modeling and simulation of the operation of the two
stereolithography apparatuses (SLA’s or SLA machines) and the resources required for their
operation.
Requirements
The following were available to execute the project:
 The author’s time
 The student version (4.2) of ProModel
 Excel Spreadsheet software
 A database containing information for a year’s worth of model shop workflow
 Limited time with model makers and the model shop manager
The System
Figure 1 shows a schematic of the system. Work orders are fed into the system and are
assigned to model makers. Once the model maker is free to work on a work order, they process
the solid model file on a workstation, save their work and then send the file to the appropriate
SLA when it is free. The SLA grows the part or parts. Once they have been grown the parts are
placed in a UV oven for one hour to finish curing. Once they have cured, they are manually
cleaned by the model makers. At this point the work order is complete.
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Work Orders
Arrive
"Shelf"
Assigned to
Model Maker
Preprocesses
Solid Model
File
Saves work
SLA
grows
5170 resin
part
SLA
grows
SOMOS
resin part
UV Cured
Cleaned
Figure 1: System Schematic
The Model
Figure 2: Model in ProModel
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Entities
Two entity types have been created in the model; both are work orders. The first represents a
work order requesting SLA parts grown using the 5170 resin (job_5170), the second represents a
work order requesting parts grown using the SOMOS resin (job_somos).
Two more entities, batch entities, were required when the variation using forced batching
(described later) was developed.
Locations, Routings and Processes
The model has the following locations:
 A gate at which the 5170 jobs arrive – from here they go to the queue
 A gate at which the SOMOS jobs arrive – from here they go to the queue
 A queue (known informally as “the shelf”) – from here the jobs go to a workstation when
either is free and a model maker is available
 Two workstations – model maker service time is used to make the job “wait” here – from here
the jobs are stored on one of the workstations’ hard drives
 The hard drives for these workstations – modeled as queues which store the jobs while the
SLA’s are occupied
 Two SLA Machines – service times with a mean of 20 hours and a standard deviation of 8
hours
 A UV oven (modeled as a conveyor) – it takes one hour for a part to traverse this conveyor
 Two cleaning stations – the jobs require a model maker at this point - they wait here for a
half-hour to represent the model maker cleaning the parts and the jobs exit the system.
Resources
Several model makers are available to spend at least a part of their time working on SLA work
orders. Typically no more than two are assigned at any given time to work on SLA work orders.
Simplifications and Assumptions
No rejected work orders
The time taken by the manager to assign or reject work orders is negligible. Typically, the
manager can process a work order in a matter of minutes. It is assumed that this does not affect
the time in system for a given job, nor do work orders queue up at the manager. That is, the
manager’s service time is much less than the work order interarrival times.
Transit times are negligible
Since time in system values are of the order of several days and service times are of the order of
several hours, it was assumed that the time taken for a work order to move from location to
location is negligible.
Model Maker Utilization is the same as Workstation Utilization
In the early versions of the model, no resources were assigned to the workstation location. It was
assumed that whenever the workstation was active, one model maker was needed to run it.
Therefore, the utilization of the model maker was assumed to be exactly that of the workstation.
As the model was refined it and the cleaning process was added, it was necessary to define
model makers as actual resources.
Number of Parts Requested per Work Order is not Handled Explicitly
Although in reality, the number of parts fabricated in a given work order can vary from one to ten,
no attribute for this was assigned to the work order entities. It was assumed that the effect of the
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DSES6620 Simulation Modeling and Analysis - Term Project Report: Rapid Prototyping Cell
Simulation
number of parts requested in a given work order is handled implicitly in the variation of processing
times. In other words, the variation in processing times is as large as it is due partly to the
variation in number of parts requested in the work orders.
SLA Machines Can Handle Two Work Orders Simultaneously
In reality, as many as five work orders may be fit into an SLA machine, if the part sizes and
quantities are sufficiently small. Conversely, only one work order may fit it requires larger parts or
many parts. This assumption is based on the fact that most of the time the work order
requirements are such that two jobs can fit, and that it is rare in practice to see more than two
jobs combined in one SLA cycle.
No Downtime
The author can recall only two times during a period of four years that the model shop maker
announced that an SLA machine was down (for a two to three day period). Therefore downtime
has been assumed to be negligible. At one time, there was only one SLA in the cell and
significant downtimes occurred when the machine had to be flushed so that it could be switched
to the other resin.
SLA Service Time = In-Process Time – Model Maker Service Time
This assumption came out of a limitation of the data. The data did not explicitly include the actual
service time for the SLA’s. The data did however, give starting and ending dates for jobs once
they left the queue, and it also gave the hours spent on the job by the model maker.
Model Makers Do Not Leave an SLA Job to Work on Another Job
Essentially, it was assumed that once a job left the queue and went into process, a model maker
did not start working on another non-SLA job until the SLA job was completed.
Data
A database of input and performance data has been growing since the implementation of the
electronic work form system about two years ago. A subset of the entire database, representing
all work orders submitted during the 12 months starting 9/1/1999 and ending 8/31/2000, was
extracted from the database.
Each record has the following fields that were used to develop the model:
Table 1: Relevant Model Shop Work Order Fields
Field
Type
start_date
end_date
System-generated date/time
System-generated date/time
Material
User-generated text
Sla
Startdate
completion_date
total_hours
Y/N attribute
User-generated text
User-generated text
User-generated number
Description
date/time electronic work order was created
date/time electronic work order was updated to
complete (only populated for completed work
orders)
material of which part is to be made (plastics,
steels, etc.)
is the part to be grown in an SLA?
date model maker started
date model maker finished
model maker hours
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Input Analysis
These figures (3 and 4) show the interarrival time distribution for all SLA work orders (those
requesting the 5170 resin and those requesting the SOMOS resin):
Interarrival Time Distribution - 5170 Jobs
250
Frequency
200
150
100
50
0
2
4
6
8
10
12
14
16
18
20
22
24
More
Hours
Figure 3
Interarrival Time Distribution - SOMOS Jobs
40
35
Frequency
30
25
20
15
10
5
or
e
32
M
30
28
26
24
22
20
18
16
14
12
10
8
6
4
2
0
Hours
Figure 4
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DSES6620 Simulation Modeling and Analysis - Term Project Report: Rapid Prototyping Cell
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Mean interarrival time for the 5170 jobs was 4.85 hours and 14.10 hours for the SOMOS jobs.
Figure 5 shows the distribution of model maker service times. No acceptable analytic distribution
was found to model this distribution, so a user-defined distribution was used. Note that this data
was subjective in that it was provided by the model maker as an estimate and was not computer
generated.
Model Maker Service Times
40
35
Frequency
30
25
20
15
10
5
0
0
8
16
24
32
40
48
56
64
72
80
88
96 More
Working Hours
Figure 5
Performance Analysis
The primary performance measure will be the time in system (mean and distribution). It is not
apparent that throughput rates, or work in process numbers, or any other measure is of direct
concern to model shop management, model makers or R&D engineers (customers). The primary
concern of all parties seems to be only how long it takes to get jobs through the system.
These figures (6 and 7) show the current time in system distribution for the 5170 jobs and
SOMOS jobs:
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DSES6620 Simulation Modeling and Analysis - Term Project Report: Rapid Prototyping Cell
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or
e
32
M
30
28
26
24
22
20
18
16
14
12
10
8
6
4
90
80
70
60
50
40
30
20
10
0
2
Frequency
Time in System - Completed 5170 Jobs
Calendar Days
Figure 6
Time in System - Completed SOMOS SLA Jobs
35
Frequency
30
25
20
15
10
5
or
e
M
32
30
28
26
24
22
20
18
16
14
12
10
8
6
4
2
0
Calendar Days
Figure 7
The mean time in system for 5170 jobs was 339 hours (14.13 calendar days) and that for the
SOMOS jobs was 315 hours (13.15 calendar days). It is interesting to note that the time in
system for each type of job is essentially the same, despite the fact that 5170 jobs are requested
about four times more than SOMOS jobs. Since each SLA machine has the same capacity and
processing speed, it might be expected that the SOMOS jobs would be handled much more
quickly.
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DSES6620 Simulation Modeling and Analysis - Term Project Report: Rapid Prototyping Cell
Simulation
Verification, Validation and Resulting Refinements
The verification and validation activities were conducted as an integral part of the development of
the model. Some of the activities served to both verify and validate, as in the case of the interarrival times. Refinements were made to the model as verification and validation processes
indicated deficiencies in the model. Among there were:
Shifts
Work orders arrive only during the first shift and the workstations are used to process the jobs
during the first shift. However, once the SLA machine is started on a job it can be (and is) left
unattended for overnight and weekend operation. The model was therefore created to run
through 24-hour days and shifts were added to those locations and resources that were available
only during shifts. As an example, without employing shifts, jobs arrived 24 hours a day, even
though in reality, the engineers were only submitting jobs during their workday. This resulted in
approximately three times too many jobs being submitted, even though the interarrival time
distribution was correct.
Gate
In order to apply the constraint of a shift on arrivals (work orders arrive only during the first shift),
it was necessary to create a queue, with a capacity of one, upstream of what was the actual
queue (which has infinite capacity). It was discovered that a shift could not be applied to an
infinite capacity queue and that an upstream single-capacity queue (referred to as a “gate”) was
needed. The arrivals are pointed to the gates and they travel to the queue from there. Separate
gates for the 5170 and SOMOS jobs were created.
Disk Drives
During verification of the single SLA machine model, it became obvious that some sort of buffer
or queue was needed between the workstation and the SLA machine. Without such a buffer, the
model described a situation where model makers could only process one job in the workstation
while the SLA machine was busy with an earlier job. Once they had processed that one job, the
model forbid them from processing any more jobs until the SLA was free and their current job
could leave the workstation and go to the SLA. This was clearly not what happened in reality,
where a model maker could save a job to the hard drive and then continue with a new job. The
hard drive was therefore modeled as a queue in its own location.
A further refinement was made based on observations of the animation. SOMOS jobs were
being caught in the disk drive queue behind 5170 jobs. Since the 5170 jobs were waiting for the
5170 SLA machine to free, they held up the SOMOS jobs even though the SOMOS SLA was free
and waiting. Unlike real hard drives, the model hard drives operated (inadvertently) on FIFO
basis. Two options to address this were available: one was to change the disk drive operation
logic from FIFO, the other was to dedicate one hard drive to 5170 jobs and the other to SOMOS
jobs. The latter approach was taken, although it should be understood that either approach
would work.
UV Oven
The UV curing location is modeled as a conveyor, since it has an essentially deterministic service
time and parts can be added at any time during the cycle.
Validation Metrics
A few of the validation metrics are presented below in Table 2:
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DSES6620 Simulation Modeling and Analysis - Term Project Report: Rapid Prototyping Cell
Simulation
Table 2: Validation Metrics
Validation Metric
System
Throughput - 5170 (per year)
Throughput - SOMOS (per year)
Model maker time (hours)
Model
LCI-95% UCI-95%
469
424
456
152
138
156
4514
5168
5342
While only the SOMOS throughput fell within the 95% confidence interval, it is clear that the
model comes close to accurately simulating the system.
Variations
The following variations on the model were created to see if they would indicate a significant
decrease (95% confidence level) in cycle time. Stream 1 was used for all random variates in
order to reduce variation.
Addition of a Third Workstation (and Model Maker)
The first change to the model will be the addition of a third workstation and model maker. The
expectation is that this will help to increase the utilization of the SLA machines thereby increasing
throughput and reducing time in system. This is a fairly practical alternative to explore since extra
workstations are available to use in this capacity. However, the additional model maker would
have to be hired or taken away from some other operation in the model shop, so a cost is
involved.
Forced Batching
Currently, the system operates such that SLA operation starts as soon as a work order comes
into it. If two work orders are ready to go to an SLA then both do go, but if only one work order is
waiting, then only one goes in. In other words, the current practice is not to wait for a second
work order before starting the SLA. So, this second variation on the model forces the model
makers to always wait for a second work order to come through before starting the SLA. The
expectation is that this might reduce the mean idle time for work orders queuing up in the hard
drive locations, thereby reducing time in system. This is the most practical alternative to study
since it would require no additional expenditures to implement in the actual system. All that
would be needed would be to tell the model makers to always make sure that two work orders are
being processed in the SLA machines. Also, it would not be difficult to undo should it prove to be
ineffective in reality.
Addition of a Third SLA Machine
This is the most costly and therefore least practical alternative to examine. However, it is easy to
implement in the model and it will be useful to determine if such expenditure would produce an
improvement. Since neither of the existing machines runs at full utilization, it is expected that the
addition of a third machine would not show significant improvement in time in system.
Results
It proved to be quite difficult to develop a well-validated model, even for a system as simple as
this one appeared. Some metrics were better validated than others. As an example, interarrival
times for the SOMOS jobs fit with a gamma distribution quite well and the simulation produced
arrivals very similar to the actual system. Conversely, the calculation used to determine SLA
service times was inaccurate and an estimate of the service time distribution had to be used.
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Simulation
Comparison of Systems
The results of the verification and validation processes will be summarized, as well as the results
of the comparison of systems. For the comparison of systems, the results will be described in
terms of a 95% confidence level as to whether any of the alternatives predict improvement in the
time in system measures. Table 2 shows the actual system mean cycle times and compares it to
those of the model and its variations:
Table 3: Comparison of Model Variations
Average Time in System (hours)
Actual System
Model of System
Model with 3rd Workstation and Model Maker
Model with 2nd SLA (for 5170 resin)
Model with forced batching
5170
339
SOMOS
315
LCI-95% UCI-95% LCI-95% UCI-95%
174
356
107
278
159
263
82
157
65
71
60
71
219
388
185
357
Conclusions
Further work needs to be done to fully validate the model. Despite this, the model does seem to
produce some evidence that the addition of a third SLA machine would improve cycle times, but
only for the 5170 resin based jobs. It may be less risky to agree that the other two variations
would not produce significant improvements in cycle time. The addition of a third workstation and
model maker does nothing to help improve conditions at the bottleneck – the 5170 SLA machine.
Likewise, it is easy to understand how forced batching could easily increase time in system
instead of decreasing it.
The best way to improve the accuracy of the model would be to return to the model shop and
collect more data on the actual service times produced by the two SLA machines. Other data
could also be collected, but these would be of little benefit without better service time data
accompanying them.
References
1. Harrell, C. et al, [2000] Simulation Using ProModel, McGraw-Hill, Inc.
2. Law, A and W.D. Kelton, [2000] Simulation Modeling and Analysis, 3rd Ed, McGraw-Hill, Inc.
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Appendices
Appendix 1: Text Printout of the Original Model
********************************************************************************
*
*
*
Formatted Listing of Model:
*
*
F:\UsersP\PBayer\RPI\SMA\project\SMA Bayer Rapid Prototyping Cell.mod
*
*
*
********************************************************************************
Time Units:
Distance Units:
Hours
Feet
********************************************************************************
*
Locations
*
********************************************************************************
Name
-------------sla_5170_gate
sla_somos_gate
queue
workstation1
workstation2
sla_5170
sla_somos
cleaning_area
hard_drive_1
hard_drive_2
uv_curing_oven
Cap
-------1
1
INFINITE
1
1
2
2
2
5
5
4
Units
----1
1
1
1
1
1
1
1
1
1
1
Stats
----------Time Series
Time Series
Time Series
Time Series
Time Series
Time Series
Time Series
Time Series
Time Series
Time Series
Time Series
Rules
Cost
--------------- -----------Oldest, FIFO,
Oldest, FIFO,
Oldest, FIFO,
Oldest, ,
Oldest, ,
Oldest, ,
Oldest, ,
Oldest, , First
Oldest, FIFO,
Oldest, FIFO,
Oldest, FIFO,
********************************************************************************
*
Entities
*
********************************************************************************
Name
------------sla_5170_job
sla_somos_job
Speed (fpm)
-----------150
150
Stats
Cost
----------- -----------Time Series
Time Series
********************************************************************************
*
Resources
*
********************************************************************************
Res
Name
Units Stats
Search
----------- ----- -------- -----model_maker 2
By Unit None
Ent
Search Path
Motion
Cost
------ ---------- -------------- -----------Oldest
Empty: 150 fpm
Full: 150 fpm
********************************************************************************
*
Processing
*
********************************************************************************
Process
Entity
-------------sla_5170_job
sla_5170_job
sla_5170_job
Routing
Location
Operation
-------------- ------------------
Blk Output
Destination
Rule
---- ------------- -------------- -------
sla_5170_gate
queue
1
1
workstation1
sla_5170_job
sla_5170_job
sla_5170_job
queue
workstation1
workstation2
Move Logic
----------
FIRST 1
FIRST 1
FIRST
GET model_maker
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DSES6620 Simulation Modeling and Analysis - Term Project Report: Rapid Prototyping Cell
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wait(mm_service())
FREE model_maker
sla_5170_job
workstation2
sla_5170_job
sla_5170_job
sla_5170_job
sla_5170_job
sla_somos_job
sla_somos_job
hard_drive_1
sla_5170
wait(N(20, 8))
uv_curing_oven
cleaning_area wait(.5)
sla_somos_gate
queue
sla_somos_job workstation1
sla_somos_job workstation2
sla_somos_job
sla_somos_job
sla_somos_job
sla_somos_job
GET model_maker
wait(mm_service())
FREE model_maker
GET model_maker
wait(4)
FREE model_maker
GET model_maker
wait(4)
FREE model_maker
hard_drive_2
sla_somos
wait(N(20, 8))
uv_curing_oven
cleaning_area GET model_maker
wait(1)
FREE model_maker
1
sla_5170_job
hard_drive_1
FIRST 1
1
1
1
1
1
1
1
sla_5170_job
sla_5170_job
sla_5170_job
sla_5170_job
sla_5170_job
sla_somos_job
sla_somos_job
sla_5170_job
hard_drive_1
sla_5170
uv_curing_oven
cleaning_area
EXIT
queue
workstation1
workstation2
FIRST
FIRST
FIRST
FIRST
FIRST
FIRST
FIRST
FIRST
1
sla_somos_job hard_drive_2
FIRST 1
1
1
1
1
sla_somos_job
sla_somos_job
sla_somos_job
sla_somos_job
FIRST
FIRST
FIRST
FIRST
1
sla_somos_job EXIT
hard_drive_2
sla_somos
uv_curing_oven
cleaning_area
1
1
1
1
1
1
1
1
1
1
1
FIRST 1
********************************************************************************
*
Arrivals
*
********************************************************************************
Entity
------------sla_5170_job
sla_somos_job
Location
-------------sla_5170_gate
sla_somos_gate
Qty each
First Time
---------- ---------1
1
Occurrences
----------inf
inf
Frequency
Logic
------------ -----------G(.481,10)
G(.481,29.3)
********************************************************************************
*
Shift Assignments
*
********************************************************************************
Locations
Resources
Shift Files
Priorities
Disable Logic
-------------- ----------- ------------------------------ ------------ ------- -----------------sla_5170_gate
D:\rah\Simulation Modeling & A 99,99,99,99 No
sla_somos_gate
cleaning_area
workstation1
workstation2
uv_curing_oven
model_maker D:\rah\Simulation Modeling & A 99,99,99,99
No
D:\rah\Simulation Modeling & A 99,99,99,99
No
********************************************************************************
*
User Distributions
*
********************************************************************************
ID
Type
Cumulative
Percentage
----------- ------------ ------------ -----------mm_service Discrete
No
18
27
10
28
3
3
1
4
1
1
4
Value
-----------2
4
6
8
10
12
14
16
18
20
22
Hanspeter Bayer – 21 December 2000
16 of 21
DSES6620 Simulation Modeling and Analysis - Term Project Report: Rapid Prototyping Cell
Simulation
sla_service Discrete
No
7
28
20
1
6
9
2
5
5
1
6
5
5
8
16
24
32
40
48
56
64
72
80
88
96
104
********************************************************************************
*
External Files
*
********************************************************************************
ID
---------(null)
(null)
Type
----------------Shift
Shift
File Name
Prompt
------------------------------------------------------------ ---------D:\rah\Simulation Modeling & Analysis\Models\shop.sft
D:\rah\Simulation Modeling & Analysis\Models\engineering.sft
Hanspeter Bayer – 21 December 2000
17 of 21
DSES6620 Simulation Modeling and Analysis - Term Project Report: Rapid Prototyping Cell
Simulation
Appendix 2: Output Results (Averaged) from the Original Model Simulation
-------------------------------------------------------------------------------General Report
Output from F:\UsersP\PBayer\RPI\SMA\project\SMA Bayer Rapid Prototyping Cell.mod [Rapid Prototyping Cell]
Date: Dec/21/2000
Time: 11:06:32 AM
-------------------------------------------------------------------------------Scenario
: Normal Run
Replication
: Average
Period
: Final Report (2190 hr to 10950 hr Elapsed: 8760 hr)
Warmup Time
: 2190 hr (Std. Dev. 0 sec)
Simulation Time : 10950 hr
-------------------------------------------------------------------------------LOCATIONS
Capacity
-------1
0
1
1
Total
Entries
------440.1
22.7472
423.829
456.371
Average
Hours
Per Entry
---------0.000000
0.000000
0.000000
0.000000
Average
Contents
---------0
0
0
0
Maximum
Contents
-------1
0
1
1
Current
Contents
-------0
0
0
0
% Util
-----0.00
0.00
0.00
0.00
(Average)
(Std. Dev.)
(95% C.I. Low)
(95% C.I. High)
2088
0
2088
2088
1
0
1
1
147.1
12.197
138.375
155.825
0.000000
0.000000
0.000000
0.000000
0
0
0
0
1
0
1
1
0
0
0
0
0.00
0.00
0.00
0.00
(Average)
(Std. Dev.)
(95% C.I. Low)
(95% C.I. High)
8760
0
8760
8760
999999
0
999999
999999
591.8
27.7361
571.96
611.64
135.306545
117.811016
51.035477
219.577614
9.36291
8.52301
3.26634
15.4595
28
11.6333
19.6786
36.3214
9.9
12.0319
1.29349
18.5065
0.00
0.00
0.00
0.00
(Average)
(Std. Dev.)
(95% C.I. Low)
(95% C.I. High)
workstation1
workstation1
workstation1
workstation1
6091.8281
1002.834446
5374.493394
6809.162806
1
0
1
1
319.4
10.926
311.585
327.215
18.157288
3.623582
15.565314
20.749262
0.946714
0.0415717
0.916978
0.976451
1
0
1
1
0.8
0.421637
0.4984
1.1016
94.67
4.16
91.70
97.65
(Average)
(Std. Dev.)
(95% C.I. Low)
(95% C.I. High)
workstation2
workstation2
workstation2
workstation2
6503.8372
1096.310488
5719.638407
7288.035993
1
0
1
1
263.8
15.8521
252.461
275.139
23.006816
3.813517
20.278980
25.734653
0.933242
0.0508992
0.896833
0.969651
1
0
1
1
0.7
0.483046
0.354474
1.04553
93.32
5.09
89.68
96.97
(Average)
(Std. Dev.)
(95% C.I. Low)
(95% C.I. High)
Location
Name
-------------sla 5170 gate
sla 5170 gate
sla 5170 gate
sla 5170 gate
sla
sla
sla
sla
somos
somos
somos
somos
gate
gate
gate
gate
queue
queue
queue
queue
Scheduled
Hours
----------2088
0
2088
2088
sla
sla
sla
sla
5170
5170
5170
5170
8760
0
8760
8760
2
0
2
2
501.5
19.179
487.781
515.219
33.760313
0.531232
33.380319
34.140308
1.9323
0.066971
1.8844
1.98021
2
0
2
2
1.9
0.316228
1.6738
2.1262
96.62
3.35
94.22
99.01
(Average)
(Std. Dev.)
(95% C.I. Low)
(95% C.I. High)
sla
sla
sla
sla
somos
somos
somos
somos
8760
0
8760
8760
2
0
2
2
82.7
7.24262
77.5193
87.8807
34.200562
2.299125
32.555981
35.845143
0.321682
0.0195715
0.307682
0.335681
2
0
2
2
0.1
0.316228
-0.1262
0.3262
16.08
0.98
15.38
16.78
(Average)
(Std. Dev.)
(95% C.I. Low)
(95% C.I. High)
2218.0259
103.7233146
2143.831866
2292.219934
2
0
2
2
582.2
22.8269
565.872
598.528
1.313142
0.219738
1.155962
1.470322
0.34379
0.0507613
0.30748
0.380099
2
0
2
2
0
0
0
0
17.19
2.54
15.37
19.00
(Average)
(Std. Dev.)
(95% C.I. Low)
(95% C.I. High)
cleaning
cleaning
cleaning
cleaning
area
area
area
area
hard
hard
hard
hard
drive
drive
drive
drive
1
1
1
1
8760
0
8760
8760
5
0
5
5
503.6
19.8169
489.425
517.775
70.359927
8.958036
63.952180
76.767675
4.06068
0.651454
3.59469
4.52667
5
0
5
5
4.1
1.91195
2.73237
5.46763
81.21
13.03
71.89
90.53
(Average)
(Std. Dev.)
(95% C.I. Low)
(95% C.I. High)
hard
hard
hard
hard
drive
drive
drive
drive
2
2
2
2
8760
0
8760
8760
5
0
5
5
82.5
7.41245
77.1978
87.8022
0.596115
0.485164
0.249073
0.943157
0.00558801
0.00447504
0.00238699
0.00878904
1
0
1
1
0
0
0
0
0.11
0.09
0.05
0.18
(Average)
(Std. Dev.)
(95% C.I. Low)
(95% C.I. High)
2634.6043
195.4924428
2494.767147
2774.441453
4
0
4
4
582.3
22.6718
566.083
598.517
2.278214
0.317537
2.051077
2.505350
0.502142
0.0469295
0.468573
0.535711
4
0
4
4
0.1
0.316228
-0.1262
0.3262
0.60
0.06
0.56
0.64
(Average)
(Std. Dev.)
(95% C.I. Low)
(95% C.I. High)
uv
uv
uv
uv
curing
curing
curing
curing
oven
oven
oven
oven
LOCATION STATES BY PERCENTAGE (Multiple Capacity)
Hanspeter Bayer – 21 December 2000
18 of 21
DSES6620 Simulation Modeling and Analysis - Term Project Report: Rapid Prototyping Cell
Simulation
Location
Name
-------------queue
queue
queue
queue
Scheduled
Hours
----------8760
0
8760
8760
%
Empty
----25.17
15.05
14.40
35.93
%
Partially
Occupied
--------74.83
15.05
64.07
85.60
%
Full
----0.00
0.00
0.00
0.00
|
|
|
|
|
|
|
|
%
Down
---0.00
0.00
0.00
0.00
(Average)
(Std. Dev.)
(95% C.I. Low)
(95% C.I. High)
sla
sla
sla
sla
5170
5170
5170
5170
8760
0
8760
8760
1.82
1.78
0.55
3.10
3.12
3.59
0.56
5.69
95.05
5.07
91.43
98.68
|
|
|
|
0.00
0.00
0.00
0.00
(Average)
(Std. Dev.)
(95% C.I. Low)
(95% C.I. High)
sla
sla
sla
sla
somos
somos
somos
somos
8760
0
8760
8760
74.18
1.74
72.93
75.42
19.48
2.03
18.03
20.93
6.34
0.98
5.64
7.04
|
|
|
|
0.00
0.00
0.00
0.00
(Average)
(Std. Dev.)
(95% C.I. Low)
(95% C.I. High)
2218.0259
103.7233146
2143.831866
2292.219934
71.90
4.29
68.84
74.97
21.82
3.84
19.07
24.56
6.28
1.37
5.30
7.26
|
|
|
|
0.00
0.00
0.00
0.00
(Average)
(Std. Dev.)
(95% C.I. Low)
(95% C.I. High)
cleaning
cleaning
cleaning
cleaning
area
area
area
area
hard
hard
hard
hard
drive
drive
drive
drive
1
1
1
1
8760
0
8760
8760
9.08
7.92
3.41
14.74
21.07
9.85
14.02
28.12
69.85
17.38
57.42
82.28
|
|
|
|
0.00
0.00
0.00
0.00
(Average)
(Std. Dev.)
(95% C.I. Low)
(95% C.I. High)
hard
hard
hard
hard
drive
drive
drive
drive
2
2
2
2
8760
0
8760
8760
99.44
0.45
99.12
99.76
0.56
0.45
0.24
0.88
0.00
0.00
0.00
0.00
|
|
|
|
0.00
0.00
0.00
0.00
(Average)
(Std. Dev.)
(95% C.I. Low)
(95% C.I. High)
2634.6043
195.4924428
2494.767147
2774.441453
58.07
4.45
54.88
61.25
41.70
4.48
38.50
44.90
0.23
0.05
0.20
0.27
|
|
|
|
0.00
0.00
0.00
0.00
(Average)
(Std. Dev.)
(95% C.I. Low)
(95% C.I. High)
uv
uv
uv
uv
curing
curing
curing
curing
oven
oven
oven
oven
LOCATION STATES BY PERCENTAGE (Single Capacity/Tanks)
Location
Name
-------------sla 5170 gate
sla 5170 gate
sla 5170 gate
sla 5170 gate
Scheduled
Hours
----------2088
0
2088
2088
%
Operation
--------0.00
0.00
0.00
0.00
%
Setup
----0.00
0.00
0.00
0.00
%
Idle
-----100.00
0.00
100.00
100.00
%
Waiting
------0.00
0.00
0.00
0.00
%
Blocked
------0.00
0.00
0.00
0.00
%
Down
---0.00
0.00
0.00
0.00
(Average)
(Std. Dev.)
(95% C.I. Low)
(95% C.I. High)
2088
0
2088
2088
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
100.00
0.00
100.00
100.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
(Average)
(Std. Dev.)
(95% C.I. Low)
(95% C.I. High)
workstation1
workstation1
workstation1
workstation1
6091.8281
1002.834446
5374.493394
6809.162806
33.36
5.16
29.67
37.05
0.00
0.00
0.00
0.00
5.33
4.16
2.35
8.30
0.93
0.49
0.58
1.28
60.38
9.33
53.71
67.05
0.00
0.00
0.00
0.00
(Average)
(Std. Dev.)
(95% C.I. Low)
(95% C.I. High)
workstation2
workstation2
workstation2
workstation2
6503.8372
1096.310488
5719.638407
7288.035993
28.87
2.98
26.74
31.00
0.00
0.00
0.00
0.00
6.68
5.09
3.03
10.32
0.60
0.33
0.37
0.84
63.85
7.68
58.36
69.34
0.00
0.00
0.00
0.00
(Average)
(Std. Dev.)
(95% C.I. Low)
(95% C.I. High)
sla
sla
sla
sla
somos
somos
somos
somos
gate
gate
gate
gate
RESOURCES
Resource
Name
------------model maker.1
model maker.1
model maker.1
model maker.1
Units
----1
0
1
1
Scheduled
Hours
----------2616.3723
55.53851402
2576.645201
2656.099399
Number
Of Times
Used
-------332.3
15.4132
321.275
343.325
Average
Hours
Per
Usage
-------5.903854
0.234677
5.735988
6.071719
% Util
-----74.99
4.34
71.88
78.09
(Average)
(Std. Dev.)
(95% C.I. Low)
(95% C.I. High)
Hanspeter Bayer – 21 December 2000
19 of 21
DSES6620 Simulation Modeling and Analysis - Term Project Report: Rapid Prototyping Cell
Simulation
model
model
model
model
maker.2
maker.2
maker.2
maker.2
1
0
1
1
2638.7527
77.73163379
2583.150702
2694.354698
332.2
15.252
321.29
343.11
5.902800
0.315315
5.677252
6.128347
74.27
3.92
71.46
77.07
(Average)
(Std. Dev.)
(95% C.I. Low)
(95% C.I. High)
model
model
model
model
maker
maker
maker
maker
2
0
2
2
5255.125
121.5093161
5168.208511
5342.041489
664.5
27.4924
644.834
684.166
5.900473
0.225776
5.738974
6.061972
74.62
4.08
71.71
77.54
(Average)
(Std. Dev.)
(95% C.I. Low)
(95% C.I. High)
RESOURCE STATES BY PERCENTAGE
Resource
Name
------------model maker.1
model maker.1
model maker.1
model maker.1
Scheduled
Hours
----------2616.3723
55.53851402
2576.645201
2656.099399
%
In Use
-----74.99
4.34
71.88
78.09
%
Idle
----25.01
4.34
21.91
28.12
%
Down
---0.00
0.00
0.00
0.00
(Average)
(Std. Dev.)
(95% C.I. Low)
(95% C.I. High)
model
model
model
model
maker.2
maker.2
maker.2
maker.2
2638.7527
77.73163379
2583.150702
2694.354698
74.27
3.92
71.46
77.07
25.73
3.92
22.93
28.54
0.00
0.00
0.00
0.00
(Average)
(Std. Dev.)
(95% C.I. Low)
(95% C.I. High)
model
model
model
model
maker
maker
maker
maker
5255.125
121.5093161
5168.208511
5342.041489
74.62
4.08
71.71
77.54
25.38
4.08
22.46
28.29
0.00
0.00
0.00
0.00
(Average)
(Std. Dev.)
(95% C.I. Low)
(95% C.I. High)
FAILED ARRIVALS
Entity
Name
------------sla 5170 job
sla 5170 job
sla 5170 job
sla 5170 job
Location
Name
-------------sla 5170 gate
sla 5170 gate
sla 5170 gate
sla 5170 gate
Total
Failed
------1393.5
59.1143
1351.22
1435.78
(Average)
(Std. Dev.)
(95% C.I. Low)
(95% C.I. High)
sla
sla
sla
sla
sla
sla
sla
sla
474.2
24.58
456.618
491.782
(Average)
(Std. Dev.)
(95% C.I. Low)
(95% C.I. High)
somos
somos
somos
somos
job
job
job
job
somos
somos
somos
somos
gate
gate
gate
gate
ENTITY ACTIVITY
Entity
Name
------------sla 5170 job
sla 5170 job
sla 5170 job
sla 5170 job
Total
Exits
------499.6
19.2769
485.811
513.389
Current
Quantity
In System
--------15.2
10.174
7.92244
22.4776
sla
sla
sla
sla
82.6
7.21418
77.4396
87.7604
2.4
3.83551
-0.343566
5.14357
somos
somos
somos
somos
job
job
job
job
Average
Hours
In
System
---------265.532492
126.899435
174.760411
356.304573
Average
Hours
In Move
Logic
-------0.000000
0.000000
0.000000
0.000000
Average
Hours
Wait For
Res, etc.
---------178.461749
121.758882
91.366744
265.556755
Average
Hours
In
Operation
--------28.606349
0.311564
28.383485
28.829213
Average
Hours
Blocked
--------58.464394
6.810305
53.592934
63.335854
(Average)
(Std. Dev.)
(95% C.I. Low)
(95% C.I. High)
192.543150
119.485236
107.074499
278.011800
0.000000
0.000000
0.000000
0.000000
139.530821
116.657838
56.084629
222.977013
26.091616
0.898898
25.448628
26.734604
26.920712
3.729184
24.253200
29.588225
(Average)
(Std. Dev.)
(95% C.I. Low)
(95% C.I. High)
ENTITY STATES BY PERCENTAGE
Entity
Name
------------sla 5170 job
sla 5170 job
sla 5170 job
sla 5170 job
%
In Move
Logic
------0.00
0.00
0.00
0.00
%
Wait For
Res, etc.
--------63.08
10.08
55.88
70.29
%
In Operation
-----------12.39
4.15
9.42
15.36
%
Blocked
------24.53
6.03
20.22
28.84
(Average)
(Std. Dev.)
(95% C.I. Low)
(95% C.I. High)
Hanspeter Bayer – 21 December 2000
20 of 21
DSES6620 Simulation Modeling and Analysis - Term Project Report: Rapid Prototyping Cell
Simulation
sla
sla
sla
sla
somos
somos
somos
somos
job
job
job
job
0.00
0.00
0.00
0.00
65.54
14.67
55.04
76.04
17.21
7.68
11.71
22.70
17.25
7.09
12.18
22.33
(Average)
(Std. Dev.)
(95% C.I. Low)
(95% C.I. High)
Hanspeter Bayer – 21 December 2000
21 of 21