The document outlines the 12 main steps in a simulation study: 1) problem formulation, 2) setting objectives and project plan, 3) model conceptualization, 4) data collection, 5) model translation, 6) verification, 7) validation, 8) experimental design, 9) production runs and analysis, 10) documentation and reporting, 11) implementation, and 12) feedback loops for additional runs if needed. It provides details on each step, including defining the problem, collecting relevant data, translating the conceptual model into a programming language, verifying the model's logic, validating the model's accuracy, designing experiments, analyzing output, documenting results, and implementing recommendations.

1. StepsIn Simulation Study
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2. Steps:
1. Problem Formulation
2. Setting of Objectives and Overall ProjectPlan
3. Model Conceptualization
4. Data Collection
5. Model Translation
6. Verification
7. Validation
8. Experimental Design
9. Production RunandAnalysis
10. Documentation and Report
11. Implementation

3. Flow Chart Representation
Problem
Formulatio
n
Setting of
Objectives and
Overall Project
Plan
Model
Conceptualization
Data
Collection
Model
Translation
Experimental
Design
Production
Run &
Analysis
More
Runs
?
Validat
e
d?
Verified
?
Documentation
& Reporting
Implementation
No
Yes
Yes
No
Yes
Yes
No
No

4. Problem Formulation
• the problem is clearly understoodby
the simulation analyst
• the formulation is clearlyunderstood
by the client
Problem
Formulatio
n
Setting of
Objectives and
Overall Project
Plan
Model
Conceptualization
Data
Collection
Model Translation

5. Setting of Objectives and Overall ProjectPlan
• determine the questions that are tobe
answered
• identify scenarios to beinvestigated
• decision criteria
• determine the end-user
• determine datarequirements
• determine hardware, software, & personnel
requirements
• prepare atime plan
• cost plan and billing procedure
Problem
Formulatio
n
Setting of
Objectives and
Overall Project
Plan
Model
Conceptualization
Data
Collection
Model Translation

6. Model Conceptualization
oevents, activities, entities, attributes,
operformance measures
odata requirements
(assumptions)
Problem
Formulatio
n
Setting of
Objectives and
Overall Project
Plan
Model
Conceptualization
Data
Collection
Model Translation
• abstract essReeanlWtioarlldfSeysatetmures
resources,As
vs
au
rm
iae
bd
lS
eys
st
,e
am
ndtheir relationships
• select correCcontcleeptvueallSoysftedmetails
LogicalModel

7. Data Collection
• collect data for input analysisand
validation
oarrival rate, arrival process, service discipline,
service rate , etc.
• analysisof the data
odetermine the random variables
ofit distribution functions
Problem
Formulatio
n
Setting of
Objectives and
Overall Project
Plan
Model
Conceptualizati
on
Data
Collection
Model Translation

8. Model Translation
Problem
Formulatio
n
Setting of
Objectives and
Overall Project
Plan
Model
Conceptualization
Data
Collection
Model Translation
Codin
g
General
Purpose
Language
(JAVA, C++, VB)
Special Purpose
Simulation
Language/
Software
(SIMEN,
ARENA,
EXTEND)

9. Verification
• the processof determining ifthe
operational logic iscorrect
odebugging the simulation software
Model Translation
Verified
?
Validated
?
No
Yes
Yes
DataCollection
No
Model
Conceptualization
No

10. Validation
• the processof determining if themodel
accurately represents the system
ocomparison of model results with collected
data from the realsystem
Model Translation
Verified
?
Validated
?
No
Yes
Yes
DataCollection
No
Model
Conceptualization
No

11. Experimental Design
• alternative scenarios to be
simulated
• type of output data analysis
(steady-state vs.terminating
simulation analysis)
• number of simulationruns
• length of eachrun
• variance reduction
Validated
?
Yes
Yes
DataCollection
No
Model
Conceptualization
No
Experimental
Design
More Runs?
Yes
Production Run
& Analysis

12. Production Run
& Analysis
• statistical tests for significance
and ranking
o Point Estimation
oConfidence-Interval Estimation
• interpretation of results
Validated
?
Yes
Yes
DataCollection
No
Model
Conceptualization
No
Experimental
Design
Production
Run &
Analysis
More
Runs?
Yes
Yes
No

13. Documentation & Report
• program documentation
oallows future modifications
ocreates confidence
• progress reports
ofrequent reports (e.g. monthly) are
suggested
oalternative scenarios
operformance measuresor criteria
used
oresults of experiments
orecommendations
Documentation
& Reporting
Implementation
No
More Runs?

14. Implementation
• Implement the study in realsystem
Documentatio
n &
Reporting
Implementation
More Runs?
No
Success
???
Failure
???
O
R

15. References
• www.engr.sjsu.edu
• www.ewp.rpi.edu/hartford

16. Thank you
!