an overview about simulation modeling in DSS

1. Nora ALHarbi
Enaam ALOtaibi
CPIS620
Dr.AbdualAllah ALGhamdi

2. Outlines
• Introduction
• Simulation Definition
• Classification of Simulation
• Major Characteristic
• Advantages and Disadvantages
• Process of Simulation
• Type of Simulation
• Simulation in Practices
• Conclusion
• References

3. Introduction
• Simulation has been used for analyzing systems and too
complex decision problems.
• Russian army used to simulate wars by holding field
exercise.
• The movie Apollo 13 illustrated the use of simulation to
train astronauts .
• NASA also uses simulations to predict rocket and
satellite trajectories.
• Simulation modeling enables organizations to make
better decisions by letting them see the impact of
proposed changes before they are implemented.

4. Definition:
• “Simulation is the process of designing a model of a real
system and conducting experiments with this model for
the purpose of either understanding the behavior of the
system and/or evaluating various strategies for the
operation of the system.”
• It is often used to conduct what-if analysis on the model
of the actual system
• It is a popular DSS technique .

5. Cont.
• Allow us to do the following:
• Model complex systems in a detailed way
• Describe the behavior of systems
• Construct theories or hypotheses that account for the
observed behavior
• Use the model to predict future behavior
• Analyze proposed systems

6. Simulation
model
Decision and
uncontrollable
variables
Measure of
performance or
behavior
input output
Cont.

7. Classification of simulation models
1- Static analysis system : Represents the
system at a particular point in time
2- Dynamic analysis system : Represents
the system behaviour over time

8. Example:
• Manufacturing facility
• Bank operation
• Airport operations
• Transportation/logistics/distribution operation
• Hospital facilities
• Computer network
• Business process (insurance office)
• Chemical plant
• Fast-food restaurant
• Emergency-response system

9. Major Characteristics of
Simulation
• Imitates reality and captures its richness both in shape
and behavior
– “Represent” versus “Imitate”
• Technique for conducting experiments
• Descriptive, not normative tool
• Often to “solve” [i.e., analyze] very complex
systems/problems
• Simulation should be used only when a numerical
optimization is not possible
• Simulation is often used to solve very complex, risky
problems

10. Advantages of Simulation
• Flexibility
• The theory is fairly straightforward
• Great deal of time compression
• Experiment with different alternatives
• The model reflects manager’s perspective
• Can handle large and complex systems
• Can answer “what-if” questions
• Produces important performance measures
• Often it is the only DSS modeling tool for non-structured
problems

11. Disadvantages of Simulation
• Can be expensive and time consuming
• Cannot guarantee an optimal solution
• Slow and costly construction process
• Cannot transfer solutions and inferences to
solve other problems (each model is unique)
• So easy to explain/sell to managers, may lead to
overlooking analytical solutions
• Software may require special skills so it is not so
user friendly

12. The process of simulation
Problem
formulation
Setting of
objectives
and overall
project plan
Model
conceptualization
Data
collection
Model
translation
Verified?
No
Validated?
No
No
Experimental
Design
Production runs
and analysis
More runs?
Documentation
and reporting
No
Implementation
Yes
Yes
Yes
Yes

13. Cont.
1-Problem formulation : (statement of the problem)
•the problem is clearly understood by the simulation analyst
•the formulation is clearly understood by the client.
2-Setting of objectives & project plan : (project proposal)
•Determine : questions that are to be answered, scenarios to
be investigated, decision criteria, end-user, hardware,
software, & personnel requirements.

14. Cont.
3- Model conceptualization : (abstract essential features)
•Contains of events, activities, entities, attributes, resources,
variables, and their relationships.
Assumed system
Conceptual model
Real World System
Logical model

15. Entity: is an object of interest in the system
Example: Health Center
Patients
Visitors
Attribute: is a characteristic of all entities, but
with a specific value “local” to the entity.
Example: Patient
Age,
Sex,
Temperature,
Blood Pressure

16. Resources: what entities compete for , it can be
changed during the simulation
Example: Health Centre
Doctors, Nurses
X-Ray Equipment
Variable: A piece of information that reflects some
characteristic of the whole system, not of specific
entities
Example: Health Centre
Number of patients in the system,
Number of idle doctors,
Current time

17. State: A collection of variables that contains all the
information necessary to describe the system at any
time
Example: Health Centre
{Number of patients in the system,
Status of doctors (busy or idle),
Number of idle doctors,
Status of Lab equipment, etc}

18. Event: An instantaneous occurrence that changes
the state of the system Example: Health Centre
Arrival of a new patient,
Completion of service
(i.e., examination)
Failure of medical
equipment, etc.
Activity: represents a time period of specified
length.
Example: Health Center
Surgery,
Checking temperature,
X-Ray.

19. Logical model
Q(t)> 0 ?
3
YESNO
2 Departure event
Q(t)=Q(t)-1B(t)=0
Generate service &
schedule new departure
Collect & update statistics
TB, TQ, TL, N
L(t)=L(t)-1
L : # of entities in system
Q : # of entities in queue
B : # of entities in server

20. Cont.
4- Data collection & analysis:
•Collect and analysis data for input by: Determine the random
variables , and Fit distribution functions.
5- Model translation:
Coding
General Purpose Language Special Purpose Simulation Language/Software
JAVA, C++, Visual BASIC
Examples:
SIMAN, ARENA, EXTEND
Examples:

21. ARENA example
public static void main(String argv[])
{
Initialization();
//Loop until first "TotalCustomers" have
departed
while (NumberofDepartures <
TotalCustomers)
{
Event evt = FutureEventList[0]; //get
imminent event
removefromFEL(); //be rid of it
Clock = evt.get_time(); //advance in time
if (evt.get_type() == arrival)
ProcessArrival();
else ProcessDeparture();
}
ReportGeneration();
}
JAVA example

22. Cont.
6- Verification: the
process of determining if
the operational logic is
correct. (Debugging software)
7- Validation: the process
of determining if the model
accurately represents the
system. (by comparison of
model results with the real
system)
Conceptual model
Logical model
Simulation model
Real World System
VERIFICATION
VALIDATION

23. Cont.
8- Experimental design : it the process of Alternative
scenarios to be simulated ,Number of simulation runs , and
Length of each run.
9- Analysis of the results: Statistical tests for significance and
ranking and for Interpretation of results.
10- Documantion &reporting: Allows to future
modifications ,Creates confidence , Frequent reports ,
Performance measures , and Recommendations.
11- Implemantion

24. Simulation Types
1. Monte Carlo ( probabilistic simulation)
2. Activity-scanning simulation
3. Event-driven simulation
4. Process-driven simulation
5. Time dependent (discrete simulation)
6. Visual simulation.

25. 1-Monte Carlo
• Specify one or more of the independent variables as a
probability distribution of values.
• It helps take risk and uncertainty in a system into
account in the results.

26. 2-Activity-scanning simulation
• It involves to describing activities that occur during in
fixed time
• Then simulating for multiple future periods the
consequences of the activities

27. 3-Event-driven simulation
• It identifies "events" that occur in a system
• Focusing on a time ordering of the events rather
than a causal or logical ordering.

28. 4-Process-driven simulation
• It focuses on modeling a logical sequence
of events rather than activities.

29. 5-Time dependent (discrete simulation)
• It refers to a situation where it is important to
know exactly when an event occurs.
• For example, in waiting line or queuing
problems, it is important to know the precise
time of arrival to determine if a customer will
have to wait or not.

30. 6-Visual simulation.
• Use computer graphics to present the impact of different
management decisions.
• Decision makers interact with the simulated model and
watch the results over time

31. Simulation Software
• A comprehensive list can be found at
– orms-today.org/surveys/Simulation/Simulation.html
• Simio LLC, simio.com
• SAS Simulation, sas.com
• Lumina Decision Systems, lumina.com
• Oracle Crystal Ball, oracle.com
• Palisade Corp., palisadae.com
• Rockwell Intl., arenasimulation.com …

32. Simulation in Practices
• Example: Simulation analysis for Red Cross blood
drives
• Red Cross collects over 6 million units of blood per year
in the US.
• The system relies heavily on repeat donors who give
blood on a regular basis.
• In the early 1990s the Red Cross was very concerned
about how to keep these donors satisfied.
• By minimize donor time especially waiting time , and the
overall time of system

33. Red Cross cont.
• So, they examined blood collection process via
simulation model to try to develop policies that would
reduce the waiting time .
• The time was broken down into three parts: the time
required to prepare the donor and insert the needle ,
blood gathering time , and the time required to
disconnect the needle and bandage the donor.
• Then , the time fit to simulation model as inputs.

34. Red Cross cont.
• They identified three arrival patterns by direct
observation and bar code scanning devices.
1. For business organization  arrivals peaked around
midmorning and midafternoon .
2. For schools in mid to late morning.
3. For open community drives around midday.
• Several different policy alternatives were considered
( increasing the number of beds )
• Result was improved performance and greater
satisfaction

35. Simulation in Practices cont.
• Example: Simulation analysis at Desiny World
• Cruise Line Operation: Simulate the arrival and check-in
process at the dock.
• Discovered the process they had in mind would cause
hours in delays before getting on the ship.
• Private Island Arrival: How to transport passenger to the
beach area?
• Drop-off point far from the beach.
• Used simulation to determine whether to invest in trams,
how many trams to purchase, average transport and
waiting times, etc..

36. Desiny World cont.
• Bus Maintenance Facility: Investigated “best” way of
scheduling preventative maintenance trips.
• Alien Encounter Attraction: Visitors move through
three areas. Encountered major variability when ride
opened due to load and unload times (therefore, visitors
waiting long periods before getting on the ride). Used
simulation to determine the length of the individual
shows so as to avoid bottlenecks.

37. Discussion:
• Some people believe that managers do
not need to know the internal structure of
the model and the technical aspects of
modeling. “It is like the telephone or the
elevator, you just use it.” Others claim that
this is not the case and the opposite is
true.

38. Conclusion
• Simulation is the common technique in
DSS
• It’s has many characteristic such as
Imitates reality and conducting experiments
• Often it is the only DSS modeling tool for
non-structured problems
• It’s applying in many area to solve the
complex problem .

39. References
• CPIS620 –Chapter 10
• - Introduction to Simulation Using SIMAN (2nd Edition)
• Evan, J. R. and D. L. Olson, Introduction to Simulation and Risk
Analysis (2nd Edition), Upper Saddle River, NJ: Prentice Hall, 2002