2. contents
When simulation is the appropriate tool and when it is not appropriate
Advantages and disadvantages of Simulation
Areas of application
Systems and system environment
Components of a system
Discrete and continuous systems
Model of a system
Types of Models
Discrete-Event System Simulation
Steps in a Simulation Study
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3. What is simulation?
Definition: It is the imitation of the operation of a real world process or system over
time.
It involves the generation of artificial history of the system and the observation of that
artificial history to draw the inferences concerning to the characteristics of the real
system.
The behavior of a system as it evolves over time is studied by developing simulation
model.
Simulation modeling can be used both as an analysis tool and a design tool.
Analysis Tool: To predict the effect of changes to the existing systems
Design Tool: To predict the performance of new systems under varying sets of
circumstances.
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4. When simulation is an appropriate tool?
To study the internal interactions of a computer system or a subsystem within a complex
system.
To study the informational, organizational and environmental changes which affects the
model’s behavior.
To gain the knowledge which may help to investigate the improvement of a model
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5. When simulation is an appropriate tool?
Cont’d
Changing the simulation i/p’s and studying the o/p’s can produce a valuable insight
Can be used as pedagogical device to reinforce analytical solution methodologies
Can be used to experiment with new designs or policies before implementation to prepare
what might happen.
To verify analytic solutions.
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6. When simulation is an appropriate tool?
Cont’d
Simulating different capabilities can determine the requirements on it.
Animation shows a system in simulated operation can be visualized.
To study the modern systems.
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7. When Simulation is not appropriate?
Should not be used when the problem can be solved with common sense
Should not be used when the problem can be solved analytically.
Should not be used if it is easier to perform the direct experiments.
Not to use simulation if costs exceeds the savings.
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8. When Simulation is not appropriate?
Cont’d
Not to be performed if the resources or time are not available
Not advised when no data available.
If managers have unreasonable expectations or if the power of simulation is over estimated ,
simulation might not be appropriate.
If the system behavior is too complex or can’t be defined , simulation is not appropriate.
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9. Advantages of simulation
New policies and all the different rules and regulation of real system can be explored.
Testing of new systems without committing resources is possible.
Hypothesis about how or why certain phenomena occur can be tested for feasibility.
Insight can be obtained about the importance of variables to the performance of the system.
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10. Advantages of simulation cont’d
Bottleneck analysis can be performed to discover where work in process, information,
Materials and so on are being delayed excessively.
It can help in understanding how the system operates rather than how individuals think the
system operates.
“what if” questions can be answered to design the new systems.
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11. Disadvantages of simulation
Model building requires special training.
Simulation results can be difficult to interpret.
Simulation modeling and analysis can be time consuming and expensive.
Can be used only in some cases when an analytical solution is possible or even preferable.
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12. Areas of Application
Manufacturing applications
Wafer fabrication
Business Process Simulation
Construction Engineering and Project management
Logistics, Supply chain and Distribution Applications
Military applications
Health Care
Additional applications
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13. System & Environment
A system is defined as a group of objects that are joined together in some regular interaction
towards the accomplishment of some purpose
E.g..: production system manufacturing automobiles
A system is often affected by changes occurring outside the system, such changes are said to
occur in the system environment.
In modelling systems, it is necessary to determine the boundary between the system and
environment
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14. Components of system
Entity: Object of interest in the system.
Attribute: Property of an entity.
Activity: Time period of specified length
State: Collection of variables necessary to describe a system at any time
Event: An instantaneous occurrence that might change the state of the system.
Terms such as
Endogenous: describes the activities and event occur within a system
Exogenous: describes the activities and events in the environment that affects the system
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16. Types of systems
Can be classified as discrete and continuous system
Discrete system is one whose state variables change only at discrete set of points in time
E. g. : Bank and customers
No. of customers change only when they arrive or service to be provided has completed.
Following figure depicts a discrete system
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18. Types of systems
A continuous system is one in which the state variables change continuously over the time
E.g. : head of water behind the time
During excess water, they do flood control, for electricity they draw water
Following figure depicts continuous system
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20. Model of a system
A model is defined as a representation of a system for the purpose of studying the system.
Model is nothing but simplification of the system
Should be sufficiently detailed to permit valid conclusions to be drawn about the real system
Different models of the same system could be required as the purpose of investigation
changes.
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21. Types of models
Models can be mathematical or physical
A mathematical model uses symbolic notation and mathematical equations to represent a
system
A physical model is larger or smaller version of an object such as the enlargement of atom or
scaled down version of solar system
Simulation models can be classified as
Static or dynamic
Deterministic or stochastic
Discrete or continuous
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22. Static model represents a system at a particular point in time
Dynamic model represents the system as they change over time
Eg: bank simulator from 9 am to 4 pm
Deterministic model model that contains no random variables
Stochastic model model which has one or more random variables as inputs.
Random inputs leads to random output
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23. Discrete event system simulation
State variable changes only at a discrete set of point in time
The simulation models are analysed by numerical rather than analytical methods
Analytical methods employ the deductive reasoning of mathematics to solve the model.
Numerical methods employ computational procedures to solve mathematical models.
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24. Steps in Simulation Study
Initialization phase (First phase)
1. Problem Formulation
2. Setting objectives and overall project plan
Model building (Second Phase)
3. Model Conceptualization
4. Data Collection
5. Model Translation
6. Verification
7. Validation
Third phase
8. Experimental Design
9. Production runs and Analysis
10. More Runs?
Documentation (Fourth phase)
11. Documentation and Reporting
12. Implementation
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26. Problem formulation
Every study should begin with the statement of the problem
Problem must be clearly understood by the analyst from those who have the problem
If the problem statement is still being developed by the analyst, it is important that the policy
makers understand and agree with the formulation.
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27. Setting objectives and overall project plan
The objectives indicate the questions to be answered by the simulation
At this point, determination should be made concerning whether simulation is the appropriate
methodology for the problem as formulated and the objectives as stated.
Should include the plans for the study in terms of the number of people involve, the cost of
study, number of days required to accomplish each phase of the work, along with the results
expected in each stage.
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28. Model conceptualization
It is not possible to provide a set if instructions that will lead to building successful and
appropriate models in every instance
Hence it is good to build simple model and build towards greater complexityy
It is not necessary to have one to one mapping between the model and real system, only
essence of real system is needed.
Involving the model user will both enhance the quality of the resulting model and increase the
confidence of the model user in the application of the model.
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29. Data collection
There is direct relation between the construction of model and collection of the needed input
data
As the model changes the required data elements can also change.
Data collection takes large portion of time, hence it is necessary to begin as early as possible
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30. Model translation
Model must be entered into a computer recognizable format
Model is converted into program to accomplish the desired result with little or no actual
coding
If the problem is amenable to solution with simulation software, the model development is
greatly reduced.
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31. Verified?
After converting the model into program, to check whether it performs properly
With complex models, it is difficult, if not impossible to translate the model successfully in its
entirely without a good deal of debugging
If the input parameters and logical structure of the model are correctly represented in the
computer, verification is completed.
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32. Validated?
Achieved through calibration of the model
An iterative process of comparing the model against the actual system behaviour and using
discrepancies between the two, the insights gained , to improve the model.
The process is repeated until the accuracy is judged acceptable
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33. Experimental design
The alternatives that are to be simulated must be determined
For each system design that is simulated, decisions need to be made concerning the length of
the initialization period, the length of simulation runs and the numbers of replications to be
made of each run.
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34. Production runs and analysis
Used to estimate measures of performance for the system designs that are being simulated.
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35. More runs?
After the run is completed, the analyst determines whether additional runs are needed and
what design those additional experiments should follows.
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36. Documentation and reporting
There are two types of documentation
Program
Progress
Program documentation – here the program is documented well so that if same program when to
be used by another analyst, it can be easily understood hence policymakers and model users can
make decisions based on analysis very easily
Progress documentation- written history of a simulation project
Tells about work done and decisions made
“It is better to work with many intermediate milestones that with one absolute deadline”
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37. implementation
The success of implementation phase depends on the previous stages
If the model user has been involved during the entire model building process and if the model
user understands the nature of the model, its outputs, the likelihood of implementation is
enhanced.
If the model and its underlying assumptions have not been properly communicated, then
implementation will probably suffer, regardless of simulation validity.
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39. Example 1
Name the several entities , attributes, events and state variables for the following systems
a) A cafeteria
b) A grocery store
c) A Laundromat
d) A fast food restaurant
e) A hospital emergency room
f) A taxicab company with 10 taxis
g) An automobile assembly line
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40. solution
a) Cafeteria
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Entities Diners (customers)
Attributes 1. Size of appetite (thurst for hunger)
2. Entree preference (choice of main course)
Activities 1. Selecting food
2. Paying for food
Events 1. Arrival at service line
2. Departure from service line
State variables 1. Number of diners in waiting line
2. Number of servers working
41. solution
b) Grocery store
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Entities Shoppers
Attributes 1. Length of grocery list
Activities 1. Checking out
Events 1. Arrival of checkout counters
2. Departure from checkout counter
State variables 1. Number of shoppers in line
2. Numbers of checkout lanes in operation
42. solution
c) Laundromat (coin based- public washing machine)
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Entities Washing machine
Attributes 1. Breakdown rate
Activities 1. Repairing the machine
Events 1. Occurrence of breakdown
2. Completion of service
State variables 1. Number of machine running
2. Number of machine in repair
3. Number of machine in waiting for repair
43. solution
d) Fast food restaurant
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Entities Customers
Attributes 1. Size of order desired
Activities 1. Placing the order
2. Paying the order
Events 1. Arrival at the counter
2. Completion of the purchase
State variables 1. Number of customers waiting
2. Number of position operating
44. solution
e) A hospital emergency room
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Entities Patients
Attributes 1. Attention level required
Activities 1. Providing the service required
Events 1. Arrival of the patients
2. Departure of the patients
State variables 1. Number of patients waiting
2. Number of doctors waiting
45. solution
f) A taxi cab company with 10 taxis
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Entities Fares
Attributes 1. Origination (start location)
2. Destination (end location)
Activities 1. travelling
Events 1. Pick up of fare
2. Drop off of fare
State variables 1. Number of busy taxi cabs
2. Number of fares waiting to be picked up
46. solution
g) Automobile assembly line
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Entities Robot welders
Attributes 1. Speed
2. Breakdown rate
Activities 1. Spot welding
Events 1. Breaking down
State variables 1. Availability of machines
47. Example 2
What are the events and activities associated
with the use of your checkbook?
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48. solution
Event
Deposit
Withdrawal
Activities
Writing a check
Cashing a check
Making a deposit
Verifying the account balance
Reconciling the checkbook with the bank statement
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