1. Discrete Event Systems Simulation
Lecture 1: Introduction
Dr. Jafar Habibi

2. Outline Modeling and Simulation Model Development Life Cycle What?
Why?
Uses
Taxonomy
Model Development Life Cycle

3. Modeling and Simulation
Definitions
Model
A (usually miniature) representation of something; an example for imitation or emulation [Merriam-Webster dictionary]
A description of observed behavior, simplified by ignoring certain details. Models allow complex systems to be understood and their behavior predicted within the scope of the model, but may give incorrect descriptions and predictions for situations outside the realm of their intended use. [
Simulation
The imitative representation of the functioning of one system or process by means of the functioning of another [Merriam-Webster dictionary]

4. System: set of objects, joined to accomplish some purpose

5. Why Simulate?
It may be too difficult, hazardous, or expensive to observe a real, operational system
Parts of the system may not be observable (e.g., internals of a silicon chip or biological system)
Uses of simulations
Analyze systems before they are built
Reduce number of design mistakes
Optimize design
Analyze operational systems
Create virtual environments for training, entertainment

6. When is simulation appropriate?
Allows access to system internals that may otherwise not be observable.
Informational, organizational, and environmental changes can be
simulated, and the effect of these changes on the model’s behavior
can be observed.
Observations based on simulations give great insight into the system behavior, and it can be determined which variables are most important and how they interact.
Analytic solutions can be verified.
Simulation allows to experiment with new designs or policies prior to implementation.
Can be used for training without the cost and disruption of on-the-job learning.
The simulated system is so complex, that its interactions can be treated only through simulation

7. When simulation is not appropriate?
Would common sense suffice?
Is there an analytical solution?
Is it easier to perform direct measurements on a physical system?
Is there a shortage of resources for implementing the simulation?
Is there a shortage of time for getting the desired results?
Is data lacking for modeling the system and beginning a simulation study?
Is there enough time and personnel to verify and validate the model?
Are the managers’ expectations unrealistic?
Is the system too complex to be modeled?

8. Models in Simulation Types of models But why model? Physical models
Simulation models
Analytical models
But why model?
Understanding
Improvement
Optimization
Decision making

9. Applications: System Analysis
“Classical” application of simulation
Telecommunication networks
Transportation systems
Electronic systems (e.g., microelectronics, computer systems)
Battlefield simulations (blue army vs. red army)
Ecological systems
Manufacturing systems
Logistics
Focus typically on planning, system design

10. Applications: On-Line Decision Aids
interactive simulation environment
analysts and
decision makers
live
data
feeds
forecasting tool
(fast simulation)
situation
database
Simulation tool is used for fast analysis of alternate courses of action in time critical situations
Initialize simulation from situation database
Faster-than-real-time execution to evaluate effect of decisions
Applications: air traffic control, battle management
Simulation results may be needed in only seconds
Here to discuss technology. The technology we are developing is realizing faster than real time interactive simulations. Current TISES simulation runs require a few hours, depending on the scenario. We’d like to see that reduced to minutes.
The specific benefits from this capability fall into two categories:
doing what you do now, better
doing things you cannot do now

11. Applications: Virtual Environments
Uses: training (e.g., military, medicine, emergency planning), entertainment
Simulations are often used in virtual environments to create dynamic computer generated entities
Adversaries and helpers in video games
Defense: Computer generated forces (CGF)
Automated forces
Semi-automated forces
Physical phenomena
Trajectory of projectiles
Buildings “blowing up”
Environmental effects on environment (e.g., rain washing out terrain)

12. A Few Example Applications
Earth magnetosphere: understand space weather
Wargaming: test strategies; training
Transportation systems: improved operations; urban planning
Parallel computer systems: developing scalable software
Computer communication
network: protocol design

13. Simulation Fundamentals
A computer simulation is a computer program that models the behavior of a physical system over time.
Program variables (state variables) represent the current state of the physical system
Simulation program modifies state variables to model the evolution of the physical system over time.

14. Defense Simulations Types of simulation Major application areas
Constructive: simulated people operating simulated equipment
Virtual: real people operating simulated equipment,
Live: real people operating real equipment
Major application areas
Analysis
Wargaming, logistics
Training
Platform level, Command level
Test and evaluation
Hardware-in-the-loop

15. Types of Simulation Models
System model
deterministic
stochastic
static
dynamic
static
dynamic
Monte Carlo
simulation
continuous
discrete
continuous
discrete
Continuous
simulation
Discrete-event
simulation
Continuous
simulation
Discrete-event
simulation

16. Stochastic vs. Deterministic
Stochastic simulation: a simulation that contains random (probabilistic) elements, e.g.,
Examples
Inter-arrival time or service time of customers at a restaurant or store
Amount of time required to service a customer
Output is a random quantity (multiple runs required analyze output)
Deterministic simulation: a simulation containing no random elements
Simulation of a digital circuit
Simulation of a chemical reaction based on differential equations
Output is deterministic for a given set of inputs

17. Static vs. Dynamic Models
Static models
Model where time is not a significant variable
Examples
Determine the probability of a winning solitaire hand
Static + stochastic = Monte Carlo simulation
Statistical sampling to develop approximate solutions to numerical problems
Dynamic models
Model focusing on the evolution of the system under investigation over time
Main focus of this course

18. Continuous vs. Discrete
State of the system is viewed as changing at discrete points in time
An event is associated with each state transition
Events contain time stamp
Continuous
State of the system is viewed as changing continuously across time
System typically described by a set of differential equations

19. Course Overview to This course is basically about going from
An actual or envisioned system
A useful simulation model of that system
Discrete event simulation
Continuous simulation
Monte Carlo simulation
Simulation software

20. Course Outcomes
Recognize mathematical parameters as if they were physical variables and vice-versa
Be able to follow general mathematical concepts of derivation of engineering or scientific result and possess the mathematical skill to link those concepts
Be able to understand the relevance of the mathematical results to physical applications
Have the ability to use computational tools for finding graphical, numerical, statistical and analytic solutions to problems
Have the ability to use systems simulations appropriate to engineering practice
Be able to identify input, output, and operating variables as appropriate in various units
Be able to identify technical relationships between the input, output and variables and use the relationships to predict mutualchanges

21. Model Development Life Cycle
Define goals, objectives of study
Develop conceptual model
Develop specification of model
Fundamentally an iterative process
Develop computational model
Verify model
Validate model

22. Determine Goals and Objectives
What does you (or the customer) hope to accomplish with the model
May be an end in itself
Predict the weather
Train personnel to develop certain skills (e.g., driving)
More often a means to an end
Optimize a manufacturing process or develop the most cost effective means to reduce traffic congestion in some part of a city
Often requires developing a business case to justify the cost
Improved efficiency will save the company $$$
Example: electronics
Even so, may be hard to justify in lean times
Goals may not be known when you start the project!
One often learns things along the way

23. Develop Conceptual Model
An abstract (i.e., not directly executable) representation of the system
What should be included in model? What can be left out?
What abstractions should be used
Level of detail
Often a variation on standard abstractions
Example: transportation
Fluid flow?
Queueing network?
Cellular automata?
What metrics will be produced by the model?
Appropriate choice depends on the purpose of the model

24. Develop Specification Model
A more detailed specification of the model including more specifics
Collect data to populate model
Traffic example: Road geometry, signal timing, expected traffic demand, driver behavior
Empirical data or probability distributions often used
Development of algorithms necessary to include in the model
Example: Path planning for vehicles

25. Develop Computational Model
Executable simulation model
Software approach
General purpose programming language
Special purpose simulation language
Simulation package
Approach often depends on need for customization and economics
Where do you make your money?
Defense vs. commercial industry
Other (non-functional) requirements
Performance
Interoperability with other models/tools/data

26. Verification Did I build the model right?
Does the computational model match the specification model?
Largely a software engineering activity (debugging)
Not to be confused with correctness (see model validation)!

27. Validation Did I build the right model?
Does the computational model match the actual (or envisioned) system?
Typically, compare against
Measurements of actual system
An analytic (mathematical) model of the system
Another simulation model
By necessity, always an incomplete activity!
Often can only validate portions of the model
If you can validate the simulation with 100% certainty, why build the simulation?

28. Summary
Modeling and simulation is an important, widely used technique with a wide range of applications
Computation power increases (Moore’s law) have made it more pervasive
In some cases, it has become essential (e.g., to be economically competitive)
Rich variety of types of models, applications, uses
As easy (actually, easier!) to get wrong or misleading answers as it is to get useful results
Appropriate methodologies required to protect against major mistakes. Even so…

29. Questions?
Courtesy of Professor Richard Fujimoto