1. Static- does not vary with time.
2. Dynamic- varies with time
Types of system
Components of a system
*Entity – is the object of interest in the system.
*Activity- represents a time period of a specific length
*Attribute- is a property of an entity
*State of the problem – is a collection of variable necessary to describe the system of the study.
*Event – is defined as the instantaneous occurrence that may change the state of the system.
*Progress of the system-is studied by following the changes in the state of the system.
Types of models
*Physical – scale model, prototype plans
*Mathematical – analytical queuing models, linear programs, simulation, etc.
Uses of a model
1. To study system behavior in the design stage before such systems are built 2. To communicate a system design
3. To predict the performance of new system under varying sets of circumstances. 4. “What if” questions are answered a about the real world system.
What is a simulation?
* It is a computer program that mimic both
* Internal behavior of the real world system
* Input processes w/c drive or control the simulated system
*simulator output – is a set of measurement concerning the observance reactions and performance of the system.
*Measurements are only estimates of what the real world measurement actually would be because an abstraction of real world system is simulated
When do we simulate
1. Gain knowledge about the improvement of the system
2. The system as yet does not exist
3. Experimentation w/ the system is expensive, too time consuming and dangerous.
Not appropriate use of modeling and simulation
1. Problem can be solved using common sense
2. Problems can be solved analytically
3. Easier to perform direct experiments
Advantages of modeling and simulation
1. Time can be compressed to allow for speed up or slow-down the phenomenon 2. Operating performance of new hardware designs, physical layouts can be tested prior to full scale implementation 3. New policies , operating procedures, information flows, can be explored w/out disrupting on-going operation of the real system 4. Answer “what if” questions
Disadvantages of modeling and simulation
1. Model building requires special training
2. Simulation modeling, execution and analysis can be time consuming and expensive 3. Hidden critical assumptions may cause the model to diverse from reality 4. Model parameters may be difficult to initialize
Classification of models
1. Deterministic – no random variables in the model
2. Stochastic (Non-deterministic or probabilistic) – model has 1 or more random variables as input
Classification of simulation models
1. Monte carlo simulation – describe system w/c are both stochastic and static 2. Continuous simulation – the system modeled are dynamic but may be deterministic and stochastic 3. Discrete event simulation – used to model systems which are assumed...