CSC401 Simulation
Week 1 – Chapter 1 – Introduction to Simulation
A simulation is the imitation of the operation of a real-world process or system
over time.
Simulation involves the generation of an artificial history of a system and the
observation of that artificial history to draw inferences concerning the operating
characteristics of the real system.
Develop a system model
Set of assumptions concerning the operation of the system.
Mathematical, logical, symbolic relationships between the entities
(objects of interest) of the system.
Validated system can be used to investigate what-if questions about the
real-world system such as
Analysis tool to predict the effect of system changes on performance
Design tool to predict the performance of new systems
Sometimes a model can be done analytically via mathematical techniques.
Many systems are too complex for mathematical solutions and require
computer-based simulation.
1.1 When simulation is the appropriate tool.
Study and experimentation of internal interactions of a complex system
Informational, organizational, environmental changes can simulated and
there effect on the model’s behavior observed.
Knowledge gained during design of simulation model is valuable.
Verify analytic solutions.
Simulation models for training.
Animation facilitates visualization.
1.2 When simulation is not appropriate
Banks and Gibson in 1997 gave ten rules for evaluating when simulation is not
appropriate.
1. and 2. Problem can be solved easily analytically or by common sense.
3. Don’t use if easier to perform direct experiments
4. Don’t use if costs exceed savings.
5. and 6. Don’t use simulation if resources or time are not available.
7. If no data is available, not even estimates, simulation is not advised.
8. Don’t use if not enough time or personnel to validate model.
9. Simulation tough if managers have unreasonable expectations
10. If system behavior too complex or undefined, simulation not appropriate.
(Human behavior)
1.3 Advantages and disadvantages or simulation
Simulation mimics what happens in real life.
Simulation model does not need to make dubious assumptions.
Output from model directly correspond to outputs from real system.
Advantages:
1. New policies and procedures can be done without disruption to real
system.
2. New hardware or physical designs can be done before committing
resources for acquisition.
3. Hypotheses can be tested for feasibility.
4. Time can be compressed or expanded for a speed-up or slow-down of
phenomena.
5. Insight can be obtained about interaction of variables.
6. Insight can be obtained about importance of variables to performance
of the system.
7. Bottleneck analysis can be performed to discover where work in
progress are being delayed excessively.
8. Simulation study aids in understanding of how the system operates.
9. “What if” questions can be answered.
Disadvantages / offset
1. Model building requires special training. Art, science, experience
required.
However vendors of simulation software may take all the pain out of
development.
2. Simulation results can be difficult to interpret. Interrelationship of
variables or randomness of observations?
However simulation software vendors have developed output-analysis
capabilities within their packages for performing very thorough analysis.
3. Simulation modeling and analysis can be time consuming and expensive.
However simulation can be performed faster today than yesterday and
will be faster tomorrow.
4. Simulation is used when an analytical solution is possible, or even preferable.
Closed-form queuing models are available.
However closed-form queuing models are not able to analyze most of the
complex systems that are encountered in practice from experience of the
authors in consulting.
1.4 Areas of application
Winter Simulation Conference is good place to learn about applications and theory.
Manufacturing applications
Shared resource capacity analysis in biotech manufactoring
Semiconductor manufacturing
Making optimal design decisions for next-generation dispensing tools
Construction engineering and project management
Building a virtual shop model for steel fabrication
Military applications
Specifying the behavior or computer-generated forces without programming
Logistics, supply chain, and distribution applications
Semiconductor supply-network simulation
Transportation modes and traffic
Simulation of freeway merging and diverging behavior
Business process simulation
Simulation’s role in baggage screening in airports
Health care
A simulation-integer-linear-programming-based tool for scheduling
emergency room staff
Simulation for risk analysis is growing: options pricing and insurance.
Simulation of large-scale systems, such as the internet backbone and wireless
networks, are growing as hardware and software increase their capability to handle
large numbers of entities in a reasonable time.
Simulation models of automated material handling systems
1.5 Systems and system environment
A system is defined as a group of objects that are joined together in some regular
interaction or interdependence toward accomplishment of some purpose.
A system is often affected by changes occurring outside the system.
Such changes are said to occur in the system environment.
In modeling systems, it is necessary to decide on the boundary between the system
and its environment.
This decision may depend on the purpose of the study.
Setting of interest rate may be a constraint or may be an activity of the system.
1.6 Components of a system
Entity – object of interest in the system
Attribute – property of an entity
Activity – represents a time period of specified length
Collection of entities that compose a system for one study may be only a subset of the
overall system
State of a system – that collection of variables necessary to describe the system at any
time, relative to the objectives of the study
Event – an instantaneous occurrence that might change the state of the system.
Endogenous – activities and events occurring within a system
Exogenous – activities and events in the environment that affect the system.
Table 1.1
1.7 Discrete and continuous systems
Although few systems are wholly discrete or continuous, usually one type of change
usually predominates for most systems
A discrete system is one in which the sate variables change only at discrete set of
points in time.
A continuous system is one in which the state variables change continuously over
time.
1.8 Model of a system
A model is defined as a representation of a system for the purpose of studying the
system.
Usually only need those aspects of the system that affect the problem under
investigation.
Chapter 3
1.9 Types of models
Models can be classified as being mathematical or physical
Mathematical models use symbolic notation and mathematical equations to represent
a system
A simulation model is a particular type of mathematical model
A static simulation model, some time called a Monte Carlo simulation, represents a
particular point in time.
Dynamic simulation models represent systems as they change over time.
Simulation models that contain no random variables are classified as deterministic.
Have a known set of inputs which will result in a unique set of outputs
A stochastic simulation model has one or more random variables as inputs.
Random inputs lead to random outputs.
Since outputs are random, they can be considered only as estimates of the true
characteristics of a model
In a stochastic simulation, the output measures must be treated as statistical
estimates of the true characteristics of the system.
Discrete models can be used to model a continuous system.
Models in text are discrete, dynamic, and stochastic
1.10 Discrete-event systems simulation
Discrete –event systems simulation is the modeling of systems in which the state
variables changes only at a discrete set of points in time
The simulation models are analyzed by numerical methods rather than by analytical
methods
Analytical methods employ the deductive reasoning of mathematics to “solve” the
model.
Numerical methods employ computational procedures to “solve” mathematical
models.
Simulation models which employ numerical models are run rather than
solved.
Artificial history of the system generated from model assumptions and
observations are collected to be analyzed and to estimate the true system
performance measures.
Large amount of data usually requires the aid of a computer
1.11 Steps in a simulation study
Figure 1.3
Problem formulation
Setting of objectives and overall project plan
The objectives indicate the questions to be answered by simulation
Model conceptualization
Art and science
Art of modeling enhance by an ability to abstact the essential features of a
problem.
Start simple and build to more complex
Involve the model user
Data collection
Interplay between construction of the model and collection of needed data
The objectives of the study dictate the kind of data to be collected
Model translation
Program the model
Use a simulation language such as Extend
Verified?
Verify the computer program prepared for the simulation model to insure it
performs properly
Validated?
Validation usually is achieved through calibration of the model, an iterative
process of comparing the model against actual system behavior and using the
discrepancies between the two, and the insights gained, to improve the model.
Experimental design
The alternatives that are to be simulated must be determined.
The decision concerning which alternatives to simulate will be a function of runs
that have been completed and analyzed.
For each system design, decisions need to be made concerning the length of
initialization period, the length of the simulation runs, and the number of
replications to be made of each run.
Production runs and analysis
Production runs, and their subsequent analysis, are used to estimate measures of
performance for system designs that are being simulated.
Chapters 11 and 12
More runs?
Given the analysis of runs that have been completed, additional runs may be
needed and may require a different experimental design.
Documentation and reporting
Program documentation
Progress documentation important to keep project on course
Better to work with many intermediate milestones with deliverables than with one
absolute deadline.
Implementation
Depends on how well previous steps have been performed
Model and underlying assumptions properly communicated important
Phase 1: problem formulation, and setting of objectives and overall design
Phase 2: model conceptualization, data collection, model translation, verification,
and validation
Phase 3: running the model, experimental design, production runs and analysis,
additional runs
Phase 4: documentation and reporting, and implementation
Problems:
Page 19 +
1, 2, 3, 4, 11