1 / 23

ISE 195Introduction to Industrial

EngineeringLecture 2

Modeling and Simulation(Topic of ISE 471 System

Performance Modeling)

Simulation Is

- Simulation very broad term methods and

applications to imitate or mimic real systems,

usually via computer - Applies in many fields and industries
- Very popular and powerful method, in fact many

surveys list simulation as among the most used

techniques - Todays goal Cover general ideas, terminology,

examples of applications, good/bad things, kinds

of simulation, software options, how/when

simulation is used

Simulation Is

- Simulation is the process of designing a model of

a real or imagined system and conducting

experiments with that model - The purpose of simulation experiments is to

understand the behavior of the system or evaluate

strategies for the operation of the system - Simulation is a descriptive technique, it

generally requires something to evaluate - Definition of Simulation The technique of

imitating the behavior of some situation or

system by means of an analogous model, situation,

or apparatus, either to gain information more

conveniently or to train personnel.

Systems

- System facility or process, actual or planned
- Many Examples
- Manufacturing facility
- Bank or other personal-service operation
- Transportation/logistics/distribution operation
- Hospital facilities (emergency room, operating

room, admissions) - Computer network
- Freeway system
- Business process (insurance office)
- Criminal justice system
- Chemical plant
- Fast-food restaurant
- Supermarket
- Theme park
- Flight-line maintenance modeling
- Simulator training systems
- Emergency-response system

What is a Model in Engineering?

- A system used to study another system
- Physical A prototype or mock-up of a system
- Live-action exercises
- Flight Simulators
- Mathematical
- Systems of Simultaneous Linear Equations
- Closed Form expressions (Force mass x

acceleration) - Logical
- A chemical reaction
- Description of input/output of a logic circuit
- Computational A combination of logical and

mathematical with a computer engine - Numerical methods
- Newtons method for finding a minimum of a convex

function - Iterative solutions to differential equations
- Computer Simulation Using a computer-based

model to mimic a real system as it evolves

through time - Includes both mathematical aspects and logical

aspects

Example 1

- An example of a simulation from the mechanical

engineers perspective - Vehicle Suspension Simulation (Inventor)
- http//www.youtube.com/watch?vL0R5elR6nck

Why Not Work With the Actual System?

- Study the system measure, improve, design,

control - Maybe just play with the actual system
- Advantage unquestionably looking at the right

thing - But its often impossible to do so in reality

with the actual system - System doesnt exist
- Would be disruptive, expensive, or dangerous
- Examples
- Examine configurations without disrupting

manufacturing operations - Examine customer flows without re-configuring the

store - Examine new tactics without endangering planes or

people

Using Models

- Study the model instead of the real system

usually much easier, faster, cheaper, safer - Can try wide-ranging ideas with the model
- Make your mistakes on the computer where they

dont count, rather than for real where they do

count - Often, just building the model is instructive

regardless of results - Model validity (any kind of model not just

simulation) - Care in building to mimic reality faithfully
- Level of detail incorporated must be determined
- Should get same conclusions from the model as

from system - More on this during verification and validation

material

Studying Mathematical or Logical Models

- If model is simple enough, use ISE mathematical

analysis get exact results, lots of insight

into model - Queueing theory
- Differential equations
- Linear programming
- But complex systems can seldom be validly

represented by a simple analytic model - Danger of over-simplifying assumptions model

validity? - The simplified model can provide valid bounds
- Often, a complex system requires a complex model,

and analytical methods dont apply what to do?

Simulation is just a sampling experiment that

is performed using a model.

When Should We Use Computer Simulation?

- Can be used to study simple systems
- Usually not necessary if an analytical solution

is available - You will often study simple systems via

simulation in classwork, its worth the effort to

search for a - Real power of simulation is in studying complex

models - Simulation can support complex models
- Good for comparing alternative designs
- More complex techniques allow optimization

using a simulation model

Advantages of Simulation

- Flexibility to model things as they are (even if

messy and complicated) - Avoid looking where the light is
- Allows uncertainty, nonstationarity in modeling
- The only thing thats for sure nothing is for

sure - Danger of ignoring system variability
- Model validity - is the system correctly captured

Youre walking along in the dark and see someone

on hands and knees searching the ground under a

street light. You Whats wrong? Can I help

you? Other person I dropped my car keys and

cant find them. You Oh, so you dropped them

around here, huh? Other person No, I dropped

them over there. (Points into the

darkness.) You Then why are you looking

here? Other person Because this is where the

light is.

Advantages of Simulation (contd.)

- Advances in computing/cost ratios
- Estimated that 75 of computing power is used for

various kinds of simulations - Dedicated machines (e.g., real-time shop-floor

control) - Advances in simulation software
- Modern Tools are far easier to use (GUIs)
- There is a down-side to this
- No longer as restrictive in modeling constructs

(hierarchical languages exist, can program down

to C) - For ISE 471 we use ARENA
- Statistical design analysis capabilities
- However, practitioners do not solely rely on

these packaged results

Dangers of Simulation Modeling

- Tendency to be too convinced by results without

validation of the model - Animation is very compelling
- Numbers are very compelling
- Results must be checked using statistical

techniques - Did you collect enough data?
- Are you sure of your conclusions?
- How sure are you about your conclusions?

ISE Simulation Models

- Monte Carlo Simulation
- Using Sampling to estimate measures from

systems - NCAA Tournament Pool Example
- Can you estimate the probability of picking the

national champion in Basketball if you could

assign probabilities to each game in your

bracket? - Could use probability theory, if you knew how to

combine probabilities - Could use simulation to try it out many times on

the computer, and see what happens in many trial

runs of the tournament - Wayne Winstons Simulation of the 2010 NCAA Mens

Basketball Tournament - http//waynewinston.com/wordpress/?p509

Example Monte Carlo Model in a Spreadsheet

Discrete Event Simulation

- A model of a system as it evolves over time

where the state of the system changes at discrete

points in time - Necessary when systems involve humans and logical

connections between components - The engine of common ISE simulation software is

built on the discrete event approach ARENA

(used in ISE 471), FlexSim, etc. - The interface for the common ISE simulation

software is built on the process flow approach.

Process Flow Description of Systems

- Systems consist of
- Entities (Customers, Parts)
- Resources (Machines, People)
- Routings (Logic, Networks)
- Input Data (Times, Probabilities)
- Performance Measures (Times, Utilizations)
- ARENA Model of a Single Server System
- (Service Counter at a Bank)
- ARENA Model of a Truck Assembly Line

Example 2 Traffic Simulators

- Vehicle Intersection Model with Pedestrians

(VisSim) - http//www.youtube.com/watch?vYq9IAzNTAz0feature

related

Example 3 Agent Based Models

- Subway Station Simulation AnyLogic Subway

Entrance Hall Model - http//www.xjtek.com/anylogic/demo_models/?applica

tion_areaPedestrianDynamics

Some Primary Uses of Simulation Models in

Operations

- Find the bottlenecks
- How are resources utilized
- Capacity planning
- Impact of configuration changes
- Understand the system dynamics

- Questions?