ISE 477- Study Guide Exam 1

Simulation

imitation of the operation of a real‐world process or system over time

System

• set of related components working together toward some purpose over time

examples of areas that simulation can be applied to.

• Airports (terminal transport),

• manufacturing (optimizing line),

• Hospital (disaster planning)

• Predictive-"What COULD happen?"

• Prescriptive- “What SHOULD we do?"

• Descriptive- “what happenED?”

⭐Describe the difference between predictive, prescriptive and descriptive mode

• Predictive

• Get Insight Into Future Outcomes;

• What COULD happen?"

• Prescriptive

• Get Insight Into the Best Possible Outcome

• What SHOULD we do?"

• Descriptive

• Get Insight Into Past Outcomes

• “what happenED?”

Simulation process

• Deterministic models- don’t involve randomness; 

• Stochastic models- involve randomness'

What is the difference between a deterministic and stochastic model?

• Deterministic models

• - DON’T involve randomness

• Stochastic models

• - involve randomness

• Static simulation models represent a certain point; don’t involve time

• dynamic simulation- Represents the system over time; involves time

What is the difference between static and dynamic simulation models?

• Static simulation models

• represent a certain point

• don’t involve time

• dynamic simulation-

• Represents the system over time;

• involves time

• Verification- does what we say the model is doing

• Validation- correctness of the model/data

 What is the difference between verification and validation?

• Verification

• - does what we say the model is doing

• Validation-

• correctness of the model/data

discrete event simulation

Dynamic, discrete-change, stochastic simulation models.

• measures of risk-  Likely and Unlikely values

• measures of error- accuracy and inaccuracy of values

What are measures of risk vs. measures of error?

• measures of risk

• -  Likely and Unlikely values

• measures of error-

• accuracy and inaccuracy of values

the more replications, the smaller the confidence interval

What is  the effect of increasing replications?

Queue disciplines

• how a server decides what specific entity in the queue will be chosen to move into service next.

• FIFO: First-In, First-Out

• LIFO: Last-In, First-Out

• Reciprocals(inverse value) of each other

• Arrival Rate (λ): The number of arrivals that happen per unit of time (e.g., customers per hour).

• Interarrival Time (I): The amount of time that passes between one arrival and the next

• Service Rate (μ): The number of customers or items a server can process per unit of time (e.g., servers completed per hour).

• Service Time (S or 1/μ): The total time it takes to finish servicing a single customer or item once service begins.

 relationship between interarrival times and arrival rates along with service times and service rates

Stable if the utilization is p <1

how to determine if a queueing system is stable or not?

• Maybe I haven’t run it enough

• The model might be wrong

• The expectation might be wrong

What are the possible causes if the model results do not match the expectation?

minimize error

Confidence Interval is to?

• Continuous time simulation- State variables can change continuously over time

• Discrete Time simulation- state variables can change only at instantaneous points in time.

What is the difference between continuous and discrete time simulations

• Continuous time simulation-

• State variables can change continuously over time

• Discrete Time simulation

• - state variables can change only at instantaneous points in time.

•  Describe the common events which would cause a transition to another state.

  
  

 Arrivals,

• departures,

• processing,

• arriving in queue

Ways to troubleshoot if the results of a model do not match the expected results? (simio)

• Look at error bar, to identify the error

• Check for typos

• Watch the simulation animations

• Go to the simio help desk

Depends on the situation

How to determine an appropriate run length for a simulation? (simio)

• Connectors require no travel time; paths include travel time

what is the difference between connector and path in Simio.

• Ex

• Random.Exponential(mean)

• ServerName.InputBuffer.Contents

• DefaultEntity.Population.TimeInSystem.Average

Simio’s dot notation. Understand the Simio Expressions that we’ve covered in class.

• This is because even though it is random, you are running the same experiment with the same values. So you’ll get the same random results unless a variable is changed.

Why do we get the same random results in Simio if we construct the same model?

• Small, built-in example models in Simio designed to illustrate how to accomplish specific modeling tasks or concepts 

• Tech support, mini modules to help people when they are stuck on certain problems

 What is Simbit used for in Simio?

Output analysis and experimentation

❓what type of information a simulation will output.

assumptions of a Jackson Network

• Poisson process

• Independently and exponentially distributed service times.

• Probabilistic routings.

• Infinite queue capacities.

• Utiliaztion p <1

⭐Units

• (Wq)- Time in queue (excluding service time)

• (W)- Time in system

• (Lq)- Number of entities in queue; ex custmor in queue

• (L) Number of entities in system ex custmor in system

Queueing forumla cheat sheet

• Arrival Rate (λ):

• The number of arrivals that happen per unit of time

• ex unit. arrival/hour

$$\lambda=\frac{20}{h}$$

• Interarrival Time (I ,IAT, 1/λ):

• The amount of time that passes between one arrival and the next

• Unit:

$IAT=\frac{1}{\lambda}$

IAT= InterArravial Time

$\lambda=$ arrival rate

**make sure to convert units to hour and vis versa

• Service Rate (μ):

• The number of customers or items a server can process per unit of time

• ex unit. servers/hour= 20/h

• Service Time (S or 1/μ):

• The total time it takes to finish servicing a single customer or item once service begins.

$ST=\frac{1}{\mu}$

St= service time

$\mu=$ service rate

$\rho$

Utilization ()

$\rho=\frac{\lambda}{c\cdot\mu}$

$\rho$ = utilization

$\lambda=$ arrival rates

$\mu=$ service rates

c= servers

$\rho<1$ ;= stable

ex queueing system

Consider an M/M/2 queueing system having 10 arrivals per hour and a mean service time of 10 minutes. What is the utilization of this system?

ex queueing system

Given an M/M/1 queue with utilization of 75%, what is the mean number of customers in the system? What is the mean number of customers in the queueing area?

steps for manual discrete calendar

Time- only times when something is happening

Event- arrivals or completion for item

Queue- only add to queue if something is in service

Service- whats being processed

Arrival- Next arrival on the list

Complete- the thing in service+ st