Optimizing the University of
Arizona Campus Recreation
Center Queue Times
1
Contents
Background.3
Data and Arena Model.3
Model Clarification.5
Results and Analysis.8
Conclusion.8
2
Background
The campus recreation center is a frequently visited location a

SIE 431
Homework 5
Oct. 24, 2014
The average time for travelers to go through the CBP process was 4.0413
minutes for one line (with an officer) and 4.0148 minutes for multiple lines (each with
their own officer).
We created this model in a group discussio

SIE 431
Homework 6
Oct. 29, 2014
4-1 This problem was somewhat simple to solve once we figured that we needed to
use the station and route modules that were discussed in class. After figuring out how
to use these new modules it was a matter of making sure

SIE 431
Homework 6 Part 2
Nov 2, 2014
4-8 The colored blocks represent if the process is functioning or not and were our effort
to fulfill the animation requirement. What somewhat confused us was what to put into
the text box on the model. We did notice 3

Category Overview
8:30:27PM
November 14, 2014
Unnamed Project
Replications:
1
Time Units:
Minutes
Key Performance Indicators
System
Number Out
Average
1,984
Model Filename: \vmware-host\Shared Folders\Documents\SIE 431\Homework 5-8
Page
1
of
6
Category Ov

Category Overview
7:33:28PM
November 13, 2014
Unnamed Project
Replications:
1
Time Units:
Minutes
Key Performance Indicators
System
Number Out
Model Filename: Model2
Average
3
Page
1
of
6
Category Overview
7:33:28PM
November 13, 2014
Unnamed Project
Repli

SIE 431/531 Simulation modeling and analysis
Fall 2014
Elements of Queuing Systems:
1) What are the similarities and differences for a queue of lawsuits waiting for trial and a
queue of shoppers waiting at the checkout counter?
2) The customer is the pers

Poisson Process (SIE 431/531, 09/29/14)
1) When does a Poisson process arise?
When many _ people (or items) choose whether or not to
be observed on the time line at a certain interval (t, dt) and each individual has a
tiny probability of being chosen.
Eac

SIE 431/531 Simulation Modeling and Analysis
The Use of the Rate Diagram: steady state solutions (9/17/2014)
Case 1) A single server and an infinite queue (unlimited queue storage space)
The interarrival time follows the exponential distribution with an a

9/24/2014
Input Analysis: Specifying Model
Parameters, Distributions
Structural modeling: what weve done so far
Logical aspects entities, resources, paths, etc.
Quantitative modeling
Numerical, distributional specifications
Like structural modeling,

3-1
We went into the run tab, clicked on setup and then under the replication parameters then
changed the number of replications to 5, then ran it. After viewing the category by replication report we
noticed that it matched the table 2-4 on page 35 in the

The first mid-term exam is scheduled on Oct. 19 (closed book and closed notes). The scope of the exam is as follows:
- Basic concepts about simulation (e.g. event lists, discrete vs. continuous simulation,
deterministic vs. stochastic model, relationship

SIE 431 Exam 1 Practice
1) Arrive rate is 0.6/ hr, in exponential distribution, what is the inter-arrival rate in use of
minutes? How to specify that in Arena Create Module?
2) A Possian Process arrives when many
people (or parts) chose whether or not
to

IIE/INCOSE SIE 431 Review Session
Fall 2015
1. Suppose the arrival is exponentially distributed with rate of 0.6 per hour.
What is the interarrival time in the units of minute? How do you specify that in
CREATE?
2. A Poisson process arises when many _ peo

3-6 We began by modifying model 3-1 adding a process called washing that closely matched the drilling
machine set-up. Next, we added an inspection center that takes 4.5 minutes and has a 75% chance of
approving the entity passed through. After that, we ad

Category Overview
8:50:35PM
November 13, 2014
Unnamed Project
Replications:
1
Time Units:
Minutes
Key Performance Indicators
System
Number Out
Average
1,998
Model Filename: C:\Users\tekibler\Desktop\Homework 5-7
Page
1
of
6
Category Overview
8:50:35PM
Nov

Let
FD (x) be the empirical probability distribution function.
FT (x) be the theoretical probability distribution function.
The Kolmogorov-Smirnov (K-S) statistic, D, is the maximum deviation between the two
distribution functions:
D = max FD ( x) FT ( x)