IE 415: SUMMER 2015
LAB 4
Introduction to Arena
1. Introduction
In this laboratory exercise there are two main objectives:
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Get introduced to using the Arena simulation software package. The TAs will
review some Arena basics.
Gain experience with the software by using Arena simulation software to
modify/enhance the single server queuing model that will be developed with
TA instruction. You will work in teams of two
2. Arena Basics
Some of the Arena basics that will covered in this lab are:
 Starting Arena.
 The model window: flowchart and spreadsheet views.
 The project bar: hiding/displaying, adding templates.
 Adding flowchart modules from the project bar to the model window.
 Connecting modules in the model window.
 Simulation run settings and running a simulation.
o Turning off animation.
 Accessing reports and some of the default performance measures Arena
tabulates.
Additional modeling techniques will be covered in other labs. There is also a large
amount of electronic Arena documentation available in pdf files in the Rockwell
software folder.
3. Single Server Queuing System Model
The lab TAs will guide you through the development of a single server queuing
model. This is the same model that was used in class to demonstrate Arena, and
the same model simulated by hand in class. The system has the following
operational features:
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Customers arrive one at-a-time to a queue of infinite capacity with an
average time between arrivals = 3.4 minutes. The probability distribution
of the interarrival times is exponential. If there is no one in queue the
arriving customer begins service upon arrival, otherwise customers in
queue are served in a First Come First Served (FCFS) order.
A single server serves a single customer at-a-time with a service time that
follows an exponential distribution with a mean of 3.3333 minutes. The
server is never idle while customers are in the system.
In addition to the basic Arena features in section 2, you will be guided through:
 Adding animated queues.
 Adding animated variables.
 Adding text to the modeling window.
 Animating a resource.
4. Process Modeling Orientation
Arena is a “process oriented” simulation modeling development system. By
“process oriented” it is meant that the model is developed by representing the
flow of entities (e.g., customers, parts, etc.) in the system being simulated. The
flow is defined by the process steps and process decisions that pertain to the entity
as it moves through the system.
It is very similar to developing a flow chart with the exception that sometimes
additional simulation modeling steps are added to the flow chart.
For the single-server queuing system the flow from the perspective of the
customer can be represented with a simple flow chart:
Enter
System
Join Queue &
Receive Service
Leave
System
Arena is a graphical development system, where models are built by dragging
Arena modules into a modeling window to represent the flow of entities.
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5. Lab Assignment
1. Build the single server queuing Arena simulation following the TA instructions.
2. For your Arena model do the following:
a. Set the run length of the model to run 160 hours per replication and base
time units to minutes. Set the replications to five. Run the model and
report the averages (over the five replications) for: total part time, number
waiting in queue, and time in queue. Also report on the utilization of the
server.
b. Next change the inter-arrival times in the model (from part 1a above) so
that they are uniformly distributed between 3.7 and 34.1 minutes. Change
the processing times to be uniformly distributed between 3.1333 and
3.5333 minutes. Save this model under a different name. Run this
modified simulation as in part 2a. Compare the results with those from
part 2a. Explain the differences?
3. Create a new Arena model similar to the model developed with the TAs except
have two separate drilling centers each with a single server of its own. In this
system each server has their own line, in contrast to s ingle line feeding two
servers. Set the average time between arrivals to 1.95 minutes (exponentially
distributed interarrivals). Each server is identical to the single server in the model
used in part 2a. Use a “Decide” module to randomly send 50% of the incoming
parts to server 1, and 50% to server 2.
a. Set the replications to five and run length to 160 hours. Run the model and
report the averages (over the five replications) for: total part time, and the
number waiting in queue (sum of value for both queues).
What to turn in
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E-mail your completed models to the TAs (faisalsaud81@gmail.com) (File
Name: Last Name-Last Name-Lab#).
Also email a Word document with the reported averages requested and an
answer to the question from 2b.
Names of team members should be in the Word document.
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