Industrial Systems Simulation Lab Report
IE-360 (Spring 2025)
Lab No.
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Registration No.:
Submission date:
Rubrics Marks
01 to 06
Dr. Imran Ahmad
M. Zakria
22PWIND0717
13-May-2025
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Department of Industrial Engineering
University of Engineering and Technology, Peshawar
Lab: 01
INTRODUCTION TO ARENA, EXPLANATION OF BASIC PROCESS,
ADVANCE PROCESS AND ADVANCE TRANSFER MODULES
1. Introduction
Arena is a discrete event simulation and automation software developed by Systems
Modeling and acquired by Rockwell Automation in 2000. In Arena, the user builds an
experiment model by placing modules (boxes of different shapes) that represent processes or
logic. Connector lines are used to join these modules together and to specify the flow of
entities. While modules have specific actions relative to entities, flow, and timing, the precise
representation of each module and entity relative to real-life objects is subject to the modeler.
2. The User interface
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PROJECT BAR
STATUS BAR
TOOL BAR
MODEL WINDOW
3. Explanation:
Arena is an easy-to-use, powerful tool that allows creating and running experiments on models of the
systems.
Any business environment, from customer service to manufacturing to health care, can benefit from
simulation. Whether analyzing an existing supply chain or a new emergency-room layout, follow five
easy steps with Arena:
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Create a basic model
Refine the model
Simulate the model.
Analyze simulation result
Select the best alternative.
4. The Arena Window:
The window of the Arena looks like the above one.
When the Arena simulation software opens up, then the next step you need is to place the right module in
right place, keep in mind that place the create module at the first place while Dispose module at the end of
the Arena interference.
The definition of a data module is carried out by clicking on the shape of then module in the Project bar to
activate its spreadsheet. Then the user can edit the data of the module.
5. Procedure:
You need to have the following steps in order for a successful launch of your idea.
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Construct the model of the project in arena using the arena provided modules. Just drag the
modules and paste it into the region of the simulation.
Adding data to the model parameter.
Run the simulation.
Analysis of the simulation results provided by Arena for the running simulation.
Modify and enhancing the model and then repeat the circle again.
In the simulation model we need to know about the various parameters such as the arrival time and the
processing time because this type of time values greatly effect the model of the simulation.
6. Summary:
Arena is a very versatile integrated simulation development tool. Constructing a simulation model involves
identifying one or more flow objects known as entities that flow through the system and then building a
flowchart of the model using Arena’s flowchart modules.
7. Conclusion:
In this lab we learn about the user interface of Simulation with ARENA software through
browsing and existing single channel queue model.
8. Questions
1. What the different types of windows of ARENA Simulation Model.
• Arena has main window or flowchart window
• Spreadsheet window
• Project bar panel
2. Which Module can be used to generate entities in the system?
Create Module is used for generating entities and we give entities arrival time and
distribution followed bythe entities.
3. What will happen if only seize and delay option is used in resource and not the releaseoption.
In that case resource will not move to the next module because process module has notreleases
the resource and this causes delay the system.
Lab: 02
MODELING AND SIMULATION OF A SINGLE CHANNEL
QUEUEING SYSTEM WITHDYNAMIC PLOTS FOR NUMBER
OF ENTITIES IN QUEUE
1. Introduction:
In this lab we will learn about single channel queue susytem that part are being generated by the create
module and going to process on drilling machine and then are being disposed and queue is being formed
because there is only one server and we will also generate the graph for numbers of parts waiting in the
queue and sustem busy graph.
2. Modules Used:
The create Module
The Entity Data Module
The process Flowchart Module
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The Resource and Queue data modules
Resource Animation
Dynamics Plots
Comments:
In this lab we have learnt about drilling process and we have quue was fomed because of theonly
one server/station to process.
Questions
a. What will happen the drill press utilization if the mean of exponential distribution
isreduced from 5 minutes to 2 minutes.
b. How can the queue length be reduces through machine capacity. Check for the
capacity of 2 machines.
By increasing capacity to two, no queue will form.
Lab: 03 MODELLING AND SIMULATION OF SPECIALIZED
SERIAL VERSUS GENERALIZEDPARALLEL PROCESSING
(CASE STUDY OF A LOAN APPLICATION PROCESS)
Introduction:
A loan application office were people are arriving with mean of 1.25 hours and every person go through
focus steps for getting the lone first step involves the checking of the credit of the customer after that
server/officer prepares the loan convent and loan pricing discussed with the customer and last Stepanova
the disbursement of the funds to the customer.
There are four steps in in this load application system.
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Check credit
Prepare covenant
Price Loan
Disburse funds
Each of the above steps follows exponential distribution with mean of 1hours. The arrival rate ofthe
follows exponential distribution with a mean of 1.25 hours.
Check which of the setting is better. Serial or generalized parallel.
Modules Used:
Create Module
In this module customers are being generaed by the mean of 1.25 hours with the random expo
distribution.
Process module 1(Check Credit)
In this credit of ustomer will be checked to check the eligibility of the customer.
Process Module 2(Preparing covenant)
Process Module 3 (Pricing the Loan):
Process Module 4 (Funds Disbursement)
Dispose Module Application Departs
Comments:
Same steps will be followed in both parallel and series steps but arrangement will be different ofboth
scenario in case of modules.
Parallel Arrangement:
Series Arrangement:
Comparison of results:
Model
Total Time in
System
Total WIP
Total
Waiting
Number
Processe d
Average
Utilizatio n
AVG
MAX
AVG
MAX
AVG
MAX
Number
Serial
12
21
16
27
11.98
22.72
117
82,70.80,80
Parallel
4.6
10
5.38
13.168
1.3122
6.82
135
87%
Questions
a. What is the effect of service time variability on the decision?
If the service time changes or variable then this could affect the average time on the customer.
If service time is low then customer will wait for the service in queue. If service time is faster
than it will lead to customer satisfaction as well as idleness of thesystem.
b. Compare the WIP with respect to both the decision. Which discission was better for
lower WIP level.
Since the average waiting in the series is approximately 12 customer and in parallel seriesaverage
customer in queue are 6. So parallel system is better for the low WIP.
Lab: 04
A case study of an Electronic Assembly and Test System.
Introduction:
This lab describes simulation of a sealed electronic assembly and test system using Arena. The system
represents the final operation of the production of two different sealed electronic units.
Objectives:
To understand basic concepts of simulation
To learn the interface of Arena
To build an electronic assembly model using basic modules in Arena
Explanation:
Simulate the model and collect statistics in each area on resource utilization, number in queue, time in
queue, and the cycle time (or total time in system) separated out by shipped parts, salvaged parts. Run the
simulation for four consecutives 8 hour shifts.
Building the model:
To build the model, open a new model window and place the required modules on the screen: two create,
two assign, four process, two decide, three record and three dispose modules. The final model window
looks like after making the connections between modules as indicated.
Adding data to the modules:
Double-click on each module icon to feed data such as processing time, distribution, resource utilization
etc., into the system.
Performing a simulation run of the model:
Run the completed model by simply clicking on the go button in the standard toolbar. After the simulation
starts to run, its speed can be changed with the slider bar on the right end of the standardtoolbar.
Viewing the results:
If the model is run to completion, Arena asks the user if they want to see the results. Click on yes to get a
window showing the Category Overview Report. Then click on the desired reports which can be related to
entities or resource or queue etc.
Performing a simulation run of the model:
Run the completed model by simply clicking on the go button in the standard toolbar. After the simulation
starts to run, its speed can be changed with the slider bar on the right end of the standardtoolbar.
Results:
Conclusion:
In this lab we have learnt about drawing the assembly model of the elctronic assembly.
Lab Report 5
Modeling and simulation of resource schedule, states, and failures using
an electronic assembly test system as a case study (Enhanced Electronic
Assembly)
Introduction:
An animation is often very useful during the verification and validation phases because it allows you to
view the entire system being modeled as it operates. If you ran the model we developed and viewed its
animation, you should have noted that it appeared to operate quite similarly to the way we described the
system. That's because validation implies that the simulation is behaving just like the real-world system,
which may not even exist. And even if the system does exist, you have to have output performance data
from it, as well as convince yourself and other nonbelievers that your model can really capture and predict
the events of the real system.
Procedure:
Let's assume that as part of this effort you showed the model and its accompanying results to the
production manager. Her first observation was that you didn't have a complete definition of how the
system works. Whoever developed the problem definition looked only at the operation of the first shift.
This system actually operates two shifts a day, and on the Second shift, there are two operators assigned
to the rework operation. This would explain our earlier observation when we thought the rework operation
might not have enough capacity. The production manager also noted that she has a failure problem at the
sealer operation. Periodically, the sealer machine breaks down. Engineering looked at the problem some
time ago and collected data to determine the effect on the sealer operation. They felt that these failures did
not merit any significant effort to correct the problem because they didn't feel that the sealer operation was
a bottleneck. However, they did log their observations, which are still available. Let's assume that the
mean uptime between failures was found to be 120 minutes and that the distribution is exponential
(which, by the way, is often used as a realistic model for uptimes if failures occur randomly at a uniform
rate over time).
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Expanding Resources:
Previously our resource was one with capacity of one. We will add two shifts and make the capacity of
resources increased to two in the second shift. For this purpose, we will use the schedule module.
Now we will define the actual schedule by clicking the schedule module.
Resource Failure:
Breakdowns occur at the sealer resource. So, for adding this breakdown in our model we do the
following.
Now we need to add this failure to our resource, so this is how we do it.
We have added it as shown above.
Lab Report 6
Use of frequency statistics in ARENA to find the optimum number of
racks to be purchased for a resource using a case study of electronic sealer
assembly.
Introduction:
To understand basic concepts of Use of frequency statistics in ARENA to find the optimumnumber
of racks to be purchased for a resource using a case study of electronic sealer assembly.
Procedure:
Frequency
Record time-persistent occurrence frequency of variable, expression, orresource
state
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Use here to record % of time rework queue is of length 0, (0, 10], (10, 20], … for info on
number of racks needed
Statistic data module (Advanced Process panel)
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Five Types of statistics, of which Frequencies is one
Specify Name (Rework Queue Stats),
Frequency Type (Value)
Specify Expression to track and categorize
Right-click in field to get to Expression Builder
Report Label (Rework Queue Stats)
Pop-up secondary spreadsheet for Categories (browse file)
Add another Frequency (in Statistic module) to give a finer description of theSealer
states
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Produces statistics on proportion of time Sealer is in each of its three possible states –
Busy, Idle, and Failed
Frequencies are not part of default Category Overview report
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Open Frequencies report from Project Bar (get separate window)
Utilization:
Two utilizations reported for each Resource
Instantaneous Utilization is the time-average of the ratio of the number of units that are busy tothe
number of units that are scheduled
Scheduled Utilization is the average number busy divided by the average number available
By definition, counts periods when zero units are scheduled as zero-utilization periodsNo
division-by-zero problem, assuming there were ever any units of the Resource scheduled at
all (if not, it shouldn’t be in the model.
Identical for fixed-capacity Resource
Can differ for Resources on a variable Schedule
If Resource capacity varies among several different positive values, it’s better to useScheduled
Utilization
Results:
All of which causes underlying random-number stream to be used differently.
Prep A/B didn’t change (other than run length and random variation) … need statisticalanalysis of
simulation output.
Sealer is more congested (it now fails)
Rework is less congested (50% higher staffing)
Frequencies report suggests one rack suffices about 95% of the time, two racks all the timeStandard vs.
Restricted Percent’s