The document summarizes the verification and validation process of a simulation model of Waldo Library at Western Michigan University. It describes 3 main problems encountered in the model - sending students to multiple locations randomly, tracing students through different service areas, and analyzing printing station capacity. Solutions to these problems involved adding attributes to distinguish student types and routes. Validation with 10 replications showed output within the 95% confidence interval of actual library data. Increasing printing, classroom, and seating capacities improved resource utilization and reduced blocking as shown through statistical analysis of 2 scenarios.

1. IEE 6300 Team 7 Report 4
April 5, 2017
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IEE 6300: Advanced Simulation Modeling and Analysis
Team 7 – Report 4
Verification and Validation of Waldo Library
Submitted by:
Dalal Kunal
Gore Tejas
Sabale Sagar
Instructor: Dr. Azim Houshyar
Industrial and Entrepreneurial Engineering
Western Michigan University

2. IEE 6300 Team 7 Report 4
April 5, 2017
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Verification of the model:
Our project model is on Western Michigan University Waldo Library located on the main campus.
The model with routings at each location is shown below:
Figure 1. Routing of ProModel at each locations
After importing the data in the Promodel, we faced the following problems and came up with the
solution to encounter them in the following ways:
Problem 1:
The customer entering the Library were visiting different stations. But it was unable to trace each
one of them. Students entering the Library visited mainly four stations i.e.
1. IT Services
2. Access Services
3. Research and Instruction
4. Seating Area

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§ To locate the path of the visiting customers, it was becoming difficult to recognize the
purpose of student after entering Library.i.e. whether he will go to one of the above
mentioned locations.
§ Also we had problems sending the students the student to both the seating areas as they
were only showing up at once seating area.
§ To solve both of the Problem, a separate attribute for each type of student entering the
Library were created to distinguish between them. An attribute was introduced with
Increment function to keep a count of the students entering the respective areas.
§ Thus to send the students randomly to all the stations, a user condition in routing rules was
applied as shown in the figure below.
Figure – 2 Routes for customer distribution at four distinctive service areas

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Problem 2:
§ Another similar problem we faced while moving further with the model that students
entering the Library after moving through the Information desk and reaching to their
respective station from one of the four main distinctive stations, it was unpredicted to trace
the type of students.
§ Access Services further has many branches where the number of students gets split. They
get split into following areas:
i) Classroom
ii) Seating area
iii) Photocopy section
iv) Art Section
v) Language Section
vi) Geography Section
vii) Sociology, English and Navel section
• Thus to send them randomly to each station was major task while debugging this situation.
So, a similar approach, as used prior to this station was used to encounter this problem.
• An attribute was created to send that particular attribute into their respective department
without creating any mess.
• After sorting this problem, we came across one more issue where students would borrow a
book at Access Services and we had no clue where they would be going.
• Thus, an attribute called borrowed book was created to trace each type of students entering
different sections under Access Services.

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Figure – 3 Logic for routes for access areas
§ To make the model realistic and run as close to the real scenario of the Waldo Library, we
tried to add queue at each locations wherever it was recorded initially in the data analysis.
§ Following scenarios were observed in Peak hours:

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a. Insufficient capacity of classroom adds to its full utilization at its
peak hours.
b. Blocking at Information Desk
§ Now, bottleneck doesn’t happen at those station and they form a queue rather than blocking
the whole process.
Figure – 4 Utilization of Information Desk station
Figure - 5 Utilization of Classroom

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So in this fashion we rectified the difficulties arrived during modeling our simulation.
Moreover, variables and their respective counters were added at each location to verify the entry
and exit of each entity during the timings mentioned in the model. Additionally, “Display”
functions were given to trace the route of the entities moving in our simulation model.
These display functions were just used to verify that customers are moving in their
designated destinations. These points were authenticated by the output we received from ProModel
which showed the utilization at every location which means that entities are entering into each
locations. In this way we concluded our simulation model verification.
Problem 3:
After adding the two Printing stations in the first floor of the Library where most of the students
visit and defining queues for them, it was necessary to analyze the scenario for different number
of printing stations.
Thus to understand the behavior of the system when two more printers were added to first floor,
this approach was achieved by using Macros function in the Promodel.
Figure – 6 Printing Capacity

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April 5, 2017
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Figure - 7 Scenario Manager Analysis
Figure - 8. Utilization for different scenarios
Figure - 9. Total number of exits in different scenarios

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April 5, 2017
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From the above outputs, it was determined that the total number of exits where highest
when we allocated 3 location capacity for Printing stations and Classroom. This meant that 3
stations would serve at a time to three different customers. Further, it also implied that the average
waiting time of Printing and classroom queue reduced, which can be observed from the graphs
shown above.
Data Validation with 10 replications:
After eliminating the difficulties observed in the simulation model we decided to run our model
for 10 replications. The results obtained after running the model are displayed below,

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April 5, 2017
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Figure 12. Statistics obtained from ProModel
Figure 13. Location summary at each location

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April 5, 2017
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Figure 14. Location Summary - % utilization
After analysis it can be observed that the simulation data (total number of exits) lied within
the range of 95% Confidence Interval. This approximately resembled the data provided by the
waldo Library Services.

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Recommendations:
• After
selecting
the
main
parameters
in
consideration
for
Simulation
of
Waldo
Library
Services,
we
initially
decided
to
select
the
Printing
stations,
classrooms
and
sitting
areas
where
we
could
make
the
maximum
differences
at
peak
hours
as
their
utilization
were
up-­‐to
its
optimum
and
led
to
blocking
of
the
system.
• After
data-­‐collection,
we
came
to
know
about
the
distributions
of
the
readings
at
the
different
stations
followed
and
were
then
processed
further
to
understand
the
behavior
of
the
system
in
detail.
• After
running
the
data
in
Pro-­‐Model
and
observing
the
main
three
stations
(Printing,
Classroom
and
sitting
area),
a
descriptive
output
was
achieved.
• Upon
intercepting
that
output,
it
was
learned
that
their
utilization
was
very
high
so
we
immediately
decided
to
optimize
its
utilization
by
increasing
the
size
of
Printers,
Classrooms
and
Sitting
Area.
• After
increasing
the
size
of
the
above
mentioned
stations,
two
different
scenarios
were
created
and
observed.
• Thus
after
increasing
the
capacity
of
the
stations,
there
was
significant
deduction
in
the
blocking
of
the
resources
utilization.
By
using
the
statistical
analysis,
it
was
proved
that
the
numbers
obtained
at
different
scenarios
were
significantly
different
and
showed
improvement
in
the
process.
Thus
it
can
be
concluded
that
by
changing
the
capacity
of
the
Printing,
Classroom
and
sitting
area,
a
better
model
was
created
and
much
better
than
the
base
model
which
was
selected
for
study

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APPENDIX:

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