Purdue University Food Truck Location
IE 431 Senior Design Final Report
Purdue University
School of Industrial Engineering – Spring 2014
Mark Lehto (CEO)
Bobby Vaziri (Cluster Leader)
Purdue University Dining Service: Tom Coleman, Director of Retail Dining
TEAM 6
Erin Russell
Ann Yaacoub
Evan Pope
Ziya Zhao
1
Executive Summary
Purdue University Dining Services currently operates between 25 to 30 retail locations, 5
dining courts, and multiple satellite locations to service the students and faculty. Purdue is now
interested in implementing a food truck on the campus. This project performed analysis on
several factors that were believed to have significant impact on locating the best possible facility
location for the Purdue food truck. To achieve this goal, our team applied our expertise on
conducting food traffic analysis. Our team collected data that took into consideration time, day of
the week, weather and location as possible factors affecting foot traffic. We conducted Minitab
analysis to conduct a multiple regression to provide a model that will give our customer the
optimal location in regards to highest passing foot traffic. This model takes into consideration the
weather, time of day, location, and day of the week in order to output an accurate estimate of the
amount of foot traffic passing by for that hour. The analysis showed that time and location had
the strongest impact on the amount of pedestrians walking by. It was discovered that Class of 50
was the best location for the day shifts and PMU was the best location for the night shifts.
However, when there was an event at the Elliott Hall of Music, Lawson showed a higher
concentration of pedestrians than PMU for the night shift. A Floyd-Warshall Algorithm was
utilized to give the shortest driving path for the truck to get from Harrison Grill to each location
that was tested. The optimal route diagrams are included. Sunday evening service was
determined with a minimax system. This gives a line in which the truck should be placed
anywhere on in order to be as close as possible to all the dorms.
2
Contents
Executive Summary .................................................................................................................... 2
Introduction, Background, & Outline of Report ........................................................................... 4
Project Schedule...................................................................................................................... 5
Project Cost ............................................................................................................................ 5
Approach Used in the Project ...................................................................................................... 6
Results ....................................................................................................................................... 8
Minitab Results ....................................................................................................................... 8
Shortest Route Results............................................................................................................. 9
Sunday Evening Results .........................................................................................................10
Summary...................................................................................................................................11
Conclusion.................................................................................................................................11
Recommendations......................................................................................................................12
Appendix ...................................................................................................................................12
Gantt Chart ...........................................................................................................................12
Minitab for Day-shift..............................................................................................................13
Node Map ..............................................................................................................................17
Route: Harrison to Lawson .....................................................................................................18
Route: Harrison to EE & Potter ..............................................................................................19
Route: Harrison to Bell Tower ................................................................................................20
Route: Harrison to CL50 ........................................................................................................21
Route: Harrison to PMU and Krannert ...................................................................................22
Distance Matrix: ....................................................................................................................23
Java Code: .............................................................................................................................23
Sunday Night Food Truck Location ........................................................................................28
Data Collection ......................................................................................................................29
Daytime 10:30-2:30 .............................................................................................................29
Night Time (8-12)................................................................................................................33
3
Introduction, Background, & Outline of Report
Purdue is a large public university that has around 50,000 students and staff operating on
West Lafayette’s campus every year. These students and staff have the opportunity to purchase
food and drinks through Purdue University Dining Services. This division of Purdue currently
operates many different options to serve the students and faculty such as Purdue Memorial Union
and also dining courts. Every year the Purdue University Housing and Food Services
organization releases a Strategic Plan. This plan includes target areas that the university would
like to grow and develop for the upcoming year. These strategic plans are based around Purdue
University's values & virtues, vision for the future, and their mission statement.
Our team has worked all semester to expand one of Purdue’s goals. The strategic plan
and goal is in regards to affordability. According to Purdue University Dining the affordability
goal is to “Establish a 5 year capital plan and to evaluate and provide new mission-centric
revenue opportunities including food truck presentation, project team”. We have collaborated
with a committee that is composed of dining and catering staff that have been working together
to find the best possible location for a food truck on Purdue’s Campus. The committee has held
meetings throughout the semester that discuss the current status and plan for the truck. The food
truck will illustrate and improve the strategic goal #4 which states, “Purdue Dining will operate
in a fiscally responsible manner to support operational and strategic growth”. The goal is to make
food more accessible to Purdue University Students in locations where food is not currently
available. The food truck will also provide more employment for students and staff.
Our group has taken the initiative to find the optimal location for the lunchtime (MondayFriday), nighttime (Thursday- Saturday), and Sunday evening shifts. In addition, our group has
found the most efficient way to reach all of the potential food truck locations. This report will
4
detail our approach, results, and our recommendations for the team of dining court personnel
who are in charge of this food truck.
Project Schedule
The stages of the project are given the Gantt chart located in the Appendix. In the Problem
Definition and Strategic Plan Development phases (WBS 1 and 2), initial meetings with our
clients helped us deepen our understanding of our project and narrow the scope. From these
meetings, we were able to come up with an approach to the problem. We determined exactly
what data we needed and ways in which we could obtain that data. We decided that counting
pedestrians would be the best way to find areas on campus with the highest concentration of
potential customers during the day and night shifts. Our Data Collection phase (WBS 3) began
on February 21, 2014 and ended on April 12, 2014. Having a sufficient amount of data marked
the beginning of our Data Analysis phase (WBS 5), where we began to create a predictive model
using Minitab. We continued to collect data and input it into our database to increase the
accuracy of the model as much as possible. After analyzing the data and utilizing the model,
recommendations for the different shifts could be made.
Project Cost
The final project cost consisted of the hours spent analyzing foot traffic, hours spent with the
TA/customer, and our group work. The final costs are seen in the table below.
Type of Work
# of Hours
Rate of work
Total Cost
Foot Traffic Analysis
52
$100
$5,200
Time with
TA/Customer
20
$200
$4,000
Time with Group
30
$100
30*4*$100= $12,000
$21,200
5
A majority of our time was used to collect our data which was collecting foot data. The time
spent with the TA consisted of planning our plan of action for our project and also ask questions
along the way. The time spent with our customer consisted of update meetings for the type of
food, type of truck, understanding the financials regarding the truck, and attending the food truck
meetings. Our group meetings included work on our proposal, status reports, final report, and the
analysis of our data.
Approach Used in the Project
The approach we used for this project was to collect pedestrian data at various locations
on campus. Our goal was to collect data during weekdays from 10:30 A.M. - 2:30 P.M. at 3
locations on campus. Those locations are the areas in front of Class of 50, between the Bell
Tower & Stanley Coulter, and between EE & Potter. The target customers are pedestrians on
campus. We documented the time, date, weather, and amount of people walking in these areas as
different variables that could potentially affect the amount of pedestrians at a given time. Our
group separated and each of us took one of these locations to count pedestrians. We all collected
data at the same time and date. This method is helpful so that we are collecting similar data and
not changing more than one variable in our observations.
The other time period we documented is the night shift, which is currently from 8 P.M. 12 A.M. These night shift areas include: in front of Krannert, in front of the PMU, and in front of
Lawson. The target group of people are those walking to and from the Purdue University bars.
We completed the observations the same way as the afternoon shifts: different locations at same
time and same date. See appendix for all the data collected.
Using the data we collected from the day and night shifts, we conducted a regression in
Minitab. Using date, time, location, and weather as variables, we set pedestrians as the response.
6
The regression equation output by Minitab can predict the best location for the food truck based
on these variables. Also, Minitab allowed us to determine which of the variables affected the
number of pedestrians. The rule of thumb that Purdue University Dining uses is that 6% of
people walking through the Union will stop and buy some amount food.
There is one more shift that we looked at and it’s the Sunday dinner shift from 5:30-9:30
P.M. The target customer for this time period is dormitory students that can’t use dining courts
during this time because they are closed. We did not count pedestrians for this time frame
because these students will be targeting the food truck, and not necessarily be pedestrians in
which we can physically count. Instead, we found a location near the dorms that is the shortest
total distance from all the dorms and the current Sunday night restaurants, Harrison Grille &
Cary Knight Spot. We did not want to take business away from these restaurants and that was
taken into consideration during our analysis.
Using the Purdue University map we found distances using minimax to find the best
location on campus for Sunday evening. Using the minmax equations without consideration of
frequency: C1= min (xi + yi), C2= max (xi+yi), C3= min(-xi+yi), C4= max(-xi+yi), and C5=
max (C2-C1, C4-C3). We combined the dorms into certain sections. The sections include:
McCutcheon & Harrison, Purdue Village, Shreve & Earhart, First Street Tower, Meredith,
Windsor, Willey and Tarkington, Owen Hall, Cary Quad, and Hill Top. Using these sections we
came up with coordinates of these locations to conduct the minimax equations. After finding the
optimal points we have a line on the Purdue Map that will be the best location for the food truck
on Sunday.
The last step we took was using an algorithm to find the best route from Harrison Hall,
which will be the food trucks home base, to each of the potential locations. Utilizing the Purdue
7
University map, we used the Floyd-Warshall Algorithm to find the optimal routes from Harrison
to each of the locations that we surveyed. We took into consideration one way streets and other
safety regulations on Purdue’s campus. We created a map of nodes that is seen in the appendix.
Using this node map we found the distances between each node and ran the algorithm to find the
shortest routes.
Results
Minitab Results
According to the Minitab results for the Day-shift, there is no unusual observations in the
normality plot and Versus Fits plots, indicating that they are normally distributed and they have
equal variance. The regression model explained 75.9% variance, meaning that it is a pretty good
model. Based on the analysis, we can tell that the Location, Time and Time2 have significant
impact on the response, which is the number of people passing by. We created quadratic forms
into the model, Temp2 and Time2 ,because there was a curve in Versus Fits plot which violated
the assumption for the regression analysis. The predicting regression equation is included in the
Minitab analysis in appendices. The average number of people passing by each location are
1653, 558,1219 for CL50, EE & Potter and Stanley Coulter & Bell Tower respectively. The
optimal location for Day-shift is CL50.
For the Night-shift regression analysis, the residual plots for response look decent and the model
explains 80.6% of variance. Location is the only factor that has a significant effect on the
response. The prediction regression equation is included in the Minitab analysis for Night-shift in
the appendices. The average number of people passing by are 181, 71 and 222 for PMU,
Krannert and Lawson respectively. From the data, it suggested that Lawson is the optimal
8
location but we have to consider the reason that Lawson is close to Elliott Hall of Music where
lots of events happen. From our observation, we noticed that as weather gets warmer, there are a
lot more people passing by PMU.
Shortest Route Results
Based on the results that we obtained from Floyd-Warshall Algorithm, the optimal routes from
Harrison Hall to each location candidate are as follows:
Harrison to PMU: (Node 1 to Node 38):1→9→15→21→24→27→30→33→45→46→38
Total distance:21.47
Harrison to Krannert: (Node 1 to Node 38):1→9→15→21→24→27→30→33→45→46→38
Total distance:21.47
Harrison to Lawson: (Node 1 to Node 19):1→3→2→4→7→13→19
Total distance:13.5
Harrison to CL50(Node 1-Node 26):1→9→15→21→24→27→30→29→26
Total distance: 16.3
Harrison to Bell Tower: (Node 1-Node 29):1→9→15→21→24→27→30→29
Total distance: 15.2
Harrison to EE & Potter:
(Node 1- Node 32):1→9→15→21→24→27→30→33→45→46→38→37→36→32 :
Total distance:28.37
The visualization of the actual routes can be found in the Appendix.
9
Sunday Evening Results
We first found the coordinates for the different sections of the dormitories from the
Purdue University Map. These coordinates along with our calculations for C1, C2, C3, and C4 are
shown below in the table:
Locations
Coordinates
C1= min
(xi+yi)
C2= max(xi +
yi)
C3= min(xi+yi)
C4= max(xi+yi)
McCutcheon
& Harrison
(2.5, 5)
7.5
7.5
2.5
2.5
Purdue
Village
(2.5,3.5)
6
6
1
1
Shreve &
Erhart
(3,5.5)
8.5
8.5
2.5
2.5
First Street
Towers
(2.5,5)
7.5
7.5
2.5
2.5
Meredith
(4,5.5)
9.5
9.5
1.5
1.5
Windsor
(4, 5.5)
9.5
9.5
1.5
1.5
Wiley &
Tarkington
(4.5, 7)
13.5
13.5
2.5
2.5
Owen
(4.5, 8)
12.5
12.5
3.5
3.5
Cary
Quadrangle
(5.5, 8)
13.5
13.5
2.5
2.5
Hill Top
(4, 9)
13
13
5
5
After we found all the coordinates along with C1, C2, C3, and C4, we found C5, which is shown
below:
C5= max (C2-C1, C4-C3)
C5= (7.5, 4) = 7.5
We then find the two coordinates that make up the line:
10
P1* = (C1-C3)/2 , (C1+C3+C5)/2 = (2.5, 7.25)
P2*= (C2- C4)/2, (C2+C4-C5)/2 = (4.25, 5.5)
So the optimal line connects points (2.5, 7.25) and (4.25, 5.5) on the map. See the appendix for
the map.
Summary
Purdue University Dining Services, our client, has been working on a project involving
implementation of a mobile food truck. After meeting with our client, we narrowed the scope of
our project to finding optimal locations for the food truck. We created a project schedule with 5
main phases - Problem Definition, Strategic Plan Development, Data Collection, Data Analysis,
and Providing Recommendations. Our team came up with a strategy to find on campus areas
with the highest student concentration during both day and night shifts. This strategy led us to
creating a regression that could tell us the significance of external factors on the number of
students in a potential location. From this model, we were able to come up with
recommendations for our client’s needs.
Conclusion
We created two multivariable regressions (one for each shift) that indicate the significance of
external factors including location, time, temperature, day of the week, and weather. For both our
day regression and night regression, results indicated that location was the most significant
variable for predicting the number of students, followed by time. We also found the shortest
route using the Floyd-Warshall Algorithm from where the truck is loaded (Harrison) to each
potential location in our model. Since the dormitory area is the ideal area for Sunday hours of
operation, we found the optimal location in this area based on the rectilinear distances of
dormitory locations.
11
Recommendations
We believe the food truck should be placed around Class of 50 for the lunch shift, PMU sidewalk
for evening, and near Meredith and the Smalle Center for sunday night. Things to consider are to
potentially move the truck for events such as concerts, sports, and the farmers market. Also
something to consider is marketing for the food truck around campus. Marketing will be helpful
for the student body to make them aware of the food truck.
Appendix
Gantt Chart
12
Minitab for Day-shift
Regression Analysis: Number of people versus Temperature, CL50, ...
* SC and Bell Tower is highly correlated with other X variables
* SC and Bell Tower has been removed from the equation.
The regression equation is
Number of people = 349 - 7.3 Temperature + 434 CL50 - 661 EE and Potter
+ 838 Time + 0.223 Temp^2 - 174 Time^2
Predictor
Coef
Constant
T
P
348.9 443.1
0.79
0.439
Temperature
-7.26
-0.30
CL50
433.8 148.3
2.93
0.008
EE and Potter
-660.7 148.4
-4.45
0.000
Time
838.1 319.8
2.62
0.015
Temp^2
0.2228
SE Coef
24.10
0.3366
0.66
0.766
0.514
13
Time^2
-174.49 62.56
S = 331.584
R-Sq = 75.9%
-2.79
0.010
R-Sq(adj) = 69.6%
Analysis of Variance
Source
DF
Regression
6
Residual Error
23
2528807
Total
29
10494611
Source
SS
7965805
DF
Seq SS
Temperature
1
890800
CL50
1
3911698
EE and Potter
1
2207362
Time
1
78122
Temp^2
1
22572
Time^2
1
855251
MS
F
1327634
12.08
P
0.000
109948
Unusual Observations
Number of
Obs
Temperature
people
Fit
SE Fit
Residual
St Resid
8
23.0
2406.0
1677.5
143.1
728.5
2.44R
30
60.0
2242.0
1659.5
192.1
582.5
2.15R
14
R denotes an observation with a large standardized residual.
Minitab for Night-shift
Regression Analysis: Response versus Temp, Time, ...
* K is highly correlated with other X variables
* K has been removed from the equation.
The regression equation is
Response = - 217 + 21.5 Temp - 141 Time + 88.7 PMU + 175 Lawson - 0.240 Temp^2
+ 22.1 Time^2
Predictor
Coef
SE Coef
T
P
15
Constant
-217.0
242.8
-0.89
0.391
Temp
21.50
15.74
1.37
0.199
Time
-140.98
82.23
-1.71
0.114
88.67
31.96
2.77
0.018
Lawson
174.67
31.96
5.47
0.000
Temp^2
-0.2400
Time^2
22.05
PMU
S = 55.3541
0.2130
16.51
-1.13
1.34
R-Sq = 80.6%
0.284
0.209
R-Sq(adj) = 70.0%
Analysis of Variance
Source
DF
Regression
6
SS
139782
23297
Residual Error
11
33705
Total
17
173487
Source
DF
Seq SS
Temp
1
22137
Time
1
19570
PMU
1
7
Lawson
1
91525
Temp^2
1
1075
Time^2
1
5468
MS
7.60
F
P
0.002
3064
16
Node Map
17
Route: Harrison to Lawson
18
Route: Harrison to EE & Potter
19
Route: Harrison to Bell Tower
20
Route: Harrison to CL50
21
Route: Harrison to PMU and Krannert
22
Distance Matrix:
Java Code:
class Graph
{
int vertex[]=new int [100];
float edges[][]=new float [100][100];
int n ,e;
static void interPoint(int path[][],int i,int j)
{
if(path[i][j]==-1)
{
return;
}
23
interPoint(path,i,path[i][j]);
System.out.print(path[i][j]+"->");
interPoint(path,path[i][j],j);
}
static void showPath(int path[][],float A[][],int n)
{
for(int i=1;i<=n;i++)
for(int j=1;j<=n;j++)
{
if(A[i][j]==1000)
{
System.out.print("从"+i+"to"+j+"no route");
System.out.print("\n");
}
else if(i==j) continue;
else
{
System.out.print("从"+i+"to"+j+"route:");
System.out.print(i+"->");
interPoint(path,i,j);
System.out.print(j);
System.out.print("Total distance:"+A[i][j]);
System.out.print("\n");
24
}
}
}
void CreateGraph(Graph G)
{
G.n=44;
G.e=108;
for(int i=1;i<=G.n;i++)
for(int j=1;j<=G.n;j++)
{
G.edges[i][j]=1000;
if(i==j)
{
G.edges[i][j]=0;
}
}
%%%Decompose big matrix into smaller ones(G.edges[i][j])%%%
System.out.println("The matrix you entered：\n");
for(int i=1;i<=G.n;i++)
{
for(int j=1;j<=G.n;j++)
25
{
System.out.print("\t"+G.edges[i][j]);
}
System.out.print("\n");
}
}
void Floyd(Graph G)
{
float A[][]=new float [100][100];
int path[][]=new int [100][100];
for(int i=1;i<=G.n;i++)
{
for(int j=1;j<=G.n;j++)
{
A[i][j]=G.edges[i][j];
path[i][j]=-1;
}
}
for(int k=1;k<=G.n;k++)
for(int i=1;i<=G.n;i++)
for(int j=1;j<=G.n;j++)
{
26
if(A[i][k]+A[k][j]<A[i][j])
{
A[i][j]=A[i][k]+A[k][j];
path[i][j]=k;
}
}
showPath(path,A,G.n);
}
}
class Floyd{
public static void main(String[]args)
{
Graph G = new Graph();
G.CreateGraph(G);
G.Floyd(G);
}
27
Sunday Night Food Truck Location
}
28
Data Collection
Daytime
10:30-2:30
Day Shift
Date
Day of week Location
Time
Number
Group
of People Comments
Member
Weather
15
degrees
2/27/2014 Thur
EE and
10:30 -
Potter
11:30
feels like 653
Ziya Zhao 1
12
degrees
10:30 2/27/2014 Thur
CL50
11:30
878
Evan
feels like -
Pope
5 degrees
15
degrees
2/27/2014 Thur
CL50
1:30 - 2:30
1412
Evan
feels like -
Pope
1
12
degrees
SC & Bell
2/27/2014 Thur
Tower
10:30-11:30
677
SC & Bell
2/27/2014 Thur
Tower
1:30-2:30
654
Erin
feels like -
Russell
5 degrees
Erin
15
Russell
degrees
29
feels like 1
15
degrees
EE and
2/27/2014 Thur
Potter
1:30 - 2:30
467
Ann
feels like -
Yaacoub
1
23
degrees
EE and
3/4/2014 Tues
Potter
12:30 - 1:30
556
Ann
feels like
Yaacoub
15
23
degrees
3/4/2014 Tues
CL50
12:30 - 1:30
2406
Evan
feels like
Pope
15
23
degrees
SC & Bell
3/4/2014 Tues
Tower
12:30-1:30
1121
Erin
feels like
Russell
15
27
degrees
sunny
SC & Bell
3/26/2014 Wed
Tower
12:30-1:30
1193
Erin
feels like
Russell
19
27
degrees
3/26/2014 Wed
CL50
12:30-1:30
1917
Evan
sunny
Pope
feels like
30
19
27
degrees
sunny
EE and
3/26/2014 Wed
Potter
feels like
12:30-1:30
538
Ziya Zhao 19
41
degrees,
SC & Bell
3/27/2014 Thur
Tower
1:30-2:30
821
Erin
rainy, feels
Russell
like 35
41
degrees,
3/27/2014 Thur
CL50
1:30-2:30
975
Evan
rainy, feels
Pope
like 35
41
degrees,
EE and
3/27/2014 Thur
Potter
1:30-2:30
532
Ann
rainy, feels
Yaacoub
like 35
EE and
3/31/2014 Mon
3/31/2014 Mon
Potter
CL50
51 and
10:30-11:30
10:30-11:30
616
2003
SC & Bell
3/31/2014 Mon
Tower
10:30-11:30
1503
Ziya Zhao sunny
Evan
51 and
Pope
sunny
Erin
51 and
Russell
sunny
47
EE and
4/1/2014 Tues
Potter
1:30-2:30
517
Ann
degrees
Yaacoub
and sunny
31
47
4/1/2014 Tues
CL50
1:30-2:30
1318
Evan
degrees
Pope
and sunny
47
SC & Bell
4/1/2014 Tues
Tower
1:30-2:30
1067
Erin
degrees
Russell
and sunny
42
degrees
SC & Bell
4/4/2014 Friday
Tower
12:30-1:30
1407
Erin
and partly
Russell
cloudy
42
degrees
4/4/2014 Friday
CL50
12:30-1:30
1669
Evan
and partly
Pope
cloudy
42
degrees
EE and
4/4/2014 Friday
Potter
and partly
12:30-1:30
583
Ziya Zhao cloudy
EE and
4/8/2014 Tuesday
Potter
53 and
11:30- 12:30
519
SC & Bell
4/8/2014 Tuesday
4/8/2014 Tuesday
Tower
CL50
11:30- 12:30
11:30- 12:30
1504
1708
Ziya Zhao cloudy
Erin
53 and
Russell
cloudy
Evan
53 and
Pope
cloudy
Evan
4/9/2014 Wednesday CL50
12:30- 1:30
2241
4/9/2014 Wednesday EE and
12:30- 1:30
597
Pope
60
Ziya Zhao 60
32
Potter
SC & Bell
4/9/2014 Wednesday Tower
Erin
12:30- 1:30
2242
Russell
60
Night
Time (812)
Night
Shift
Day of
Date
week
Number of
Location
Time
People
Group
Comments
Member
Weather
36-- Cloudy
In front of
2/21/2014 Fri
2/21/2014 Fri
PMU
Lawson
Feels like
8-9
8-9
215 None
Evan Pope 32
Symphony
36-- Cloudy
Orchestra at
Feels like
340 Elliot
Erin Russell 32
36-- Cloudy
Feels like
2/21/2014 Fri
Krannert
8-9
70 None
Ziya Zhao
X in
36-- Cloudy
Memoria
2/21/2014 Fri
Mall
8-9
101 36°
In front of
2/22/2014 Sat
PMU
32
Ann
Feels like
Yaacoub
32
33- light
9 - 10
137 32° - rain/snow
Evan Pope snow feels
33
like 26
33- light
snow feels
2/22/2014 Sat
Lawson
9 - 10
275 Elliot Event
Erin Russell like 26
33- light
snow feels
2/22/2014 Sat
Krannert
9 - 10
29 rain/snow
Ziya Zhao
X in
33- light
Memoria
2/22/2014 Sat
Mall
82 32° - rain/snow
Ann
snow feels
Yaacoub
like 26
In front of
3/1/2014 Sat
3/1/2014 Sat
PMU
Krannert
like 26
23 feels like
8-9
8-9
141
66 23°
Ziya Zhao
12
Ann
23 feels like
Yaacoub
12
23 feels like
3/1/2014 Sat
Lawson
8-9
126
Erin Russell 12
3/27/2014 Thur
Lawson
8-9
367
Evan Pope 50. Rain
Ann
3/27/2014 Thur
Krannert
8-9
81
Yaacoub
50, rainy
a little bit
3/27/2014 Thur
PMU
8-9
256
Ziya Zhao
rainy
30 felt like
3/29/2014 Sat
PMU
11 - 12
73
Ziya Zhao
17
30 felt like
3/29/2014 Sat
Krannert
11 - 12
64
Evan Pope 17
30 felt like
3/29/2014 Sat
Lawson
11 - 12
151
Erin Russell 17
34
41 feels like
4/6/2014 Sat
Lawson
10 - 11
162
Erin Russell 37
41 feels like
4/6/2014 Sat
Krannert
10 - 11
63
Evan Pope 37
41 feels like
4/6/2014 Sat
PMU
10 - 11
83
Ziya Zhao
37
4/12/2014 Sat
PMU
9 - 10
361
Ziya Zhao
70
4/12/2014 Sat
Krannert
9 - 10
120
Evan Pope
70
Ann
4/12/2014 Sat
Lawson
9 - 10
130
Yaacoub
70
35