Food Truck BY: TEAM 6

Team Members• Erin Russell: Project Manager

• Ann Yaacoub: Project Scheduler

• Evan Pope: Data Measurement

• Ziya Zhao: Data Analyst

Where is Cheese Wheels?

Background Information• Purdue University Dining Courts: Tom Coleman

• Affordability Goal of “Establish a 5 year capital plan and to evaluate and provide new mission-centric revenue opportunities including food truck presentation, project team”.

• Our Goals for this semester included: o providing optimal locations for lunch (monday-friday), night

(thursday-saturday), sunday night shift, and finding optimal routes from Harrison to our optimal locations

Project Schedule

Approach Used: Lunch and Dinner Shifts • Counted Foot Traffic: Mon-Friday (10:30-2:30PM) & Thursday-Saturday (8PM-12AM)

• Minitab Regression to determine the biggest factors that determine the number of people

• 6% of people will purchase food

Approach Used: Floyd-Warshall Algorithm • Nodes,Arcs, Edges

• All nodes to all others

• Optimal path and optimal subpath

• Intermediary nodes

Approach Used: Floyd-Warshall Algorithm

Approach Used: Minimax• Used the

equations for C1, C2, C3, C4,C5

• Found best location with respect to Rectilinear distance (P1* & P2*)

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

Results: Day Shift Regression 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 SE Coef T P

Constant 348.9 443.1 0.79 0.439

Temperature -7.26 24.10 -0.30 0.766

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 0.3366 0.66 0.514

Time^2 -174.49 62.56 -2.79 0.010

S = 331.584 R-Sq = 75.9% R-Sq(adj) = 69.6%

Results: Night Shift RegressionThe 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

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

PMU 88.67 31.96 2.77 0.018

Lawson 174.67 31.96 5.47 0.000

Temp^2 -0.2400 0.2130 -1.13 0.284

Time^2 22.05 16.51 1.34 0.209

S = 55.3541 R-Sq = 80.6% R-Sq(adj) = 70.0%

Results: Shortest Path 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

Results: Sunday Minimax Analysis

Conclusions and Recommendations • Best locations:

o CL50- Day shift o PMU sidewalk-- Night Shift o Near Meredith-- Sunday Night

• Things to Considero Special Events: Concerts, Farmers Market, Sports

Questions?