United Parcel Service NAAFN - Ground

1. United Parcel Service NAAFN - Ground Senior Design Final Presentation April 15, 2008 Jessel Dhabliwala, Megan Patrick, Tom Pelling, Nathan Petty, Vikas Venugopal, Selin Yilmaz This presentation has been created in the framework of a student design project and it is neither officially sanctioned by the Georgia Institute of Technology nor the United Parcel Service.

2. Outline Client Description Problem Description Design Strategy Truckload Demand Generator Independent Lane Scheduling Model Integrated Approach Stochastic Optimization Results Conclusion

3. UPS North American Air Freight Network (NAAFN) Large Third Party Logistics (3PL) Product Offerings of NAAFN Next Day Second Day Economy/Deferred Delivery Ground Truck Movements Scheduled Movements Ad-hoc Movements Client Description

4. Regional Hub Ground Network SEA GEG PSC BGR PDX YUL YOW PWM BTV BOI ALB PSM YYZ MSP GRB ROC SYR BOS BDL PVD FNT BUF MKE MSN SWF ELM GRR BGM DTW JFK CID ABE ERI EWR RFD MDW TOL CLE RNO TTN OMA DSM FWA MDT SLC JFK Gateway PHL SAC ORD Gateway SBN PIT MLI OAK BWI PIA CMH DEN CMI IND DAY PKB IAD SJC MCI CVG FAT RIC STL CRW COS LEX ORF ICT EVV SDF ROA LAS TRI GSO JLN WIL LAX Gateway RDU TYS TUL LAX ONT BNA FYV AVL MEM CLT FAY ILM OKC CHA LIT FLO ABQ AMA GSP PHX FSM HSV CAE ATL SAN LBB CHS ATL Gateway TUS BHM DFW JAN SHV ELP MAF JAX MOB ACT BTR TLH IAH MSY AUS LRD MCO SAT TPA FLL BRO MIA Gateway

5. Truckload Procurement Process Determine Scheduled Moves per Lane Build Routes to Cover Scheduled Moves Obtain Carrier Bids for Routes Award Route Contracts to Winning Bidders

6. Project Focus Determine Scheduled Moves per Lane Build Routes to Cover Scheduled Moves Obtain Carrier Bids for Routes Award Route Contracts to Winning Bidders

7. UPS’s Current Method Determine Scheduled Moves per Lane Average Weekly Demand Weekly Operating Days Moves per Day =

8. Operational Process Scheduled Routes are Executed Weekly Scenario One Scenario Two Demand < Scheduled Incur scheduled truck cost OR Demand > Scheduled Dispatch ad-hoc truck(s)

10. Design Strategy Independent Lane Scheduling Model Detailed Cost Estimation Model Truckload Demand Generator Detailed Cost Estimation Model Integrated Model

11. Truckload Demand Generator Input Functionality Output Shipment Level OD Demand Data Chain Tables Demand Histogram Conversion Includes: OD data, Day of departure, Actual weight, GAD weight, Miles Assigns OD freight to sequences of lanes Convert to truckloads required on each lane each day Trucks per lane per day

13. Design Strategy Independent Lane Scheduling Model Detailed Cost Estimation Model Truckload Demand Generator Detailed Cost Estimation Model Integrated Model

14. Independent Lane Scheduling Model Input Functionality Output Histograms from TDG Lane Cost Estimates • Ad-hoc • Scheduled Independent Lane Scheduling Model Number of moves to schedule on each lane per day Minimize expected costs per lane per day

15. Multiple Linear Regression R-squared = 0.752 Ad-hoc Cost Estimation • Where • YAB = Ad-hoc cost from city A to city B • β, α, d, C = Factor coefficients • rAB = Distance in miles from city A to city B • INi, OUTi = Indicates a move into or out of state i

16. ILSM Functionality • Treats each lane individually • Decides amount of moves to schedule (TS) • Distributions (Ta) approximated by TDG • Lane Cost = CS*TS+ E[CAH*MAX(0,TA-TS)] • Where: • CS = Cost of scheduled move on lane • CAH = Cost of an ad-hoc move on lane • TS = Amount of scheduled trucks • TA= Amount of actual trucks needed

17. Detailed Cost Estimation • Total Cost: Scheduled cost + Ad-hoc cost • Two-Phase Approach • Phase I: Deterministic integer program • Covers complete lane moves with pre-existing routes • Minimizes scheduled truck dispatch costs • Phase II: Compute ad-hoc cost • Uses route schedule from phase I • Computes necessary ad-hoc moves and cost per day from historical data

18. ILSM Results Possible Causes of Higher Cost: Unbalanced routes

19. Design Strategy Independent Lane Scheduling Model Detailed Cost Estimation Model Truckload Demand Generator Detailed Cost Estimation Model Integrated Model

20. Integrated Model • Integrates Scheduling and Costing • Stochastic programming model • Uses pre-existing NAAFN routes and costs • Selects routes to execute weekly • Sample Average Approach • Generates n random weeks from TDG • Covers all moves in each scenario • With a route or an ad-hoc move • Minimize Sample Average Cost per Week

21. Xpress Model • Inputs • Pre-existing routes, route costs, ad-hoc costs, and TDG frequency histograms • Functionality • Random variables represent CDF of histogram • Number of constraints = n * number of unique lane-day combinations • n = 50 • Over 125,000 constraints • Run time of three minutes

22. Integrated Model Results • Results: • Concerns: • Extremely risky to depend on unscheduled moves • Sensitivity of model to ad-hoc cost estimates

23. Improvements • “Scheduling” Ad-hoc Movements • One-way moves • Occur consistently throughout the year • Scheduled at ad-hoc price

24. Conclusions • Suggested Method • Integrated model • Further Improvements • Scheduling one-ways • Significant Potential for Cost Savings • Scheduling daily may save $6M annually

25. Recommendations • Implementation • Estimate daily demand per lane from TDG • Generate new “candidate” routes • Estimate new route costs • Solve Integrated Model to select routes for bids

26. Questions