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

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 Average Weekly Demand Weekly Operating Days Moves per Day = Determine Scheduled Moves per Lane

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

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

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

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

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

15 Multiple Linear Regression R-squared = Ad-hoc Cost Estimation Where Y AB = Ad-hoc cost from city A to city B β, α, d, C = Factor coefficients r AB = Distance in miles from city A to city B IN i, OUT i = Indicates a move into or out of state i

16 Treats each lane individually Decides amount of moves to schedule (T S ) Distributions (T a ) approximated by TDG Lane Cost = C S *T S + E[C AH *MAX(0,T A -T S )] Where: C S = Cost of scheduled move on lane C AH = Cost of an ad-hoc move on lane T S = Amount of scheduled trucks T A = Amount of actual trucks needed ILSM Functionality

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 ScheduledAd-hocTotal UPS 2007$70,900,000$4,900,000 $75,800,000 ILSM$63,900,000$18,300,000 $82,200,000

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

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 ScheduledAd-hocTotal UPS 2007 $70,900,000$4,900,000 $75,800,000 SA n=10 $33,000,000$39,200,000 $72,200,000 SA n=50 $32,300,000$37,100,000 $69,400,000

23 Improvements “Scheduling” Ad-hoc Movements –One-way moves Occur consistently throughout the year Scheduled at ad-hoc price Scheduled w/ Routes Scheduled One-Way Total Scheduled Ad-hocTotal UPS 2007 $70,900, $70,900,000$4,900,000 $75,800,000 ILSM $63,900,000$6,500,000$70,400,000$18,300,000 $88,700,000 SA n=50 $32,600,000$21,000,000$53,600,000$15,800,000 $69,400,000

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