Lyudmila Y. Bzhilyanskaya, J.P. Klingenberg and Michael J. Ravnitzky

1. Optimization of the United States Postal Retail Network by applying Geographic Information System (GIS) and Econometric Tools The views represented in the paper are solely those of the authors and not necessarily those of the Postal Regulatory Commission Lyudmila Y. Bzhilyanskaya, J.P. Klingenberg and Michael J. Ravnitzky

2. From 2002 to 2011 for the United States Postal Service Retail Revenue 22% Customer Retail Transactions 28% Why Optimization? http://www.

3. Analysis Overview Major Elements of Optimization Strategy Factors that complicate Optimization Consider interests of both customers and Postal Operator Analyze Postal network as a transportation network Data collection and database development MAX(Revenue) / MIN (Costs) for Postal Operator Customer Access to Post Offices Modeling Econometric Regression Model Spatial Optimization Models Driving distance vs. straight-line distance Broader than population access

4. Customer and Supplier Sides in Optimization Supply Supplier MIN or MAX Function Excess Supply Optimization Problem Equilibrium Excess Demand Set of Constraints Customer Demand

5. Spatial Approach to the Analysis Source: Denver Council of Government

6. International Access Standards: Would they Work in the U.S?

7. Walk-In Revenue as a function of Socio-economic Variables • Hypothesis:there is a relationship between postal revenue and socio-economic variables • Tools:SAS, ArcGIS, MS Access, MapPoint, Excel • Study Area: USA at 5-digit ZIP code level • Analyzed Data [for 2008] • United States Postal Service walk-in-revenue • Geographic coordinates for post office locations • Employment, # of establishments and households • Adjusted gross income and # of tax returns

8. Final Regression Model Employment by place of work Postal Walk-In Revenue Households Average Adjusted Income per Tax Return

9. Model Calibration Results

10. Application for Performed Regression Analysis • Helps postal operator estimate spatial demand • Allows evaluation of actual revenue vs. predicted • Supports decision making in postal retail network reorganization • Provides a building block for future analysis in other regions or countries • Can be transferred into forecasting regression model

11. Optimization of Customer Access Under Multiple Constraints • Analytical Task:optimize customer access under demand and supply constraints • Tools:IBM LogicNet, SAS, MS Access, Excel • Study Area: State of Alaska • Analyzed Data: [for 2010] • United States Postal Service walk-in-revenue, number of retail windows, geographic coordinates for post offices • Population, # of households by Census Block and 5-digit ZIP code, geographic coordinates for the center of each Census Block

12. Multi-criteria Optimization Model Average Weighted Distanceij MIN Customer Demandi > 99% Max. Possible Distanceij < D i= 1,..n j=1,..m k=1,..w Walk-In Revenuej > R Capacityi < C i k n – number of post offices m – number of census blocks w – number of retail windows

13. Optimization of Customer Access - Case of Alaska

14. Alaska Multi-Objective Analysis The trade-off between the # of post offices and the average weighted distance from customers (population center of each Census Block) to the post office. (Distance is measured as driving distance) Average Distance to Post Offices # of Post Offices

15. Future Study • Include costs as constraints or optimization function • Define more refined market areas • Run models for the entire nation or specific regions • Model access to post offices for employees of nearby businesses • Include data on postal competitors into analysis • Use projected data and develop forecasting model

16. Thank You.