Staff Scheduling at USPS Mail Processing & Distribution Centers

1. Staff Scheduling at USPS Mail Processing & Distribution Centers A Case Study Using Integer Programming

2. General Observation Companies and organizations that build, or make use of the latest technology in their business practices, rarely make use of the latest technology in planning and scheduling!

3. Service Area in City

4. Processing & Distribution Center

5. USPS Scheduling Problem Flow patterns & Union rules & facility configuration local policies Mail arrival Equipment scheduler Staff scheduler Weekly staff assignments profiles & volume Worker demand

6. Staff Planning and Scheduling Long-term planning: Fix size and composition of permanent workforce Mid-term scheduling: Determine days off and shift assignments Short-term scheduling: Overtime, individual tasks, requests, part-timers Real-time control: Emergencies, absenteeism, and other disruptions

7. Long-Term Staff Scheduling Goal : Minimize labor costs • Categories • Full-Time Regulars, Part-Time Regulars • Part-Time Flexibles Skills (15 Categories)  • Input Data • Labor Requirements (1/2 hour increments) • Labor Costs by Worker Type

8. Determine optimal amount of equipment • Daily mail arrivals • Mail flow configuration • Machine parameters Equipment counts Operations analysis (simulation) Model Components for Long-TermStaff Scheduling INPUT INPUT • Work rules Equipment schedules • Labor ratio Personnel scheduling (optimization) Shifts Days off Tours

9. Computational Flow Modeling language Optimization engine Initial output Post-processing Weekly schedules Input data Staff levels and shifts (FT, PT) Days-off scheduling (Visual Basic) OPL Studio (ILOG) FT, PT (Visual Basic) Microsoft ExcelSpreadsheets CPLEX Breaks (OPL Studio)

10. Shift Optimization Model Minimize (Full time costs + Part time costs) Subject to 1. Cover all time periods during the week 2. Ensure sufficient lunch breaks are assigned 3. Adhere to days off requirements 4. Meet other labor rules and policies

11. Portion of IP Model

12. Size of Typical Staff Planning Model • Number of Constraints = 1100 • Number of Integer Variables = 1500 • Number of Logic Variables = 336 • Solution Times: seconds  years

13. Post-Processors • Days-Off Scheduling • Greedy algorithm for assigning days off • Small integer program for 2-days off in a row • Lunch Break Assignments • Transportation problem • Greedy algorithm • Task Assignments • Multi-commodity network flow problem • Tabu search

14. Modeling Issues • Time to run, # of runs, how often • Users and their skills • GUI sophistication • Training requirements • Version control • Help desk availability

15. Who Is The Customer ? • USPS Headquarters Contracting Officer Facility Managers Facility Industrial Engineers Information Technology Manager

16. Everybody Wants Something More • Headquarters – Implementation in 9 months system-wide Contracting Officer – Statement of Work is just a starting point (don’t expect any more money, though, for additional work) Plant Manager – More modeling features IT Manager – It will take years to provide the data you want

17. Model “Creep” • 10-hour shifts, 4-day a week schedules • Some schedules 2 days off in row, others not necessary • Worker assignments during the day • At least “X” workers per shift • No more than 1 shift every “Y” hours

18. Implementation • Prototype written in OPL Studio to demonstrate concepts Web Access – Java • CPLEX is optimization engine • 1600 variables (all integers) • 1500 constraints Two Test Sites – Dallas and Philadelphia

19. SOS Menu

20. Workstation Sets

21. Output Report

22. Number of constraints Number of variables Total cost per week Number of full-timers Number of part-timers % 2 days off in a row Baseline model 1092 888 $96,280 101 25 68.9 Ratio 3:1 1092 888 $95,040 96 32 65.6 Ratio 5:1 1092 888 $97,880 105 21 63.5 Consecutive off-days 2127 1440 $103,600 108 27 100 6 hr/6 day workers 1140 936 $95,952 100 25 72.4 Variable start time 684 837 $95,800 101 25 62.1 Part-time flexible 1092 1308 $94,976 100 -- 67.8 Computational Results

23. Parametric Analysis

24. Benefits of Flexibility

25. Observations and Lessons • The Customer is Not Always Right Sometimes a Good Product will Sell Itself but it Pays to Have a Champion Don’t Expect the Customer to Understand his Business from Your Point of View Data are Always a Problem

26. Observations and Lessons (cont.) • Do not Try to Explain Optimization to Anyone Who Does not Have an Advanced Degree Nobody Reads Manuals so Make Sure the Interfaces are Simple and Clear However, Don’t Underestimate the Intuition of the Customer

27. Skill Categories 