Jacob Feldman, Ph.D. jacobfeldman@openrules.com 732-662-7233 NJ office 732-306-0685 cell

1. Experience with CP Products & Applications Jacob Feldman, Ph.D. jacobfeldman@openrules.com 732-662-7233 NJ office 732-306-0685 cell

2. Outline • Customer’s CP Applications • Brief Review • Two examples of workforce management apps • From Batch to Interactive Engines • Interactive Constrained Environments • Architecture • Constrained GUI and Interactive Constraint Engine • Practical Examples

3. Selected Customer’s Applications • IRS, Washington DC • Strategic Workforce Planning: integrated use of constraint and linear programming. The objective: maximize long-term revenue given an initial workforce, potential workload, and expected budget. • Major Wall Street Institutions • Decision engine for a wealth management system • Minimization of Rules Violations • Rule Processing System for large financial portfolios integrated with ILOG CPLEX • Keyspan Energy, Long Island • Interactive Scheduling engines for maintenance and construction jobs • FAA, Maryland • Decision Support Tool for the Reduced Vertical Separation Minimum.RVSM was intended to increase airspace capacity, improve aircraft operating efficiency at the minimum cost.  Developed a Logistic Simulator to monitor airspace operations that allocated scarce resources to flights and airports in a way to provide the required coverage of flights over a fixed period. 

4. Selected Customer’s Applications (2) • Exigen Group • Optimal selection of calling plans for telecom vertical solution • Insurance underwriting • CPRail, Canada • Grain Train Configuration and Scheduling • Sandia National Labs, New Mexico • Supply Chain Scheduling for textile industry • FastMan (Ottawa) / Manugistics • Manufacturing Scheduling Engine for a supply-chain software vendor • Solution Dynamics / I2 Technologies • Optimal Container Loading for Warehouse management systems • CNN, Atlanta • Advertisement/Show Scheduling • MCI, Colorado Springs • Configuration and scheduling system for teleconferencing • Solution Dynamics, NJ • Transportation routing with different business constraints to possible routes

5. IRS Strategic Compliance Planning Model • Internal Revenue Service (IRS): 150,000 people nationwide, virtually unlimited workload, limited yearly budget. US Government wants to make IRS more revenue-driven while satisfying many other constraints and policies • The objective of the SCPM is to identify that allocation of resources by location, position, and grade for each year of a defined planning period which will tend to maximize the collection of taxes owed (including direct and indirect revenues). • In two words: Reshape workforce to maximize revenue over years

6. Generic Object Model TRANSACTIONS CareerLadder, Transfer, Fire Consumes HIRE TRANSACTIONS

7. Examples of Constraints • Expected annual budgets • Allocation of funds to non-compliance activities • IRS personnel rules and practices • Attrition • Minimum coverage • Minimum staff size (geographically dependent) • “Front-loaded" funding

8. Examples of Constraints (2) • Geographic movement of employees • Transfer between positions (e.g., Revenue Agent to Appeals) • Competitive promotions • Reductions in force • Annual comparability pay increase • Locality pay • Base salaries • Training requirements

9. Limitations • The amount of money available • nationwide • per location • The availability of work bundles • The direct time resources needed to work each bundle • The time and expense required to obtain needed skills

10. Workforce Measurements • Capacity: Hours / FTEs / Cost • Max Requirement: Hours / FTEs / Revenue • Min Requirement (to support min coverage) • After each Workload-Driven Engine Run: • Workload Requirement: • Required Hours / FTEs • Produced Revenue • Workforce Surplus: Hours / FTEs / Cost

11. Workload Measurements • For each year and total: • Coverage Percent • Total Yield • Required Workforce Hours / FTEs / Cost • Produced Revenue based on actual Y/toC

12. Transitions Between Positions and Grades • Engine generates “vertical” and “horizontal” transitions to cover missing FTEs required by selected workload bundles • One more dimension: locations Time P1 P2 P3 P4 P5 G13 G12 G11 G9

13. SCPM Framework • Hybrid use of CP (Solver/Constrainer) and LP (CPLEX/GLPK) for optimal workload/workforce allocations • Interactive Workforce Modifications with automatic constraint propagation • Using rule engine for workload forecasting and generation • “Small” and “Big” engines working under control of the same constraints • Live Demo

14. Scheduling and Resource Allocation for a Utility Company • LILCO – gas and electric utility • Batch constraint-based engine that allocates resources (teams of specialists, equipment, vehicles) to different field services jobs under control of constraints/rules • Initial success • A practical need for constant customer interaction • Scheduling Engine as a company employee that constantly “learns and adjusts” to the real-world situations • “Big” and “small” constraint engines working under control of the same constraints • Live Demo

15. LILCO – Job View

16. LILCO – Resource View

17. CP Products • Horizontal CP Products • ILOG Solver/Scheduler™ (used) • Java Constrainer™ (developed, transferred to Exigen) • Vertical Products • Utility Scheduler™ (developed) • Resource Allocation & Scheduling engines for service and utility jobs • Load Optimizer (developed, transferred to I2) • Truck loading software

18. Java Constrainer • Integer, Boolean, and Floating-point constrained variables with very efficient domain implementations • All major Constraints and Constrained Expressions • Ability to handle Symbolic Expressions (!) • Generic reversible environment • Efficient event notification and constraint propagation mechanisms • Predefined search Goals and Selectors • Ability to write problem specific constraints and search algorithms • Basic scheduling classes (jobs, resources) and constraints on them

19. Java Constrainer vs. C++ Solver:Expressiveness • The same logical constraint in: • ILOG C++ Solver: • IlcIfThen(IlcAbs(x-5)>10, x*z<IlcPower(y,5)) • Pure Java Constrainer: • x.sub(5).abs().more(10).imply((x.mul(z).less(y.power(5))) • Java Constrainer with symbolic expressions: • “|x-5|>10 -> x*z<y**5” • Samples with Sources

21. Java Constrainer vs. C++ Solver:Efficiency • "100 queens": C++ = 0.12 Java = 0.13 • "500 queens": C++ = 3.72 Java = 3.40 • "1000 queens": C++ = 18.93 Java = 17.31

22. Interactive Constraint Satisfaction • Interactive Constrained Environments • Architecture • Problems • Approaches

23. Interactive Problem Definition • A Quote from 1999 Constrainer’s Brochure: “AI increasingly confronts real world problems, in expert systems and robotics, for example, we are increasingly likely to encounter situations where, rather than searching for a solution to a problem, we must, in a sense, search for a problem we can solve" • Eugene C. Freuder. Partial Constraint Satisfaction, 1996   • Today interactive problem definition under control of rules/constraints defined by business (non-technical!) people became even more important

24. Interactive Constraint Propagation • When it comes to managing jobs and resources, change is the name of the game.  • Users want to update and fine-tune schedule in a flash, allowing: • a scheduling engine to react to the constant changes • inform them about possible consequences of manual overrides • automatically adjust the schedule  after manual changes • These problems are common whether you are trying to assign people to jobs, connect plants to the supply chain, or to minimize travel time for different field service crews • Dynamic constraint propagation becomes a must functionality of any practical constraint-based system. It assumes the ability to add and/or remove (!) constraints on the fly to redefine the problem and find the appropriate solution

25. Between 0 to 100% Scheduling Automation • It is unreal to achieve 100% automation for scheduling: • Permanent changes in job priorities and resource availability • The efficiency and constraint satisfaction are crucial • Compromise between time and quality • 0% automation still should allow users to do scheduling better what they did before: • Warn a user about problems without being too “talkative” • Allow a user to violate constraints • A “Big” Scheduling Engine should be able to provide 100% automation, but with respect to manual overrides. There are no pure “advisory” or “manual” modes: always a combination!

26. Typical CP Engine Scheduling Engine uses a specialized search strategy to solve this CSP. It modifies the state of the constrained environment J2 J1 J3 R1 R2 R3 Scheduling Engine

27. Batch Scheduling Engines J2 J1 J3 DB GUI R1 R2 R3 Scheduling Engine

28. Initial Step to Constrained Graphical Interface J2 J1 J3 DB GUI R1 R2 R3 Scheduling Engine Java and Constrainer C++ and ILOG

29. Common Core for both GUI and Scheduling Engine J2 DB J1 J3 GUI R1 R2 R3 Scheduling Engine

30. InteractiveConsistent Constrained Core - CCC User Actions: ”Small” Engines Manual and automatic Actions modify the state of the constrained environment under control of the same constraints Action Action Action Action J2 J1 J3 R1 R2 R3 Action “Big Scheduling Engine”

31. CCC: Business & Constrained Objects • Business Objects • reflect the current state of the “visual” objects • allow non-monotonic changes • Constrained Object • only monotonic changes • when not in action, they are in the initial state with static constraints propagated • after actions executed, save their state in the business object and restore

32. Consistent Constrainer Core: Business & Constrained Cores • Constrained • Action • Business • Action • Request • Business • Core: • Job(s) • Resource(s) • Skill(s) • Assignment(s) • Constrained • Core: • Constrained • Job(s) • Constrained • Resource(s) Web Browser • Response • Question • Event Windows Scheduling Clients Live Scheduling Server

33. Constrained Graphical Interface vsInteractive Constraint Engine • Constrained Graphical Interface: • Constraint Engine “always” runs (demons are active) • Constraint Engine could handle GUI events • Immediate business actions can add/remove constraints • Interactive Constraint Engine: • Engine can run for a long time • Engine provides a GUI update every X sec • A user can pause/resume the Engine’s run • While the Engine is paused, a user can add/remove constraints, and “resume” engine execution or start it over

34. How to deactivate constraints? • Different approaches • Attempt to restore the state of constrained variables after a constraint removal • Restore everything • Differentiate between removable (soft) and frozen (hard) constraints defined by business users • Activation of a removable constraint creates a choice point • Recent new findings • Use of filtering algorithms for soft constraints (J-C Regin, J-F Puget, T. Petit) can dramatically improve efficiency • Latest ILOG Solver’ infeasible constraints can call a user-defined demon instead of causing a global failure. This allows an application to determine how to react to constraint failures. Can be used for constraint removal? • Looking for more discussions and further research

35. Interactive Constraint Engine • Implementation ideas: • Engine’s run is a special CCC goal “activate” • Check GUI events: • in a choice point • every 10 sec, after 200 failures, etc. • Pause: • saves the state of all decision variables in the business objects • Allow a user to modify the saved values • Resume: • Add saved values to the list of activated constraints • Activate the same goal

36. A User At a Choice Point Choice Point Choice Point Choice Point Choice Point Batch Search Interactive Search

37. Chess as a model for the Interactive CSP (1) • Man vs Man

38. Chess as a model for the Interactive CSP (2) • Man vs Computer Kasparov Deep Blue

39. Chess as a model for the Interactive CSP (3) • (Man+Computer) vs (Man+Computer) • Analogies: • Racing (Formula-1) • Player = Scheduling Analyst, Plant Manager • Game = Process of Ongoing Improvements

40. Choice Point Visualization • Any chess position (choice point) has a “shape” favorable or unfavorable at this particular moment to this particular player • “I can hear the moves” (V. Nabokov. “The defense”) • Dynamically configurable strategies • “They tuned it to play against me personally” • Kasparov about the “Deep Blue”

41. Interaction in a Choice Point • A man with a computer in a choice point • What to present to a human about the current search situation: • Search Size Estimate • Search Time Estimate • Search Complexity Estimate • Preferable resulting positions (no knights, “boring” position, minimum inventory, minimized overtime, etc.)

42. Thank You.Q&A