1. MICANTS Gabor Karsai Benoit Dawant Greg Nordstrom Chris vanBuskirk Karlkim Suwanmongkol Patrick Norris Jonathan Sprinkle (Vanderbilt/ISIS) Jon Doyle Robert Laddaga Vera Ketelboeter (MIT) George Bloor (Boeing) Russ Currer (Idea Services) Lt Martin (MAG-13 VMA-513)

2. MICANTS Research Goals How to useHow to use 1.Model-Integrated Computing, and 2.Agent/Negotiation technology to solve complex resource management problems in (Autonomic) Logistics to solve complex resource management problems in (Autonomic) Logistics To demonstrate the feasibility of the technology through real- life example(s)To demonstrate the feasibility of the technology through real- life example(s) Roles  Vanderbilt/ISIS: MIC, implementation, and demonstration  MIT: Concepts, algorithms  Boeing: Modeling, domain knowledge  Idea Services: Domain expertise and scenarios, customer interface http://www.isis.vanderbilt.edu/Projects/micants/micants.htm

3. Demonstration problem Vanderbilt/ISIS

4. Application Summary Vision: Agent-supported Maintenance Process discrepancy report MMCO Flight Schedule Shop Maintenance Schedule Assign mechanic negotiate W/C OIC Goal:Assistance through offering negotiated options options approve report options approve negotiate options approve Autonomic response MMCO (sister squadron) Agents: “Helpers” for the users Implement CO’s intent, business rules, and user guidance Negotiate solutions autonomically Offer options for approval Commander’sIntent CAUTION: Simplified picture MAPLANT MAintenance PLanning AgeNTs Maintenance Schedule maintains Current focus: Negotiation between Flight and maintenance schedule

5. Resource Allocation Architecture Scheduling and negotiation as CSP Negotiating agent Messaging Coordination Engine Data structures representing domain constraints Constraint SAT mapper (encoding) Standard SAT Problem Solver (Tableau,WSAT,ISAMP) Standard SAT Problem Solver (Tableau,WSAT,ISAMP) Explicit management of constraints during negotiation/scheduling “High-performance” encoding techniques Domain-independent SAT techniques Standard SAT Interface (CNF, etc.) Schedule Domain-specific API to the scheduler Complexity management:  Encoding strategy  SAT Other agent

6. Approach Encoding a scheduling problem as binary SAT Task constraints From: Maintenance Plan and Manual Precedence, Starts after, Ends before, Coherence Resource constraints: Capacity (mechanics and tools) Flight requirements Guidance: Preferences for scheduling certain tasks for certain times SCALING SCALING: Polynomial in #Tasks, #Resources, #Slots

7. The current objective Negotiated, joint scheduling of flight operations and maintenance 1. Long-term version: - today  Time span: 5 weeks  Daily inspections 2. Short-term version: - in the works  Time span: next day  Based on current status (snapshot) and tomorrow’s flight schedule

8. CAMERA / MICANTS Integration Plan The two systems interact using a well-defined messaging protocol to facilitate negotiation between the flight schedule and maintenance schedule. The objective is to explore trade-offs between the two aspects to achieve global optimization w.r.t. some metric (e.g., generation rate, CRP, etc.) Negotiation between MAPLANT and SNAP5. MAPLANT provides a "best effort“ estimates for the number aircrafts, their capabilities, other attributes, and negotiable and non-negotiable constraints associated with them. SNAP creates a schedule based on this. Scheduling flight operations based on plane availability and capabilities 4. MAPLANT provides a "best effort" estimates for the number of available aircrafts over time. SNAP creates a schedule based on these estimated generation rates. Scheduling flight operations based on plane availability 3. SNAP supplies n-week flight schedule to MAPLANT which uses that to generate a long term maintenance plan Scheduling/planning of long-term, strategic, scheduled maintenance actions w.r.t. long-term flight schedule 2. SNAP supplies daily flight schedule to MAPLANT which in turn generates a daily maintenance plan Scheduling/planning of short-term, tactical, corrective maintenance actions in light of the flight schedule 1.

9. Skeleton Demo Scenario OPSMaintenance Guidance MAPLANT SNAP First Cut Plan Refined Ops Plan Approx. Maintenance Plan (A/C status) Refined Maintenance Plan Demo Negotiation

10. MAPLANT Scheduler Context Diagram For schedule refinement RESULTS Flight Scheduler Guidance

11. Progress to Date Flight schedule driven maintenance scheduling Richer and realistic data sets (via XML) VMA-513 personnel roster  Full complement of personnel (202 maintainers)  Ranks, quals by workcenter (10 WCs) Calendar-based maintenance  New inspections (7-, 14-, 28-, 56-, 112-, 182-, 364-, 365-, 448-day, 30mo)  Mechanic requirements  Tool requirements Tools  Defined schema  Code to load/save/manipulate/query  Still need accurate counts Maintenance plan represents typical healthy squadron Architectural, scheduler refinements Tool-aware constraint generation Optimization/scaling work (in progress) Implemented Aircraft Availability Report algorithms

12. MAPLANT Input Data Flight schedule A/C type Sortie times (start, end, duration) Pit/turn info Event Priorities Mission types Ordnance Maintenance plan Special inspection types Side numbers Due dates Usage-based inspections Repair manual Inspection manual Job type Task breakdowns Task duration Equipment requirements Personnel requirements Task sequencing requirements Bold = In today’s demo Guidance Shift length Shift start Holidays Pit requirements (“extra” ready aircraft) Maintenance schedule Shift-level (coarse grain) granularity “Locked” maintenance actions Technical directives Aircraft inventory Type BUNO Side number Status & EOC codes Frame number Engine number Lifecounts Roster Manpower, skill level, qualifications, MOS MAFs Support equipment inventory Type, location

13. What is being demonstrated Generation of a maintenance look-ahead reflecting: –Flight ops requirements –Aircraft special inspection requirements –5 week’s worth of scheduled inspections –Aircraft type, availability and status (up/down) –6 Night aircraft, 10 Radar aircraft –Maintenance guidance (shift durations, holidays, pit info) Iterative refinement –Phase 1: Shift-level granularity –Phase 2: Tasks scheduled down to the hour (configurable) Integrated MS Project ® -based reporting Phase1: Coarse Scheduling Phase2: Fine-grain Scheduling MS Project MAPLANT Scheduler Feedback to flight scheduler Initial flight schedule

14. Input Data: Aircraft Total Aircraft: 16 MC Aircraft: 12 of 16 6 Night, 10 Radar Aircraft

15. Input Data: Personnel Roster size: 202

16. Input Data: Flight Schedule

17. Input Data: Maintenance Plan

18. Input Data: Guidance

19. Input Data: Inspections

20. Input Data: Tools Total Tools: 225

21. Output: Maintenance Schedule Gantt chart view

22. Output: Maintenance Schedule Calendar view

23. Output: Maintenance Schedule Resource usage view

24. Output: A/C Availability

25. Experimental data summary Scalability of the Scheduler  Platform: 500Mhz/256Mb machine, Win2K + Java  Lookahead: 5-week  #Tasks: ~600  #Mechanics: ~200  #Days: ~40  #Variables: ~100K  #Constraints: ~1M  Encoding: ~2 minutes (for coarse-grain)  Solving: ~2 minutes (for coarse-grain; 1/2 I/O overhead)  MS-Project +4 minutes  Other: +3 minutes (e.g. fine-grains)

26. Scalability Memory usage

27. Scalability CPU usage

28. Methods for control Computational complexity Through problem size: change window size Assigning “importance” to sets of constraints (needs MAXSAT) Dynamic behavior Via the SAT solver engine Using different negotiation strategies w.r.t. flight scheduler

29. Negotiation Technology MIT

30. Resource management through automated negotiation Key concept: Resolving resource conflicts through negotiation Dynamic Negotiation Strategies: “Scripts” for negotiation Plans specify structure of complex negotiations Composing complex strategies from elemental methods Dynamic Negotiation Goals: Changing the objectives on-the-fly Strategic progression changes goals Changing situation changes goals, then strategy Dynamic Negotiation Preferences: Changing priorities  “Invention” of preferences to cover new situations  Toughening or liberalizing negotiation position Dynamic Negotiation Organization: Changing partners Relation of agent to others depends on strategy, situation, and history Construct “proximity groups” along different relational dimensions Structure strategies to exploit these proximity groups

31. Controlling negotiation Specifying negotiation preferences Modifying negotiating positions Allocating negotiation effort Address these using qualitative preferences

32. Qualitative preferences Traditional numerical utility models limit flexibility, stability, and perspicuity Rely on identifying independent factors beforehand Confuse specific tradeoffs with fundamental preferences Qualitative, generic preferences express underlying structure, indicate dependencies, and simplify statement and revision Speed > Optimality Cost > Reliability

33. Computing with preferences Interpret as preference ceteris paribus Preference other things being equal Much weaker than unrestricted preference Other semantics possible Supports important deliberative inferences (e.g., dominance) Constructive utility functions

34. Utility construction methods Work with Michael McGeachie of MIT Inputs: sets of qualitative preferences Output: numerical utility function over individuals Specification: output function satisfies input preferences Multiple algorithms developed with different properties

35. Constraint-based negotiation Negotiating positions characterized by Constraints on acceptable agreements Preferences over negotiating actions Objective versus positional preferences Objective preferences compare possible agreements Positional preferences compare negotiating positions Qualitative preferences structure objective preferences and state positional preferences

36. Positional preferences Positional preferences guide constraint changes Weaken or strengthening constraints To speed solution construction or failure determination Differentiate alternative deals by making different modifications of base preferences

37. Resource allocation Positional preferences guide sequential effort Concurrent negotiations and strategies require resource allocation preferences Qualitative preferences express fundamental guidance for resource allocation

38. Plans Timeline May 2001:  Finishing integration with Flight Scheduler  Customer demonstration Summer 01:  Short-term scheduling  Data warehouse integration Late Summer 2001:  Initial assessment milestone demonstration Framework refinements Constraints with preferences (MAXSAT) Sophisticated constraint management in scheduler MS Project as input/control for the scheduler Complexity experiments Joint scaling properties (with flight scheduler)