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DECISION MODEL -MILITARY AIRLIFT RESOURCE ALLOCATION

DECISION MODEL -MILITARY AIRLIFT RESOURCE ALLOCATION. Barry Stannard Sajjad Zahir Faculty of Management University of Lethbridge E.S. Rosenbloom I.H. Asper School of Business University of Manitoba. Presentation Outline. Introduction Problem Scenario Formulation of the Mathematical Model

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DECISION MODEL -MILITARY AIRLIFT RESOURCE ALLOCATION

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  1. DECISION MODEL -MILITARY AIRLIFT RESOURCE ALLOCATION Barry Stannard Sajjad Zahir Faculty of Management University of Lethbridge E.S. Rosenbloom I.H. Asper School of Business University of Manitoba

  2. Presentation Outline • Introduction • Problem Scenario • Formulation of the Mathematical Model • Assessing Relative Importance of each Mission • An Example and Discussion of the Results • Conclusions • Future Development

  3. Introduction • Capacity Planning Problem • n variable length airlift mission requests (tasks) from several users • with many priority levels • constrained assignment to m airframes (parallel machines) • Solution combines • sequential goal programming • analytic hierarchy process

  4. Problem Scenario • Several airlift users from gov’t departments • Foreign affairs, DND … • Hundreds of airlift requests exceed capacity • date, time, load, origin, destination, numbers of airframes … • An ordered list of priorities • one for emergency and code 1 VIPs • others for deployed peacekeeping forces, scheduled fights … • Current output is non-optimized Gantt chart

  5. Formulation of Mathematical Model What should the model handle? • priority of the mission • number of taskable airframes & overtasking • user flying hours budget • total fleet flying hours limit • linked airlift missions • level of importance of mission to user • degree of mission training value to airlift system • degree of effective use of specific airframe

  6. Formulation of Mathematical Model

  7. AHPAssessing Relative Mission Importance

  8. AHPAssessing Relative Mission Importance

  9. Mission Aggregate Value from Expert Choice

  10. An Example • Modeled representative set of 33 mission requests • from users 2, 4, 5, 6, and 7 simulating planning inputs • taskable airframes (TACd) set equal to 6 • overtasking enabled • overtasking set to a limit of 2 days for the period • user hours budget set for each user • total fleet hours (YFR) set for the period • linked missions 17 & 19 modeled • linked missions 21, 22, 23, 24, 25 modeled • at least four must be accepted or all must be declined

  11. Formulation – Final Iteration of Run 4

  12. Model Results • Initial Plan (Run 1, 31 requests): • Mission 4 not selected due to lack of user hours • Missions 14, 16, 24, 29, and 31 not selected • lack of taskable airframes • Only four of linked missions 21 to 25 selected • no airframes available on days 11 and 21 • Re-plan (Run 2, 32 requests): • Fifth airframe planned for days 12 to 20 • for linked missions 21 to 25 • Additional request (#32) from user 4 planned

  13. Model Results - Continued • Re-plan (Run 3, 33 requests): • User 7 still wants mission 31, agrees to start on the 27th vs the 25th as originally requested • User 7 introduces mission 33 as an additional request • Problem: mission 33 accepted but mission 28 (user 6) now dropped (unacceptable) • Re-plan (Run 4, 33 requests): • Solution. Overtask MOB (Wing) to provide and extra airframe for days 24 and 25. • Final Iteration of Run 4 confirms solution

  14. Abridged Summary of Excel Runs

  15. Conclusions • AHP and sequential goal programming work well for aggregate capacity planning of this type • model is flexible, computationally quick and handles: • priority of the mission • number of taskable airframes & overtasking • user flying hours budget • total fleet flying hours limit • linked airlift missions • level of importance of mission to user • degree of mission training value to airlift system • degree of effective use of specific airframe model handles all the • Management can change the importance values by changing decision criteria (Table 1) • enables transition from peace to other levels of tension (incl war)

  16. Future Development • As a capacity planning model, we have a first step towards a Decision Support System that supports: • multiple fleets of airframes (C130s, Airbus, …) • our prototype model only used one fleet • multiple main operating bases (MOB) for each fleet • single MOB per fleet in our model • develop optimal mission assignment to specific MOB • sliding time windows for begin/end dates (automated) • currently done manually in our prototype • optimal base-level scheduling by airframe tail number • incorporate crew duty time limitations

  17. Questions?

  18. Q & A

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