Abstract : Well-publicized lost opportunities for U.S. and coalition air forces to strike enemy leadership targets in Afghanistan and Iraq demonstrate the importance of Time Sensitive Targeting. How do we pair the weapon and weapons delivery platform with their target? The available platforms (aircraft, manned or unmanned) may be on the ground in an alert status, loitering airborne, or on their way to attack other targets. The problem is compounded by the facts that we actually wish to (1) create multiple strike packages simultaneously, (2) recompose existing strike packages that are disrupted by the new plans, (3) minimize such disruptions, (4) satisfy minimum kill probabilities, and (5) avoid the attrition of tasked assets. This thesis develops an automated, optimizing, heuristic decision aid, RAPTOR, that rapidly revises a current Air Tasking Order (ATO) to meet the requirements above. RAPTOR identifies, verified, near-optimal ATO revisions, on a desktop PC, in less than two seconds, for a typical scenario with 40 aircraft, 4 new targets and thousands of potential strike packages. RAPT-OR allows decision makers the ability of adjusting risk acceptance in the formulation of possible courses of action by manipulating friendly attrition importance in formulating a solution.
[1]
Paul R. Weaver,et al.
Development and evaluation of an automated decision aid for rapid re-tasking of air strike assets in response to time sensitive targets
,
2004
.
[2]
Sushil J. Louis,et al.
Strike Force Asset Allocation using Genetic Search
,
2002,
IC-AI.
[3]
Davi Rogerio da Silva Castro,et al.
Optimization Models for Allocation of Air Strike Assets with Persistence
,
2002
.
[4]
Gregory Dobson,et al.
Worst-Case Analysis of Greedy Heuristics for Integer Programming with Nonnegative Data
,
1982,
Math. Oper. Res..
[5]
Gerald G. Brown,et al.
Extracting embedded generalized networks from linear programming problems
,
1985,
Math. Program..