Robust Multi-Scenario Optimization of an Air Expeditionary Force Force Structure Applying Genetic Alogorithms to the Combat Forces Assessment Model
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The United States Air Force is increasingly facing more diverse threat situations while existing force structure levels are being reduced and proposed compositions are being severely scrutinized for relevance, affordability, and effectiveness. Military planners are struggling with the question of how to generate a single force structure that can adequately respond to a multitude of threat scenarios in an uncertain future while at the same time being tasked to prove just how effective their choice will be. In the past, modeling has been effective in showing how a force can respond to a single t...
The United States Air Force is increasingly facing more diverse threat situations while existing force structure levels are being reduced and proposed compositions are being severely scrutinized for relevance, affordability, and effectiveness. Military planners are struggling with the question of how to generate a single force structure that can adequately respond to a multitude of threat scenarios in an uncertain future while at the same time being tasked to prove just how effective their choice will be. In the past, modeling has been effective in showing how a force can respond to a single threat scenario but a new modeling technique needs to be developed for constructing a robust force capable of success across a gambit of scenarios. This thesis proposes a meta-heuristic approach to solving the planner's multi-scenario optimization problem. The approach makes use of an existing single scenario optimizer, the Combat Forces Assessment Model (CFAM), a public domain genetic algorithm, GENESIS, and a Visual Basic controller module to link them together. The approach is demonstrated by finding a robust AEF strike force tasked against three notional AEF threat scenarios.
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