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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 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.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
