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The proliferation of low-cost, attritable unmanned aerial systems represents a disruptive transformation in modern warfare, compelling fundamental reassessment of military doctrine, force structure, and strategic investment priorities. This doctoral dissertation employs quantitative force structure optimization modeling to investigate the strategic implications of mass attritable systems for United States military transformation during the 2022-2030 period. The Force Structure Optimization Model (FSOM) integrates Lanchester-based combat effectiveness functions with linear programming optimization across five operational scenarios: high-intensity conventional conflict, limited regional conflict, counter-insurgency operations, gray zone warfare, and humanitarian assistance/disaster relief. Utilizing empirically calibrated data from the Oryx Equipment Loss Database, SIPRI Arms Transfers Database, and Congressional Budget Office publications, the analysis determines optimal allocation ratios between exquisite legacy platforms and mass attritable systems under budget constraints. Results demonstrate that optimal attritable allocations range from 65% to 85% depending on operational scenario, significantly exceeding current force structure assumptions. The research reveals a cost-effectiveness ratio of 8.3:1 favoring attritable systems over traditional platforms, with force resilience advantages of 2:1 in capability retention under sustained attrition. Hypothesis testing indicates that the traditional 30% aviation budget threshold for attritable systems is overly conservative (H1 not supported), while manned-unmanned teaming produces 11.3% average effectiveness improvement (H2 partially supported). The findings provide empirical foundation for defense budget allocation, supporting the Department of Defense Replicator initiative and informing force structure decisions for great power competition. Theoretical contributions include extension of Lanchester attrition theory to heterogeneous force compositions and development of scenario-dependent optimization methodologies for defense planning applications.