This book investigates the application of two-sided matching algorithms to optimize the US Navy's enlisted assignment process. The authors compare the Deferred Acceptance (DA) algorithm with Linear Programming (LP) through simulation, aiming to improve the likelihood of both commands and sailors achieving their preferred assignments. While DA ensures stable matches, it can exhibit bias depending on its implementation. The study explores how these algorithms can enhance the integrity and efficiency of the Navy's personnel allocation system.

This technical study presents a comparative analysis of matching algorithms for a specific military application. The research focuses on the operational efficiency and fairness of personnel assignments, suggesting improvements over existing methods. The work is likely to be of interest to specialists in operations research, economics, and public policy related to labor markets and military logistics. Its findings aim to provide a data-driven framework for optimizing complex assignment systems.