Troop to Task Optimization: Algorithmic Scheduling for USMC

In the dynamic and complex field of military operations, commanders at the small unit level face the daunting challenge of developing and implementing effective plans within constrained timeframes. Traditional methods, often reliant on manual planning and decision-making, are not only time-consuming but also prone to suboptimal outcomes, thereby underscoring the urgent need for a more efficient and effective solution. Our research delves into this critical issue by exploring and comparing the efficacy of various algorithms in solving the troop-to-task problem, a fundamental concern that directly impacts the operational readiness and effectiveness of military units.The centerpiece of our investigation is the assessment of three distinct algorithmic approaches: the Greedy Algorithm, the First-Fit Algorithm, and the Genetic Algorithm. Each of these methodologies offers unique perspectives and potential benefits in the context of military planning and execution. We find that the Genetic Algorithm provides more optimal schedules but takes significantly longer to run than the other two algorithms. Making the most appropriate algorithm situationally dependent on available planning time and scheduling complexity. We have made our troop-to-task scheduling framework open-source, in hopes that future researchers will leverage it to develop and compare their troop-to-task planning algorithms.