Learnable Agent-Based Modeling and Spatio-Temporal Graph Networks for Large Scale Mobilization Operations (LSMO) Analysis

The U.S. Army Reserve (USAR) seeks to enhance Large Scale Mobilization Operations (LSMO) efficiency for Large-Scale Combat Operations (LSCO). Challenges in the current mobilization process affect deployment readiness and timeliness. The USAR aims to improve LSMO through advanced computing, software tools, and modeling techniques, with an initial focus on deterministic and stochastic agent modeling validation. These techniques, supported by tools like Python and RStudio, are effective in modeling complex processes like mobilization, enhancing adaptability and resilience. The goal is to increase readiness and support for combatant commanders, improving mobilization efficiency and maintaining a versatile force for modern military operations. Analytics HQ (AHQ) leverages modern, open-standards, advanced analytics software and frameworks to deliver rapid insights through comprehensive, true self-service capabilities that seamlessly integrate software development, modern data management and advanced analytics within a secure and compliant zero-trust framework. This novel and unified approach to software, analytics and DoD cyber lays the foundations for organizations to adopt a digital-first strategy, thereby enabling data-driven decision-making across the USAR enterprise. AHQ will provide the USAR with modern and advanced data analytics capabilities, based on an open architecture, for the purpose of modeling LSMO, analyzing the performance of the LSMO simulation models, applying optimization techniques to the models and operationalizing the models in a manner that can be consumed by USAR stakeholders, decision makers and combatant commanders. AHQ will develop, deploy and integrate novel modeling techniques that streamline large scale mobilization processes for USAR. Through the analysis of previous exercises and insights from mobilization experts, AHQ aims to pinpoint areas for improvement and deploy targeted capabilities in these areas. Moreso, agent simulation modeling, complex process forecasting and complex process optimization have numerous commercial applications, such as in supply chain forecasting, where predictive modeling significantly enhances operational efficiency and risk management in complex business processes. AHQ's approach not only aims to improve military mobilization for the USAR, but provide insights for other military and civilian industries as well, such as supply chain management, weather forecasting, finance, or even integrated circuit design. By leveraging complex modeling with advanced analytics, AHQ seeks to enable a more agile and efficient mobilization process, boosting USAR readiness and effectiveness in supporting national security needs.