Babak Aslani - DSS for Safety-Critical Systems

Drone as First Responder (DFR) Feasibility Study

Emergency response systems face increasing challenges in ensuring rapid, coordinated, and efficient deployment of resources during critical incidents. Traditional response models are often limited by travel times, resource availability, and communication inefficiencies. To address these challenges, this research develops a data-driven Decision Support System (DSS) for evaluating the operational feasibility and deployment strategies of drone-assisted emergency response (DFR) programs. The proposed methodology integrates optimization models, geospatial analytics, and real-time data processing to support rapid dispatch planning, coverage analysis, and coordination between police and fire agencies. A digital twin platform is designed to model, simulate, and visualize DFR operations under varying geographic, temporal, and demand conditions, enabling dynamic what-if analysis and scenario-based evaluation. From a managerial perspective, this work offers actionable insights into reducing response times, enhancing situational awareness, and improving resource allocation efficiency across urban regions, such as Northern Virginia. Additionally, a comprehensive cost–benefit analysis quantifies potential savings, performance gains, and operational trade-offs compared to traditional response models, supporting data-informed policy and investment decisions for public safety agencies.

Funding Agency: Northern Virginia Emergency Response System (NVERS).

Role: Postdoctoral Research Fellow

Modern defense operations involve complex, dynamic mission environments where commanders must make rapid, high-stakes decisions under uncertainty and resource constraints. Traditional planning approaches often lack the analytical rigor and adaptability necessary to address evolving mission objectives in real-time. To overcome these challenges, this research develops an optimization-based Decision Support System (DSS) for mission planning and resource allocation in defense and aerospace applications. The proposed methodology integrates advanced mathematical optimization, machine learning, and simulation-based analysis to support data-driven decision-making under operational uncertainty. These components work together to generate feasible, high-quality plans that adapt to changing mission conditions while satisfying logistical and operational constraints. From a managerial and operational perspective, the framework enhances mission readiness, strategic agility, and analytical transparency, empowering Air Force research and operations teams to evaluate alternative strategies, allocate resources efficiently, and improve overall mission effectiveness.

Funding Agency: Air Force Research Laboratory (AFRL).

Role: Postdoctoral Research Fellow