Development of a Real-Time Decision Support System for Proactive Response, including Recourse Planning, under Uncertain Active Cascading Emergencies PI: P. Mirchandani, co-Pis: R. Maciejewski & D. Morton Arizona Sate University, Tempe, Arizona Problem Statement and Objective::The project team will develop algorithms, procedures, and visualization for a decision support system (DSS) that will assist emergency responders, federal, state and local, to coordinate and proactively respond to emergencies and disasters (D&E) that are cascading in a highly uncertain manner. Although there has been considerable research that have studied post-event impacts and have conducted post-evet simulations, and have subsequently developed “what-if” plans for responding to D&E, instead of using a strategy that learns in real time the cascading dynamics and subsequently recommends response actions that incudes recourse plans as more and more is learnt.in time and space, as proposed here Methodology and Data Requirements: 1) Brainstorm with stakeholders on sources of data – historical, streaming from sensors, intermittent from detectors, from social media, from organizations (NOAA , CNN, …); 2) Develop a case study and an associated virtual reenactment of a D&E for research team and stakeholders to refine developed algorithms and systems; 3) Develop analytics for real-time dynamic (a) generation of possible scenario trees, (b) recourse setting and allocation of response recourses (c) management of evacuation – contra- flows, shelter location's, dynamic routing/sheltering advisories, and (d) supply chaining and logistics for food, water and fuel; 4) Develop a prototype DSS and visualizations for a case study. Impact Statement: Eventual adaption of the DSS designed in this project could have significant broader impacts in terms of lesser casualties and fatalities and lesser property damage. It could also result in improved operational efficiency and productivity of several national, state, and local agencies, including improved coordination among them. Further, intellectual contributions include new algorithms for (a) Real-time Data Fusion Predictive Analytics; (b) Decision-making under uncertainty using probabilistic scenario trees; (c) Stochastic Optimization, and (d) Real-time Visualization. Relevance to DHS and Roles and Responsibilities: Potential DHS components benefiting from this effort include FEMA, NPPD, OCIA (the team has a point of contact). The Team also has POCs for local emergency management offices, traffic agencies, and national labs which may collaborate with the team. Timeline and Deliverables (By 30 days) Refine scope of the project including tasks and planned completion times. Also develop budget. Anticipated budget for this project $250/year for two years. (By 60 days) Review literature and provide a state of the art and practice report. Also, meet POCs and develop a stakeholder group for developing a system architecture and concepts of operations. (By 90 days) Provide a quarterly report with a preliminary design of the DSS based a brainstorming with stakeholders on available sources of data (historical, streaming from sensors, intermittent from detectors, from social). Also will provide a detailed list of research tasks and anticipated results.