Advanced Disaster Relief and Aid Planning Tool

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SciTech 2026_Advanced Disaster Relief and Aid Planning Tool.pdf (35.85 MB)
Author(s)
Thomas, Jade R.
Edwards, Autumn
Conroy, Anna
Surrency, Chloe
Tan, Yaw Tung
Kallou, Eva
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Organizational Unit
Daniel Guggenheim School of Aerospace Engineering
The Daniel Guggenheim School of Aeronautics was established in 1931, with a name change in 1962 to the School of Aerospace Engineering
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Research Publications
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Permanent Link
https://hdl.handle.net/1853/80595
Abstract
Defense acquisition and science and technology (S&T) investment decisions are inherently uncertain, requiring a careful balance between short-term capability procurement and long-term innovation. To explore this trade-space in a transparent, data-rich environment, the Advanced Disaster Relief and Aid Planning Tool (ADAPT) leverages Humanitarian Aid and Disaster Relief (HADR) operations as an unclassified analog for military missions. ADAPT integrated agent-based and discrete-event modeling with an interactive dashboard to assess how alternative fleet compositions, technology infusions, and acquisition strategies affect mission outcomes such as amount of aid delivered, aid delivery time, and asset utilization. The 2025 iteration of ADAPT expands the framework by introducing three major advancements: (1) expanded quantification of risk within the dashboard; (2) a redesigned Tabletop Exercise (TTX) format featuring structured participant roles, defined objectives, and dynamic scenario vignettes; and (3) integration of uncertainty and variability in risk, cost, and vignettes to mirror real-world acquisition environments. These enhancements enable participants to explore procurement and S&T investment tradeoffs interactively, observing how decision strategies evolve under uncertainty. Through multiple TTX campaigns based on Australia’s 2016 Cyclone Winston response, participants developed adaptive acquisition strategies, most notably a 25/75 allocation between investment and procurement, emerging from live negotiation and risk-balancing. The results demonstrate how risk-informed, role-driven experimentation can illuminate decision dynamics in complex acquisition environments and support data-driven recommendations for future defense and humanitarian operations.
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Date
2026-01
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Paper
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