Value-focused robust optimization for disaster response
The risk of natural disasters and national emergencies drives the need for real-time decision-making to guide the allocation of critical resources and the design of resilient and effective supply chains to respond effectively to these events. From the allocation of fuel during a hurricane to the deployment of medical supplies during a pandemic, the ability to proactively respond based on real-time data can mitigate the negative impacts of future catastrophic events.
This research is developing a decision-support system (DSS) for robust proactive resource allocation strategies based on the evolution of a disaster. This proactive response uses novel mathematical models to integrate probabilistic forecasting and logistics optimization to predict potential impacts and provide actionable decision support, accounting for cost and benefits. This approach has been implemented in the field during the pandemic by the City of Austin with a staged alert system for pandemic response. Future implementations for hurricane response and food supply chain disputations are currently in development.
This approach will address real-world scenarios to guide the allocation of critical resources and develop efficient and resilient supply chains. This system could mitigate the impact of disasters and national emergencies.
Research Leadership Team
Principal Investigator: Pitu Mirchandani, Arizona State University
Co-PI: David Morton, Northwestern University
Co-PI: Lauren Davis, North Carolina A&T State University
Co-PI: Ross Maciejewski, Arizona State University
Operations research and systems analysis