DRU: Contending with Materiel Convergence: Optimal Control, Coordination, and Delivery of Critical Supplies to the Site of Extreme Events (NSF-CMMI-0624083)

Research Goals

The overall goal of this project submitted to the Human and Social Dynamics (HSD) solicitation (NSF 06-509) is to develop methodologies and tools to foster an accelerated convergence between the dynamic needs and supplies of critical resources (e.g., blood, water) to the site of an extreme event. These methodologies will be based on concepts from the social sciences, control theory, and robust stochastic optimization of dynamic supply chains with the aim of reducing adverse impacts of convergent low priority goods, while expediting the flow of high priority supplies to various response related sites. Achieving the overall goal of this project requires a modeling framework that integrates concepts from the social sciences, control theory, robust and stochastic optimization of supply chains, to bridge the gap between dynamic demand and supply of critical resources (i.e., resources available on site, private donations, resources provided by emergency agencies) after an extreme event. These mathematical procedures would be used to help advise the general public about donation priorities; thus reducing the probability of a repeat of the experience with previous extreme events in which a massive influx of non-priority donations hampered the flow of critical resources.

The work will lead to scientific contributions in the social sciences, control theory, robust and stochastic optimization, dynamic modeling of supply chains; and to improve the Nation's emergency response capabilities. As a part of the effort to promote learning education at all levels, the research team will engage both undergraduates and middle school students in research activities with specific emphasis on members of underrepresented groups.


Objectives:

  1. Synthesize the social sciences’ state of the art thinking about convergent behavior
  2. Determine hazard, communication, organizational, and demographic features that impact convergence and supply needs
  3. Provide robust estimates of the dynamic resource requirements after an extreme event (e.g., blood and water), i.e., what is needed
  4. Estimate the amount of critical resources available on site / in adjacent areas, i.e., what is available
  5. Estimate optimal pre-positioning strategy (in the case of anticipated extreme events)
  6. Estimate the dynamic pattern of unmet needs, i.e., the difference between items (1) and (2) above. This provides estimates of what needs to be transported to the impacted area from elsewhere
  7. Estimate, on the basis of control theory, the donation priorities needed from the general public and emergency agencies
  8. Find out, in dynamic fashion, the most effective ways to deliver, store and distribute the critical supplies needed, estimated in (5)
  9. Ensure the models developed are consistent with social sciences’ state of the art thinking
  10.  Identify institutional impediments to coordinated multi-institutional private and public sector response to extreme events, and formulate mechanisms to overcome these obstacles
  11.  Identify ways in which tighter integration of the information technology systems can foster coordination

 

Estimation of Immediate Resource Requirements

  • Empirical estimation of immediate resource requirement needs after a disaster.
  • Prepositioning appropriate amounts of a relatively small number of critical supplies could go a long way toward reducing delivery times
  • Emergency response agencies must have in place regional blanket purchase agreements (RBPA), because they would expedite purchasing of the critical supplies needed












Models to Forecast Immediate Resource Requirements


  • Statistical analyses of the requests made by the State of Louisiana to the Federal Emergency Management Agency (FEMA) using the Action Request Forms
  • Providing insight into the resource requirements after a disaster, their temporal evolution, the key types of commodities requested, as well as their relative importance
  • Autoregressive integrated moving average (ARIMA) models were estimated for the requests of key commodities


Identified Key Commodities


 


Integrative Freight Demand Management

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