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NISAC  National Infrastructure Simulation and Analysis Center




NISAC Tools: LogiSims

Overview

LogiSims provides all-purpose decision support for resource allocation and planning prior to, during, and after both natural and manmade disasters.

Approach and Objective

LogiSims Process Flow

LogiSims Process Flow
(Click graphic to enlarge)

LogiSims is an integrated software application suite that allows NISAC to assist local, state, regional, and federal officials responsible for disaster planning and response to more effectively use the resources available to them for disaster preparedness and response. LogiSims blends a combination of empirical data and model predictions to enumerate and characterize hazards, determine direct and secondary infrastructure impacts, and optimize resource allocation and distribution, providing valuable input into disaster preparedness and response planning.

The process used by LogiSims to optimize the planning and preparation for a disaster or the real-time response to a disaster includes five distinct steps, each of which may blend a combination of empirical data and model predictions.

  • Enumeration/characterization of potential hazards: An empirical or simulated spatial
    characterization of each hazard is made along with an assessment of its relative likelihood.
  • Determination of direct impacts: The direct impact of each hazard on critical infrastructure
    components and the population is assessed.
  • Determination of secondary impacts: The secondary impacts of each hazard are assessed based on interdependencies between critical infrastructures or between critical infrastructures and the population.
  • Optimization of resource allocation: The available resources are allocated to optimize
    overall disaster response performance with respect to a specified metric and under a given set
    of constraints (such as overall costs).
  • Disaster mitigation strategic decisions: A number of what-if optimization scenarios are run, for example, as a function of the overall cost constraint, to allow policy makers to determine when the incremental costs outweigh the incremental benefits relative to other funding avenues.

Where Applied

Optimization Approach

Optimization Approach
(Click graphic to enlarge)

NISAC has used LogiSims for the following applications:

  • Understanding intensive care unit (ICU) resource needs and requirements for NBIC H1N1 Flu Study
  • Prototype application of potable water pre-staging and distribution for hurricane disasters.
  • Prototype application of electric power restoration resource needs for hurricane disasters.

Confidence in the Model

NISAC has confidence in the model because it accounts for the uncertainty in resource needs through a well-accepted scenario (ensemble) base approach in order to determine the best resource allocations in the expected sense.

Research Challenges

Submodels and their relationships in the application of HCSim to IND incidents

Submodels and their relationships in the application of HCSim to improvised nuclear device (IND) incidents (Click graphic to enlarge)

  • Simultaneous optimization of three combinatorial problems: positioning, inventory, and routing (multi-stage)
  • Uncertainty awareness
  • Application driven constraints and objectives
  • Competing objectives of efficient resource distribution and resource survivability

Documentation/References

  • R. Bent, C. Coffrin, and P. Van Hentenryck. Vehicle, Location, and Inventory Routing for Disaster Relief. INFORMS Annual Meeting, October 2009, San Diego, CA.
  • R. Bent, B. Daniel, and P. Van Hentenryck. Randomized Adaptive Decoupling for Large-Scale Vehicle Routing with Time Windows in Disaster Response. Eleventh INFORMS Computing Society Conference (ICS 2009). January 2009, Charleston, SC.
  • R. Bent and P. Van Hentenryck. Randomized Adaptive Spatial Decoupling for Large-Scale Vehicle Routing with Time Windows. Proceedings of the Twenty-Second Conference on Artificial Intelligence (AAAI 2007), 173-178, July 2007, Vancouver, BC.
  • R. Bent and P. Van Hentenryck. A Two-Stage Hybrid Algorithm for Pickup and Delivery Vehicle Routing Problems with Time Windows. Computers and Operations Research, Volume 33 (4): 875-893, 2006.
  • R. Bent and P. Van Hentenryck. A Two-Stage Hybrid Local Search for the Vehicle Routing Problem with Time Windows, Transportation Science, Volume 38 (4): 515-530, 2004.

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