Casualty Evacuation and Distribution Using B-767 and C-9 Aircraft

Abstract

The objective of this thesis was to develop and evaluate a casualty evacuation and distribution system using B-767 and C-9 aircraft. For a European conventional conflict, an average casualty rate between 1600 and 1900 per day was considered for a 60-day period. Casualties were distributed among all potential members of the National Disaster Medical System. The system was modelled using SLAM simulation and FORTRAN computer code. The performance of the system was measured by the average time each patient spent in the evacuation system, beginning with the time the patient was released (medically cleared) to fly. The time ended when the patient arrived at the final destination airport. The factors in the model affecting the mean time in the system (TIS) include predeparture ground time, flying time, number and capacity of aircraft and casualty rate. Response surface equations were generated from the experimental results for selected combinations of factor levels. The prediction equations provide an accurate measure of the performance of the system, while saving the time and expense of conducting simulation experiments. The equations can be used to determine either the required number of aircraft or the necessary aircraft capacity given a specified criterion value of mean TIS and expected casualty rate.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1986
Accession Number
ADA172506

Entities

People

  • William B. Ewing Jr

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Aeromedical Evacuation
  • Air Force
  • Aircrafts
  • Algorithms
  • Analysis Of Variance
  • Birds
  • Casualties
  • Commercial Aircraft
  • Computer Programming
  • Computers
  • Department Of Defense
  • Evacuation
  • Experimental Design
  • Medical Evacuation
  • Operations Research
  • Statistics
  • Transportation

Readers

  • Aviation Safety and Air Traffic Management
  • Computational Modeling and Simulation
  • Medical or Health Care Field.