The Queuing Manpower Model (QMAN).

Abstract

The Queuing Manpower Model (QMAN) is an analytic personal computer (PC) based model for the determination of maintenance manpower requirements. The model applies a queuing algorithm to Air Force Specialty (AFS)/crew size clusters to determine necessary manning to meet flying demands. This value is then compared to work load and crew size manpower demands to determine actual requirements. The Turbo Pascal implementation of QMAN provides rapid manpower estimations that compare favorably with the Air Force standard maintenance manpower model, the Logistics Composite Model (LCOM). The inherent speed of an analytic model, such as QMAN, contrasts with the lengthy run-times required by large and complicated Monte Carlo simulations like LCOM. These lengthy run-times lead to lengthy analysis due to the need for multiple simulation runs before "optimal" manpower requirements can be determined. QMAN makes possible various types of analyses that were not previously feasible due to LCOM time demands. Examples of these include the determination of the effect of increased maintainer productivity, shorter flying days, changing wing structure, and alternative occupational structures on manpower requirements. Additionally, QMAN serves as a tool for assessing and reducing manpower costs for aircraft systems under development or modification.

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 1996
Accession Number
ADA316799

Entities

People

  • David M. Quick
  • Jeffrey H. Grobman
  • William M. Weaver

Organizations

  • Armstrong Laboratory

Tags

Communities of Interest

  • Air Platforms
  • Human Systems
  • Weapons Technologies

DTIC Thesaurus Topics

  • Air Force
  • Aircraft Maintenance
  • Aircrafts
  • Algorithms
  • Computer Programs
  • Computers
  • Information Science
  • Logistics
  • Maintenance
  • Manpower
  • Manpower Utilization
  • Monte Carlo Method
  • Operating Systems
  • Personnel Management
  • Random Variables
  • Reliability
  • Simulations

Readers

  • Aerospace logistics and air mobility.
  • Computer Science.
  • Statistical inference.