A Variable Flow Modelling Approach To Military End Strength Planning

Abstract

The purpose of this thesis is to develop a model to assist military manpower planners in meeting prescribed end strength requirements. To achieve this, I have developed a variable flow model capable of both optimizing accessions and also optimizing transition probabilities. I use the Marine Technician category of the Royal Australian Navy as the subject of the thesis, as it is currently facing large manpower deficits and could benefit from the recommendations. I compare forecasts using current and optimized parameters against each other, and the results show that optimizing transition probabilities is the most efficient way of meeting manpower targetswhile maintaining the current hiring policyfor the Marine Technician category. I also conduct a risk analysis by simulating the effect of changes in the transition rate on the differential between the forecast and desired end strengths. Again, the transition probability optimization model performs better than the status quo situation. Recommendations are made for future research to improve the implementation of optimized transition probabilities and also for ways of limiting the attrition rate, which is the only variable not under the control of the Royal Australian Navy.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2016
Accession Number
AD1030900

Entities

People

  • Benjamin K Grossi

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Biomedical
  • Human Systems

DTIC Thesaurus Topics

  • Air Force
  • Attrition
  • Computer Simulations
  • Dynamic Programming
  • Employment
  • Linear Programming
  • Manpower
  • Marine Corps
  • Markov Chains
  • Markov Models
  • Mathematical Models
  • Operations Research
  • Optimization
  • Probability
  • Risk
  • Risk Analysis
  • Technicians

Readers

  • Mathematical Modeling and Probability Theory.
  • Military Mobilization and Reserve Forces Studies.
  • Systems Analysis and Design