Identification of Preferred Operational Plan Force Mixes Using a Multiobjective Methodology to Optimize Resource Suitability and Lift Cost

Abstract

AFIT research in support of the Advanced Logistics Project is directed at developing a Mission-Resource Value Assessment Tool for rationally assigning relative value to resources and identifying alternative force mixes to logistics and operational planners. Research of factors that affect force mix composition has been strictly limited to how the operating environment of USAF combat aircraft influences their performance in specified aerospace missions. In contrast, this research makes use of an aircraft's designed suitability to perform specified aerospace missions in order to examine the tradeoff between mission suitability and the amount of lift needed to deploy and operate the asset. An Evolutionary approach was applied to a tri-objective constrained optimization problem with 15 decision variables with the goal of producing five Pareto optimal sets of force mixes corresponding to five progressively larger sortie capability levels. Analysis of the results include absolute performance comparisons using different operating parameter settings, and time complexity in relation to problem scale. Preliminary results were also generated from a version of the algorithm that uses a solution repair function. These results help to assess the viability of using a multi-objective fast messy genetic algorithm to identify well balanced force mixes.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2001
Accession Number
ADA390974

Entities

People

  • David J. Wakefield

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Cyber
  • Energy and Power Technologies
  • Human Systems
  • Space

DTIC Thesaurus Topics

  • Air Force
  • Aircrafts
  • Algorithms
  • Computational Science
  • Computer Programming
  • Computer Programs
  • Computers
  • Deployment
  • Evolutionary Algorithms
  • Genetic Algorithms
  • Logistics
  • Mathematical Models
  • Mathematical Programming
  • Multiobjective Optimization
  • Operating Systems
  • Optimization
  • Systems Engineering

Readers

  • Aerospace logistics and air mobility.
  • Computational Modeling and Simulation
  • Operations Research

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy
  • AI & ML - Machine Learning Algorithms
  • Biotechnology
  • Space