Combat Logistics Force Sizing to Ensure Endurance Reliability.

Abstract

A methodology is developed for analysis of Combat Logistics Force performance in wartime conditions with stochastic demand. The imposition of randonmess on consumption, transit, and commodity transfer rates is intended to faithfully represent the dynamic environment in which logistics ships operate. An object oriented computer simulation is used to generate data for measuring the days of supply onhand for naval forces in various scenarios. This data is then used to construct cumulative probability distributions with which to compare the ability of different Combat Logistics Force configurations to sustain these naval forces. Analysis results quantity the impact of employing multi-product station ships with carrier battle groups in terms of the probability of these groups falling below some percentage of capacity measured in days of supply. The impact of additional shuttle ships is demonstrated, as well as the consequence of withdrawing shuttle ship operations from an advance logistics support base. Finally, the simulation is used to find a Combat Logistics Force configuration which minimizes the probability of naval forces exhausting their supplies of propulsion fuel, aviation fuel, provisions, and non-specific ordnance. In these experiments, unclassified approximations of the North Korea and Baltic major regional contingencies are modelled and run independently.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1995
Accession Number
ADA305963

Entities

People

  • David H. Salzer

Organizations

  • Naval Postgraduate School

Tags

DTIC Thesaurus Topics

  • Aviation Fuels
  • Computer Simulations
  • Computers
  • Logistics
  • Logistics Support
  • Mathematical Models
  • Munitions
  • Naval Operations
  • Navy
  • North Korea
  • Operations Research
  • Probability
  • Probability Distributions
  • Random Variables
  • Reliability
  • Simulations
  • Transport Ships

Readers

  • Aerospace logistics and air mobility.
  • Computational Modeling and Simulation
  • Logistics and Supply Chain Management.