A Methodology for Assessing System Availabilities with Finite Queues, Component Redundancy, and Spare Components.

Abstract

Logistics support has always been a key element of military combat effectiveness as well as an important element in commercial transportation systems. This research develops an original method to assess the system availability of a small number of vehicles or machines which overcomes some of the flaws of existing models. This small calling population of machines is also known as a finite-source queueing environment. The research method also incorporates backorder distributions based on sampling without replacement which is more accurate in this environment than the more common method of sampling with replacement. Although this general approach can be computationally burdensome, this work proposes two original methods to significantly reduce the number of computations necessary to find a solution. They include eliminating the tails of individual failure distributions and selectively ignoring other components in the system because of their insignificant downtime contributions. The new method also allows the user to include component redundancy and component spares. Finally, an optimization technique is developed to allow the user to optimize operating hours given a system availability goal.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1995
Accession Number
ADA303324

Entities

People

  • Theodore P. Lewis

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Air Platforms
  • Biomedical
  • Ground and Sea Platforms
  • Human Systems
  • Weapons Technologies

DTIC Thesaurus Topics

  • Air Force
  • Computer Programming
  • Computer Programs
  • Computers
  • Data Mining
  • Data Science
  • Databases
  • Information Processing
  • Information Science
  • Knowledge Management
  • Logistics
  • Maintenance
  • Operations Research
  • Queueing Theory
  • Random Variables
  • Statistical Algorithms
  • Systems Engineering

Fields of Study

  • Computer science

Readers

  • Logistics and Supply Chain Management.
  • Statistical inference.
  • Systems Analysis and Design