On Maximizing the Expected Lifetime of Replaceable Systems.

Abstract

Consider the following model. A system has one vital component with n spares. When the vital component fails, the system fails. Derman, Lieberman, and Ross have considered the problem of maximizing the time until failure of the system. They obtained optimal schedules when the lifetime distributions of the spares were known. This paper treats several different cases of this model and finds optimal schedules together with their properties. Assuming only the first two moments of the spare component lifetime distributions are known, the minimax replacement schedules is obtained. These minimax replacement schedules are then compared with schedules based on different amounts of information. When the spares are different from each other, it must be decided in which order they should be used. A general sufficient condition is given under which the greedy order is maximal. This condition applies when the complete lifetime distribution is known, or for any minimax schedule. Two special cases are also considered. The first is the case in which groups of spares may be used in parallel. In the second special case, an additional spare will become available at some future time. Originator-supplied key words include: Reliability; Optimal replacement; Optimal schedule; Optimal maintenance; and Minimax schedule. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1984
Accession Number
ADA150003

Entities

People

  • M. M. Perkins

Organizations

  • Stanford University

Tags

Communities of Interest

  • Air Platforms
  • C4I

DTIC Thesaurus Topics

  • Aircrafts
  • Algorithms
  • Computational Science
  • Discrete Distribution
  • Distribution Functions
  • Equations
  • Game Theory
  • Guarantees
  • Inequalities
  • Inspection
  • Maintenance
  • Military Research
  • Operations Research
  • Probability
  • Reliability
  • Test And Evaluation
  • United States

Fields of Study

  • Engineering

Readers

  • Game Theory.
  • Mathematical Modeling and Probability Theory.
  • Software Engineering