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      • Department of Industrial Engineering
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      An adaptive bayesian replacement policy with minimal repair

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      Author(s)
      Gürler, Ü.
      Dayanık, S.
      Date
      2002
      Source Title
      Operations Research
      Print ISSN
      0030-364X
      Electronic ISSN
      1526-5463
      Publisher
      Institute for Operations Research and the Management Sciences (INFORMS)
      Volume
      50
      Issue
      3
      Pages
      552 - 558
      Language
      English
      Type
      Article
      Item Usage Stats
      216
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      179
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      Abstract
      In this study, an adaptive Bayesian decision model is developed to determine the optimal replacement age for the systems maintained according to a general age-replacement policy. It is assumed that when a failure occurs, it is either critical with probability p or noncritical with probability1−p, independently. A maintenance policy is considered where the noncritical failures are corrected with minimal repair and the system is replaced either at the first critical failure or at age , whichever occurs first. The aim is to find the optimal value of that minimizes the expected cost per unit time. Two adaptive Bayesian procedures that utilize different levels of information are proposed for sequentiallyupdating the optimal replacement times. Posterior density/mass functions of the related variables are derived when the time to failure for the system can be expressed as a Weibull random variable. Some simulation results are also presented for illustration purposes.
      Keywords
      Bayesian analysis
      Statistical decision making
      Replacement of industrial equipment
      Probability theory
      Maintenance
      Variables (Mathematics)
      Business expenses
      Corporate policies
      Cognitive processes
      Production factors
      Financial accounting
      Financial economics
      Permalink
      http://hdl.handle.net/11693/48620
      Published Version (Please cite this version)
      https://doi.org/10.1287/opre.50.3.552.7750
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      • Department of Industrial Engineering 758
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