Optimal burn-in procedure for mixed populations based on the device degradation process history

Ji Hwan Cha, Gianpaolo Pulcini

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17 Scopus citations


Burn-in is a method of ‘elimination’ of initial failures (infant mortality). In the conventional burn-in procedures, to burn-in an item means to subject it to a fixed time period of simulated use prior to actual operation. Then, the items which failed during burn-in are just scrapped and only those which survived the burn-in procedure are considered to be of satisfactory quality. Thus, when the items are subject to degradation phenomena, those whose degradation levels at the end of burn-in exceed a given failure threshold level are eliminated. In this paper, we consider a new burn-in procedure for items subject to degradation phenomena and belonging to mixed populations composed of a weak and a strong subpopulation. The new procedure is based on the ‘whole history’ of the degradation process of an item periodically observed during the burn-in and utilizes the information contained in the observed degradation process to assess whether the item belongs to the strong or weak subpopulation. The problem of determining the optimal burn-in parameters is considered and the properties of the optimal parameters are derived. A numerical example is also provided to illustrate the theoretical results obtained in this paper.

Original languageEnglish
Pages (from-to)988-998
Number of pages11
JournalEuropean Journal of Operational Research
Issue number3
StatePublished - 16 Jun 2016

Bibliographical note

Funding Information:
The authors would like to thank the Editor and referees for helpful comments and suggestions, which has improved the presentation of this paper. The work of the first author was supported by Priority Research Centers Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (No. 2009-0093827 ).

Publisher Copyright:
© 2015 Elsevier B.V.


  • Burn-in
  • Cost optimization
  • Degradation process
  • Mixed population
  • Non-homogeneous gamma process


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