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Economic optimization in a fixed sequence of unreliable inspections

Author

Listed:
  • T Avinadav

    (Tel Aviv University)

  • T Raz

    (Tel Aviv University)

Abstract

Given a fixed sequence of unreliable inspection operations with known costs and inspection error probabilities of two types (classifying good items as defective and vice versa), we develop a model for selecting the set of inspections that should be activated in order to minimize expected total costs (inspection and penalties). We present an efficient branch and bound algorithm for finding the optimal solution, and two variations of a greedy heuristic that can be applied jointly to provide very good solutions at a O(n2) computational complexity. The conclusions are backed by a factorial experiment that included 1440 problem instances.

Suggested Citation

  • T Avinadav & T Raz, 2003. "Economic optimization in a fixed sequence of unreliable inspections," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(6), pages 605-613, June.
    • Handle: RePEc:pal:jorsoc:v:54:y:2003:i:6:d:10.1057_palgrave.jors.2601543
    DOI: 10.1057/palgrave.jors.2601543
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    References listed on IDEAS

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    1. Liou, Ming-Jong & Tseng, Sheng-Tsaing & Lin, Tsong-Ming, 1994. "Optimal sequence of partial inspections subject to errors," International Journal of Production Economics, Elsevier, vol. 33(1-3), pages 109-119, January.
    2. Raz, Tzvi & Bricker, Dennis, 1993. "Sequencing of inspection operations subject to errors," European Journal of Operational Research, Elsevier, vol. 68(2), pages 251-264, July.
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    Cited by:

    1. Jafari, L. & Makis, V., 2015. "Joint optimal lot sizing and preventive maintenance policy for a production facility subject to condition monitoring," International Journal of Production Economics, Elsevier, vol. 169(C), pages 156-168.
    2. Chernonog, Tatyana & Avinadav, Tal, 2016. "A two-state partially observable Markov decision process with three actionsAuthor-Name: Ben-Zvi, Tal," European Journal of Operational Research, Elsevier, vol. 254(3), pages 957-967.

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