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Technical Note---Note on “Myopic Heuristics for the Random Yield Problem”

Author

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  • Karl Inderfurth

    (Faculty of Economics and Management, Otto-von-Guericke-University Magdeburg, 39106 Magdeburg, Germany)

  • Sandra Transchel

    (Department of Logistics, University of Mannheim, 68131 Mannheim, Germany)

Abstract

Bollapragada and Morton (1999) present several well-performing heuristics for solving the periodic inventory problem with random yield and demand. Their approach is essentially based on a transformation of the single-period problem into a standard newsvendor problem with deterministic yield and random demand which, however, is supply dependent. In our note, we show that their evaluation of the respective optimality condition is not correct. This explains the steady deterioration of their myopic heuristics for parameter constellations that correspond to increasing service levels. Some computational investigations reveal that the performance of the heuristics can become quite poor if service levels are high and exceed those values for which results are reported in the original study. Nonetheless, up to now these heuristics are still the best ones available for solving the joint random yield problem.

Suggested Citation

  • Karl Inderfurth & Sandra Transchel, 2007. "Technical Note---Note on “Myopic Heuristics for the Random Yield Problem”," Operations Research, INFORMS, vol. 55(6), pages 1183-1186, December.
    • Handle: RePEc:inm:oropre:v:55:y:2007:i:6:p:1183-1186
    DOI: 10.1287/opre.1070.0420
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    References listed on IDEAS

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    1. repec:hal:journl:hal-02312467 is not listed on IDEAS
    2. Candace Arai Yano & Hau L. Lee, 1995. "Lot Sizing with Random Yields: A Review," Operations Research, INFORMS, vol. 43(2), pages 311-334, April.
    3. Mordechai Henig & Yigal Gerchak, 1990. "The Structure of Periodic Review Policies in the Presence of Random Yield," Operations Research, INFORMS, vol. 38(4), pages 634-643, August.
    4. Srinivas Bollapragada & Thomas E. Morton, 1999. "Myopic Heuristics for the Random Yield Problem," Operations Research, INFORMS, vol. 47(5), pages 713-722, October.
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    Cited by:

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