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A new analysis of a lot‐size model with partial backlogging

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  • David Rosenberg

Abstract

We reformulate the cost equation for the lot‐size model with partial back‐logging. The formulation is in terms of “fictitious demand rate,” a new inventory decision variable that simplifies the analysis. Using decomposition by projection, we obtain an optimal solution in a straightforward manner. The form of the solution sheds additional light on the behavior of the model. Some of these insights are elucidated by numerical examples.

Suggested Citation

  • David Rosenberg, 1979. "A new analysis of a lot‐size model with partial backlogging," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 26(2), pages 349-353, June.
    • Handle: RePEc:wly:navlog:v:26:y:1979:i:2:p:349-353
    DOI: 10.1002/nav.3800260214
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    Cited by:

    1. Achin Srivastav & Sunil Agrawal, 2020. "Multi-objective optimization of mixture inventory system experiencing order crossover," Annals of Operations Research, Springer, vol. 290(1), pages 943-960, July.
    2. Joaquín Sicilia & Luis San-José & Juan García-Laguna, 2012. "An inventory model where backordered demand ratio is exponentially decreasing with the waiting time," Annals of Operations Research, Springer, vol. 199(1), pages 137-155, October.
    3. Yonit Barron, 2024. "Shortage Policies for a Jump Process with Positive and Negative Batch Arrivals in a Random Environment," Mathematics, MDPI, vol. 12(9), pages 1-30, April.
    4. S. Sarkar & B. C. Giri, 2020. "A vendor–buyer integrated inventory system with variable lead time and uncertain market demand," Operational Research, Springer, vol. 20(1), pages 491-515, March.
    5. Wee, Hui-Ming & Huang, Yen-Deng & Wang, Wan-Tsu & Cheng, Yung-Lung, 2014. "An EPQ model with partial backorders considering two backordering costs," Applied Mathematics and Computation, Elsevier, vol. 232(C), pages 898-907.

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