A dynamic programming approach for batch sizing in a multi-stage production process with random yields

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Abstract

This paper proposes a dynamic programming approach to modeling and determining batch sizes in a single period, multi-stage production process with random yields for each stage. To improve the computational performance of the proposed approach, a statistical bound is developed. A key decision incorporated into the model is whether to continue onto the next stage of processing or to scrap the entire current batch of product. This decision is based on the expected total profit from the remaining items for processing following the removal of all defectives. The decisions involving the locations of test stations after stages are also incorporated into the modeling approach.

Original languageEnglish (US)
Pages (from-to)1399-1406
Number of pages8
JournalApplied Mathematics and Computation
Volume218
Issue number4
DOIs
StatePublished - Oct 15 2011

All Science Journal Classification (ASJC) codes

  • Computational Mathematics
  • Applied Mathematics

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