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Conclusions and Open Research Problems

In: Markovian Demand Inventory Models

Author

Listed:
  • Dirk Beyer

    (M-Factor)

  • Feng Cheng

    (Office of Performance Analysis and Strategy)

  • Suresh P. Sethi

    (The University of Texas at Dallas)

  • Michael Taksar

    (University of Missouri)

Abstract

The Markovian demand approach provides a realistic way of modeling real-world demand scenarios. It allows us to relax the common assumption of demands being independent over time in the inventory literature. By associating the demand process with an underlying Markov chain, we are able to capture the effect of environmental factors that influence the demand process. Although the modeling capability is significantly enhanced by the incorporation of Markovian demands in inventory models, the simplicity of the optimal policies normally exhibited in the classical inventory problems is still preserved. Specifically, we show that the (s, S)-type policies shown to be optimal for a large class of inventory models with independent demands continue to be optimal for Markovian demand models, with one difference. That is, with Markovian demands, the (s, S) values depend on the state of the Markov process.

Suggested Citation

  • Dirk Beyer & Feng Cheng & Suresh P. Sethi & Michael Taksar, 2010. "Conclusions and Open Research Problems," International Series in Operations Research & Management Science, in: Markovian Demand Inventory Models, chapter 0, pages 211-213, Springer.
    • Handle: RePEc:spr:isochp:978-0-387-71604-6_10
    DOI: 10.1007/978-0-387-71604-6_10
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