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Discrete-Time Markov-Reward Models of Production Systems

  • Ranga Mallubhatla
  • Krishna R. Pattipati
  • N. Viswanadham
Part of the The IMA Volumes in Mathematics and its Applications book series (IMA, volume 73)

Abstract

In this paper we consider the discrete-time version of performability modeling. The discrete-time approach is well-suited for the performance studies of Automated Manufacturing Systems (AMS) in the presence of failures, repairs and reconfigurations. AMS exist in various configuration states and this transitional behavior is modeled using discrete-time Markov chains. In addition, the performance in each configuration state is modeled by a Markov reward structure, that is similar to the continuous-time versions. In this paper, we derive recursive expressions for the conditional densities and moments of the cumulative reward function, when the underlying Markov chain describing the evolution of the configuration states is homogenous. Examples are used to illustrate the methods obtained in the paper.

Keywords

Buffer Size Material Handling System Mission Time Finite Buffer Irreducible Markov Chain 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag New York, Inc 1995

Authors and Affiliations

  • Ranga Mallubhatla
    • 1
  • Krishna R. Pattipati
    • 1
  • N. Viswanadham
    • 2
  1. 1.Department of Electrical and Systems EngineeringUniversity of ConnecticutStorrsUSA
  2. 2.Department of Computer Science and AutomationIndian Institute of ScienceBangaloreIndia

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