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Two Approximations as a Basis for the Optimization of Production in Unreliable Markovian Long Transfer Lines

  • Jean Mbihi
  • Roland P. Malhamé
  • Javad Sadr
Part of the Advances in Computational Management Science book series (AICM, volume 4)

Abstract

Optimizing buffer sizes in manufacturing transfer lines has been a long standing problem. By providing some amount of decoupling between various production stages, parts buffering can significantly help increasing manufacturing productivity in the face of potential individual machine failures and part processing time variability. However, buffering comes at a cost, both in terms of occupied space and frozen capital within the plant. Transfer line decomposition methodologies have aimed, among other goals, at simplifying the analysis of this question. Two approximations, the machine decoupling approximation and the socalled demand averaging principle, are presented. They lead to a characterization of the transfer line, when controlled via a class of KANBAN like production policies, as a collection of isolated failure prone machines with random failures described by recursively coupled statistical parameters. Subsequently, the well developed single machine theory can be used as a building block in the analysis and optimization of the line. The approximations are tested via regenerative Monte Carlo simulation, and illustrative dynamic programming based transfer line optimization results are reported.

Keywords

Sojourn Time Transfer Line Storage Cost Active Portion Markov Chain Representation 
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 Science+Business Media New York 2002

Authors and Affiliations

  • Jean Mbihi
  • Roland P. Malhamé
  • Javad Sadr

There are no affiliations available

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