Group technology in production management: The short horizon planning level.

*(English)*Zbl 0583.90044Numerical algorithms are proposed for optimal partitioning of a set of tasks into production subsystems under the following conditions:

(1) the number of production subsystems equals the number of part families, where a set of part types is partitioned into subsets called part families, (2) one and only one production subsystem corresponds to each part family, (3) one and only one part family corresponds to each production subsystem, and (4) these partitions minimize the number of tasks performed in production subsystems.

An example is worked out in some detail to show the efficiency of the algorithms, which depend however for its speed on the choice of the distance function chosen to be minimized.

(1) the number of production subsystems equals the number of part families, where a set of part types is partitioned into subsets called part families, (2) one and only one production subsystem corresponds to each part family, (3) one and only one part family corresponds to each production subsystem, and (4) these partitions minimize the number of tasks performed in production subsystems.

An example is worked out in some detail to show the efficiency of the algorithms, which depend however for its speed on the choice of the distance function chosen to be minimized.

Reviewer: J.K.Sengupta

##### MSC:

90B30 | Production models |

90B35 | Deterministic scheduling theory in operations research |

62H30 | Classification and discrimination; cluster analysis (statistical aspects) |

62P20 | Applications of statistics to economics |

65C99 | Probabilistic methods, stochastic differential equations |

##### Keywords:

data analysis; group technology; production management; non-hierarchical clustering; production scheduling; optimal clustering; optimal grouping; Numerical algorithms; optimal partitioning of a set of tasks; production subsystems; part families
PDF
BibTeX
XML
Cite

\textit{H. Garcia} and \textit{J. M. Proth}, Appl. Stochastic Models Data Anal. 1, 25--34 (1985; Zbl 0583.90044)

**OpenURL**

##### References:

[1] | Cluster Analysis for Applications, Academic Press, New York, 1973. · Zbl 0299.62029 |

[2] | and , Group Technology, Butterworth, London, 1973. |

This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.