Advertisement

Modelling Production Management Problems by Knowledge-Based Approximations

  • Marino Nicolich
  • Agostino Villa
Chapter
Part of the Microprocessor-Based and Intelligent Systems Engineering book series (ISCA, volume 9)

Abstract

Production Management (PM) in a discrete multi-product manufacturing enviroment consists in deciding, for each order of final products from customer,
  1. (A)

    when the manufacturing system must initiate to process items

     
  2. (B)

    by what machining center (or sequence of machining centers) the items must be processed

     
  3. (C)

    in what rates the working time of each machining center must beshared and assigned to items of different product families

     

Formally the PM problem can be started in terms of a dynamic scheduling problem, for given due date constraints of orders from customers and for given release constraints of material from suppliers.

A formal solution of this problem, suitable for a practical use in a multi-products multi-center industrial department, has not yet been found.

Approximation is mandatory. But it is not an easy task, since an approximated solution of a combinatorial optimization problem must be found, together with some criteria to evaluate the approximation accuracy.

In author’s opinion this problem can be approached by using the wide experience of industrial managers, thus adopting knowledge-based criteria from production management practice.

The contents of this contribution will be focused on such an approach, first introducing a K-B approximation methodology, then analyzing its applicability in solving PM problems.

Keywords

Product Family Machine Center Dynamic Schedule Part Family Internal Order 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    A. Kusiak - “The generalized Group Technology concept” - Int. J. Prod. Res., 1987, vol 25, n. 4, 561–569.CrossRefGoogle Scholar
  2. [2]
    A. Villa, S.Rossetto-“Rule based production planning in flexible ma-nufacturing systems” - Int. J. Flex. Manufacturing Systems, 1989, vol.2,5–24.Google Scholar
  3. [3]
    A. Iwainsky, E. Canuto, O. Taraszow, A. Villa - “Network decomposition for the optimizazion of connection structures”- Networks, 1986, vol 16, 205–235.MathSciNetzbMATHCrossRefGoogle Scholar
  4. [4]
    A. Villa - “Decision architectures for production planning in multi-stage multi-product manufacturing systems”- Annals of Operations Research, 1989, vol. 17, 51–68.CrossRefGoogle Scholar
  5. [5]
    A. Villa, M. Arcostanzo - “DOPP - Dynamically Optimized Production Planning” - Int. J. Prod. Res., 1988, vol. 26, n. 10, 1637–1650.CrossRefGoogle Scholar
  6. [6]
    J. L. Burbidge “The introduction of Group Technology” - Halsted Press, J. Wiley & Sons, New York, 1975.Google Scholar
  7. [7]
    A. Kusiak, W. S. Chow “Efficient solving of the Group Technology problem” - J. Manufacturing Systems, 1989, vol.6, n. 2, 117–124.MathSciNetCrossRefGoogle Scholar
  8. [8]
    H.G.Campbell, R.A.Dudek, M.L.Smith - “A heuristic algorithm for the n job m machine sequencing problem” - Management Sci.,1970,vol.16,n.10.Google Scholar
  9. [9]
    G. L. Nemhauser, A. H. Rinnooy Kan and Todd (eds) - “Handbook of operations research and management science” - Elsevier, Amsterdam, 1989.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 1991

Authors and Affiliations

  • Marino Nicolich
    • 1
  • Agostino Villa
    • 1
  1. 1.Istituto di Fisica Tecnica e di Tecnologie IndustrialiUniversita’ degli Studi di UdineUdineItaly

Personalised recommendations