Advertisement

An Evolutionary Algorithm for the Sequence Coordination in Furniture Production

  • Carlo Meloni
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2264)

Abstract

In the material flow of a plant, parts are grouped in batches, each having as attributes the shape and the color. In both departments, a changeover occurs when the attribute of a new part changes. The problem consists in finding a common sequence of batches optimizing an overall utility index. A metaheuristic approach is presented which allows to solve a set of real-life instances and performs satisfactorily on a large sample of experimental data.

Keywords

Evolutionary algorithms sequencing manufacturing systems 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Agnetis, A., Detti, P., Meloni, C., Pacciarelli, D., 1999, Set-up coordination between two stages of a supply chain. Tech.Rep. n. 32-99, Dipartimento di Informatica e Sistemistica, Università La Sapienza, Roma.Google Scholar
  2. 2.
    Agnetis A., Detti P., Meloni C., Pacciarelli D., 2001, A linear algorithm for the hamiltonian completion number of the line graph of a tree. Information Processing Letters 79, 17–24.Google Scholar
  3. 3.
    Agnetis A., Detti P., Meloni C., Pacciarelli D., 2001, The minimum cardinality dominating trail set problem (MDTS). Proceedings of the AIRO 2001 conference, CUEC, Cagliari.Google Scholar
  4. 4.
    Bertossi, A.A., 1981, The edge hamiltonian problem is NP-complete, Information Processing Letters, (13), 157–159.Google Scholar
  5. 5.
    Chatterjee, S., Carrera, C., Lynch, L.A., 1996, Genetic algorithms and traveling salesman problems, European Journal of Operational Research (93), 490–510.Google Scholar
  6. 6.
    Crama, Y., Oerlemans, A.G., Spieksma, F.C.R., 1996, Production planning in automated manufacturing, Springer, Berlin.Google Scholar
  7. 7.
    Detti, P., Meloni, C., 2001, A linear algorithm for the Hamiltonian completion number of the line graph of a cactus. In: H. Broersma, U. Faigle, J. Hurink and S. Pickl (Eds.) Electronical Notes in Discrete Mathematics vol 8 (2001), Elsevier Science Publishers.Google Scholar
  8. 8.
    Hertz, A., Kobler, D., 2000, A framework for the description of evolutionary algorithms, European Journal of Operational Research (126), 1–12.Google Scholar
  9. 9.
    Holland, J.H., 1976, Adaption in natural and artificial systems, University of Michigan Press, Ann Arbor MI.Google Scholar
  10. 10.
    Jawahar, N., Aravindan, P., Ponnambalam, S.G., Aravind Karthikeyan, A., 1998, A genetic algorithm-based scheduler for setup-constrained FMC, Computers in Industry (35), 291–310.Google Scholar
  11. 11.
    Korte, B., Vygen, J., 2000, Combinatorial Optimization: Theory and Algorithms, Springer, Berlin.Google Scholar
  12. 12.
    Meloni, C., 2001, The splittance of a graph and the D-Trails problem. Preprint n.449, Centro Vito Volterra, Università Tor Vergata, Roma.Google Scholar
  13. 13.
    Reeves, C.R., 1993, Modern heuristic tecniques for combinatorial problems, Blackwell Scientific Pubblications, Oxford.Google Scholar
  14. 14.
    Reeves, C.R., 1995, A genetic algorithm for flowshop sequencing, Computers And Operations Research, 22, 5–13.Google Scholar
  15. 15.
    Wolsey, L.A., 1998, Integer Programming, Wiley-Interscience Publication, New York.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Carlo Meloni
    • 1
  1. 1.Dipartimento di Informatica e AutomazioneUniversitá di Roma TreRomaItaly

Personalised recommendations