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The Optimal Diversity Management Problem

Author

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  • Olivier Briant

    (Laboratoire Mathématiques Appliquées de Bordeaux, 351 cours de la libération, 33405 Talence, France)

  • Denis Naddef

    (Laboratoire Informatique et Distribution---IMAG, Institut National Polythechnique de Grenoble, 51 avenue Jean Kuntzmann, 38330 Montbonnot, France)

Abstract

In some industries, a certain part can be needed in a very large number of different configurations. This is the case, e.g., for the electrical wirings in European car factories. A given configuration can be replaced by a more complete, therefore more expensive, one. The diversity management problem consists of choosing an optimal set of some given number k of configurations that will be produced, any nonproduced configuration being replaced by the cheapest produced one that is compatible with it. We model the problem as an integer linear program. Our aim is to solve those problems to optimality. The large-scale instances we are interested in lead to difficult LP relaxations, which seem to be intractable by the best direct methods currently available. Most of this paper deals with the use of Lagrangean relaxation to reduce the size of the problem in order to be able, subsequently, to solve it to optimality via classical integer optimization.

Suggested Citation

  • Olivier Briant & Denis Naddef, 2004. "The Optimal Diversity Management Problem," Operations Research, INFORMS, vol. 52(4), pages 515-526, August.
    • Handle: RePEc:inm:oropre:v:52:y:2004:i:4:p:515-526
    DOI: 10.1287/opre.1040.0108
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Vié, Marie-Sklaerder & Zufferey, Nicolas & Cordeau, Jean-François, 2019. "Solving the Wire-Harness Design Problem at a European car manufacturer," European Journal of Operational Research, Elsevier, vol. 272(2), pages 712-724.
    2. Sergio García & Martine Labbé & Alfredo Marín, 2011. "Solving Large p -Median Problems with a Radius Formulation," INFORMS Journal on Computing, INFORMS, vol. 23(4), pages 546-556, November.
    3. Bader F. AlBdaiwi & Diptesh Ghosh & Boris Goldengorin, 2011. "Data aggregation for p-median problems," Journal of Combinatorial Optimization, Springer, vol. 21(3), pages 348-363, April.
    4. C. Beltran-Royo & J.-P. Vial & A. Alonso-Ayuso, 2012. "Semi-Lagrangian relaxation applied to the uncapacitated facility location problem," Computational Optimization and Applications, Springer, vol. 51(1), pages 387-409, January.
    5. Sourour Elloumi, 2010. "A tighter formulation of the p-median problem," Journal of Combinatorial Optimization, Springer, vol. 19(1), pages 69-83, January.
    6. Duran-Mateluna, Cristian & Ales, Zacharie & Elloumi, Sourour, 2023. "An efficient benders decomposition for the p-median problem," European Journal of Operational Research, Elsevier, vol. 308(1), pages 84-96.
    7. Agard, Bruno & Penz, Bernard, 2009. "A simulated annealing method based on a clustering approach to determine bills of materials for a large product family," International Journal of Production Economics, Elsevier, vol. 117(2), pages 389-401, February.
    8. Menezes, Mozart B.C. & Ruiz-Hernández, Diego & Guimaraes, Renato, 2016. "The component commonality problem in a real multidimensional space: An algorithmic approach," European Journal of Operational Research, Elsevier, vol. 249(1), pages 105-116.
    9. Sáez-Aguado, Jesús & Trandafir, Paula Camelia, 2012. "Some heuristic methods for solving p-median problems with a coverage constraint," European Journal of Operational Research, Elsevier, vol. 220(2), pages 320-327.
    10. Baud-Lavigne, Bertrand & Agard, Bruno & Penz, Bernard, 2016. "Simultaneous product family and supply chain design: An optimization approach," International Journal of Production Economics, Elsevier, vol. 174(C), pages 111-118.
    11. Baud-Lavigne, Bertrand & Agard, Bruno & Penz, Bernard, 2012. "Mutual impacts of product standardization and supply chain design," International Journal of Production Economics, Elsevier, vol. 135(1), pages 50-60.

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