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A simulated annealing method based on a clustering approach to determine bills of materials for a large product family

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  • Agard, Bruno
  • Penz, Bernard

Abstract

Defining an efficient bill of materials for a family of complex products is a real challenge for companies, largely because of the diversity they offer to consumers. To remain competitive, their products are available with many variants and options, which leads to a difficult question: "How can a bill of materials be defined so that all possible products can be built efficiently?" One way to answer it is to define a set of components (called modules), each of which contains a set of primary functions. An individual product is then built by combining selected modules. This industrial problem leads, in turn, to the complex optimization problem we study in this paper, which we propose to solve using a simulated annealing method based on a clustering approach.

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  • 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.
    • Handle: RePEc:eee:proeco:v:117:y:2009:i:2:p:389-401
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    References listed on IDEAS

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    1. Lamothe, Jacques & Hadj-Hamou, Khaled & Aldanondo, Michel, 2006. "An optimization model for selecting a product family and designing its supply chain," European Journal of Operational Research, Elsevier, vol. 169(3), pages 1030-1047, March.
    2. Olivier Briant & Denis Naddef, 2004. "The Optimal Diversity Management Problem," Operations Research, INFORMS, vol. 52(4), pages 515-526, August.
    3. Jayashankar M. Swaminathan & Sridhar R. Tayur, 1998. "Managing Broader Product Lines through Delayed Differentiation Using Vanilla Boxes," Management Science, INFORMS, vol. 44(12-Part-2), pages 161-172, December.
    4. Hau L. Lee & Christopher S. Tang, 1997. "Modelling the Costs and Benefits of Delayed Product Differentiation," Management Science, INFORMS, vol. 43(1), pages 40-53, January.
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    Cited by:

    1. Van den Broeke, Maud & Boute, Robert & Cardoen, Brecht & Samii, Behzad, 2017. "An efficient solution method to design the cost-minimizing platform portfolio," European Journal of Operational Research, Elsevier, vol. 259(1), pages 236-250.
    2. Mascle, Christian, 2013. "Design for rebirth (DFRb) and data structure," International Journal of Production Economics, Elsevier, vol. 142(2), pages 235-246.
    3. 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.
    4. Anzanello, Michel J. & Fogliatto, Flavio S., 2011. "Selecting the best clustering variables for grouping mass-customized products involving workers' learning," International Journal of Production Economics, Elsevier, vol. 130(2), pages 268-276, April.
    5. Zhang, Xinyan & Huang, George Q., 2010. "Game-theoretic approach to simultaneous configuration of platform products and supply chains with one manufacturing firm and multiple cooperative suppliers," International Journal of Production Economics, Elsevier, vol. 124(1), pages 121-136, March.
    6. 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.
    7. Brecht Cardoen & Jeroen Beliën & Mario Vanhoucke, 2015. "On the design of custom packs: grouping of medical disposable items for surgeries," International Journal of Production Research, Taylor & Francis Journals, vol. 53(24), pages 7343-7359, December.

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