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Assembly line balancing: Joint precedence graphs under high product variety

Author

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  • Nils Boysen
  • Malte Fliedner
  • Armin Scholl

Abstract

Previous approaches for balancing mixed-model assembly lines rely on detailed forecasts of the demand for each model to be produced on the line (model mix). With the help of the anticipated model mix a joint precedence graph for a virtual average model is deduced, so that the mixed-model balancing problem is reduced to the single-model case and traditional balancing approaches can be employed. Today's ever increasing product variety often impedes reliable forecasts for individual models. Instead, forecasts for the estimated occurrences of each product feature (e.g., percentage of cars with air conditioning) are merely obtainable. This paper shows how the generation of joint precedence graphs can be altered to account for this fundamental change in information. For the first time a tractable approach to provide the information necessary to balance mixed-model assembly lines carrying considerable product variety is presented.

Suggested Citation

  • Nils Boysen & Malte Fliedner & Armin Scholl, 2009. "Assembly line balancing: Joint precedence graphs under high product variety," IISE Transactions, Taylor & Francis Journals, vol. 41(3), pages 183-193.
    • Handle: RePEc:taf:uiiexx:v:41:y:2009:i:3:p:183-193
    DOI: 10.1080/07408170801965082
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    Citations

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    Cited by:

    1. Boysen, Nils & Bock, Stefan, 2011. "Scheduling just-in-time part supply for mixed-model assembly lines," European Journal of Operational Research, Elsevier, vol. 211(1), pages 15-25, May.
    2. Battaïa, Olga & Dolgui, Alexandre, 2013. "A taxonomy of line balancing problems and their solutionapproaches," International Journal of Production Economics, Elsevier, vol. 142(2), pages 259-277.
    3. Klindworth, Hanne & Otto, Christian & Scholl, Armin, 2012. "On a learning precedence graph concept for the automotive industry," European Journal of Operational Research, Elsevier, vol. 217(2), pages 259-269.
    4. Otto, Christian & Otto, Alena, 2014. "Multiple-source learning precedence graph concept for the automotive industry," European Journal of Operational Research, Elsevier, vol. 234(1), pages 253-265.
    5. Sternatz, Johannes, 2015. "The joint line balancing and material supply problem," International Journal of Production Economics, Elsevier, vol. 159(C), pages 304-318.
    6. Otto, Alena & Scholl, Armin, 2011. "Incorporating ergonomic risks into assembly line balancing," European Journal of Operational Research, Elsevier, vol. 212(2), pages 277-286, July.
    7. Boysen, Nils & Schulze, Philipp & Scholl, Armin, 2022. "Assembly line balancing: What happened in the last fifteen years?," European Journal of Operational Research, Elsevier, vol. 301(3), pages 797-814.
    8. Emde, Simon & Boysen, Nils, 2012. "Optimally locating in-house logistics areas to facilitate JIT-supply of mixed-model assembly lines," International Journal of Production Economics, Elsevier, vol. 135(1), pages 393-402.
    9. Sternatz, Johannes, 2014. "Enhanced multi-Hoffmann heuristic for efficiently solving real-world assembly line balancing problems in automotive industry," European Journal of Operational Research, Elsevier, vol. 235(3), pages 740-754.
    10. Emde, Simon & Gendreau, Michel, 2017. "Scheduling in-house transport vehicles to feed parts to automotive assembly lines," European Journal of Operational Research, Elsevier, vol. 260(1), pages 255-267.

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