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Benefits of robust multiobjective optimization for flexible automotive assembly line balancing

  • Manuel Chica
  • Joaquín Bautista
  • Jesica de Armas
Article

Abstract

Changing conditions and variations in the demand are frequent in real industrial environments. Decision makers have to take into account this uncertainty and manage it properly. One clear example is the automotive industry where manufacturers have to assume an uncertain and heterogeneous demand. For instance, automotive manufacturers must adapt their decisions when balancing the assembly line by considering different flexible solutions. Our proposal is using robust multiobjective optimization and simulation techniques to provide managers with a set of robust and equally-preferred solutions for assembly line balancing. We study a Nissan case where the demand of each product family is uncertain. The problem is addressed by considering a robust multiobjective model for assembly line balancing based on a high number of production plans. After the selection of six different assembly line configurations, we study the implications of robustness metrics based on workstations’ overload. We show that the adverse managerial effects of not having flexible line configuration when demand changes are alleviated. For the real Nissan automotive case, our analysis and conclusions show the managerial and industrial benefits of using robust assembly lines. We also encourage decision makers to use robust multiobjective optimization methods for selecting the most flexible decisions.

Keywords

Flexibility Assembly line balancing Uncertain demand Robust optimization 

Notes

Acknowledgements

This work has been partially supported by the Spanish Ministry of Economy and Competitiveness (Projects FHI-SELM2 TIN2014-57497-P, NEWSOCO TIN2015-67661-P, TRA2013-48180-C3-P, and TRA2015-71883-REDT) and FEDER. Likewise we want to acknowledge the support received by the Department of Universities, Research & Information Society of the Catalan Government (2014-CTP-00001).

Supplementary material

10696_2018_9309_MOESM1_ESM.pdf (66 kb)
Supplementary material 1 (pdf 65 KB)

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Electrical Engineering and ComputingThe University of NewcastleCallaghanAustralia
  2. 2.ETSEIBUniversitat Politcnica de CatalunyaBarcelonaSpain
  3. 3.Department of Economics and BusinessUniversitat Pompeu FabraBarcelonaSpain

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