Benefits of robust multiobjective optimization for flexible automotive assembly line balancing
- 465 Downloads
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.
KeywordsFlexibility Assembly line balancing Uncertain demand Robust optimization
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).
- Borshchev A, Filippov A (2004) From system dynamics and discrete event to practical agent based modeling: reasons, techniques, tools. In: Proceedings of the 22nd international conference of the System Dynamics Society, Citeseer, vol 22Google Scholar
- Chica M, Juan A, Cordon O, Kelton D (2017) Why simheuristics? Benefits, limitations, and best practices when combining metaheuristics with simulation. https://doi.org/10.2139/ssrn.2919208
- Greco S, Figueira J, Ehrgott M (2005) Multiple criteria decision analysis. Springer’s International series. Springer, BerlinGoogle Scholar
- Juan AA, Chica M, de Armas J, Kelton WD (2016) Simheuristics: a method of first resort for solving real-life combinatorial optimization problems. In: Keynote papers of the OR58 annual conference, Portsmouth, UK, pp 147–156Google Scholar
- Singh HK, Isaacs A, Ray T, Smith W (2008) Infeasibility driven evolutionary algorithm (IDEA) for engineering design optimization. In: AI 2008: advances in artificial intelligence, pp 104–115. SpringerGoogle Scholar