Integrating Life Cycle Sustainability Assessment Results Using Fuzzy-TOPSIS in Automotive Lightweighting 05-14-03-0022
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SAE International Journal of Materials and Manufacturing-V130-5EJ
This article presents the application of the Life Cycle Sustainability Assessment (LCSA) methodology for integrating environmental, economic, and social assessment results by the direct application of Fuzzy-Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The aim of this work is to test the applicability of LCSA methodology as a potential tool to support the design phase, providing solutions tailored to its application in the automotive sector. To validate the proposed procedure, two alternative design solutions for a car dashboard are used as case study.
In response to the need of methods and tools for evaluating and comparing sustainability of alternative design solutions, LCSA is seen as one of the most promising method, but which needs further testing with real cases to solve some methodological challenges.
This research provides one of the first examples of LCSA application in the automotive sector, with the effort to provide practical suggestions by facing the methodological steps in a comprehensive way.
As a result, two important methodological aspects were addressed within the goal and scope and the results integration and interpretation phases. In particular, a clear list of indicators to measure sustainability is provided, together with a transparent procedure to identify their relevance for the sector. In addition, the application of Fuzzy-TOPSIS method was tested, and a three-level approach to present LCSA results is delivered (single score, sustainability dimensions’ contributions, and stakeholders’ points of view).
Citation: Zanchi, L., Delogu, M., Dattilo, C., Zamagni, A. et al., "Integrating Life Cycle Sustainability Assessment Results Using Fuzzy-TOPSIS in Automotive Lightweighting," SAE Int. J. Mater. Manf. 14(3):317-341, 2021, https://doi.org/10.4271/05-14-03-0022. Download Citation
Laura Zanchi, Massimo Delogu, Caterina Antonia Dattilo, Alessandra Zamagni, Francesco Del Pero