Multi-period optimal design of an aerospace CFRP waste management supply chain: Data set, variables and criteria for the development of a multi-objective MILP model.

Clicks: 360
ID: 72607
2020
This paper presents the data set, variables and criteria for the development of a multi-objective and multi-period Mixed Integer Linear Programming (MILP) model for the deployment and design of an aerospace CFRP (Carbon Fibre Reinforced Polymer) waste supply chain. It involves ε-constraint, lexicographic techniques and Multiple Criteria Decision Making (MCDM) tools. In this model, the deployment of new recycling sites (Grinding, Pyrolysis, Supercritical Water, Microwave) is established. The system is optimised by bi-criteria optimisation including an economic objective based on cost minimisation or Net Present Value (NPV) maximisation and an environmental one (minimisation of Global Warming Potential). The presentation of the global strategy, the results and their discussion have been presented in a companion paper (Vo Dong, P.A., Azzaro-Pantel, C., Boix, A multi-period optimisation approach for deployment and optimal design of an aerospace CFRP waste management supply chain, Waste Management, Volume 95, 2019, Pages 201-216 [1]). The data were acquired by literature analysis, by use of Simapro v7.3 software tool and EcoInvent database, by use of institutional sources (Eurostat for energy prices) or from Airbus and Boeing websites for aircraft deliveries and calculation of CFRP content. The model was created by the authors within the framework of SEARRCH (Sustainability Engineering Assessment Research for Recycling Composite with High value) project supported by ANR (Agence Nationale de la Recherche Scientifique). The case study of CFRP waste supply chain in France has supported the deployment analysis.
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dong2020multiperioddata Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Dong, Anh Vo;Azzaro-Pantel, Catherine;Boix, Marianne;
Journal Data in brief
Year 2020
DOI 10.1016/j.dib.2019.104766
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