Combining multi-attribute decision-making methods with multi-objective optimization in the design of biomass supply chains. (8th May 2018)

• Record Type: Journal Article
• Authors: Wheeler, J.; Páez, M.A.; Guillén-Gosálbez, G.; Mele, F.D.
• Is Part Of: Computers & chemical engineering. Volume 113(2018)
• Page Start: 11
• Page End: 31
• Publication Date: 2018-05-08
• Subjects: Sustainability -- Multi-criteria optimization -- Environmental impact -- Biorefinery design -- Decision-making; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2018.02.010
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: We calculate weighting factors that reflect experts' preferences on the basis of four well-known multi-attribute methods. A comparison of the different multi-attribute methods is performed. A weighting-based approach is used to solve multi-objective optimization problems. The proposed framework is applied to the sugar/bioethanol industry in Argentina. Abstract: Multi-objective optimization (MOO) is widely applied in sustainability problems where several objectives must be accounted for in the analysis. Unfortunately, its complexity grows with the number of objectives, which hampers its practical use. In this paper, we simplify MOO problems via their combination with multi-attribute decision-making (MADM) methods. The approach identifies a unique Pareto solution of the MOO problem, which best reflects the decision-makers' preferences, by using weighting factors generated via four well-known MADM methods: SWING, SMART, AHP and TRADE OFF. The capabilities of this approach are illustrated through its application to the design and planning of a sugar/ethanol supply chain using questionnaires filled in by academic experts in the problem. We find that the weights obtained using MADM algorithms may well differ from the ones given by standard life-cycle assessment methods employed in systems engineering problems. Overall, our approach simplifies the MOO problem by identifying solutions consistent with the decision-makers' preferences and by providing valuable insight on how these preferences are articulated in practice.