A novel fuzzy-prospect theory approach for hydrogen fuel cell component supplier selection for automotive industry

Abstract

The burgeoning automotive sector is an energy-intensive industrial sector, amounting to 94 M tons of oil equivalents (MTOE). Owing to more significant environmental concerns, there is a dire need to transition the energy-intensive mobility space to sustainable mobility. In this direction, hydrogen is being researched as a potential alternative clean fuel for the automotive sector; however, in the context of developing countries, hydrogen fuel cell technology is at a very nascent stage. Various policies and research projects are underway to realize the shift, but they must be underpinned through a robust supply chain network. The selection of an optimal supplier is key to the development of a robust hydrogen supply chain network. To this end, this work proposes the selection of an appropriate supplier for supplying critical components for different research and development projects of hydrogen fuel cells. This is still a lacuna in the context of developing nations. To aid in addressing this issue, a novel TODIM (TOmada de Decisao Interativa Multicriterio) decision-making framework has been proposed to select the optimal supplier to supply critical hydrogen fuel cell components. Within the proposed approach, the linguistic evaluations provided by experts are modeled using a more generalized linear Diophantine linguistic uncertain fuzzy set that circumvents the algorithmic restrictions on the falsity and truth grades. The linguistic assessments from the experts were aggregated using the linear Diophantine uncertain linguistic power Einstein weighted averaging (LDULPEWA) operator. The power Einstein operations take care of the interaction between falsity and truth grades, which is important for the accuracy of the results. Finally, the TODIM approach was used to deduce the final ranking results. The proposed method considers the specialists' willingness to take risks during the decision-making process. Such a decision-making framework is still a lacuna in the domain of decision science. The supposed results were tested for their stability through sensitivity analysis. A past case study was solved using the proposed approach to validate the rankings. A comparison of the suggested strategy with the existing prominent decision-making frameworks was also carried out to clearly highlight the strengths and weaknesses of the proposed approach.