A Synthetic Decision-Support System Based on Multi-Facet Fuzzy Sets with Dynamic Parameter Settings for Next-Gen Battery Technology Investments

New generation battery technology investments play a key role in the transition process from fossil fuels to renewable energy. The main problem related to the subject is that decision makers experience uncertainty about which of these numerous criteria affecting investment performance are prioritized. The lack of comprehensive models in the literature for systematically prioritizing these criteria creates a significant gap. The aim of this study is to determine the priority strategies to increase the performance of new generation battery technology investments. In this context, an innovative decision-making model is developed by integrating multi-facet fuzzy sets, logarithmic least-squares and WASPAS techniques. This study makes a significant contribution to the literature by prioritizing the performance indicators of new generation battery technology investments via an innovative decision-making model. The development of multi-facet fuzzy sets in this study provides an important contribution to the literature. Moreover, dynamic decision-making opportunity is provided by redefining membership degrees with different parameter sets for each scenario. This provides the opportunity to make clearer decisions based on scenarios and dynamic evaluations in complex decision-making processes. The main findings of the study indicate that circularity and compatibility with existing manufacturing infrastructure are priorities in improving the performance of these projects.