Knowledge and accuracy measures for dual hesitant fuzzy sets with their applications in pattern recognition and multiple attribute decision making

As the world shifts towards sustainable transportation, electric vehicles (EVs) have emerged as a promising alternative to traditional fossil fuel-based vehicles, driving the need for robust charging infrastructure. The optimal location of electric vehicle charging stations (EVCSs) is a critical factor in promoting the widespread adoption of electric vehicles. This paper addresses the EVCS location problem using a multi-attribute decision-making (MADM) approach under a dual hesitant fuzzy environment. Dual hesitant fuzzy sets (DHFSs) represent a significant extension of hesitant fuzzy sets (HFSs), allowing each element to have multiple values of membership and non-membership, ranging from 0 to 1. This feature allows DHFSs to effectively capture the diverse perspectives and opinions of decision-makers in complex environments. In this research, knowledge and accuracy measures specifically designed for DHFSs are proposed. These measures are then leveraged to develop a pioneering DHFS-based MADM model, which effectively harnesses the unique capabilities of DHFSs to handle complex decision-making scenarios. A real-world case study demonstrates the effectiveness of the approach in selecting optimal EVCS locations, considering factors such as construction costs, energy losses, grid infrastructure, ecological impact, and social acceptance. The results highlight the robustness and practicality of the proposed approach, providing valuable insights for decision-makers and stakeholders.