A Hybrid AI and Yager’s Fuzzy Framework for Optimizing Multi -Criteria Decisions in Smart Cities

This research introduces an innovative approach that blends hybrid artificial intelligence (AI) with Yager's fuzzy logic framework to enhance decision -making in smart cities, with a particular emphasis on urban transportation options in Isfahan, Iran. The methodology unfolds through several key steps: first, it identifies important criteria like cost, travel time , environmental impact, safety, and user satisfaction. Next, it assigns weights to these criteria using a pairwise comparison method and creates a fuzzy decision matrix. By applying Yager’s fuzzy aggregation operator, the approach combines fuzzy scores, which are then converted into clear, actionable values. A real-world case study illustrates how this methodology works by evaluating three transportation options: the Bus Network, Metro System, and Ride -sharing Services. The findings highlight the Metro System as the best option, showcasing how this framework supports thoughtful and informed decision -making for sustainable urban growth. Overall, this research plays a crucial role in enhancing urban transit by integrating cutting -edge AI techniques with fuzzy logic in decision -making processes.

کلیدواژه ها:

Hybrid ، Multi -Criteria Decision ، Yager's Fuzzy ، Smart Citie ، Artificial Intelligence