Urban transportation planning involves evaluating multiple conflicting criteria such as accessibility,cost-effectiveness,and environmental impact,often under uncertainty and incomplete information.These complex decisi...Urban transportation planning involves evaluating multiple conflicting criteria such as accessibility,cost-effectiveness,and environmental impact,often under uncertainty and incomplete information.These complex decisions require input from various stakeholders,including planners,policymakers,engineers,and community representatives,whose opinions may differ or contradict.Traditional decision-making approaches struggle to effectively handle such bipolar and multivalued expert evaluations.To address these challenges,we propose a novel decisionmaking framework based on Pythagorean fuzzy N-bipolar soft expert sets.This model allows experts to express both positive and negative opinions on a multinary scale,capturing nuanced judgments with higher accuracy.It introduces algebraic operations and a structured aggregation algorithm to systematically integrate and resolve conflicting expert inputs.Applied to a real-world case study,the framework evaluated five urban transport strategies based on key criteria,producing final scores as follows:improving public transit(−0.70),optimizing traffic signal timing(1.86),enhancing pedestrian infrastructure(3.10),expanding bike lanes(0.59),and implementing congestion pricing(0.77).The results clearly identify enhancing pedestrian infrastructure as the most suitable option,having obtained the highest final score of 3.10.Comparative analysis demonstrates the framework’s superior capability in modeling expert consensus,managing uncertainty,and supporting transparent multi-criteria group decision-making.展开更多
This paper introduces fuzzy N-bipolar soft(FN-BS)sets,a novel mathematical framework designed to enhance multi-criteria decision-making(MCDM)processes under uncertainty.The study addresses a significant limitation in ...This paper introduces fuzzy N-bipolar soft(FN-BS)sets,a novel mathematical framework designed to enhance multi-criteria decision-making(MCDM)processes under uncertainty.The study addresses a significant limitation in existing models by unifying fuzzy logic,the consideration of bipolarity,and the ability to evaluate attributes on a multinary scale.The specific contributions of the FN-BS framework include:(1)a formal definition and settheoretic foundation,(2)the development of two innovative algorithms for solving decision-making(DM)problems,and(3)a comparative analysis demonstrating its superiority over established models.The proposed framework is applied to a real-world case study on selecting vaccination programs across multiple countries,showcasing consistent DM outcomes and exceptional adaptability to complex and uncertain scenarios.These results position FN-BS sets as a versatile and powerful tool for addressing dynamic DM challenges.展开更多
文摘Urban transportation planning involves evaluating multiple conflicting criteria such as accessibility,cost-effectiveness,and environmental impact,often under uncertainty and incomplete information.These complex decisions require input from various stakeholders,including planners,policymakers,engineers,and community representatives,whose opinions may differ or contradict.Traditional decision-making approaches struggle to effectively handle such bipolar and multivalued expert evaluations.To address these challenges,we propose a novel decisionmaking framework based on Pythagorean fuzzy N-bipolar soft expert sets.This model allows experts to express both positive and negative opinions on a multinary scale,capturing nuanced judgments with higher accuracy.It introduces algebraic operations and a structured aggregation algorithm to systematically integrate and resolve conflicting expert inputs.Applied to a real-world case study,the framework evaluated five urban transport strategies based on key criteria,producing final scores as follows:improving public transit(−0.70),optimizing traffic signal timing(1.86),enhancing pedestrian infrastructure(3.10),expanding bike lanes(0.59),and implementing congestion pricing(0.77).The results clearly identify enhancing pedestrian infrastructure as the most suitable option,having obtained the highest final score of 3.10.Comparative analysis demonstrates the framework’s superior capability in modeling expert consensus,managing uncertainty,and supporting transparent multi-criteria group decision-making.
文摘This paper introduces fuzzy N-bipolar soft(FN-BS)sets,a novel mathematical framework designed to enhance multi-criteria decision-making(MCDM)processes under uncertainty.The study addresses a significant limitation in existing models by unifying fuzzy logic,the consideration of bipolarity,and the ability to evaluate attributes on a multinary scale.The specific contributions of the FN-BS framework include:(1)a formal definition and settheoretic foundation,(2)the development of two innovative algorithms for solving decision-making(DM)problems,and(3)a comparative analysis demonstrating its superiority over established models.The proposed framework is applied to a real-world case study on selecting vaccination programs across multiple countries,showcasing consistent DM outcomes and exceptional adaptability to complex and uncertain scenarios.These results position FN-BS sets as a versatile and powerful tool for addressing dynamic DM challenges.
