A Multiple Attribute Decision-Making Model for Firefighting Integrating Entropy Weights and Preferences with Intuitionistic Fuzzy Sets

Context In the realm of Smart Firefighting, research on multi-attribute decision modeling has yet to develop a decision-making model for AI-assisted fire rescue plans that integrates both subjective and objective information. Meanwhile, while studies using intuitionistic fuzzy sets provide valuable support for multi-attribute decision-making, they often focus only on data fuzziness and neglect decision-makers’ preferences for alternative plans and attributes. Objective The primary objective of this study is to develop a sophisticated multi-attribute decision-making model tailored specifically for urban fire emergency decision-making scenarios that integrates both subjective and objective information. Method To overcome the limitations of previous research, we develop a multi-attribute decision-making model for firefighting that integrates entropy weights and preference weights with intuitionistic fuzzy sets. Specifically, we first preprocess the decision attributes and experts’ preferences, transforming real-valued attributes into intuitionistic fuzzy numbers and determining the attribute weights based on expert preferences. We then introduce an overall framework for multi-attribute decision-making, comprising two models: the Fuzzy Entropy Weighted Multi-Attribute Decision-Making model, FEW-MADM and the Intuitionistic Fuzzy Sets based Integrated Entropy-Weighted and Preference-Weighted Multi-Attribute Decision-Making model IFPS-EWMADM, which realize the harmonious balance between data-driven insights and human expertise. Results We demonstrate an illustrative example in urban firefighting to evaluate the effectiveness of the proposed models. The results show that the FEW-MADM model is suitable for situations where the plan dataset contains no unexpected values, while the IFPS-EWMADM model is appropriate for scenarios involving complex and uncertain data. In addition, we conducted a sensitivity analysis, focusing on the conditions under which the original optimal solution would remain effective when key data in the case fluctuated within different ranges. Conclusion The proposed models calculate optimal solutions for various fire area sizes, which not only aid emergency management personnel in devising effective dispatching plans but also enable the analysis and summarization of historical cases. This dual functionality significantly propels the development of “smart firefighting” and markedly enhances the overall efficiency and effectiveness of fire emergency management.