A multi-stage decision-making model for urban fire emergency with multi-granularity uncertain linguistic information and prospect theory

  • Xuemei Zhou orcid

    Key Laboratory of Linguistic and Cultural Computing Ministry of Education, Northwest Minzu University, Lanzhou 730030, China
  • Nady Slam orcid

    Key Laboratory of Linguistic and Cultural Computing Ministry of Education, Northwest Minzu University, Lanzhou 730030, China
Article ID: 3861
DOI: https://doi.org/10.59400/adecp3861
Keywords: multi-granularity uncertain language; prospect theory; multi-stage decision-making; two-tuple linguistic model; random forest; urban fire emergency; dynamic reference point

Abstract

Existing fire emergency decision-making models often struggle with accurately handling multi-granularity uncertain linguistic information, loss aversion, and a lack of adaptability to dynamic fire evolution. To address these gaps, this study adopts two-tuple linguistic representation (2TLR) for quantifying multi-granularity linguistic information and combines the Analytic Hierarchy Process (AHP) with the entropy weight method (EWM) to determine the ability weights of the experts. Furthermore, a six-dimensional dynamic reference point is generated via the random forest algorithm, and the integration of prospect theory (PT) with a sequential decision-making framework (SDF) is implemented for the dynamic optimization of response plans. Validation through real-world cases demonstrates that the proposed Multi-stage Prospect Selection (M-PS) model outperforms both the TOPSIS method and the single PT model, compared with these two methods, the proposed M-PS model can effectively prioritize the avoidance of high-risk scenarios, accurately reflect decision-makers’ loss aversion tendency, and realize dynamic decision-making through updating the decision plan sequentially, thereby providing reliable support for urban fire emergency management. At the same time, in this study we conduct a comparative analysis of core metrics between existing methods and the proposed M-PS model. The evaluation across five dimensions demonstrates that the proposed M-PS model delivers superior performance.

Published
2026-02-11
How to Cite
Zhou, X., & Slam, N. (2026). A multi-stage decision-making model for urban fire emergency with multi-granularity uncertain linguistic information and prospect theory. Advances in Differential Equations and Control Processes, 33(1). https://doi.org/10.59400/adecp3861
Issue
Vol. 33 No. 1 (2026)
Section
Articles

Copyright (c) 2026 Xuemei Zhou, Nady Slam

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

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