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The optimal emergency decision-making method with incomplete probabilistic information.

Ming Fu1, Lifang Wang2, Bingyun Zheng1

  • 1School of Management Science and Engineering, Anhui University of Finance & Economics, BengBu, 233030, Anhui, China.

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This study introduces a novel emergency decision-making algorithm using hesitant fuzzy probabilistic linguistic sets and a maximization gap model. It effectively handles limited time and incomplete information for critical situations.

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Area of Science:

  • Decision Sciences
  • Artificial Intelligence
  • Risk Management

Background:

  • Emergencies like natural disasters and chemical spills demand rapid, informed decisions.
  • Limited time and incomplete information often hinder effective emergency response.

Purpose of the Study:

  • To develop an advanced emergency decision-making algorithm.
  • To address challenges posed by time constraints and data uncertainty in crisis situations.

Main Methods:

  • Utilized hesitant fuzzy probabilistic linguistic sets for data collection, accommodating multiple values, probabilities, and unknown information.
  • Proposed a maximization gap model to derive unknown parameters and differentiate between similar alternatives.
  • Employed a dynamic hesitant probability fuzzy weighted arithmetic operator for information aggregation.

Main Results:

  • The proposed algorithm demonstrated effectiveness and accuracy in a practical instance.
  • The maximization gap model successfully distinguished alternatives with subtle differences.
  • The algorithm outperformed existing methods in comparative analyses.

Conclusions:

  • The developed algorithm provides a robust framework for emergency decision-making under uncertainty.
  • The novel approach enhances the ability to make timely and reasonable decisions during crises.
  • The maximization gap model is a key contribution for parameter estimation in complex decision scenarios.