A Closed-Loop Method for Multiperiod Intelligent Information Processing with Cost Constraints under the Fuzzy...

Area of Science:

• Decision Sciences
• Fuzzy Mathematics
• Emergency Management

Background:

• High-quality decision-making is critical, especially during emergencies, relying heavily on accurate data.
• Traditional decision-making models often struggle with the inherent hesitation and fuzziness of complex problems.
• Cost-effectiveness is a key consideration in selecting optimal solutions, particularly when multiple interventions are required.

Purpose of the Study:

• To develop a decision-making framework that prioritizes cost reduction in emergency situations.
• To introduce a multiround decision-making model capable of handling complex, evolving scenarios.
• To utilize continuously probabilistic linguistic sets for robust data collection in uncertain environments.

Main Methods:

• Employed continuously probabilistic linguistic sets to capture decision-maker hesitation and problem fuzziness.
• Developed a multiround decision-making model featuring a closed-loop structure for iterative adjustments.
• Integrated a closed-loop control module for real-time system verification and accuracy enhancement.

Main Results:

• The proposed algorithm demonstrated superior effectiveness compared to existing methods.
• Experiments confirmed significant cost reduction in emergency response scenarios.
• The closed-loop system facilitated timely adjustments, enhancing overall decision accuracy.

Conclusions:

• The novel decision-making algorithm and multiround model offer a more effective and cost-efficient approach to emergency management.
• Continuously probabilistic linguistic sets provide a robust data structure for handling uncertainty in decision-making.
• The closed-loop control mechanism is vital for maintaining accuracy and adaptability in dynamic situations.