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Dynamic nonlinear simplified neutrosophic sets for multiple-attribute group decision making.

Junda Qiu1, Linjia Jiang2, Honghui Fan1

  • 1School of Computer Engineering, Jiangsu University of Technology, Changzhou, 213001, PR China.

Heliyon
|March 19, 2024
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Summary

This study introduces dynamic nonlinear simplified neutrosophic sets (DNSNS) to model changing expert preferences in group decision-making. A novel aggregation model and decision algorithm are presented for improved real-time analysis.

Keywords:
Aggregation modelDecision algorithmDynamic nonlinear simplified neutrosophic setMultiple-attribute group decision makingTOPSIS

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Area of Science:

  • Decision Science
  • Fuzzy Set Theory
  • Artificial Intelligence

Background:

  • Expert preferences are dynamic and complex, posing challenges for traditional decision-making models.
  • Existing methods often struggle with real-time data and subjective uncertainty.

Purpose of the Study:

  • To propose a dynamic nonlinear simplified neutrosophic set (DNSNS) for real-time expert preference representation.
  • To develop an aggregation model and decision algorithm for multiple-attribute group decision-making (MAGDM) problems.

Main Methods:

  • Introduced DNSNS with measures for similarity, entropy, and distance.
  • Projected DNSNS time series into 3D curves to represent variance.
  • Developed a plant growth simulation algorithm (PGSA)-extended aggregation algorithm.
  • Proposed a TOPSIS and projection theory-based decision algorithm.

Main Results:

  • Established a DNSNS aggregation model without data preprocessing.
  • Calculated optimal aggregation preference curves and collective matrices.
  • Obtained overall rankings of alternatives for MAGDM problems.
  • Demonstrated feasibility and effectiveness through a case study.

Conclusions:

  • The proposed DNSNS model effectively captures real-time changing expert preferences.
  • The developed aggregation and decision algorithms provide a robust solution for MAGDM.
  • The approach offers a novel way to handle uncertainty in dynamic decision environments.