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Neutrosophic aggregation operators and their applications in the software site selection.

Sumbal Ali1, Muhammad Rahim1, Sanaa A Bajri2

  • 1Department of Mathematics and Statistics, Hazara University, Mansehra, 21300, KPK, Pakistan.

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This study introduces a novel neutrosophic set ( NS) framework to manage membership degree influence in decision-making. The new model enhances aggregation operators for improved Multiple Criteria Decision Making (MCDM) applications.

Keywords:
Aggregation operatorsDecision makingOperational lawsOptimizationα,β,γ-neutrosophic sets

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Area of Science:

  • Decision Sciences
  • Information Science
  • Mathematics

Background:

  • Conventional neutrosophic sets (NS) struggle with managing membership degree influence during aggregation.
  • Existing models lack robust mechanisms to balance membership degree (MD), indeterminacy membership degree (IMD), and non-membership degree (NMD).

Purpose of the Study:

  • To extend the concept of neutrosophic sets by introducing the neutrosophic set ( NS).
  • To develop a new Multiple Criteria Decision Making (MCDM) model that effectively handles the influence of MD, IMD, and NMD using specific parameters.
  • To introduce novel aggregation operators for combining neutrosophic information.

Main Methods:

  • Introduction of the neutrosophic set ( NS) with defined operational laws.
  • Development of a series of aggregation operators (AOs) tailored for neutrosophic information.
  • Formulation of a new MCDM model utilizing the proposed AOs.

Main Results:

  • The proposed NS framework effectively manages the influence of membership, indeterminacy, and non-membership degrees.
  • A case study on software office location selection demonstrates the practical applicability and effectiveness of the new MCDM model.
  • The developed aggregation operators successfully combine neutrosophic information for decision-making.

Conclusions:

  • The introduced neutrosophic set and MCDM model offer a significant advancement over existing approaches.
  • The model's validity and effectiveness are confirmed through comparative analysis, highlighting its potential for real-life decision challenges.
  • This research provides a robust framework for enhancing decision-making processes involving uncertain and imprecise information.