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Multi-Criteria Group Decision-Making q-Rung Neutrosophic Interval-Valued Soft Set TOPSIS Aggregating Operator for the Selection of Diagnostic Health Imaging

In: Fuzzy Optimization, Decision-making and Operations Research

Author

Listed:
  • M. Palanikumar

    (Saveetha Institute of Medical and Technical Sciences, Department of Mathematics, Saveetha School of Engineering)

  • V. Sreelatha Devi

    (Saveetha Institute of Medical and Technical Sciences, Department of Mathematics, Saveetha School of Engineering)

  • Chiranjibe Jana

    (Vidyasagar University, Department of Applied Mathematics with Oceanology and Computer Programming)

  • Gerhard Wilhelm Weber

    (Poznan University of Technology, Faculty of Engineering Management)

Abstract

The q-Rung neutrosophic interval-valued soft set (q-Rung NSIVSS) is a generalization of interval-valued fuzzy soft set (IVFSS) and fuzzy soft set (FSS). The q-Rung NSIVSS aggregation was discussed through TOPSIS aggregated operation (AO). The TOPSIS method is an effective method for multi-criteria group decision-making (MCGDM), which is an extension of FSS. The objective is to find an ideal solution that is positive and negative based on q-Rung NSIVSS, aggregating TOPSIS, using a score function. Optimal alternatives are presented in order to determine closeness values. To strengthen our conclusions, we provide practical examples. This results in the outcome of the models for which “q” is provided. Comparing the existing models to those that have been proposed allows us to measure the validity and usefulness of the models under consideration. There is a great deal of fascination and interest in the most recent discoveries.

Suggested Citation

  • M. Palanikumar & V. Sreelatha Devi & Chiranjibe Jana & Gerhard Wilhelm Weber, 2023. "Multi-Criteria Group Decision-Making q-Rung Neutrosophic Interval-Valued Soft Set TOPSIS Aggregating Operator for the Selection of Diagnostic Health Imaging," Springer Books, in: Chiranjibe Jana & Madhumangal Pal & Ghulam Muhiuddin & Peide Liu (ed.), Fuzzy Optimization, Decision-making and Operations Research, chapter 0, pages 499-517, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-35668-1_22
    DOI: 10.1007/978-3-031-35668-1_22
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