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Multi-criteria decision making approach based on SVTrN Dombi aggregation functions

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Abstract

The neutrosophic set is constructed for modelling of situations specifically with incomplete, indeterminate and inconsistent information. In the study, Dombi operations have been introduced on two single-valued trapezoidal neutrosophic numbers (SVTrNNs). Here, Dombi operation on SVTrNNs, some new averaging and geometric averaging operators namely SVTrN Domi weighted averaging (SVTrNDWA) operator, SVTrN Dombi ordered weighted averaging (SVTrNDOWA) operator, SVTrN Dombi hybrid weighted averaging (SVTrNDHWA) operator, SVTrN Dombi weighted geometric (SVTrNDWGA) operator, SVTrN Dombi ordered weighted geometric (SVTrNDOWGA) operator and SVTrN Dombi hybrid weighted geometric (SVTrNDHWGA) operator have been proposed. Further, some properties of these operators such as idempotency, boundedness, monotonicity and commutativity have been investigated. Next, we have constructed a multi-criteria decision-making (MCDM) method in SVTrN environment based on SVTrNDWA and SVTrNDWGA operators. We have given an application of the present MCDM method for selecting the best contractor for a road construction company. We have also compared the current approach with the existing procedure and have given the sensitivity analysis of the proposed plan.

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Acknowledgements

The authors wish to thank the anonymous editor and reviewers for their valuable comments and helpful suggestions which greatly improved the quality of this paper. The second author was supported by the research Grant S-0064-1439, Deanship of Scientific Research, University of Tabuk, Tabuk-71491, Saudi Arabia.

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  1. Department of Applied Mathematics with Oceanology and Computer Programming, Vidyasagar University, Midnapore, 721102, India

    Chiranjibe Jana & Madhumangal Pal

  2. Department of Mathematics, University of Tabuk, Tabuk, 71491, Saudi Arabia

    G. Muhiuddin

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  1. Chiranjibe Jana
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Correspondence to Chiranjibe Jana.

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Jana, C., Muhiuddin, G. & Pal, M. Multi-criteria decision making approach based on SVTrN Dombi aggregation functions. Artif Intell Rev 54, 3685–3723 (2021). https://doi.org/10.1007/s10462-020-09936-0

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