An extension of the MABAC and OS model using linguistic neutrosophic numbers: selection of unmanned aircraft for fighting forest fires
The paper presents a new approach to the treatment of uncertainty and subjectivity in the decision-making process based on the modification of Multi-Attributive Border Approximation area Comparison (MABAC) and an Objective–Subjective (OS) model by applying Linguistic Neutrosophic Numbers (LNN) instead of traditional numerical values. By integrating these models with LNN it was shown that it is possible to a significant extent to eliminate subjective qualitative assessments and assumptions by decision makers in complex decision-making conditions. On this basis, a new hybrid LNN–OS–MABAC model was formed. This model was tested and validated on a case-study in which the optimal unmanned aircraft were selected to combat forest fires. After defining the criteria and their attributes, they were prioritized using the LNN–OS model, in which the weights of the criteria and their attributes are a combination of the objective values obtained by the method of maximum deviation and the subjective values of the criteria obtained by expert examination using LNN. The ranking and selection of the optimal unmanned aircraft from those on offer with similar characteristics was carried out using the LNN–MABAC model. Testing of the model showed that the proposed model based on LNN provides an objective expert evaluation by eliminating subjective assessments when determining the numerical values of criteria. A sensitivity analysis of the LNN–OS–MABAC model, carried out through 54 scenarios of changes in the weight coefficients, showed a high degree of stability in the solutions obtained when the alternatives were ranked. The results were validated by comparison with LNN extensions of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) model.
First published online 2 May 2022
Keyword : firefighting UAV, LNN, MABAC, MCDM, objective–subjective (OS) model, TOPSIS
This work is licensed under a Creative Commons Attribution 4.0 International License.
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