# A Group Decision-Making Approach in MCDM: An Application of the Multichoice Best-Worst Method
###### Abstract  
Multicriteria decision-making (MCDM) techniques have successfully been used to address a wide range of real-world decision-making issues. The best-worst method (BWM) is one of the several deterministic MCDM approaches. A recently proposed method called the multichoice best-worst method (MCBWM) takes into account several linguistic terms for pairwise comparisons of relative preferences among the criteria. It has been shown that the MCBWM approach has advantages over BWM: it reduces the calculation and determines optimal weight values by providing the choices for the optimal solution. This paper proposes a unique method for group decision-making based on MCBWM. We extended the MCBWM to solve group decision-making problems. A novel solution approach was developed and validated for multiple problems. Two examples and one case study were solved using the proposed approach to demonstrate the validity and application of the proposed method. The results were further compared with existing models to validate the proposed approach. We found that the obtained ranking order for all problems is the same and that the proposed model has a higher consistency ratio than the existing approaches. This method can be extended to other mathematical programming models for collective decision making in uncertain situations. mathematical programming; multicriteria decision-making; best-worst method; multichoice; multichoice best-worst method; group decision-making +
Footnote †: journal: Applied sciences  
1-9 numeric scale, which makes it easier to calculate. Following its introduction in 2015, a large number of its extensions have also been proposed by many researchers. Extensions in a deterministic and uncertain environment were also proposed. Recently, [PERSON] et al. [15] reported a weighting model of BWM and applied it to environmental performance evaluation, [PERSON] et al. [16] proposed a fuzzy BWM method [PERSON] et al. [17] proposed a BWM with interval-value and normal distribution; [PERSON] et al. [18] demonstrated multiple approaches to MCDM using hesitant fuzzy BWM. [PERSON] et al. [19] used stratified BWM for the selection of sustainable waste disposal technology. Many applications can be found in the literature. Recently, [PERSON] et al. [5] prioritized strategies for handling the COVID-19 outbreak, [PERSON] and [PERSON] [20] reported the applicability of BWM in the study of wireless networks, [PERSON] et al. [21] performed an Identification and analysis of shale development in India, [PERSON] et al. [22] evaluated and ranked of Six Sigma enablers, and [PERSON] et al. [23] applied BWM in water rights allocation. [PERSON] et al. [24] developed a framework for a housing infrastructure system considering flood resilience. [PERSON] et al. [25] identified obstacles and difficulties in developing solar energy. [PERSON] et al. [26] evaluated the risks of accidents by applying a combination of BWM and GRA in a material-handling problem. [PERSON] et al. [27] applied a hybrid of BWM and the entropy method. There are many more applications of the best-worst method in modern-day problems. In BWM, both the directions and the strength of the relative importance are indicated by the decision maker by performing a pairwise comparison of criterion \(i\) over \(j\)[12]. The strength of preference is represented by a numeric scale value from \"equally important\" to \"extremely important\". In some situations, the strength of importance may be represented by multiple pairwise comparison values. This may happen due to conflicting criteria, lack of information, and decision-maker perspective when making the proper judgment. To handle multiple values for the strength of importance of criterion \(i\) over \(j\), recently, a novel extension of the best-worst method WAS proposed by [PERSON] et al. [28], known as the multichoice best-worst method (MCBWM). The basis of the method is multichoice mathematical programming [29; 30], where multiple choices are associated with parameters.