# A Group Decision-Making Approach in MCDM: An Application of the Multichoice Best-Worst Method
###### Abstract
[PERSON] and [PERSON] [45] applied BWM along with the logistic regression approach to a flood susceptibility mapping problem. [PERSON] et al. [46] developed a stochastic BWM and validated it on a medical devices industry problem. However, none of these models consider multichoice parameters. So, this research gap motivated us to incorporate multiple choices into group decision-making models. The motivations behind this study were as follows:  
* In the prevalent literature there are many group decision-making methods, but no group decision-making method has assumed multiple choices in pairwise comparisons. * [PERSON] et al. [28] demonstrated the usefulness of the multichoice best-worst method having a lesser inconsistency ratio than the BWM, in comparison. So, incorporating multiple choice for group decision making may provide a better approach. * The deviation in experts' opinions is also a kind of uncertainty, which may be handled using multiple choices in MCDM. * In group decision making, the inclusion of MCBWM may provide a new approach to solving MCDM problems for finding an optimal weight value while minimizing inconsistency. * The multichoice group decision-making framework considers experts' information as a choice; and, in the solution, it identifies that choice for which the inconsistency is minimized. So, after solving the model, it provides the opinion of the expert for which the inconsistency is minimized. * Overall, this approach determines those pairwise comparisons collected from experts for which the inconsistency is minimized. So, in this study, we introduced the concept of multiple choices in group decision making using MCBWM [28]. The pairwise comparisons of all \(k\) experts are transformed into a new multichoice pairwise comparison set, where all the preferences of experts are considered in the choice set. A choice set is a set of pairwise comparisons from a preference scale where no two comparison scale values are repeated. The flexibility of the decision-making problems was improved by considering multichoice parameters in reference comparisons. The proposed approach is novel in group decision making within the methods of the best-worst approach and among all MCDM problems. The multiple choices in pairwise comparison for comparing the criteria lead to accommodating all possible opinions of \(k\) experts regarding the pairwise comparison of a particular criterion for the other. In this study, we extended the MCBWM application toward group decision making. The results were finally compared with those of existing methods [36]. The main objectives of this study were as follows:  
* While considering different kinds of group decision-making methods, our primary objective was to develop an MCDM framework for group decision making incorporating the flexibility of choosing multiple choices for pairwise comparisons;
* To verify the proposed model by applying it to multiple problems;
* To assess the obtained results of the developed model's framework with those of existing models. The contribution of this study is the approach involving the use of a multichoice set in a scenario of group decision-making problems. It handles a large number of models using one multichoice mathematical programming model and provides optimal weights for the criteria. The proposed approach is a simple approach. We hope that, in the near future and through novel extensions achieved by incorporating belief theory, confidence levels, and uncertainty theories, this method can be extended to a more impactful methodology for handling MCDM problems. The rest of this paper is structured as follows: Section 2 outlines the preliminaries. The developed models are described in Section 3. In Section 4, an experimental study on two numerical problems and one case study is described. Finally, Section 5 provides our conclusion and a discussion on the limitations of the presented study and future work.