The BWM is a recent addition to the MCDM arsenal that quickly became popular due to its efficiency and increased consistency in pairwise comparisons. Researchers continue to enrich the BWM literature with extensions and new applications. Some recent examples of theoretical contributions are Brunelli and Rezaei (2019
) where the authors develop a multiplicative BWM, and Mohammadi and Rezaei (2019
) where a probabilistic BWM is provided to handle group decision-making situations to aggregate different decision makers’ preferences to find the optimal criterion weights using a Bayesian hierarchical model. Rezaei et al.
) segment the suppliers by using the BWM, and then utilize the results for supplier development. Ahmad et al.
) propose a BWM-based framework for supply chain management applications in the oil and gas industry. The model takes into account many external factors such as economic and political stability. Ahmadi et al.
) develop a BWM-based framework to reveal the social sustainability of supply chains. van de Kaa et al.
) use BWM for the selection of biomass thermochemical conversion technology. A case study is conducted in the Netherlands. Ren et al.
) reveal the importance of criteria affecting sustainability assessment of the technologies for the treatment of urban sewage sludge by using the BWM. The TOPSIS method is preferred to complete the selection of the most satisfactory technology among the three alternatives. Guo and Zhao (2017
) use a fuzzy BWM in order to handle linguistic terms (statements) of experts that make the process more imprecise. Aboutorab et al.
) combine the BWM with Z-numbers to handle uncertain information occurring during the decision-making process. The proposed model is applied to supplier development. Gupta (2018
) proposes a model that integrates BWM with VIKOR, and utilizes it to rank the criteria of service quality for airlines and to select the best airline company. van de Kaa et al.
) rank the key success factors which have an impact on the substitution of standards with the help of BWM. Nawaz et al.
) utilize Markov Chain and BWM for cloud service selection which is a complex process because of the different satisfaction terms of decision makers. Omrani et al.
) develop a hybrid model using multi-response Taguchi-neural network-fuzzy best-worst method and TOPSIS to select the best power plant among the alternatives. Rezaei et al.
) prioritize the components of the logistics performance index by utilizing BWM. Salimi and Rezaei (2018
) develop a model to evaluate R&D performance of 50 companies by using BWM. Shojaei et al.
) integrate Taguchi Loss Function, BWM, and VIKOR to evaluate the performance of airports. Kheybari et al.
) use BWM to choose the best location for bioethanol facilities in a sustainable way. Liao et al.
) propose a hesitant fuzzy BWM-based model to evaluate the success of hospitals, and conduct a comparative study to discuss the pros and cons of the proposed model. Malek and Desai (2019
) focus on the barriers to sustainable production. They benefit from the BWM to rank the barriers in their importance. Hashemizadeh et al.
) propose a Geographic Information System-based BWM method for the site selection of a solar photovoltaic power plant. Ecer and Pamucar (2020
) integrate fuzzy BWM with the traditional Combined Compromise Solution and use the proposed methodology for the selection of a sustainable supplier. Muravev and Mijic (2020
) integrate BWM with the Multi-Attributive Border Approximation Area Comparison method to select the most suitable provider. Singh et al.
) utilize BWM to rank the enablers that help to apply environmental lean six sigma effectively. A case study is conducted in Indian Micro-Small and Medium Enterprises. In the study of Alidoosti et al.
) conversion technologies are measured from a socially sustainable perspective using BWM. Dwivedi et al.
) introduce a balanced scorecard model integrated with BWM in an insurance firm. This marks the first application in the insurance domain to evaluate performance across two distinct time periods. In a related study, Rahmati and Darestani (2022
) adopt a BWM-TOPSIS hybrid model to determine the weights of criteria within the performance aspects of the balanced scorecard for insurance companies. Bayanati et al.
) present a methodology that integrates BWM and fuzzy VIKOR to assess and prioritize companies in the tire industry. The focus is on evaluating environmental risks associated with the industry’s sustainable supply chains. El Baz et al.
) explore the incorporation of sustainability factors in the implementation of Industry 4.0 technologies. They use BWM to prioritize sustainability drivers and externalities. In their research, Kheybari et al.
) propose a hybrid methodology that takes into account the significance of human health while making decisions related to the temporary locations of hospitals, using the BWM technique.
Recently, the focus of research changed from individual decision-making to group decision-making procedures due to the needs of concurrent engineering practices and multi-disciplinary studies. This shift makes MCDM applications more complex. As a result, finding solutions to group decision-making that can handle the complexity originating from the dissimilarities of expert opinions has become an attractive area for MCDM research. The BWM is also redesigned for group decision-making practices by some researchers. Mou et al.
) develop a more structured group decision-making method for the BWM in an uncertain environment with intuitionistic fuzzy multiplicative preferences. They firstly aggregate preferences by using a fuzzy multiplicative weighted geometric aggregation operator and they generate their own algorithm using max-min programming to rank the criteria. Guo and Zhao (2017
) propose a fuzzy BWM based on a model that uses the graded mean integration representation. A non-linearly constrained model is established to find the fuzzy weights and select the alternatives. Hafezalkotob and Hafezalkotob (2017
) develop a new group decision-making tool to overcome the subjectivity of the preferences by transforming the preferences into fuzzy numbers. The proposed model is helpful to aggregate the preferences of decision-makers from different levels in the organizational hierarchy. The preference degrees of decision makers and criteria are simultaneously handled as fuzzy numbers. Tabatabaei et al.
) introduced a novel approach for calculating the global weights of criteria and alternatives. Additionally, they proposed a new consistency ratio and a unified model capable of handling all formulations simultaneously. Haseli et al.
) presented a fresh group decision-making approach for the BWM, termed G-BWM, which facilitates the examination of experts’ inclinations in implementing democratic decision-making by leveraging the BWM framework. In the methodology proposed by Dehshiri et al.
), new programming approaches were devised to determine the criteria weights, reduce the number of constraints in the regular BWM methods, and combine the aggregation steps to elucidate the importance of criteria in the group decision-making process.
The majority of the previous studies evaluate the alternatives by utilizing other MCDM methods than the BWM such as AHP, TOPSIS, VIKOR, ELECTRE. Interestingly, although the research emphasizes that it is a favourable method for criterion-weighting, the BWM is not used in the alternative selection phase. Furthermore, existing BWM literature assumes that the experts will agree on one best and one worst criterion. However, this reconciliation may be very difficult to achieve for highly diverse markets, or for experts who hold very different opinions on a decision-making problem. Thus, this study makes the following important contributions:
Next, we give the details of our proposed methodology based on the BWM for alternative selection to close the above-mentioned gaps.