Advances in Best-Worst Method

In this study, we propose a probabilistic group decision-making method based on the Best-Worst Tradeoff method (BWT) and the Bayesian approach. BWT is a pairwise comparison method that is used to elicit the tradeoffs among a set of attributes (criteria) in a multi-criteria decision-making problem. While BWT is suitable for a single decision-maker situation, Bayesian BWT is suitable for aggregating the tradeoffs among a number of criteria coming from a number of decision-makers or experts. The proposed method aggregates the scaling constants (weights), and assigns a confidence number (between zero and one), to inform about the confidence we have about the ranking order of the criteria. We demonstrate how the method is used in a real-world setting. Data is collected from three experts on ranking a number of European seaports that are performing differently with respect to a number of relevant criteria. We think that the method has great potential in real-world group decision-making problems.

In recent years, there has been a growing recognition of the importance of stakeholder involvement in decision-making processes. To address this need, Multi-Actor Multi-Criteria Analysis (MAMCA) has emerged as a group decision-making framework that takes into account the preferences of key stakeholders. MAMCA provides a flexible structure that aims to capture the various points of view of stakeholders involved in the decision-making process. After the group evaluation, MAMCA encourages stakeholders to engage in discussions and negotiations to reach a consensus solution. However, sometimes it is challenging to reach a consensus solution as stakeholders normally hold conflict interests. Furthermore, during the evaluation, stakeholders may struggle to understand the weight elicitation methods, which can lead to elicitation results that do not reflect their preferences or expectations. Consequently, the Best-Worst Method (BWM) effectively addresses these challenges by simplifying the elicitation process and promoting consistency among judgments, ultimately enhancing the reliability and robustness of decision-making outcomes. This paper proposes a robust group decision-making framework based on MAMCA that incorporates BWM as the weight elicitation method. The proposed framework integrates elicited criteria weights and their consistency ratios from BWM into the consensus-reaching model to further increase the consistency of the results and identify consensual solutions that all stakeholders can accept. The effectiveness of the proposed framework is demonstrated through a logistics study.

It may be unrealistic to expect all experts to be specialized in all aspects of the problem and to reach full agreement in a multi-criteria group decision-making (MCGDM) process. This paper concerns obtaining solutions for a group decision-making problem, where consistency and consensus of decision-makers (DMs) are both considered. More specifically, the group decision-making (GDM) method is grounded on the best-worst method (BWM), called the consistent and consensual BWM. The method aims to minimize the inconsistencies of decision-makers’ (DMs’) comparisons and proposes collective weights based on the DMs’ consensus. The reliability of the results is enhanced by including the reliability of the DMs’ pairwise comparisons. The validity of the proposed method is indicated by conducting a case study on the salespersons’ performance evaluation problem. To do so, the salespersons’ performance evaluation criteria for a large selling company in Iran are identified at a particular time horizon. Then, the weights of the criteria are calculated using the proposed technique. For comparison analysis, we modify the conformity measure and total deviation to be compatible with the results of the GDM model.

The Best-Worst Method (BWM) is a popular multi-criteria decision-making tool to prioritize alternatives or criteria via a set of subjective pairwise judgments. Deriving the priority weights from best-to-others and others-to-worst preferences is one of the key issues, and several prioritization methods have been proposed to address it. However, their behavior and performances in different situations are yet to investigate. In this study, we analyze the performance of four prioritization methods from theoretical and experimental perspectives. For this purpose, we first show that when the given preference is fully multiplicative consistent, the prioritization methods produce the same weight priority, and it can directly obtain through the analytic formulae without solving the optimization model. For inconsistent preferences, the prioritization methods are compared in terms of deviation from the original preferences and total order violation measures. Simulation experiments suggest that Euclidean distance and order violations metric based measures could lead to different choices of prioritization methods.

The classical Best-Worst Method (BWM) and its expansion form in the multiple criteria decision-making problem under different backgrounds are widely used to calculate the weights of criteria. The traditional BWM uses the accurate value based on Saaty’s scale to describe a decision maker (DM)’s preferences. However, a DM may be unsure about his preference and may give several possible values to express his preferences. In this situation, the hesitant multiplicative elements may be truly reflected the DM’s preference relation. This paper incorporates the BWM, the hesitant multiplicative preference relations (HMPR), and proposes HMBWM. Three different models are proposed to determine the weights from hesitant multiplicative best-to-others (HMBO) and hesitant multiplicative others-to-worst (HMOW) vectors. Finally, a case study of choosing commercial endowment insurance products is constructed to illustrate the practicality and correctness of the proposed model.

Industry 4.0 has been considered a significant conduit for sustainable goods and processes, and green entrepreneurship are being held up as a solution for many social and environmental challenges. However, green entrepreneurs face certain challenges and uncertainty in incorporating digitisation (such as Industry 4.0) in sustainability activities. This article discusses the role and barriers that industry 4.0 and green entrepreneurs confront for environmental sustainability in Small and Medium Enterprises (SMEs). Therefore, we begin with a study identifying barriers based on a case study of SMEs and outline recent contributions exploring this role. Theoretical supports (Resource-Based View (RBV), Natural Resource-Based View (NRBV), and Stakeholder Theory (ST)) are used to support this case study. With expert opinion, multi-criteria decision-making modelling (MCDM), such as the “Best-Worst Method” (BWM), is used to assess and rank the barriers. The findings show that among the main category of barriers are “technology-related barriers”, whereas in the sub-category, “minimal technological resources and lack of technological infrastructure and facilities” are the top barriers to Industry 4.0 and green entrepreneurship on environmental sustainability. We then summarise the papers and conclude with suggestions for further research.

Supplier selection is one of the most important strategic activities that has a significant impact on the output quality of any company, as it involves multiple criteria for the evaluation and selection of suppliers. The goal of this research is to evaluate and select suppliers for the National Iranian South Oil Company. The opinions of company experts and managers were used for evaluations. The weighting of criteria was done using the Best and Worst method, and the suppliers were ranked using the fuzzy VIKOR. Fuzzy preference relations were used to incorporate the ambiguities and uncertainties that existed in experts’ judgments about the priority of each supplier considering research criteria. The results of using BWM to prioritize contributing factors in supplier selection indicate that the Financial System criterion “production price” is the most important factor in supplier selection. Finally, some practical strategies for managers are provided.

The automotive industry, like other industries, has been affected by the fourth industrial revolution. The intelligentization of manufacturing systems in the automotive industry is one of the achievements of this industrial revolution. Implementing a new manufacturing system requires continuous attention to the variables and conditions called Critical Success Factors (CSF). To successfully implement smart manufacturing, first, it is essential to assess the smartness level of an industry to have a better picture of that. To do this assessment, the importance of CSFs should be determined. Then the industry’s current performance should be evaluated based on CSFs. This assessment provides managers with a clear understanding of the condition of the industry, which can influence the effectiveness of their decisions. Therefore, this research aims to identify CSFs and evaluate the automotive industry’s performance. One approach to study this is using an importance-performance analysis (IPA). This approach is applied to the case of Iran’s two largest car manufacturers automotive industry. When studying importance, the Best-Worst Method is used. Due to incomplete information and uncertainty in the field of smart manufacturing, interval numbers have been used for more detailed analysis. Our results showed that although the two car manufacturers have performed relatively well in some factors, they have not performed well in important ones, such as customization and digitization of products and required technological infrastructure for using Industry 4.0. So, it seems these need to change the priority of these two care manufacturers’ attention to CSFs of smart manufacturing implementation. The results of this research can be suitable and useful for the managers of car manufacturers in Iran and other countries similar to Iran in terms of economic, political, and social conditions.

Today, it is vital for humanitarian organizations operating in many different fields to select volunteers from the determining criteria according to the needs of the institution. Failure to select volunteers in accordance with the institution's needs may cause the volunteers to be unable to continue their duties and interrupt the institution's work. When choosing volunteers, it is essential for effective volunteer management to classify (expert, experienced, inexperienced, and insufficient) them according to the institution’s needs and assign them starting from the volunteer in the best class volunteer when necessary. The aim of this study is to determine the criteria weights in the classification of volunteers who want to participate in search and rescue activities in humanitarian aid organizations according to the criteria determined before their assignment. In this study, a questionnaire is applied to field experts in Turkey to determine the most important and least important criteria, then criteria weights are calculated by using the Best-Worst Method (BWM). The findings obtained as a result of our study are evaluated and suggestions are made on how to improve it for future studies.

Understanding the perceptions and needs of patients regarding the quality of the emergency department (ED) is crucial for enhancing the performance of provided health care services. As an acknowledged quality measurement method in many industries, SERVQUAL has been used for hospitals and EDs. Although it is convenient to define the gaps between customer expectations and the quality of received services, SERVQUAL requires an explicit prioritization strategy to decide between further actions. In addition, this method heavily relies on self-reported data from participants who might be subject to many behavioral anomalies while assigning direct values to their expectations. To address these shortcomings, we propose a novel service quality measurement methodology by integrating the SERVQUAL method with Best-Worst Method (BWM). First, by treating the five main dimensions of the SERVQUAL method as our main criteria, we identified several ED-specific sub-criteria and structured a questionnaire following the BWM procedure. Then by analyzing the collected data, the weights of the main and sub-attributes were calculated and discussed. The results show that Reliability and Assurance are the two most important attributes for both patience and accompanying people with patients while evaluating ED service quality. The main contribution of this study is providing insights into the quality measurement of healthcare services from a multi-criteria decision-making perspective and helping practitioners and researchers design patient-oriented services.

The formation of avalanches is related to the land structure, climatic conditions, and snow cover. It is usually seen in mountainous and sloping terrains without vegetation. In Turkey, especially in Eastern Anatolia and the Black Sea Region, which have high elevations, avalanche events are observed. This study aims to perform a risk analysis by integrating the Bayesian Best-Worst method (BWM) and Geographic Information System (GIS) for Tunceli province, which is the scene of significant avalanche events. Bayesian BWM is a method that improves the original BWM by effectively integrating the preferences of multiple experts. In the study, 16 sub-criteria, such as elevation, slope, and the number of snowy days, were determined, and experts evaluated these criteria through questionnaires created. The weight of each criterion were calculated using the Bayesian-BWM. By integrating the criteria weights from the Bayesian-BWM model into GIS, the risky places for natural avalanche disasters in Tunceli province were determined, according to which the risk in the northern part of the study area is identified as high.

Snow avalanches are one of the most frequent natural hazards in high mountain regions. In this study, a map of the susceptibility of the Šar Mountains to snow avalanches was determined. The study area is located in the southern part of Serbia, which has the Status of a National park. Geographic information systems (GIS) and remote sensing are used to analysis and cartographical presentation of nine the most important elements of natural conditions which have an influence on avalanche development. Then, by applying the best-worst method (BWM) for each of the criteria was given a weighting coefficient depending on its importance for the avalanche occurrence. A synthetic map of snow avalanche susceptibility was created by processing geospatial data in the GIS software. The obtained results show that high susceptibility covers 16.9% of the territory, while 10.7% of the total area is very highly susceptible. The final results may be useful to decision-makers, local self-governments, emergency management services, and mountaineering services to mitigate human and material losses from snow avalanches. This study is the first to use the BWM methodology for snow avalanche hazard analysis.

The development and use of renewable energy is the undeniable necessity of societies to achieve sustainable development and economic-environmental goals. Considering Iran's strategic position and the possibility of exploiting all kinds of sustainable energy sources, the above research seeks to investigate and provide strategies to reduce the obstacles to the development of renewable energy in conditions of uncertainty. In the qualitative part, after identifying the obstacles and strategies to reduce those obstacles in the direction of the development of renewable energy, customization of options has been done through interviews with experts. The quantitative part in the first step includes the use of the Best-Worst Method for weighting the obstacles, and the second step of the quantitative method includes the ranking of strategies with the grey TOPSIS technique. Based on the results of adjusting the structures, trade and foreign direct investment and the allocation of subsidies and tax support are of higher priority to reduce the political-legal, industrial, etc. obstacles.