Advances in Best-Worst Method

This article presents an overview of the Best-Worst Method (BWM) applications in health and gives observations and concluding remarks about reviewed articles. BWM is a multi-criteria decision-making (MCDM) method developed to evaluate alternatives that a group of criteria characterizes. It has attracted the attention of many researchers from different disciplines. Sixty articles that applied BWM or one of the variants have been reviewed to provide an overview of the applications of BWM in the area of health. Investigated studies are divided into six groups: healthcare system evaluation studies, hospitals, performance and service quality studies, diagnosing and treatment-related studies, healthcare supply chain studies, healthcare support systems applications and occupational health and safety studies. Each article is analyzed in terms of specific application area in health, motivation of the study, integration of the BWM with other methods, if any, and essential application findings for both academicians and practitioners. With the motivation of giving a general idea of BWM applications in health, the impressions obtained and suggestions are presented at the end. The findings indicate that BWM is a favourable method for health-related decision-making problems, and it will be preferred more in health applications in the future due to its ability to adapt to other methods, practicality and reliable results.

More and more often, scholars in the field of multi-criteria decision analysis (MCDA) seem to be overly adverse towards inconsistency. While this has some reasonable justifications, hiding the dirt under the rug, by not even trying to let possible inconsistencies emerge, can have negative effects on the decision process. In other words, there may be some merit in having a decision maker being consistent when he is given the possibility of being inconsistent, but there isn’t any in having a fully consistent decision maker when he cannot be inconsistent. In the latter case, consistency of preferences cannot, by all means, be associated to the reliability of judgements. These concepts are illustrated by taking into account some recently introduced methods whose common inspiration is the Best-Worst Method.

The current multi-criteria decision-making (MCDM) ranking methods provide suggestions on the superiority of an alternative to other alternatives mainly based on the alternatives’ performance difference and the weights (relative importance) of the criteria as absolute values, while the aim of the MCDM is to only provide the relative importance of criteria/alternatives for the decision under study. In this paper, we put forward a way for ranking alternatives with respect to multiple criteria that considers only the relative importance of every pair of criteria/alternatives and provides a ranking that is agnostic to the alternative performance normalization and prevents the rank reversal phenomenon. We apply the proposed procedure to an example to show its inner mechanism.

The resolution of Multi-criteria Decision-Making (MCDM) problems driven by human knowledge involves collecting their opinions, which usually implies the emergence of inconsistencies. The Best-Worst Method (BWM) was proposed to reduce such inconsistencies and, consequently, obtain more reliable solutions for MCDM problems. Classically, the BWM finds the optimal weights for a set of criteria from the preferences of only one stakeholder, but lately it has been extended to deal with multi-criteria group decision-making (MCGDM) problems. However, when several Decision-Makers (DMs) take part in a decision process, disagreements may appear among them. If these conflicts are neglected, experts may feel unsatisfied with the solution chosen by the group or even question the decision process. Therefore, this contribution proposes an extension of the BWM to smooth disagreements and obtain consensual solutions in MCGDM problems. To do so, an optimization model is introduced which derives a collectively agreed solution for the criteria weights. Additionally, such an optimization model is based on linear programming, which provides accurate results and the ability to deal with hundreds or thousands of DMs.

The best worst method (BWM) has been regarded as an ideal alternative to the analytic hierarchy process (AHP) between multi-criteria decision making (MCDM) methods since it reduces the number of pairwise comparisons and maintains the consistency between judgments. In real decision-making problems, decision-makers are not always sure about their decisions and mainly express their preferences with degrees of uncertainty. Moreover, they often face problems that must distinguish a prominent alternative among a shortlist with similar traits, which raises the severity of decision making in an uncertain environment. To deal with these situations, we propose a new linear fuzzy BWM based on a fuzzy interval scale, which is called fuzzy additive BWM. The proposed method maintains the features of the original BWM and also decreases the complexity of fuzzy calculations by eliminating the fuzzy multiplication operation. In addition, a fuzzy consistency index is proposed to check the validity of the input data. Also, fuzzy consistency ratio and total deviation are introduced for evaluating the obtained results by the fuzzy additive BWM. An example is applied and solved by the fuzzy additive BWM to indicate the method’s efficiency. Finally, comparative analyses are conducted to show the advantages and suitability of the new BWM method.

Transmission System Operators (TSOs) are responsible for the construction, maintenance, and operation of the high-voltage grid. The Dutch TSO TenneT, has been appointed by the Dutch government to oversee all the projects required for the connection of the offshore wind farms in the North Sea with the high-voltage grid. An important step in the lifecycle of such projects is the invitation to tender and the selection of the awarded contractor, based on different exclusion, selection, and award criteria. This study focuses on identifying such sustainable criteria suitable for the TSO market and proposes a multi-criteria decision-making process based on the Best-Worst Method (BWM) for assessing their importance. Additionally, this study proposes using a Maturity Model for evaluating the performance of existing sustainable measures, related to sustainable goals. Next, following an Importance-Performance Analysis (IPA), the study concludes by identifying and proposing which sustainable goal can be prioritised. TenneT is used as a case study where its experts helped identify the research problem and took part in the IPA, helping to identify the sustainable goals to be prioritised.

The world has recently tended to turn to renewable energy sources due to the serious negative impact of fossil fuels on the environment. One of the top priorities for solar energy is the selection of the most suitable location of the solar power plant to be established. In this study, the most suitable solar power plant location selection for Tunceli, a province located in the eastern part of Turkey, is studied with an approach based on Geographic Information System (GIS) and the best-worst method (BWM). A weighting was performed using the BWM model for 16 sub-criteria under four the main criteria of “climatic conditions, geographical conditions, land conditions, and location (distance to crucial points)”. Afterward, maps were created for these criteria, and a 6-point scale in the legends of each map for each sub-criteria was applied. Finally, the most suitable locations were determined with GIS-based calculations.

Solar energy is a renewable energy source and is known as one of the primary clean energy sources all over the world. One of the most important decisions to be taken to make optimum use of solar energy is the selection of the best solar panel. Both quantitative and qualitative criteria affect the best solar panel selection. For this reason, this problem can be considered as a Multi-Criteria Decision Making (MCDM) problem. The aim of this study is to the prioritization of solar panel selection criteria using Fuzzy Best Worst Method (F-BWM). The contributions of this study are to propose a set of criteria for solar panel selection and to present a new application area for F-BWM.

Additive manufacturing systems (AMS) have been realized as one of the cutting-edge technologies that can revolutionize the traditional way of producing goods. It is expected that by deploying AMS, the manufacturing firms can effectively manage the trade-off between volume-variety and cost-flexibility. This study is developed at the outset to explore the level of competitive capabilities, namely cost, quality, delivery speed and reliability, flexibility, performance, and innovativeness, achieved by deploying AMS through an operations management lens. In this work, the Best-Worst method (BWM) is used to compute the weights of the competitive capabilities within AMS using the opinions of five informed respondents from AM domain. The competitive capabilities are further prioritized based on their aggregated weights. The results demonstrated that flexibility and innovativeness are the most critical competitive capabilities that can be retained through AMS implementation. Contrary, when employing AMS, delivery speed and cost are the least achieved capabilities over which firms need to make a possible compromise. This study will benefit academicians and readers in understanding the core competencies of AMS. Based on the desired competitive capabilities, a particular firm can align its production facilities in line with AMS.

The entrepreneurship ecosystem is a widely used approach that facilitates start-ups’ growth. To date, there is a research gap concerning the influence of the entrepreneurship ecosystem on start-ups’ success during different lifecycle stages (bootstrapping, seed, and creation stage). Using Bayesian Best-Worst Method, the importance of various entrepreneurship ecosystem attributes was examined on the entrepreneurial success of start-ups during their lifecycle in the Dutch agri-food sector. To measure success, five indicators were considered: profitability, sales growth, employment growth, market share, and return on investment. The results suggest that the ecosystem attributes of Leadership, Network Density, Talent, and Capital are critical for the success of start-up in each life stages. Furthermore, the study highlights that the ecosystem attributes of Talent and Companies present differences in the creation stage compared to the previous stages. More specifically, Talent becomes even more important, in addition to programs for cooperation between larger companies and start-ups requiring more attention. Finally, using Importance-Performance analysis, the findings indicate that the Dutch entrepreneurship ecosystem seems to be efficient and supportive for the creation and success of entrepreneurial start-up firms.

The concept of Theory of Constraints (TOC) was introduced by Eliyahu Goldratt, an Israeli Physicist, in his book THE GOAL in 1984. TOC is a management philosophy that focuses on the management of the constraint that limits the performance of a system. Although TOC has benefited organizations within many domains, namely – manufacturing, supply chain, project management, sales, and finance, TOC implementation in the outbound supply chain remains the topic of investigation. Thus, it is crucial to determine the constraints to implement TOC in the outbound supply chain. Further, there is a set of certain Key Performance Indicators (KPI) of the distributors that gets enhanced by implementing TOC’s solution in the outbound supply chain. This study proposes multi-criteria decision-making based Best-Worst method to prioritize the most important constraint to implement TOC’s solution in the outbound supply chain. Further, we prioritize the KPIs for the distributors in the outbound supply chain. This study is expected to aid researchers and practitioners in determining the critical constraints and KPIs to focus on and improve the outbound supply chain performance.

Additive manufacturing (AM) is a well-known technology applied in different industrial applications which have gained more attention over the last three decades. The crucial aspect of AM is designing and managing the supply chain for AM parts. The most critical strategic decision in the initial process of supply chain management is selecting and evaluating suppliers. Selecting an appropriate supplier can lead to reducing costs in supply chain management. Therefore, there is a need to choose a reliable supplier to enhance the performance of their supply network. For the first-time, supplier selection criteria evaluation for the AM domain is examined in this study. This study proposes multi-criteria decision-making based on the best-worst methods to prioritize AM firm’s raw material supplier selection criteria. The best-worst method is generally applied to get the criteria weight. The reliability of the comparisons is checked using a consistency ratio. Then the most important and least important criteria are obtained and considered while selecting a supplier in AM based on the result. Finally, the study concluded the importance of supplier selection for AM. This study further provides a promising avenue for future research opportunities.