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The superiority of BWM over other weighting methods for obtaining the weight values of the attributes is that it achieves high-confidence results with a reasonable number of pairwise comparisons. Although the best-worst method (BWM) is a well-known multi-criteria decision-making (MCDM) method that has been successfully utilized in almost all scientific areas to solve challenging real-life problems, no research has comprehensively examined the state-of-the-art in this regard. The present study depicts a detailed overview of publications concerned with BWM during the period 2015–2022. Based on the information obtained from the Scopus database, this work presents a big picture of current research on BWM. In other words, this paper analyzes the existing literature about BWM and identifies thematic contexts, application areas, emerging trends, and remaining research gaps to shed light on future research agendas aligning with those gaps. Further, the most recent BWM research is analyzed in the top ten scientific areas, from engineering to materials science. “Engineering”, “computer science”, and “business, management, and accounting” are the hottest fields of BWM research. China is the most active country regarding “engineering” and “computer science”, whereas India is the leader in “business, management, and accounting”. The study also reveals that there are still many research gaps in BWM research. The big picture taken in this study will not only showcase the current situation of BWM research but will also positively impact the direction and quality of new research. [ABSTRACT FROM AUTHOR]

This paper proposes an integrated approach to group decision-making (GDM) by using stochastic preference analysis (SPA) and best-worst method (BWM). BWM preparation algorithm is proposed to obtain the best and worst of experts and the relative importance degree. Meanwhile, expert weights model and expert's priority vector model are proposed. Furthermore, the stochastic composite rank acceptability index, stochastic composite expected priority vector, stochastic composite expected rank and stochastic composite confidence factor are developed based on SPA to describe the ranks of alternatives based on SPA. Finally, a group stochastic preference analysis-best worst method (GSPA-BWM) algorithm is developed by analyzing the judgments space through Monte Carlo simulation. The experts can use this method to choose some of the outcomes which they find most useful to make reliable decisions. Examples and comparison analyses show that the proposed method is effective. [ABSTRACT FROM AUTHOR]

Shelters are vital for affected people after a disaster because of the accommodation, safety, and security. In this paper, we analyze the shelter site selection criteria for disaster preparedness applying the best–worst method under interval type-2 fuzzy sets. The proposed approach utilizes the advantages of fewer pairwise comparisons with the best–worst method and better reflection of uncertainty with interval type-2 fuzzy sets. For this reason, the criteria are determined based on a literature review and the opinion of nine disaster experts. The experts have worked as disaster officers in a variety of locations, including Sivrice (Elazığ), Pazarcık, and Elbistan (Kahramanmaras) and Syrian refugee camps such as Nizip container city. In this step, 6 main criteria and 25 sub-criteria are evaluated using the proposed approach. According to the nine experts' opinions, the most important main criterion is determined as proximity. Distribution center capacity, adequate distribution logistics personnel, available electricity, distance to settlement, and landslides and flooding are also determined as the five most important sub-criteria. For disaster preparedness, responsible organizations and managers should consider these important criteria for temporary shelter site selection. [ABSTRACT FROM AUTHOR]

As a powerful tool for improving customer satisfaction, quality function deployment (QFD) can convert customer requirements (CRs) into engineering characteristics (ECs) during product development and design. Aiming to address the deficiencies of traditional QFD in expert evaluation, CRs' weight determination and ECs' importance ranking, this paper proposes an enhanced QFD model that integrates hesitant fuzzy binary semantic variables, the Best–Worst Method (BWM), and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). The objective is to determine the prioritization of product engineering characteristics. Indeed, hesitant fuzzy linguistic term sets (HFLTS) have found extensive application in decision-making problems. Compared to other fuzzy language methods, HFLTS offers greater convenience and flexibility in addressing decision-makers' hesitations and uncertainties. Initially, the combination of hesitant fuzzy linguistic term sets with interval binary tuple language variables is employed to articulate the uncertainty in the assessment information provided by QFD team members. Subsequently, the improved BWM and TOPSIS methods based on HFLTS are used to improve the accuracy of the importance ranking of engineering characteristics by determining the weights of CRs and prioritizing ECs in two stages. Finally, the feasibility and effectiveness of the proposed method are validated through an illustrative example. [ABSTRACT FROM AUTHOR]

Seeking the greatest possible return on long-term investments, investors naturally seek equities of the best-performing companies that fit their investment timeframe. Long-term investment success rests on selecting the best companies, which requires a challenging analysis reviewing voluminous and often-conflicting data about companies and understanding broader economic forecasts. This paper undertook a case study deployment of MCDM methodologies to examine the suitability and effectiveness of Multi-Criteria Decision-Making (MCDM) methods in assessing and ranking the best stocks for portfolio inclusion. A combination of MCDM techniques comprised a methodology to evaluate and rank Saudi Arabian banking stocks based on their performance in the Saudi stock market. Specifically, the paper combined the Best–Worst Method (BWM) and Ranking Alternatives by Perimeter Similarity (RAPS) for the analysis. BWM calculated each criterion's relative impact (weight) in selecting a stock. RAPS then used the weighting to rank the results of the investigation. The study's findings yielded encouraging results regarding using an integrated MCDM technique to derive optimal banking sector securities in the expansive Saudi stock market. The novel application of the robust RAPS technique combined with BWM encourages continued and increased use of MCDM techniques in financial matters and broader application in evaluating equities. [ABSTRACT FROM AUTHOR]

In the course of global trends, the development and application of information technologies have emerged as a valuable source of the economy at both the micro and the macro levels. According to the results of the best practice, the application of a piece of software with failures is known to possibly to serious consequences. The analysis and elimination of potential failures in commercial software is the problem that represents one of engineers' most important tasks. In this paper, a new integrated model for the evaluation and ranking of software failures is proposed. The cost aspect is integrated into the traditional severity index. The assessment of the severity indices, as well as a possibility of detection, is based on the Failure Mode and Effects Analysis - FMEA framework. The weights of the overall severity index, the occurrence of failures, and the possibilities of detection are calculated by applying the best-worst method. The determination of the rank of the identified failures is given by applying conventional Technique for Order of Preference by Similarity to Ideal Solution - TOPSIS. The priority of the activities that are undertaken in order to eliminate the identified failures corresponds to the obtained rank of the failures. The proposed model is illustrated by real-life data. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

One of the most important issues in multi-criteria decision making is the number of requited judgments decision-maker/analyst has to perform. This paper presents a comparison of the results obtained by standard analytic hierarchy process (AHP), limited AHP, and best-worst method (BWM) if the number of criteria is 6, 7, and 8. The examples show that BWM's results are comparable with the results if standard AHP is used, while the limited version of AHP is generally inferior to the other two methods. [ABSTRACT FROM AUTHOR]

Quality Function Deployment (QFD) is commonly used to translate customer requirements into design specifications in the product conceptual design phase. House of Quality (HOQ) is an essential part of QFD to map different requirements of product solutions using a design relationship matrix. In traditional approaches, the way of deciding weights in the relationship matrix can lead to a distortion in ranking of technical measures. Although some methods like Analytic Hierarchy Process have been proposed to improve this problem, they require additional data collection and process. This paper introduces two effective approaches based on Best–Worst Method and Full Consistency Method (FUCOM) to decide weights of technical measures in the relationship matrix of HOQ. The novel integrated approach of FUCOM-QFD is developed for rapid weight allocation in the relationship matrix. The proposed BWM-QFD and FUCOM-QFD approaches use consistency ratio and total deviation measures in reliable decision-making. They not only reduce data collection, but also improve inconsistency problems. The vector-based pairwise comparison and consistency measure are two important features of the proposed approaches. A case study demonstrates improvement in the efficiency and accuracy of weighing solutions for technical measures compared to the existing methods. [ABSTRACT FROM AUTHOR]

Choosing the best schools from a group of schools is a multi-criteria decision-making (MCDM) problem. In this paper, we have represented a method that uses the fusion of two multi-criteria decision-making methods, Best–Worst Method (BWM) and Analytic Hierarchy Process (AHP), to rank some of the user preferred alternatives. The system considers the choice of the user and the quality of the alternatives to rank them. User preferences on the criteria are taken as inputs in the form of best–worst comparison vectors to measure the choice of the user. These values are applied to calculate the numeric weights of each of the criteria. These weights reflect the preference of the user. A dataset of secondary schools in Bangladesh has been compiled and used for automatic quantitative pairwise comparison on the alternatives to calculate the score of each alternative in every criterion, which reflects its quality in that criterion. These scores are calculated using AHP. The weights of the criteria as well as the scores of these alternatives in those criteria are then used to calculate the final score of the alternatives and to rank them accordingly. An extensive experimental analysis and comparative study is reported at the end of this paper. [ABSTRACT FROM AUTHOR]

Method: This paper endeavors to introduce a new approach to modal split estimation. In the frame of the research, a customized model of the recently created Best-Worst Method (BWM) is applied to evaluate mode choice alternatives by transport experts. The integrated BWM model is tested on a real-world case study in Budapest, the capital of Hungary, where a small number of selected experts estimate the modal split of three different groups clustered based on the distance of their commuting. Results: The results clearly demonstrate the popularity of public transport among all groups, while car is estimated to be used primarily by short- and mid-distance commuters. The coherence of the responses is tested along with sensitivity analysis and rank correlation comparison. Moreover, the final results are compared to the official modal split data of the city. Recommendations: Based on the findings, it can be concluded that the application of BWM results in competitive accuracy compared to the mainstream methodologies, moreover BWM needs significantly less cost and time effort during the survey procedure. [ABSTRACT FROM AUTHOR]

Metaverse has made sense for organizations regarding new opportunities it is likely to offer, and studies on Metaverse-related subjects in different areas have increased incredibly. Advertising is an area where Metaverse-related studies are few. For the first time, this paper prioritizes Metaverse ad appeals and performs the Parsimonious Best-worst method (BWM-P) for this purpose. BWMP, an improved version of the BWM method, allows consistent results to be achieved quickly by reducing the number of pairwise comparisons. Regarding the findings, Metaverse experience is the paramount Metaverse ad appeal, followed by product familiarity and NFT of advertisement, respectively. Further, standardization and Metaverse bonuses are the other significant Metaverse ad appeals. The present research contributes to existing knowledge by analyzing ad appeals to ensure the effectiveness of advertising strategies in the Metaverse world. [ABSTRACT FROM AUTHOR]

Concerning the overwhelming advantages of solar energy, controlling and exploiting solar energy by using solar panels is one of the main fields of research in the domain of renewable energy. The choice of solar panel technology is highly significant to exploit as much energy as possible. In this paper, the main goal is to select the best technology for solar panels by investigating nine technologies from the first, second, and third generations of solar panels. Moreover, five sustainable criteria of electrical, mechanical, economic, technical, and climate, and 20 subcriteria are given for making decision analysis. Then, the best–worst method is employed according to the experts' opinions for weighting the criteria and for comparing the ranking of the solar energy technologies a framework based on the MULTIMOOSRAL multiple criteria decision‐making method is proposed. Finally, sensitivity analysis will be conducted on the ranking of the technologies. By exploiting the proposed methodology, CIS/CIGS and Perovskite Solar cell are ranked 1 and 2 as the best solar panel technologies for the selected locations. [ABSTRACT FROM AUTHOR]

This paper reports a coherent method for ranking fruit juice products in the Iranian food industry by the best–worst method by examining the factors affecting product quality. To achieve the aim, a combination of laboratory methods and mathematical calculations was used. The statistical population in this study included samples of products of several food companies (SH, S, P) in Iran. Some factors affecting the quality of fruit juice products including alcohol, sugar, degrees Brix, and pH were considered and several samples of apple and grape juice from different brands were prepared and analyzed. In the first stage, to determine the value of the parameters of each sample was determined by the standard analysis methods. Then, the best–worst method was applied to measure the selection criteria of juice producers, and then the function of linear values of the piece was used to rank the Juice products of the studied companies (SH, S, P). The results showed the following order for the studied brands: P-Apple > S-Grape > SH-Apple > SH-Grape > S-Apple. This study showed that the multi-criteria decision theory is a promising method to rank food products of different companies. [ABSTRACT FROM AUTHOR]

Double hierarchy hesitant fuzzy linguistic term set (DHHFLTS) is a newly developed complex linguistic expression model, and has been well applied to multi-criteria decision-making (MCDM) problems. However, the determination of criteria weights and the innovation of decision-making methods are still two issues that worth exploring in this field. At present, conventional weight-determination methods sometimes have the disadvantages of complicated calculation and low consistency of the obtained results. On the other hand, the existing methods for linguistic information sometimes cannot consider the uncertainty of information caused by ignorance. Considering that the best-worst method (BWM) is a weight-determination method, which can not only greatly simplify the calculation process, but also improve the consistency degree of the results. Dempster-Shafer evidence theory (DSET) can better deal with information uncertainty caused by ignorance. Therefore, this paper extends the BWM and DSET to double hierarchy hesitant fuzzy linguistic environment to solve the above two problems respectively, and the DHHFL-BWM-DSET method is proposed. First, the weight of each criterion is derived based on the BWM-based weight-determination method. Then, inspired by DSET, we propose a DSET-based MCDM method which can not only obtain the decision results of a single decision maker, but also integrate the decision information of multiple decision makers to obtain more rational results. Therefore, decision makers can choose the method based on the specific situation. Finally, taking the selection of financial products as an example, it shows that the method proposed in this paper has some breakthroughs and advantages. [ABSTRACT FROM AUTHOR]

With the rapid development of unmanned aerial vehicles (UAVs) and their applications in problems such as power line inspection, selecting the appropriate UAV supplier according to several sustainable attributes has attracted many interests. In this regard, an integrated multiattribute group decision-making (MAGDM) method based on the best-worst method (BWM) and MULTIMOORA method with two-dimension linguistic intuitionistic fuzzy variables (2DLIFVs) is proposed in this paper for the selection of UAV suppliers. First, the 2DLIFV is utilized to represent the uncertain, fuzzy, and linguistic evaluations of the experts on the evaluation attributes. Second, the two-dimension linguistic intuitionistic fuzzy BWM (2DLIF-BWM) is introduced to compute the weights of the attributes. Then, a novel expert weight calculation method that combines the uncertainty degree and consensus degree of the experts is introduced. Next, the 2DLIF-MULTIMOORA method is proposed, where the aggregation operators and distance measures of the 2DLIFVs are used to determine the ranking results of different alternatives. Finally, a real case of selecting a sustainable UAV supplier for power line inspection is presented to illustrate the process of the proposed method. The experimental results are further analyzed through sensitivity and comparative analyses to show the feasibility and effectiveness of the proposed method. From the results, it can be found that the proposed method can more flexibly represent the uncertain assessments while providing reasonable and reliable results. [ABSTRACT FROM AUTHOR]

Iterative multi-attribute reverse auctions in practice create certain difficulties for both the buyer and participating bidders. While the buyer faces the problem of creating the right attribute weights, the bidders have difficulty in adjusting the attribute values in each round. In this paper, we present an iterative multi-attribute reverse auction mechanism based on integrated data envelopment analysis (DEA) and best–worst method (BWM) with an objective of reducing the intervention of the buyer in the determination of the winner and also easing up the preference elicitation process. Unlike the typical scoring auctions, the proposed mechanism does not require the buyer to estimate the characteristics of the participating sellers in order to determine the optimal scoring function. As there will be no other intervention from the buyer during the winner determination process, the proposed method makes the procurement process impartial and corruption-free. Besides solving the buyer's problem, the proposed mechanism is also associated with an optimal bid determination method (OBDM) to assist the sellers in formulating improvised bids in iterative rounds of the auction. Simulation experiments show that the proposed OBDM benefits both the buyer and sellers. For the buyer, it provides higher expected utility and attribute values as per his preferences; for the seller, it gives a better expected profit and a higher probability of winning. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak has currently led to serious social and economic consequences. In poor and developing countries, there are more challenges and barriers to tackling the pandemic. The study's aim is to propose a hybrid approach to multiple-criteria decision-making (MCDM) models for determining the most efficient intervention strategies. The methodology is a combination between the Best-Worst Model (BWM) and Group Best-Worst Model (GBWM) to estimate the efficiency score of intervention. Based on the background of knowledge, five groups of stakeholders including Academicians, Entrepreneurs, Commons Residents, Social Workers, Health Workers are considered decision-makers (DMs). A set of nine potential strategies was evaluated and prioritized by all DMs. The findings have shown that different groups of stakeholders prioritized differently the importance of criteria due to their interests. In the context of Vietnam, however, the Availability of Health Systems is prioritized as the most important intervention. The results and proposed model of this paper contributed to MCDM literature as well as a good reference to apply practically in many different countries. [ABSTRACT FROM AUTHOR]

In the real world, there are complex decision‐making problems in which a variety of intertwined factors and hierarchical structures exist, and it is difficult or impossible to solve these problems using classical methods. The prioritization of circular economy (CE) adoption barriers is one such complex problem that cannot be solved using current classical methods. Hence, in this paper, for the first time, a new method, namely, FBWDS, is developed. FBWDS derives from the combination of fuzzy best–worst method (BWM), fuzzy decision‐making trial and evaluation laboratory (DEMATEL), and Supermatrix structure. This proposed method establishes weights of the intertwined factors in hierarchical networks under uncertainty; these weighted factors and their interdependencies are calculated using the fuzzy BWM and fuzzy DEMATEL techniques, respectively. In addition, the Supermatrix structure is applied to integrate the results of these two techniques. The proposed approach efficiency is evaluated in the field of the CE adoption barriers using data from a cable and wire industry in Iran. The results showed that "high setup costs," "financial limitations," and "absence of public awareness about CE" rank as the most challenging barriers, and "lack of standards for designing recycled products" and "absence of standard system to evaluate performance" are the least important barriers to CE adoption in the cable and wire industry. [ABSTRACT FROM AUTHOR]

As a response to the urgent call for recovery actions against the COVID-19 crisis, this research aims to identify action priority areas post COVID-19 toward achieving the targets of the sustainable development goals (SDGs) within the 2030 Agenda for Sustainable Development launched by the United Nations (UN). This paper applies a mixed-method approach to map the post-COVID-19 SDGs targets on a fuzzy action priority surface at the country level in Iran, as a developing country, by taking the following four main steps: (1) using a modified Delphi method to make a list of the SDGs targets influenced by COVID-19; (2) using the best–worst method, as a multi-criteria decision-making tool, to weight the COVID-19 effects on the SDGs targets achievement; also (3) to weight the impact of the SDGs targets on the sustainable development implementation; and finally (4) designing a fuzzy inference system to calculate the action priority scores of the SDGs targets. As a result, reduction of poor people proportion by half (SDG 1.2), development-oriented policies for supporting creativity and job creation (SDG 8.3), end the pandemics and other epidemics (SDG 3.3), reduction of deaths and economic loss caused by disasters (SDG 11.5), and financial support for small-scale enterprises (SDG 9.3) were identified as the highest priorities for action, respectively, in the recovery agenda for sustainable development post COVID-19. The provided fuzzy action priority surface supports the UN's SDGs achievement and implementing the 2030 Agenda for Sustainable Development in Iran. It also serves as a guideline to help the government, stakeholders, and policy-makers better analyze the long-term effects of the pandemic on the SDGs and their associated targets and mitigate its adverse economic, social, and environmental consequences. [ABSTRACT FROM AUTHOR]

The evolution of smart cities depends on the effective integration of advanced technologies within urban environments. It necessitates a profound and nuanced analysis of urban residents’ aspirations and perceptions called User Preferences (UPs) concerning the Quality of Service (QoS) provision, both in its current state and in envisaged improvements across diverse sectors. This paper introduces a novel, hybrid methodology designed to interpret and evaluate residents’ feedback in the context of service provision that contributes to the smart city paradigm. The methodology integrates Intuitionistic Preference Relations (IPRs) with the Best-Worst Method (BWM) within the ambit of Multi-Criteria Decision Making (MCDM). IPRs play a pivotal role in this framework, providing a refined mechanism for capturing a spectrum of preferences, encompassing both affirmative and negational aspects through positive membership and non-membership functions. This aspect is particularly pertinent in group decision-making scenarios, where it facilitates aggregating diverse preference data by applying intuitive fuzzy arithmetic and weighted averaging operators. Additionally, the proposed methodology incorporates scoring and accuracy functions to prioritize and delineate the most feasible alternatives, adeptly navigating the intricate dynamics characteristic of urban settings. The BWM further augments this approach by providing a structured yet efficient, means of evaluating alternatives against multiple criteria. It begins with identifying the most and the least preferred criteria, followed by pairwise comparisons to establish their relative significance. It was applied in a hypothetical numerical scenario to appraise the efficacy of this proposed method. A comparative analysis was conducted utilizing the Analytic Hierarchy Process (AHP), another established pairwise comparison-based method. This comparison’s results underscore the proposed method’s superiority over AHP in several key metrics, including consistency ratio, minimum violation, total deviation, and overall conformity. In summary, the approach presented in this paper not only confronts the complexities inherent in urban environments but also lays the groundwork for more comprehensive and informed decision-making processes, thereby facilitating the evolutionary journey towards realising smart cities.

Failure mode and effects analysis (FMEA) is a predictive reliability analysis technique, which is widely used to improve the reliability and safety of products in products design, manufacture and service phases. However, traditional FMEA has many shortcomings in practical application, resulting in poor accuracy of analysis results. In this paper, based on meta-action failure modes, a risk assessment and ranking method based on cloud model is proposed. First, the domain expert's assessment of failure modes' attributes is transformed into a cloud model. Then, the best–worst method (BWM) and cloud model are combined to calculate the cloud weight of each attribute, and the weight of each expert to risk factors of each failure mode is evaluated by cloud distance. Finally, the comprehensive cloud expression of each failure mode is synthesized and compared. The proposed evaluation method not only has the advantages of cloud model in dealing with fuzziness and randomness, but also integrates the advantages of BWM, and fully takes into account the differences of experts in assigning weights to different failure modes' attributes. Finally, the effectiveness of the proposed method is verified by comparing the risk assessment results of the CNC machine tool's rotation-meta-action failure modes with different risk assessment methods. [ABSTRACT FROM AUTHOR]

• Investigate a novel TrFBWM to yield the TrF criteria weights. • Develop the TrFZSG to rank the alternatives. • Propose a TrF-BWM-ZSG by fusinging the BWM and ZSG. This paper investigates a hybrid emergency decision-making (EDM) method called TrF-BWM-ZSG by combining the BWM (best-worst method) and ZSG (zero-sum game) in trapezoidal fuzzy (TrF) environment. Firstly, this paper defines the entropy and similarity for trapezoidal fuzzy numbers (TrFNs). Based on the entropy and similarity measures, a bi-objective programming model is constructed to distribute the weights for experts. Then, based on the multiplicative consistent trapezoidal fuzzy reference comparisons (TrFRCs), a novel TrFBWM is investigated to yield the TrF criteria weights. This novel TrFBWM highlights two innovations: (i) A crisp goal programming weight-determining model is constructed by using the multiplicative consistent TrFRCs; (ii) A reasonable consistency ratio is defined, in which the consistency index is determined according to the consistent transitivity of TrFRCs. Afterwards, the TrFZSG is developed to achieve the alternatives' ranking order by extending the classical ZSG into TrF environment. Lastly, the availability of the TrF-BWM-ZSG is corroborated through a real EDM case of Henan rainstorm disaster. The advantages of the TrF-BWM-ZSG are manifested by several valuable comparisons. Some desirable results derived by the TrF-BWM-ZSG are received: (i) The optimal alternative selected by the proposed TrF-BWM-ZSG suits the actual rescue situation; (ii) The proposed TrF-BWM-ZSG owns high generality since TrFN can reduce to crisp number, interval and triangular fuzzy number; (iii) The developed novel TrFBWM is helpful to preserve more inherent information of TrFNs. [ABSTRACT FROM AUTHOR]

In the context of the accelerated iteration of financial technology, the layout of financial technology in the banking industry is also accelerating and shifting gears. Actively cooperating with financial technology companies has become an important channel for banks to cope with challenges and achieve transformational development. Assessing and selecting the best financial technology company is one of the core issues that banks should address in establishing cooperative relationships. In this paper, an integrated decision-making framework based on the best-worst method (BWM) and TODIM (an acronym for interactive and multi-criteria decision making in Portuguese) is proposed for financial technology selection under the interval type-2 fuzzy (IT2F) environment. Within the proposed framework, the preference information of decision-makers for alternatives and pairwise comparison among criteria is captured by the interval type-2 fuzzy sets (IT2FSs), and the criteria weights are calculated by the IT2F-BWM method. Moreover, the IT2F-TODIM considering the psychological behavior state of decision-makers is introduced to determine the alternative ranking. The practicability and reliability of the proposed methodology are verified by a case study with sensitivity analysis and comparative analysis. This study contributes theoretically and practically to relevant literature by providing an effective framework capable of handling decision issues under uncertainty. [ABSTRACT FROM AUTHOR]

This paper focuses on committee–market standards battles for the case of vehicle-to-grid technology in Europe. In this battle, standards such as CHArge de MOve (CHAdeMO) and Combined Charging System (CCS) Combo are competing. The paper identifies relevant factors with the help of a literature review and expert interviews. Furthermore, the importance weights were established for the factors. The paper ends with a discussion and conclusion in which the theoretical contributions, practical implications, limitations, and recommendations for further research are discussed. [ABSTRACT FROM AUTHOR]

Reliability allocation is an important part of product design. Whether the result of reliability allocation is reasonable or not is directly related to the ability of the selected subsystem to meet design requirements. This paper proposes a new reliability allocation method to overcome the limitations of traditional reliability allocation methods. These limitations include weak ability to express and process fuzzy information, unreasonable use of data and lack of integration with real working conditions of products. Firstly, rough probabilistic linguistic term set (RPLTS) is proposed by combining rough set theory (RST) and probabilistic linguistic term set (PLTS). Moreover, RPLTS is extended to Muirhead mean (MM) operator and Best-Worst method (BWM), rough probabilistic linguistic weighted MM (RPLWMM) operator and rough probabilistic linguistic BWM (RPLBWM) are proposed to improve the ability of experts to express and process fuzzy information. Secondly, the aggregation results of the RPLWMM operator are only used as subsystem ranking, which not only improves the rationality of data use but also provides the basis for experts to establish the judgment matrix, which reduces the influence of experts' subjective factors. In particular, this paper introduces the proportional hazards (COX) model into reliability allocation for the first time, combining the reliability allocation results of key subsystems with the real working conditions and improving the pertinence and accuracy of reliability allocation. Finally, the advantages of the proposed method are illustrated by a numerical control machine tool case analysis. [ABSTRACT FROM AUTHOR]

Standardization can be achieved in multiple ways; firms may join forces and develop standards in standardization committees, they may compete directly on the market in standards battles, or governmental agencies may impose standards. This paper studies criteria for the selection of standards in a situation in which these three forms of standardization occur simultaneously (multi-mode standardization). The paper attempts to arrive at weights for these criteria by applying them to the case of phosphorus recovery from municipal waste water, a technological process that fits the transition to a circular economy but that is still lacking standardization. A contribution is made to the standardization literature by empirically studying the case of multi-mode standardization and by applying standard success criteria to the area of water treatment. [ABSTRACT FROM AUTHOR]

Purpose – Blockchain technology enhances information management in healthcare supply chains by securing healthcare information and providing medical resource traceability. However, there is no decision framework to support blockchain implementation for managing information, especially in emerging economies’ healthcare supply chains. This paper develops a hierarchical decision model for implementing blockchain technology for information management in emerging economies’ healthcare supply chains. Design/methodology/approach – This study uses 20 health supply chain experts in Ghana to rank 17 decision criteria for implementing blockchain for healthcare information management using the best-worst method (BWM) multi-criteria decision technique. Findings – The results show that “security” and “privacy,” “infrastructural facility” and “presence of training facilities” are the top three critical factors impacting blockchain adoption in the health supply chain for healthcare information management. Other sub-factors are prioritized. Practical implications – To implement blockchain effectively to enhance information management in the healthcare supply chain, health institutions, blockchain technology providers and state authorities should concentrate on the highly critical factors extracted from the study. Originality/value – This is the first study that develops a hierarchical decision model for implementing blockchain technology in emerging economies' health supply chains.

High-tech companies are rapidly growing in the world. Research and development (hereafter R&D) department strength is the main asset that allows a firm to achieve a competitive advantage in high-tech businesses. The allocated budget to this sector is finite; thus, integration, human resource, risk and budget limitations should be considered to choose the most valuable project in the best portion of time. This paper investigates a case study from a high-tech company in Iran to prioritize the most attractive technologies for the R&D department. The case consists of twenty three technology options and the goal is to find the most attractive projects to sort them out for implementation in the R&D department. In this research, scholars proposed the best-worst method (henceforth BWM) to find the weight of the criteria of the attractive technologies in first step and utilize the newly developed method total area based on orthogonal vectors (henceforward TAOV) to sort the selected technologies based upon the identified criteria. Project integration is one of the least-noticed subjects in scientific papers; therefore, the researchers presented a zero or one linear programming (ZOLP) model to optimize and schedule the implementation procedure on the project risk, budget and time limitation simultaneously. The results indicate that starting few but attractive projects in the first years and postponing the rest to the future, helps a firm to manage funds and gain profit with the least amount of risk. [ABSTRACT FROM AUTHOR]

This paper presents a novel failure mode and effect analysis (FMEA) framework as a formal design method to ensure safety and reliability. FMEA is used to identify potential failure modes (FMs), and it is crucial to determine the weights of risk factors and prioritize FMs. In this work, we propose a comprehensive framework that integrates the Dempster-Shafer theory, best-worst method (BWM), stochastic multi-objective acceptability analysis (SMAA), and measurement of alternatives and ranking according to compromise solution (MARCOS) to address this problem. To capture the uncertainty caused by the loss of information, the Dempster-Shafer theory is applied for dealing with the uncertainty about risk factors and FMs in terms of linguistic information. Based on the comprehensive evidential preference interval vectors of risk factors constructed by Dempster-Shafer theory, an evidential BWM combined with SMAA is proposed to determine the optimal set of risk factor weights. Meanwhile, based on the constructed interval belief interval decision matrix of FMs to risk factors constructed by Dempster-Shafer theory, an evidential SMAA-MARCOS method is proposed for determining the risk priority of FMs. Further, we conduct a case study to evaluate the risk of equipment in an automobile manufacturing enterprise. A sensitivity and comparative analysis are also conducted to demonstrate the effectiveness and superiority of the proposed framework. • An evidential SMAA method is proposed to identify the best and worst risk factors. • An evidential BWM is proposed to determine the weights of risk factors. • An evidential SMAA-MARCOS method is proposed to rank FMs. • The FMEA framework using evidential BWM and SMAA-MARCOS method is proposed. [ABSTRACT FROM AUTHOR]

Surveying citizen preferences on transportation modes when commuting is a major issue in urban transport planning. Most of the current methods approach the problem through the attributes of choices thus forecasting the demand indirectly. This paper aims to analyze a survey of commuting students and university staff by two direct preference models: the Analytic Hierarchy Process and the Best-Worst Method. Both techniques are based on pairwise comparisons; consequently, the commuting transport alternatives can be directly compared with each other, and the results are comparable, too. However, the two methods differ in the number and the nature of comparisons and in the consistency check, thus they can be regarded as competitors. A realworld case study on commuting student groups provides a better understanding of the proposed methodology. As a result, it can be stated that despite their low utilization in the transportation field, both the Analytic Hierarchy Process and the Best-Worst Method are applicable to mode choice preference surveys, and they produce comprehensive final outcomes. Therefore, the well-known tools of mode choice can be extended by Multi-Criteria-Decision-Making techniques to increase the efficiency of transport demand prediction. The extension is beneficial to avoid the bias of other methods in converting attribute evaluations to real mode choice decision, as both models, especially the Best-Worst approach, requires less cost and time than the mainstream techniques. [ABSTRACT FROM AUTHOR]

Industry 4.0 is gaining significant attention and recognition in both academic and practical spheres since to its potential to deliver substantial advantages to various industries and enable novel applications. This research aims to investigate the fertilizer production industry, evaluating its current state and identifying the barriers impeding the implementation of Industry 4.0 in this domain. The first investigation on Industry 4.0’s current state in Bangladesh entailed visiting and surveying five significant fertilizer companies. Approximately 50% of the components associated with Industry 4.0 have been successfully integrated into these manufacturing facilities. In order to ascertain the causes behind this negative circumstance, a research study was undertaken to uncover the barriers that are hindering the successful Industry 4.0 implementation. Based on the views expressed by a board of ten experts from industry and university, it has been determined that nine out of the twelve issues initially identified are deemed to be significant hurdles. A widely known decision-making approach based on numerous factors, the Best–Worst Method (BWM) was used to evaluate the ranking of these nine difficulties. Insufficient knowledge about Industry 4.0 was determined to be the most significant barrier, while the possibility of job loss was determined to be the least significant. The ranking was further improved by conducting a sensitivity analysis. Subsequently, many pragmatic strategies for overcoming these obstacles are discussed. This study is expected to have a substantial impact on the fertilizer business in Bangladesh by providing valuable insights to industry owners, policymakers, and stakeholders, therefore facilitating their adoption of the fourth industrial revolution.Article Highlights: The overall Industry 4.0 implementation is not so satisfactory in the fertilizer industry of Bangladesh. Lack of knowledge and possibility of job loss have been identified as the best and the worst challenges respectively. This article provides some pragmatic solutions and policy recommendations for implementing Industry 4.0. [ABSTRACT FROM AUTHOR]

One of the important decisions in the supply chain is the supplier selection affecting different sectors and objectives of a chain significantly. In supplier selection area, product/technology selection, product/technology transfer method selection, and product/technology supplier selection are three debatable factors. Hence, in this paper, criteria influencing these three indicators will be identified and their importance will be ranked by Gray-DEMETEL and Best-Worst Method (BWM). Then, the priority of the options will be determined using Gray Analytical Network Process (GANP), which is one of the strongest decision-making methods. This paper attempts to demonstrate the superiority of the BWM method in determining the importance of criteria while comparing previous methods in this group. The compliance rate obtained for the BWM confirms this claim. In order to investigate the proposed approach, a case was conducted with 5 final suppliers, 3 technologies and 5 technology transfer methods, which ended with the selection of the best supplier with the highest technology and capable of transferring the technology by the optimal transfer method.