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The circular economy (CE) seeks to maintain products and materials at their highest utility and value. The organisational and governmental policy have seised onto the CE philosophy to advance socio-economic and environmental development. CE remains an essentially contested concept – making its utilisation as a foundation for managerial and policy decisions challenging. Circularity assessment has not been systematically adopted, especially within supply chain management. Using critical scholarly and practical evidential foundation, we proposed a comprehensive set of metrics that can be utilised in supplier selection, monitoring, and development for circularity. These metrics include the macro, meso, and micro levels. A group decision-making method integrating best-worst method (BWM), regret theory (RT), and dual hesitant fuzzy sets (DHFS) for circular economy and circularity (CEC) supplier evaluation and selection is introduced – providing instrumental value for the identified metrics typology. The proposed BWM-DHFE-RT integrative analytical method can accommodate decisionmaker psychological behaviour under uncertainty while simultaneously capturing divergent or conflicting opinions of different decision-makers. An illustrative business scenario is utilised to demonstrate the application of the proposed method. Though the proposed CE performance metrics and methodology are used for CEC supplier management reasons they have broader applicability. Future research and application directions are discussed. [ABSTRACT FROM AUTHOR]

Energy Service Companies (ESCO) provide various services to their customers through Energy Performance Contracts (EPCs), including technical support, financial assistance, installation, maintenance, and insurance services. However, emerging risks during this extended process can lead to uncertainties for energy efficiency implementations, ESCOs, and their clients. Understanding of uncertain factors of such energy efficiency applications is therefore crucial. This study aims to evaluate the risks that emerge in the stages of EPC implementations in Türkiye. An expert survey was conducted to assess these risks, with participants consisting of energy managers, representatives from Turkish ESCOs, experts from the financial sector, academics, and decision-makers. Based on the survey responses, the Best–Worst Method (BWM), a multi-criteria decision-making approach, was employed to evaluate the risks in EPCs. Binary comparison vectors, indicating the relative importance of evaluated risk factors, were formulated using the BWM, enabling the calculation of risk weights and the analysis of response consistency. It is revealed that “Financial and Market Risks” is the most important, and “Technological Risks” is the least important risk criterion for EPCs in Türkiye. In the end, policy recommendations were developed to improve EPC applications in Türkiye. [ABSTRACT FROM AUTHOR]

Objective: Today, there is increasing competition among various organizations, and one of the competitive tools is supply chain management. Existing pressures and disruptions for companies have led to working under uncertain conditions. Therefore, the supply chain must be made resilient. Such a chain is of great importance in the oil industry, which is one of the country's most essential activities. Accordingly, this study seeks to identify and prioritize the factors affecting a resilient supply chain in the oil industry. Methodology: This research was conducted in one of the active companies in the oil industry. Initially, semi-structured interviews were conducted with 10 experts from the company, who were purposefully selected, and analyzed using the thematic analysis technique. Findings: This analysis identified 13 factors. In the next step, using the Best- Worst Method (BWM), these factors were ranked, with financial factors, flexibility, the use of experienced managers, the use of backup suppliers, and foresight being ranked first to fifth, respectively. Conclusion: Overall, it is recommended to focus on these underlying factors as the foundation of supply chain resilience. [ABSTRACT FROM AUTHOR]

The Best–Worst method (BWM) is one of the latest contributions to pairwise comparisons methods. As its name suggests, it is based on pairwise comparisons of all criteria (or possibly other objects, such as alternatives, sub-criteria, etc.) with respect to the best (most important) and the worst (least important) criterion. The main aim of this study is to investigate the path and scale dependency of the BWM. Up to now, it is unknown whether the weights of compared objects obtained by the method differ when the objects are compared first with the best object, and then with the worst, or vice versa. It is also unknown if the outcomes of the method differ when compared objects are presented in a different order, or when different scales are applied. Therefore, an experiment in a laboratory setting is performed with more than 800 respondents university undergraduates from two countries in which the respondents compare areas of randomly generated figures and the relative size of objects is then estimated via the linearized version of the BWM. Last but not least, the accuracy of the BWM is examined with respect to different comparison scales, including Saaty's nine-point linguistic scale, an integer scale from 1 to 9, and a continuous scale from 1 to infinity. [ABSTRACT FROM AUTHOR]

With the use of cutting-edge technologies like artificial intelligence (AI), robotics, and the Internet of Things (IoT), Industry 5.0 represents a breakthrough move towards a sustainable and human-centered industrial future. Industry 5.0 endeavors to transform industries such as consumer electronics by emphasizing sustainability and collaboration, in contrast to its predecessors, who only concentrated on automation and efficiency. Along with improved manufacturing efficiency and product innovation, this change in the consumer electronics sector also redefines the human-machine interaction. This paper proposes a novel hybrid integrating model that combines the Entropy Weight Method (EWM), Best-Worst Method (BWM), and an acronym in Portuguese for Interactive Multicriteria Decision Making (TODIM) using single-valued neutrosophic trapezoidal numbers to evaluate Industry 5.0 enablers. The EWM provides objective weight for criteria, while the BWM captures the subjective preferences of decision-makers. The TODIM method ranks alternatives based on these weighted criteria using single-valued neutrosophic trapezoidal numbers, which effectively handle uncertainties and imprecise information inherent in decision-making processes. The proposed hybrid model effectively evaluates the Industry 5.0 consumer electronics sector using an empirical study emphasizing personalization, sustainability, resilience, and smart manufacturing criteria. The model enhances decision-making by balancing objective metrics with subjective preferences, thus guiding stakeholders toward informed and sustainable technological investments. The hybrid EWM-BWMTODIM method with single-valued neutrosophic trapezoidal numbers demonstrated robustness in accommodating subjective and objective criteria weights. Sensitivity analysis revealed variations in aggregation methods and values significantly influenced final rankings, emphasizing the method's adaptability and responsiveness to decision-maker preferences and environmental changes. [ABSTRACT FROM AUTHOR]

Considering the double uncertainty caused by the ambiguity of statistical data and the ambiguity produced by the subjective assessment of decision makers, the crisp values of criteria may be insufficient to model the multi-criteria decision-making (MCDM) problem in the real world. This paper proposes a fuzzy best-worst method (FBWM) based on a dual-interval solution algorithm to extend the best-worst method (the most recent MCDM method) to fuzzy environments. The reference comparisons for the best criteria and for the worst criteria are represented by fuzzy numbers. Then, according to the BWM method, a nonlinear constrained optimization problem with fuzzy parameters is formulated. We decompose the membership function in fuzzy numbers into several interval numbers of special form and solve the aforementioned fuzzy BWM problem by compound interval algorithm to obtain fuzzy weights of different criteria. Meanwhile, an integral type-reduced method is proposed for determining the fuzzy consistency ratio in order to assess the reliability of the FBWM results. The viability of the new algorithm to expand the BWM method into fuzzy environments has been validated through three numerical examples. In contrast to the existing FBWM method, the proposed method avoids the arithmetic operation between fuzzy numbers during the solution process, directly transfers the uncertain information in the membership function corresponding to the fuzzy comparison vector to the result, and generates fuzzy weight value, which indicates that the proposed algorithm is able to obtain accurate BWM results in fuzzy environments. The results of the study provide new solution ideas for multi-criteria optimization problems under uncertainty. [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]

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.

Since 2020, due to the Covid-19 pandemic, planners and designers have proposed numerous changes for urban spaces and mobility concepts to comply with governments' anti-contamination restrictions. Several European cities have implemented strategies for developing cycling, favouring the design of temporary (pop-up) or permanent cycle lanes and state incentives to purchase bicycles and electric micro-mobility vehicles. However, existing research suggests that design optimisation must pursue specific geometric, functional and safety standards for cyclists and space sharers (pedestrians/cars). It is thus necessary to analyse the perceptions of citizens and commuters for optimisation of the planning and design of these infrastructures. A multi-criteria evaluation was used to determine optimal infrastructure schemes (temporary and permanent) for the metropolis of Palermo in Sicily, Italy. First, three groups of cyclists (citizens, technicians and commuters) were selected. Then, applying the best–worst method, a series of design alternatives were evaluated by the target groups. The results showed a correlation between different types of users in terms of those who live in the city compared with commuters in the city for the first time. The results of this work lay the foundation for improved planning and design strategies in sustainable urban mobility plans and the Italian Biciplan guidelines. [ABSTRACT FROM AUTHOR]

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]

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]

Recent advancements in simulation technology facilitated maritime training in various modalities such as full-mission, desktop-based, cloud-based, and virtual reality (VR) simulators. Each of the simulator modality has unique pros and cons considering their technical capabilities, pedagogical opportunities, and different organizational aspects. On the other hand, enhanced training opportunity and diversity of training depends on the proper utilization of simulators. In this context, the absence of an unbiased, transparent, and robust simulator selection process poses a complex decision-making challenge for the maritime instructors and decision-makers at the institutions. In this study, a hybrid multi-criteria decision-making (MCDM) approach is proposed to evaluate four major types of simulator modalities. For the evaluation, a MCDM framework is developed based on 13 key factors (sub-criteria) for simulator selection grouped under three higher-level criteria—technical, instructional, and organizational criteria. Data was collected using a structured best-worst method (BWM) survey from subject matter experts. The Bayesian BWM is used for ranking of the 13 sub-criteria, and the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) is used to evaluate the four simulator modalities utilizing sub-criteria ranking scores from Bayesian BWM. The results reveal that the regulatory compliance of simulators is the most important criterion, while the cost of simulators is considered the least important criterion during the simulator selection process. Overall, full-mission simulators are the most preferred followed by VR simulators, cloud-based and desktop simulators. However, a sensitivity analysis demonstrated context-specific preferences for certain simulator types over others. [ABSTRACT FROM AUTHOR]

Climate-related vulnerability indices are increasingly being utilized to enhance the creation of better disaster management strategies and to better understand and anticipate the effects of disasters related to climate change. This study evaluates the climate change vulnerability of the cities in Türkiye through focusing on their exposure, susceptibilities and adaptive capacities to climate change. Data from social, economic and environmental sub-indicators were assessed and most relevant indicators were aggregated with the goal of constructing a composite Climate Change Vulnerability Index (CCVI). The CCVI includes six forms of capital leading to socio-economic and environmental sustainability i.e. social, public utility and transport, economics, land cover, meteorology and atmospheric conditions, and natural disaster capitals, and will be assessed combining each of these forms of capitals and its three dimensions (exposure, susceptibility and adaptive capacity). Stakeholder-driven structured methodology that discovers and ranks context-relevant indicators and sets weights for aggregating indicator scores by using the Best-Worst method (BWM) and stepwise weight assessment ratio analysis (SWARA) method are utilised. The indicators are aggregated through application of the BWM and SWARA weights using a linear aggregation method. From BWM and SWARA, the highest weights were obtained for meteorological conditions and land cover which are more than 0.36 and 0.22, respectively. The lowest weights were assigned to social characteristics and economy both of which were smaller than 0.10. The findings indicated that the cities on the northern, western and southern coasts as well as the cities in south-east region are the most vulnerable to climate change. The construction of CCVI can be used as part of decision-making process to minimize hazards and exposure to risk of climate change for the cities of Türkiye.

The rail network is essential for sustainable transportation, offering various advantages such as reduced greenhouse gas emissions and congestion relief. However, ensuring safety within the rail network is crucial for its long-term viability and public acceptance. Derailment incidents have significant implications for safety, efficiency, and sustainability. This study employs the Best-Worst Method (BWM) to identify and weigh the parameters affecting derailment incidents. The research methodology involved conducting an extensive literature review to extract influential parameters, which were subsequently classified. Additionally, a rigorous data collection process was undertaken to ensure the reliability of the findings. The BWM was then applied, utilizing the expertise of five carefully selected domain experts who met specific selection criteria based on their experience and reputation in the field of railway safety. This expert panel provided valuable insights to determine the relative importance of the identified parameters. The calculated weights revealed the criticality of factors such as fractures in railway lines, illegal rail width, unauthorized locomotive speed, and defects in wagon wheels. Conversely, falling cargo train parts, improper load distribution, and subsidence of the railway line had relatively lesser influence. The results of this study offer valuable information for decision-makers and stakeholders in the rail industry, facilitating resource allocation and the implementation of targeted strategies to enhance rail safety.

ABSTRACTThis study talks about the challenges faced by the project manager to implement a digitized supply chain quickly and ways to overcome them in the healthcare industry. A combination of the Best-Worst Method and fuzzy DEMATEL is used to study the factors that would pose a challenge to a Project Manager. The output from the Best-Worst Method is fed as the input to the fuzzy DEMATEL method. The study showed that Presenteeism is one of the most influential factors in the system. It also shows that a Project Manager must be someone with a good knowledge of the business and its working. The findings indicate that for a project to become successful during novel working conditions, the Project Manager and the project team as a whole are responsible and need to come together.

Selecting an appropriate business model innovation for sustainable performance is a complex decision that requires a decision support tool. However, despite the importance of business model innovation (BMI) for sustainable performance, there has been limited investigation into how a hierarchical enabler framework grounded in service-dominant logic contributes to the sustainability of service firms. This study examines the critical enablers of service business model innovation (SBMI) for sustainable performance within the utility sector, particularly the electricity supply sector in Ghana. Using the best–worst method (BWM), this study identifies and prioritizes three main enablers and eleven sub-enablers, addressing a notable gap in understanding their impact on sustainable performance. The findings reveal that service value creation innovation is the most critical primary enabler, with human capital, technological platforms, and value-based pricing constituting the top three sub-enablers for sustainability performance. This study contributes to the service-dominant logic and BMI discourse by providing a novel hierarchical framework that aids managerial decision making in service-oriented firms, particularly in developing economies. The results underscore the need for utility companies to prioritize investments in key areas, such as human capital, technological advancements, and customer-centric approaches, to drive sustainable business practices and improve overall performance. [ABSTRACT FROM AUTHOR]

The current study aimed to identify the most and the least important situational properties of second language willingness to communicate (L2WTC) in an EFL context. After reviewing the related literature, 24 influencing factors were identified and then they were prioritized. 180 TEFL students participated in the study to answer the research questionnaire. A quantitative research approach applying paired comparison questionnaire was employed. Data analysis was done using Excel spreadsheet for sorting data and calculating the mean and WinQSB software for solving linear programming model. The results showed that, among the selected variables, "the size of the group," "familiarity with topics under discussion," and "interlocutors and familiarity with them" were determined to be the first most important situational variables which highly influence L2WTC. The findings also showed that "attitudes toward the learning situation," "course evaluation criteria," and "alignment with the classroom norms" were the least important factors influencing L2WTC. The significance of the study lies in its theoretical contributions and pedagogical implications it has for the field of second language teaching and learning. [ABSTRACT FROM AUTHOR]

Resilience is the sustainable competitive advantage of suppliers in the supply chain, and the ability of resilient suppliers to manage risk and perform better in supply than traditional suppliers in the event of disruption has driven the complexity of the current supply chain. Therefore, studying how to select a resilient supplier is necessary for establishing a supply chain with flexibility in the case of interruption. A hybrid fuzzy Multi-Criteria Group Decision-Making (MCGDM) framework is developed in this paper for Resilient Supplier Selection Problems (RSSPs). First, Probabilistic Uncertain Linguistic Term Sets (PULTSs) are introduced to deal with the subjectivity and uncertainty of experts' assessments. Second, considering that experts may have different views on the relative importance of resilient criteria depending on their different knowledge backgrounds, the Probabilistic Uncertain Linguistic Best–Worst Method (PUL-BWM) is constructed to determine the weights of resilient criteria under different experts. In addition, given that the traditional Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) cannot handle the information metrics with negative values or reflect the correlation of information, the extended TOPSIS method based on a novel Probabilistic Uncertain Linguistic Synthetic Correlation Coefficient (PULSCC) is constructed to select the optimal resilient supplier. The novel PULSCC also overcomes the drawbacks of the existing correlation coefficient between PULTSs by considering the mean, variance, and information completeness of PULTSs. Finally, an example of resilient supplier selection in the automotive industry is performed to validate the applicability and feasibility of the proposed approach. The sensitivity and comparative analyses are conducted to demonstrate the effectiveness and superiority of the proposed framework. [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]

Maintaining interconnected infrastructures such as transportation, communication, power grids, and pipeline networks is paramount in emerging economies. One of the critical interruptions is the targeted attacks on the operating cyber-physical systems to disconnect operations, inspection, or monitoring of the system. Therefore, adopting a cybersecurity system (or platform) that provides holistic protection is vital for protecting the integrity of critical infrastructure networks. As such, this research aspires to provide a decision support system for cybersecurity managers or practitioners (in the Indian power sector) to select the best and appropriate platform for protection against cyber-attacks. A three-phase method is adopted. First, a literature search followed by an expert panel discussion identified alternatives (cybersecurity platforms) and selection criteria. Next, a questionnaire was developed. Thirdly, a hybrid Best-Worst Improved and COmprehensive distance-Based RAnking (BWM-I and COBRA) method was proposed and applied to evaluate the cybersecurity platform alternatives. Four alternatives (Cloud-Based Platforms, Web-Based Platforms, Application-Based Platforms, and AI-Based Platforms), six primary criteria, and fifteen unique sub-criteria were identified. Responses were collected from 80 power utility managers on a pan-India basis, ranking "End-to-End Coverage" criteria and the AI-Based platform as best. This approach identified the best cybersecurity platform that, if adopted, can be extended to other critical infrastructures, with an appropriate adjustment in the selection criteria. The study can be helpful to practitioners in evaluating cybersecurity platforms. Furthermore, it addresses a research gap in its application in a developing country like India. [ABSTRACT FROM AUTHOR]

With consideration for the extensive resources consumption and environmental degradation being on the rise today, implementing green development strategy to pursue both socioeconomic growth and the coordinated of environment sustainability, has become an increasingly important issue in modern enterprise supply chain operations management. Hence, the appropriate green supplier selection (GSS), viewed as a core issue in green supply chain management (GSCM), requires continuous research in this field to obtain a complete perception on GSS practices. It can be regarded as a multi-attribute group decision-making (MAGDM) problem that involves many conflict and unmeasurable evaluation criteria. In view of the superiority of multi-granularity extended probabilistic linguistic term sets (MGEPLTSs) in modeling such issues on potential ambiguity, complexity and uncertainty in actual GSS practices, we propose a novel integrated MAGDM methodology for GSS problems, by integrating the BWM (best–worst method) with the VIKOR (VIšekriterijumsko KOmpromisno Rangiranje) technique under the MGEPLTSs environment. First, by introducing the multi-granularity and probabilistic linguistic term sets, the MGEPLTSs are proposed to represent and quantify the decision information of GSCM practitioners. Then, the BWM is introduced to the MGEPLTSs environment to compute the weights of decision-making panels and evaluation attributes in GSS problems, by building the fuzzy mathematical programming model, respectively. Finally, we extend a multi-granularity extended probabilistic linguistic VIKOR method to calculate the compromise measure of alternatives considering the group utility maximization and the individual regret minimization, thereby achieving the full ranking of alternatives. A GSS case is conducted to illustrate the feasibility of the proposed approach, and the sensitivity analysis and comparative analysis with other similar approaches are presented to demonstrate its effectiveness and advantages. [ABSTRACT FROM AUTHOR]

During COVID-19 pandemic, the logistics sector has witnessed supply chain disruptions all across the globe. Digitalisation and sustainable solutions have become the priority for the logistics companies to deal with this crisis situation. Managing logistic operations for sustainability has become a major challenge. Therefore, the main objective of the study is to identify the critical success factors (CSFs) for achieving circular economy goals through emerging technologies during this pandemic. The most important factors, which will impact sustainability goals through the use of technologies are extracted from the literature review and then finalised after discussion with supply chain professionals. The importance rating of critical success factors for achieving CE goals is found using Best-Worst Method (BWM). The novelty of this study lies in exploring applications of emerging technologies for achieving circular economy goals in logistics sectors during COVID 19 pandemic. These factors are further prioritised from strategy perspectives. The prioritisation of CSFs will help logistics companies in developing strategies for digitalisation and achieving sustainable goals during the uncertain business environment. It will also contribute to formulating effective policies for long-term business continuity through technological and sustainable achievement. [ABSTRACT FROM AUTHOR]

Manufacturers are adopting Lean Six Sigma4.0 (LSS4.0) to improve their operations. However, poor project selection results in substantial losses. The aim of this study is to provide a framework for LSS4.0 project selection in an industrial context using the Best-Worst-Method (BWM)-based 'multi-criteria decision making' (MCDM) technique. First, 25 project selection criteria were pre-selected using a literature review and expert opinions. All criteria were then grouped into six different dimensions, closely related to the LSS4.0 principles, based on expert opinion and similarity with literature findings. To finalize the selection, a rigorous evaluation is undertaken using corrected item-total correlation (CIMTC) and importance index analysis methods, resulting in a final set of 22 project selection criteria, prioritized using BWM. The practical applicability of this method is demonstrated by a case study in a Moroccan manufacturing context. Operational and technical feasibility, strategic orientation, finance and business development emerged as top priorities. This research can help senior managers make informed choices about projects and recognize potential in uncertain scenarios. [ABSTRACT FROM AUTHOR]

Lithium-ion (Li-ion) batteries, despite their prevalence, face issues of resource scarcity and environmental concerns, prompting the search for alternative technologies. This study addresses the need to assess and identify viable metal-ion battery alternatives to Li-ion batteries, focusing on the rapidly industrializing context of Vietnam. It acknowledges the criticality of developing a sustainable, cost-effective, and resource-efficient energy storage solution that aligns with the country's growth trajectory. The primary objective is to evaluate the suitability of emerging metal-ion batteries—specifically sodium-ion (SIB), sodium-ion saltwater (SIB-S), magnesium-ion (MIB), and zinc-ion (ZIB)—for Vietnam's energy storage needs, guiding future investment and policy decisions. A Fuzzy Multiple-Criteria Decision-Making (MCDM) approach is employed, incorporating both quantitative and qualitative criteria. This study utilizes the Fuzzy Best-Worst Method (BWM) to determine the relative importance of various performance indicators and then applies the Bonferroni Fuzzy Combined Compromise Solution (Bonferroni FCoCoSo) method to rank the battery alternatives. The SIBs emerged as the most promising alternative, scoring the highest in the overall evaluation. The MIBs and SIB-saltwater batteries displayed competitive potential, while the ZIBs ranked the lowest among the considered options. This research provides a strategic framework for energy policy formulation and investment prioritization. It contributes to the field by applying a fuzzy-based MCDM approach in a novel context and offers a structured comparative analysis of metal-ion batteries, enhancing the body of knowledge on sustainable energy storage technologies. [ABSTRACT FROM AUTHOR]

The aim of this study was to identify and prioritize the Complications of using weapons by using content analysis method and hierarchical approach of best - worst in Khuzestan province. The present study is mixed in terms of applied-exploratory purpose and in terms of the nature of the research type. This study is also descriptive-survey in terms of data collection. The statistical population of this study was a group of professors with expertise in the field of research at Shahid Chamran University of Ahvaz. Samples were selected using purposive sampling method. In the qualitative part to collect data from the semistructured interview method and in the quantitative part; A researcher-made questionnaire was used. Qualitative phase data of the research were analyzed using content analysis method and quantitative data were analyzed using bestworst method. SPSS and Lingo software were used for data analysis. The findings of the study included identifying and investigating seven main complications for the use of weapons. The findings also showed that in terms of relative importance, the social effects of the use of weapons have the highest relative importance (weighing 0.21) and environmental effects have the least relative importance (weighing 0.085). [ABSTRACT FROM AUTHOR]

In this study, the existence of anchoring bias—people's tendency to rely on, evaluate, and decide based on the first piece of information they receive—is examined in two multi‐attribute decision‐making (MADM) methods, simple multi‐attribute rating technique (SMART), and Swing. Data were collected from university students for a transportation mode selection. Data analysis revealed that the two methods, which have different starting points, display different degrees of anchoring bias. Statistical analyses of the weights obtained from the two methods show that, compared to Swing (with a high anchor), SMART (with a low anchor) produces lower weights for the least important attributes, while for the most important attributes, the opposite is true. Despite their differences in anchoring bias, analytical approaches supported by empirical studies suggest that both methods (SMART and Swing) overweigh the less important attributes and underweigh the more important attributes. As such, we examined whether the best‐worst method (BWM), which has two opposite anchors in its procedure (a possible promising anchoring debiasing strategy), could produce results that are less prone to anchoring bias. Our findings show that the BWM is indeed able to produce lower weights (compared to SMART and Swing) for the less important attributes and higher weights for the more important attributes. This study shows the vulnerability of MADM methods with a single anchor and supports the idea that MADM methods with multiple (opposite) anchors, like BWM, are less prone to anchoring bias. [ABSTRACT FROM AUTHOR]

The aim of this study is to develop an inland terminal location selection methodology. This methodology is viewed from the perspective of the shipping line designing the inland transport chain while also taking the objectives of multiple other stakeholders into account. To that end, we develop a consensus model for a group Best-Worst Method (BWM) in order to aggregate the evaluations of the various stakeholders. The proposed method is applied to a real-life case study involving the Maersk shipping line, in which nine experts representing three different types of stakeholders assess six possible locations. After the evaluation, the market volume potential is identified as one of the most important criteria. Furthermore, a sensitivity analysis indicates that a varying influx of the container volume has no impact on the most desirable location. [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]

In developing countries, the interaction between rapid urban expansion and population growth brings forth a host of challenges, particularly concerning essential services like healthcare. While interest in site suitability analysis for identifying optimal hospital locations to ensure equitable and secure healthcare access is on the rise, the absence of a holistic study that encompasses social and environmental aspects in the assessment of hospital site suitability is evident. The objective of this research is to introduce a hybrid methodology that combines Geographic Information Systems (GIS) with Multi-Criteria Decision-Making (MCDM) weighting methods. This methodology aims to create hospital site suitability maps for districts 21 and 22 in Tehran, taking into account socio-environmental factors. In addition to the conventional Analytical Hierarchical Process (AHP) weighting method, this study employs two relatively less-explored methods, the Best-Worst Method (BWM) and Step-wise Weight Assessment Ratio Analysis (SWARA), to enhance the analysis of hospital site suitability. In the SWARA method, there are minimal variations in weights among criteria, indicating that all socio-environmental factors (e.g., distance from existing hospitals, distance from main roads, distance from green spaces) hold significant importance in the decision-making process. Additionally, the findings indicate that the western part of the study area is the most suitable location for the construction of a new hospital. To achieve the average hospital bed availability in Tehran, an additional 2206 beds are required in the studied area, in addition to the existing facilities. Considering the ongoing urban development, population growth, and the potential for natural disasters and epidemics, it becomes essential to enhance the healthcare system by increasing the number of hospitals and available hospital beds. The sensitivity analysis showed that GIS-based SWARA-WLC was the most suitable and stable model for determining hospital site suitability in the study area. This methodology can be adapted for use in other regions and further improved by incorporating additional criteria. In conclusion, the study recommended three specific alternative sites for establishing a new hospital in the study area. [ABSTRACT FROM AUTHOR]

Irrigation network is a set of hydraulic channels and related structures that is created with the aim of transferring, distributing and delivering water fairly as well as efficiently. However, due to constraints resulting from type of management and maintenance, climate changes, social and cultural characteristics of users, and financial issues, their performance are usually faced with major challenges. Qazvin irrigation network is one of the modern networks in the country and after more than 40 years of implementation of this network, many problems and challenges in the field of agricultural water management are observed in this network. The purpose of the present research is to identify the important challenges in the field of agricultural water management in Qazvin plain irrigation network, to investigate the importance of challenges and performance in relation to the challenges, and finally to provide practical strategies to improve the state of the system with a new approach by using Importance-Performance Analysis technique. The method of conducting the study to identify the challenges has been library studies and then using Delphi technique. Furthermore, the importance of the challenges has been identified by using Best-Worst method. Evaluating the responce of the officials authorities to the challenges using Importance-Performance Analysis technique, demonstrates that the challenges of lack of enough motivation in farmers to reduce agriculture water consumption, lack of governmental supports for established organizations, lack of supervision on implementation of crop patterns and cultivation area, budget issues, and lack of sufficient training for farmers, in ordre are five challenges in the first quadrant of the importance-performance matrix that should be prioritized for improvement. Several practical solutions to solve the challenges for Qazvin plain irrigation network such as doing more support from government in terms of using suitable irrigation methoed while considering the approved crop pattern and providing clear information for the statue of current situation and making coordination among all types of involved sectors are presented in the results section. Introduction Statistics show that a significant part of the world's fresh water is used for agriculture. So it is essential to pay special attention to water management in this section. In this research, agricultural water management challenges in Qazvin plain irrigation network has been studied. The purpose of the present research is to identify the important challenges in the field of agricultural water management in Qazvin plain irrigation network, to investigate the importance of challenges and performance in relation to the challenges, and finally to provide practical strategies to improve the state of the system with a new approach by using ImportancePerformance Analysis technique. Materials and Methods The method of conducting the study to identify the challenges has been library studies and then using Delphi technique. Using library studies and experienced consulting engineers, the basic questionnaire was prepared. The questionnaire was distributed among Qazvin irrigation network experts. Using 3 rounds of Delphi technique, the experts reached a consensus about challenges. Furthermore, the importance of the challenges was identified by using Best-Worst method. Best-Worst method is a multi-criteria decision-making method which requires less comparison data and leads to more reliable results. Also, the performance of challenges was evaluated by officials. Then, Importance- Performance Analysis was used to help managers on how to deal with challenges. Results and Discussion 26 important challenges were identified by library studies and Delphi technique. Using Best-Worst method showed that the challenges of a) lack of enough motivation in farmers to reduce agriculture water consumption, b) lack of supervision on implementation of crop patterns and cultivation area, c) budget issues, d) Improper structure of water conveyance and distribution, e) lack of fair distribution of operation and maintenance services between the main and on farm irrigation network are the five most important challenges in the field of agricultural water management in Qazvin plain irrigation network, respectively. Evaluating the performance of the officials in relation to the challenges and using Importance- Performance Analysis showed that challenges of a) lack of enough motivation in farmers to reduce agriculture water consumption, b) lack of governmental supports for established organizations, c) lack of supervision on implementation of crop patterns and cultivation area, d) budget issues, and e) lack of sufficient training for farmers, are five challenges in the first quadrant of the importance-performance matrix that should be prioritized for improvement, respectively. The agricultural water management challenges in Qazvin plain irrigation network can not be attributed solely to the government, private sector or water users, but it can be said that the government plays the main role. In most of the challenges, due to more power, the government can play a more prominent role to reduce the challenges, but without the help of the private sector and water users, it can not achieve its goals or the solutions will not be sustainable and long-term. In order to succeed in managing challenges, planning, monitoring, motivating, supporting, delegating necessary power by the government to the private sector and water users and coordination between different sectors is essential. [ABSTRACT FROM AUTHOR]

The present study proposes a hybrid framework combining multiple methods to determine the optimal values of design variables in a flexible manufacturing system (FMS). The framework uses a multi-objective response surface methodology (RSM) to achieve optimum performance. The performance of an FMS is characterized using various weighted measures using the best–worst method (BWM). Subsequently, an RSM approximates the functional relationship between the FMS performance and design variables. The central composite design (CCD) is used for this aim, and a polynomial regression model is fitted among the factors. Eventually, a bi-objective model, including the fitted and cost functions, is formulated and solved. As a result, the optimal percentage for deploying the FMS equipment and machines to achieve optimal performance with the lowest deployment cost is determined. The proposed framework can serve as a guideline for manufacturing organizations to lead strategic decisions regarding the design problems of FMSs. It significantly increases productivity for the manufacturing system, reduces redundant labor and material handling costs, and facilitates production. [ABSTRACT FROM AUTHOR]

The weaponized unmanned aerial vehicle (UAV) swarms have posed a significant threat to maritime civilian and military installations. For effective defense deployment, threat assessment has become a critical part of maritime defense decision-making. However, due to the uncertainty of threat information and the ignorance of decision-makers' psychological behaviors, there are great challenges in obtaining a reliable and accurate threat assessment result to assist in maritime defense decision-making. To this end, this paper proposes an integrated threat assessment method for maritime defense against UAV swarms based on improved interval type-2 fuzzy best-worst method (IT2FBWM), prospect theory and VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje, in Serbian). Firstly, the improved IT2FBWM is designed by introducing interval type-2 fuzzy set (IT2FS) and entropy-based information to obtain attribute weights with high reliability. Then, the hybrid fuzzy scheme covering IT2FS and interval number is constructed to express the uncertainty of different types of threat information. Next, VIKOR is extended to hybrid fuzzy environment and combined with prospect theory to consider the influence of psychological behaviors of decision-makers. Finally, the improved IT2FBWM and extended VIKOR are integrated to determine the threat ranking of targets and the priority defense targets. A case study of maritime threat assessment is provided to illustrate the performance of the proposed method. Moreover, sensitivity and comparative experiments were conducted, and the results indicate that the proposed method not only obtain the reliable threat assessment result but also outperforms the other methods in terms of attribute weight determination, decision preference consideration and decision mechanism. [ABSTRACT FROM AUTHOR]

Making a balance between the demand for healthcare services and the response to it requires the operation of new hospitals. But the important problem is finding the optimal for the construction of a hospital. The present research used a mixed method for determining the optimal location for a hospital in District 2 of Tehran. The mixed method is based on the Geospatial Information System (GIS), Best-Worst Method (BWM), and Multi-Criteria Decision-Making Methods, WASPAS, and TOPSIS. Fewer pairwise comparisons in the weighting process of the proposed method have increased the accuracy and reliability of decision-making results. The combination of spatial modeling and ranking has also reduced the search space for suitable places to build a hospital. For this purpose, after determining the appropriate criteria, the weighting process was carried out with BWM, and the spatial layer of each criterion was prepared using GIS. Based on the weighting results, the criteria of distance from existing hospitals and distance from healthcare centers have had the highest and lowest weight, respectively. In the next step, the land suitability map was prepared by combining the spatial layers. Almost 88% of the spatial modeling results corresponded with the realities of the region, and the western half, especially the southwestern part, for the construction of a new hospital, had a higher proportion than other parts. Finally, sites number 2, 5, and 8 among 11 candidate sites were determined as the most optimal places for the construction of a new hospital in the studied area. It is suggested that one or more hospitals should be built in the designated optimal sites in accordance with the population living in areas without optimal access to hospital facilities so that in addition to improving health spatial equity, the cost of citizens’ access to hospitals could be reduced. [ABSTRACT FROM AUTHOR]

In real-life applications, there generally exist Decision Makers (DMs) who have preferences over outputs and inputs. Choosing appropriate weights for different criteria by DMs often arises as a problem. The Best-Worst Method (BWM) in Multiple Criteria Decision-Making (MCDM) depends on very few pairwise comparisons and just needs DMs to identify the most desirable and the least desirable criteria. Unlike MCDM, Data Envelopment Analysis (DEA) does not generally assume a priority for an output (an input) over any other outputs (inputs). The link between DEA and MCDM can be introduced by considering Decision-Making Units (DMUs) as alternatives, outputs as criteria to be maximized, and inputs as criteria to be minimized. In this study, we propose a linear programming model to embed DEA and BWM appropriately. We first propose a modified BWM linear programming model to satisfy all conditions that DMs can assume. We then illustrate how a conventional DEA model can be developed to include the BWM conditions. From our approach, the MCDM problem to obtain the optimal weights of different criteria are measured. At the same time, the relative efficiency scores of DMUs corresponding to the MCDM criteria are also calculated. We provide the foundation of measuring the efficiency scores when most desirable and the least desirable inputs and outputs are known. To show the process of the proposed approach, a numerical example (including 17 DMUs with seven inputs and outputs) is also discussed. [ABSTRACT FROM AUTHOR]

Social enterprises have emerged as promising solutions for addressing environmental and societal challenges. To enhance their access to financial capital and align their business proposals with the preferences of impact investors, it is crucial to understand how Philanthropic venture capitalists (PhVCs) evaluate potential portfolio companies. This study introduces a framework to assist PhVCs in making more informed investment decisions, fostering successful, long‐term partnerships between investors and investees. The proposed approach begins by determining the significance of investment criteria using the Best‐Worst Method (BWM). Subsequently, the performance of portfolio companies is evaluated, ultimately identifying the most favorable investment opportunities. We apply this framework to a Dutch philanthropic venture capitalist, and our analysis reveals that 'Potential' and the 'Management team' rank highest in terms of investment criteria importance, while 'Assessment of the deal' and 'External environment' carry lower significance. Furthermore, our approach, incorporating the BWM outcomes, considers investors' personal preferences and offers a method for ranking portfolio companies based on their value and performance. [ABSTRACT FROM AUTHOR]

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.

Electric vehicle charging stations (EVCSs) are essential for solving the energy consumption and endurance anxiety problems of car owners. EVCSs also promote sustainable development in urban economies without relying on fossil fuels. This research proposes a hybrid approach that integrates the Bayesian network with best-worst method (BN-BWM) and a geographical information system (GIS) to address the site selection problem for electric vehicles (EVs). BN-BWM is employed to address the indicator system, which consists of nine criteria from three aspects. BN-BWM calculates the final distribution of the total preference of all decision-makers. Then, a GIS is utilized for spatial analysis and superposition analysis to determine appropriate sites for charging stations (CSs). The novelty of this study lies in the development of a new decision-making method based on the combination of BN-BWM and a GIS. This method is not only more innovative but also highly operational and convincing regarding the accuracy of the weight results. This research provides feasible and reliable ideas for the site selection and construction of CSs. It can also help EV companies and government personnel carry out strategic planning. The study verified the applicability and effectiveness of the developed hybrid method in sixteen administrative regions in Beijing. According to the results, 1) an indicator system consisting of nine criteria is established, and roads, charging stations, and slopes are identified as the most sensitive criteria for site selection; 2) Three alternative stations (ASs) are identified as the most suitable sites for the establishment of CSs.

Abstract With the promise of great benefits for industry and new possibilities for a wide range of applications, Industry 4.0 is quickly rising to prominence as one of the most important and widely discussed topics in academia and in practice. In this study, we look at the steel production business and identify the current state of affairs as well as the barriers to implementing Industry 4.0 there. Initially, the current state of Industry 4.0 was uncovered by visiting and surveying four prominent steel companies in Bangladesh. Less than fifty percent of Industry 4.0 components are now implemented in these plants. To determine the reasons for this unfavorable situation, research was conducted to identify the obstacles impeding the implementation of Industry 4.0. On the basis of the preferences of 9 experts (4 from industry and 5 from academia), 9 of the 12 proposed challenges have been selected as significant obstacles. The ranking of these nine difficulties was then assessed utilizing the popular best–worst method (BWM), a multi-criteria decision-making approach. It was determined that “high capital investment” was the most significant barrier, while “lack of government support” was the least significant. In addition, a sensitivity analysis was conducted, which improved the ranking’s quality. Then, several viable techniques for overcoming these challenges are discussed.

Introduction: With the emergence of new automation and mechanized technologies in the production and processing of agricultural products in Iran, which aim to accelerate the food supply process, adopting appropriate management models in the field of maintenance becomes inevitable. This is crucial to maintain and enhance the operational reliability of agricultural machinery, tools, and equipment. Furthermore, proper management of various physical assets in the agricultural industry, including operation and maintenance, is one of the most important requirements. This is due to their crucial role in ensuring readiness and high availability during the seasons of planting, cultivating, and harvesting agricultural products. These needs differ from that of other continuous production processes. Materials and Methods: To achieve an efficient model in the field of maintenance, the following steps have been investigated:a) Reviewing and identifying the most important criteria and sub-criteria driving the maintenance management. This is based on the previous literature and the experts’ opinion.b) Evaluating and prioritizing the main criteria and the interactions between their sub-criteria using the Best-Worst Method (BWM).c) Providing improved solutions for maintenance management of Iranian agro-industries.We decided to employ BWM because, compared to similar methods, it (i) provides more reliable pairwise comparisons, (ii) reduces the possible anchoring bias that may occur during the weighting process by respondents, (iii) is the most data-efficient method, and (iv) provides multiple optimal solutions which increase flexibility when accessing the best weight point. The process of weighting by BWM is summarized in five steps:1) Determine a set of evaluation criteria identified by the experts or decision-makers.2) Identify the most important (Best) and the least important (Worst) criteria according to the experts or decision-makers, each of which may have their own Best and Worst.3) Determine the preference of the Best criterion over all the other criteria using a number from 1 to 9 (where 1 represents equal importance and 9 represents extremely more important).4) Determine the preference of all the decision criteria over the Worst criterion.5) Compute optimal weights. Results and Discussion: According to the preliminary surveys, the most important criteria in the excellence maintenance model were identified as “organizational management”, “human-related factors”, and “organizational aspects”, respectively. The results of the BWM revealed that sub-criteria such as "top management support," "fund allocation and inventory resource management," and "appropriate maintenance strategies" had the greatest impact on maintenance management in agro-industries, with global weights of 0.108, 0.075, and 0.067, respectively. Additionally, these findings were compared to previous research conducted in the field of agricultural and production system maintenance models. Conclusion: The findings of this study could assist managers in revising and developing maintenance management models in the agro-industries. Future studies could consider calculating the interactions among the criteria that were omitted in this study to simplify the evaluation process which might improve the accuracy of weighing criteria. This can be achieved through the combination of the Decision Making Trial and Evaluation Laboratory (DEMATEL) and structural equation modeling.