Priority Weights Research Articles

The ‘sense of terroir’ among wine-consuming social media users in Italy

ABSTRACT The purpose of this study is to explore wine consumer appreciation towards terroir-based communication in the social media context. The Analytic Hierarchy Process (AHP) approach was applied to elicit consumer preferences for alternative Instagram posts including image-based and text-based signals about wine terroir. An online survey was administered to a sample of Italian wine consumers who use social media. Results showed that image-based posts were largely preferred to text-based posts. Among image posts, the visual appearance of the grape variety and the natural label, which means a combination of an intrinsic cue with an extrinsic one, gains higher user attention in comparison with the other signals. Among text posts, posts proposing a ‘terroir association’ received higher priority than those explaining a ‘terroir concept’. These results were tested analysing real Instagram posts of an Italian winery. The number of likes received by the different terroir-related posts reflected the AHP-estimated global priority weights highlighting that actual consumer appreciation agreed with the evaluation determined by using the proposed AHP model. The study revealed that the AHP was a useful approach to evaluate wine consumer engagement with digital communication. It contributed to understanding how wineries can effectively convey wine terroir meanings via social media.

Faktor-Faktor yang Mempengaruhi Keputusan dalam Memilih Universitas Negeri di Indonesia: Studi pada Siswa-Siswi SMA Kelas XII Sumatera Utara

The objective of the research is to find out the main priority which highly influenced students to continue their study to the universities, especially public universities in Indonesia. Various facilities and services provided by the universities become the consideration in choosing the universities according their talent, interest, and selection. These alternatives are presented in 5 (five) criteria and sub-criteria which will be analyzed, and which factor that will become their main priority in choosing the universities according to their willingness and expectation. The research uses AHP method by calculating the weight of priority based on the response to the questionnaires in 111 Grade XII Senior Hight School students as the respondents from high ranking (A) schools in North Sumatera. The result of the research shows that of the five criteria, the criterion of University Image has the highest position with the weight of 0.33. In the highest priority of sub-criterion of University Image is International Accreditation with the weight of 0.24, and in sub-criterion of Tuition is cost of living, in sub-criterion of Tuition is cost of living, in sub-criterion of Location is the university which is located in urban area with the weight of 0.13, the sub-criterion of Facility and Infrastructure provided by the university is IT facility for Learning Media with the weight of 0.16. Therefore, based on the main priority of the criteria and each criterion which have been obtained, it is expected that the management of the public universities in Indonesia know what has to be heeded and improved in the interest of the prospective students.

ANALISIS SISTEM TRANSPORTASI LOGISTIK (STUDI KASUS: PERGUDANGAN KOTA PAREPARE)

Parepare is a commercial city that distributes various types of commodities and industrial products every day to lots of whether inside or outside South Sulawesi. Therefore, the role of warehousing is very important to support the distribution process. This research aims to determine the characteristics and choice of transportation modes for goods and warehouse commodities using the Analytical Hierarchy Process method based on surveys and interviews with 200 respondents. The results of the research show that the characteristics of the transportation modes that used are pick up, 2 axle trucks, 3 axle trucks and trailer trucks, dominated by commodity yields of 56.50%, load prices of 85 - 120 million by trailer trucks and loads above 30 tons with more than 15 hours of travel time. The choice of the most efficient mode of transportation for distributing goods and warehousing commodities is a 2 axle truck with a priority weight percentage of 0.30%.

Analisis Pemilihan Moda Angkutan Komoditi Ayam Petelur di Kabupaten Sidenreng Rappang – Sulawesi Selatan

Sidenreng Rappang Regency - South Sulawesi is one of the largest contributors of laying chicken commodities and is distributed to various regions. This research aims to determine the characteristics and choice of transportation modes. This research uses a descriptive quantitative method based on surveys and interviews with 170 respondents using AHP analysis. The results of the research show that the characteristics of the transportation used are pick-up trucks (2 tons), single-wheel trucks (5 tons), 2-axle trucks (8 tons). The dominant departure time during the day is 12.00-14.00 WITA, at night 22.00-00.00 WITA, while the arrival time at the distribution point during the day is predominantly 14.00-16.00 WITA, and at night 00.00-02.00 WITA. The dominant spread is to Makassar City, while outside the province, namely West Sulawesi Province. The choice of efficient transportation mode used in the process of distributing laying hens is the Pick Up mode with a priority weight percentage of 0.61%.

E-learning Quality and the Learners’ Choice Using Spherical Fuzzy Analytic Hierarchy Process Decision-making Approach

E-learning in the context of Industry 4.0 and the outbreak of the COVID-19 pandemic has transformed traditional education. However, the smooth transition from face-to-face education to e-learning remains a challenging task, given concerns about e-learning quality. This study aims to explore the quality criteria and the adoption of e-learning via the spherical fuzzy analytic hierarchy process (SF-AHP). The extended technical acceptance model is used as a theoretical framework for constructing quality in an adoption hierarchical model. The input data derived from in-depth interviews of 20 experts in the field and the SF-AHP calculator have generated the priority weights of quality criteria in the model of e-learning adoption. The findings confirm the role of three major criteria, in order of importance, as follows: system, resources and core factors. The results highlight system factors as most crucial, including aspects such as governmental policies and institutional leadership, which are essential for setting a conducive environment for e-learning. Resource factors are ranked second, emphasizing the importance of IT applications, human capital and facilities to support e-learning infrastructure. Core factors, though ranked lower, are vital in ensuring the effectiveness of e-learning through course materials, instruction, and learner support. The weights of 14 sub-criteria have further shed light on policies to promote e-learning quality and its adoption. The implied priority of each weight a valuable guideline for the stakeholders’ actions to reach the targeted goals under the constraint resources.

Implementation of the Analytical Hierarchy Process Method for the Muaro Jambi Community Health Center Service Performance Assessment System

Analytic Hierarchy Process (AHP) in this research is used for decision making in assessing the performance of Puskesmas services. The aim of this research is to show the effectiveness and usefulness of the AHP method in assisting complex decision-making processes. Next, the collected data was analyzed using the AHP method to produce priority levels and weights for the main criteria used, namely health promotion (C1), environmental health (C2), maternal and child health including family planning (C3), efforts to improve nutrition (C4), efforts to prevent infectious diseases (C5), treatment efforts (C6), health development efforts (C7). The processes in the system include, user data processing, criteria data processing, value category data processing, sub-criteria data processing, alternative data processing and assessment data processing. Meanwhile, the output data produced includes ranking data reports and final results reports in graphical form. The results of the research on the performance assessment system for the highest health center services, namely Tanjung Public Health Center with a score of 0.743389, were ranked number 1.

Evaluating modern intrusion detection methods in the face of Gen V multi-vector attacks with fuzzy AHP-TOPSIS.

The persistent evolution of cyber threats has given rise to Gen V Multi-Vector Attacks, complex and sophisticated strategies that challenge traditional security measures. This research provides a complete investigation of recent intrusion detection systems designed to mitigate the consequences of Gen V Multi-Vector Attacks. Using the Fuzzy Analytic Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), we evaluate the efficacy of several different intrusion detection techniques in adjusting to the dynamic nature of sophisticated cyber threats. The study offers an integrated analysis, taking into account criteria such as detection accuracy, adaptability, scalability, resource effect, response time, and automation. Fuzzy AHP is employed to establish priority weights for each factor, reflecting the nuanced nature of security assessments. Subsequently, TOPSIS is employed to rank the intrusion detection methods based on their overall performance. Our findings highlight the importance of behavioral analysis, threat intelligence integration, and dynamic threat modeling in enhancing detection accuracy and adaptability. Furthermore, considerations of resource impact, scalability, and efficient response mechanisms are crucial for sustaining effective defense against Gen V Multi-Vector Attacks. The integrated approach of Fuzzy AHP and TOPSIS presents a strong and adaptable strategy for decision-makers to manage the difficulties of evaluating intrusion detection techniques. This study adds to the ongoing discussion about cybersecurity by providing insights on the positive and negative aspects of existing intrusion detection systems in the context of developing cyber threats. The findings help organizations choose and execute intrusion detection technologies that are not only effective against existing attacks, but also adaptive to future concerns provided by Gen V Multi-Vector Attacks.

Optimal Wind Farm Siting Using a Fuzzy Analytic Hierarchy Process: Evaluating the Island of Andros, Greece

In recent decades, fuzzy logic and fuzzy multi-criteria decision-making systems have been applied in several fields. This paper aims to determine the optimal wind farm siting solution in a fuzzy environment. Therefore, the main research question of the present paper is whether and to what extent the uncertainty in the researcher’s judgments affects the ranking of wind farm siting solutions. The fuzzy analytical hierarchy method is applied to an existing case study of wind farm siting on the island of Andros, examining the stability of the final priorities of the alternatives under a regime of gradual increases in ambiguity, as well as whether the introduced ambiguity in the model corresponds to any uncertainty the researcher has during the process of scoring the criteria and alternatives. Five assessment criteria (wind potential, ground slope, distance from road network, distance from high-voltage network, and social acceptance of local population) and eight eligible suitable alternatives (A1–A8) for wind farm siting are considered in the computations. The methodology includes the fuzzification of initial decision-maker judgments, the calculation of fuzzy intermediate priorities (weights), the defuzzification of fuzzy intermediate priorities (weights), and the synthesis of intermediate priorities into final priorities of alternatives, according to the procedures of the crisp AHP (CAHP). Under the assumptions of the initial case study, the results show that the final priorities are quite robust when faced with increased ambiguity. In almost all the examined cases, the alternative initially chosen as the best, A1, is dominant, followed by A3. In addition, in all cases, social acceptance favors alternative A1, and wind velocity favors alternative A8. Therefore, fuzzy multi-criteria methods can be applied to determine an optimal wind farm siting solution when criteria with qualitative characteristics are used and the manifestation of preferences involves strong elements of subjectivity.

Driving factors for developing integrated farming: Multi-criteria decision-making analysis

Integrated farming (IF) by temporal and spatial mixing of crops, livestock, fishery, and allied activities in a single farm, is considered a critical multifunctional option for smallholder farmers, who form the backbone of Indian agriculture, to ensure sustainable livelihoods, the productivity of agricultural enterprises, stability of farm income, food and nutritional security. However, the adoption of IF still remains low. To explore the critical factors of IF systems and prioritize them for stakeholders’ decision-making and development of strategies, the current research was undertaken to integrate SWOT (Strengths, Weaknesses, Opportunities, and Threats) - AHP (Analytic Hierarchy Process) analysis. We used a multi-stage sampling to select 60 practicing IF adopters in West Bengal state of eastern India for the collection of pair-wise comparison data on 32 SWOT factors using a close-ended questionnaire and subjected to AHP analysis to understand the quantitative importance of each factor of SWOT. Results of the multi- criteria analysis showed the total priority weight of the opportunity component (0.450) was the highest, followed by strength (0.341), weakness (0.114), and threat components (0.095). Based on the global priority weight of all 32 SWOT factors, sustainable livelihood security” was the key driving factor (0.081) followed by promotion of organic farming (0.072), better risk management (0.063) and incorporation of high-value crops (0.063) of opportunity component. Increased farm production and productivity (0.058) and enhancement in income (0.055) of strength component also played as vital driving factors. There was no factor of weakness and threat components within the first 10 important factors. The insights of this study may help improve extension services to smallholder farmers for prioritizing strategies in the adoption process of IF. Additionally, it can also help policymakers to design targeted policies, remove barriers, foster innovation, and promote sustainable practices, contributing to food security and environmental sustainability.

Forecasting the probability of successful deployment of internet of things and cloud computing in the building sector through consistent fuzzy preference relations method

Purpose This study aims to identify significant factors, analyse them using the consistent fuzzy preference relations (CFPR) method and forecast the probability of successful deployment of the internet of things (IoT) and cloud computing (CC) in Gujarat, India’s building sector. Design/methodology/approach From the previous studies, 25 significant factors were identified, and a questionnaire survey with personal interviews obtained 120 responses from building experts in Gujarat, India. The questionnaire survey data’s validity, reliability and descriptive statistics were also assessed. Building experts’ opinions are inputted into the CFPR method, and priority weights and ratings for probable outcomes are obtained to forecast success and failure. Findings The findings demonstrate that the most important factors are affordable system and ease of use and battery life and size of sensors, whereas less important ones include poor collaboration between IoT and cloud developer community and building sector and suitable location. The forecasting values demonstrate that the factor suitable location has a high probability of success; however, factors such as loss of jobs and data governance have a high probability of failure. Based on the forecasted values, the probability of success (0.6420) is almost twice that of failure (0.3580). It shows that deploying IoT and CC in the building sector of Gujarat, India, is very much feasible. Originality/value Previous studies analysed IoT and CC factors using different multi-criteria decision-making (MCDM) methods to merely prioritise ranking in the building sector, but forecasting success/failure makes this study unique. This research is generally applicable, and its findings may be utilised for decision-making and deployment of IoT and CC in the building sector anywhere globally.

Spherical Fuzzy Multicriteria Decision Making for Evaluating Healthcare Service Quality of Hospitals During the Global Pandemic

AbstractThis study identifies hospitals in Iran that have demonstrated exceptional performance in service quality during the COVID-19 pandemic based on the proposed integrated multicriteria decision-making (MCDM) process. Although the coronavirus has been eradicated in most countries, occasional outbreaks of COVID-19 variants have occurred, affecting many individuals, particularly in Iran. The pandemic caused an influx of hospital visits, with people seeking treatment for various illnesses. However, the abrupt onset of the pandemic and its global impact challenged hospitals’ ability to provide timely care, leading to a noticeable decline in service quality. Identifying the top-performing hospitals is crucial for benchmarking and enhancing healthcare quality. To assess hospital service quality, the study employed a customized SERVQUAL model, which helped identify key factors that served as criteria and subcriteria for the evaluation process. The priority weights of these factors were then obtained using the spherical fuzzy analytic hierarchy process. For each SERVQUAL criterion, the hospitals were evaluated using the spherical fuzzy weighted aggregated sum product assessment method, resulting in respective rankings of the hospitals. Finally, an integrated Borda−Copeland method was utilized to generate the aggregate evaluation ranking, a feature that serves as an important departure from the literature. The contribution of this work lies in developing an integrated approach that intends to serve as a benchmark not only for hospitals in different countries but also for those confronting similar challenges and offers guidance for seeking insights from top-performing hospitals in comparable situations.

Cost, Usability, Credibility, Fairness, Accountability, Transparency, and Explainability Framework for Safe and Effective Large Language Models in Medical Education: Narrative Review and Qualitative Study.

The world has witnessed increased adoption of large language models (LLMs) in the last year. Although the products developed using LLMs have the potential to solve accessibility and efficiency problems in health care, there is a lack of available guidelines for developing LLMs for health care, especially for medical education. The aim of this study was to identify and prioritize the enablers for developing successful LLMs for medical education. We further evaluated the relationships among these identified enablers. A narrative review of the extant literature was first performed to identify the key enablers for LLM development. We additionally gathered the opinions of LLM users to determine the relative importance of these enablers using an analytical hierarchy process (AHP), which is a multicriteria decision-making method. Further, total interpretive structural modeling (TISM) was used to analyze the perspectives of product developers and ascertain the relationships and hierarchy among these enablers. Finally, the cross-impact matrix-based multiplication applied to a classification (MICMAC) approach was used to determine the relative driving and dependence powers of these enablers. A nonprobabilistic purposive sampling approach was used for recruitment of focus groups. The AHP demonstrated that the most important enabler for LLMs was credibility, with a priority weight of 0.37, followed by accountability (0.27642) and fairness (0.10572). In contrast, usability, with a priority weight of 0.04, showed negligible importance. The results of TISM concurred with the findings of the AHP. The only striking difference between expert perspectives and user preference evaluation was that the product developers indicated that cost has the least importance as a potential enabler. The MICMAC analysis suggested that cost has a strong influence on other enablers. The inputs of the focus group were found to be reliable, with a consistency ratio less than 0.1 (0.084). This study is the first to identify, prioritize, and analyze the relationships of enablers of effective LLMs for medical education. Based on the results of this study, we developed a comprehendible prescriptive framework, named CUC-FATE (Cost, Usability, Credibility, Fairness, Accountability, Transparency, and Explainability), for evaluating the enablers of LLMs in medical education. The study findings are useful for health care professionals, health technology experts, medical technology regulators, and policy makers.

A stakeholder-driven holistic approach to mitigate drought in Ramsar wetlands: evaluation of nature-based solutions using interval analytic hierarchy process

Abstract Wetland ecosystems are vital in maintaining the ecological balance of the wider area. The increase of frequent and intense droughts due to accelerated climate changes poses a threat to wetlands as fragile ecosystems which further require a holistic approach and cooperation between stakeholders to define long-term sustainable solutions. This paper focuses on identifying nature-based solutions to mitigate drought in Ramsar-designated sites through understanding the preferences of stakeholders for effective implementation. An interval version of the analytic hierarchy process is proposed as a systematic framework for selecting solutions considering multiple objectives (climate change mitigation, biodiversity preservation, and human welfare) and six alternatives applicable to Ramsar wetlands. The Serbian case study demonstrates the evaluation of alternatives using interval values in pairwise comparison matrices and priority weights were computed by linear programming. Top-ranked measures identified by three experts involve increasing water availability, supporting agroforestry practices, and utilizing natural reservoirs. Mulch and wastewater reuse are excluded due to implementation investments. The added value of the proposed approach is that the results can be used by managers and decision-makers in many ways; for example, weights of the alternatives could indicate resource allocation, while rankings serve as indicators for optimizing the number of applied solutions.

Analysis Of Ship Main Engine Procurement Process In The Shipyard Industry Using Supply Chain Operation Reference Method (SCOR) (Case Study: PT XYZ)

As a sector playing a crucial role in ship construction and repair, the shipbuilding industry faces significant challenges in material procurement, where materials constitute 50-70% of the total project cost. One of the materials requiring paramount attention is the ship's main engine. Effective supply chain management is critical to enhancing efficiency and effectiveness in the procurement process of ship engines. However, supply chain performance measurement research, especially in ship engine procurement, still needs to be improved. Therefore, this study aims to identify relevant performance metrics, analyze critical performance indicators, and provide recommendations based on the SCOR (Supply et al.) framework to improve ship engine procurement. The research results present 24 metrics distributed into four performance attributes based on SCOR 12.0, with 22 selected metrics aligned with the company's context. Analysis using the AHP method indicates that the reliability attribute has the highest weight (0.296), with the metric "Percentage of procurement received on time as per request" being the top priority (weight 0.143). This study provides a holistic view of crucial aspects of the ship engine procurement supply chain, supporting companies in enhancing their performance in the procurement process.

Investigating information security risk management in Yemeni banks: An CILOS-TOPSIS approach

In today's digital age, the banking sector faces increasing challenges in ensuring operational resilience, protecting customer assets, and maintaining a competitive edge. Prioritizing information security risk management (ISRM) practices is crucial to effectively address these challenges. This paper aims to demonstrate the effectiveness of the multi-criteria decision-making (MCDM) method in evaluating and improving ISRM practices in Yemeni banks. The study employs an integrated CILOS-TOPSIS model, considering two criteria and five sub-criteria, with criteria weights determined using the CILOS method. The results highlight the significance of specific criteria in ISRM, with the existence of a comprehensive business continuity and disaster recovery plan (C2.1) standing out as a top priority (weight: 0.266). Additionally, the frequency of data backups and the presence of an active backup policy (C2.2) and the adequacy of physical security measures (C1.1) are identified as crucial factors (weights: 0.228 and 0.203, respectively). Furthermore, the TOPSIS method is employed to rank 13 banks based on these criteria, revealing the top-performing banks as B10, B4, B13, B1, and B12. Conversely, the 7th, 5th, and 6th ranked banks require attention for improvement. The paper provides comprehensive details on criteria weighting, bank ranking, and recommendations for enhancements. The findings presented in this paper offer valuable insights to decision-makers in the banking sector, enabling them to effectively guide their efforts and allocate resources to areas, controls, and banks that require greater attention.

Calibration of Priority Points for Sensitization Status of Kidney Transplant Candidates in the United States.

A primary change to the national organ allocation system in 2014 for deceased donor kidney offers was to weight candidate priority based on sensitization (i.e. calculated panel reactive antibody percentage [CPRA%]) using a sliding scale. Increased priority for sensitized patients could improve equity in access to transplantation for disadvantaged candidates. We sought to evaluate the effect of these weights using a contemporary cohort of adult US kidney transplant candidates. We used the national Scientific Registry of Transplant Recipients to evaluate factors associated with sensitization using multivariable logistic models and rates of deceased donor transplantation using cumulative incidence models accounting for competing risks and multivariable Cox models. We examined 270,912 adult candidates placed on the waiting list between Jan,2016-September,2023. Six-year cumulative incidence of deceased donor transplantation for candidates with cPRA%=[80-85) and [90-95) was 48% and 53% respectively, as compared to 37% for candidates with cPRA%=[0-20). In multivariable models, candidates with high cPRA% had the highest adjusted hazards for deceased donor transplantation. There was significant effect modification such that the association of high cPRA% with adjusted rates of deceased donor transplantation varied by region of the country, gender, race/ethnicity, prior dialysis time and blood type. Results indicate that the weighting algorithm for highly sensitized candidates may overinflate the need for prioritization and lead to higher rates of transplantation. Findings suggest re-calibration of priority weights for allocation is needed to facilitate overall equity in access to transplantation for prospective kidney transplant candidates. However, priority points should also account for subgroups of candidates who are disadvantaged for access to donor offers.

Parameter-Free Interval Priority Weight Estimation Methods Based on Minimum Conceivable Ranges Under a Crisp Pairwise Comparison Matrix

Methods for interval priority weight estimation from a crisp pairwise comparison matrix were proposed in the interval analytic hierarchy process assuming the vagueness of human evaluation. The interval priority weights estimated by the conventional method do not reflect the intrinsic vagueness in the given pairwise comparison matrix (PCM). This paper proposes parameter-free methods based on minimal conceivable ranges for estimating interval priority weights from a crisp pairwise comparison matrix. The estimated interval priority weight vectors are required to satisfy (1) the potential reproducibility, (2) the normality, and (3) the preservation of the perfect consistent data. Estimation methods of interval priority weights are proposed based on the minimum possible range. We show those proposed methods satisfy the required three properties. The estimation problem of interval priority weights potentially has multiple solutions with which the associated interval PCMs are identical to one another. To make the further investigation simpler, we use an interval priority weight vector among multiple solutions such that the sum of the center values of interval priority weights is one. We compare the estimation methods of interval priority weights from the viewpoint of estimation accuracy by numerical experiments. Namely, by generating crisp pairwise comparison matrices randomly under true interval PCMs, we evaluate the accuracies of the estimated interval priority weight vectors by comparing the true interval priority weight vectors.

Assessing the performance of urban heritage conservation projects – influencing factors, aspects and priority weights

Heritage conservation in urban areas involves complex systems often faced with the dilemmas of maintaining the built form’s historical character, improving infrastructure, and managing development through stakeholder cooperation. At present, the performance of any conservation project is solely vested in conserving the built fabric. Evaluation tools for urban heritage conservation projects do not have provisions for measuring the subjective value of stakeholders who are part of the heritage setting. This study tries to identify and prioritise the factors that need to be considered when developing a conservation project performance assessment model for an urban heritage conservation project from the perspective of experts that can be further evaluated from the perspective of stakeholders. As these complex systems can be better viewed in the context of developing countries, the case of India is adopted. From a literature review, factors that contribute to the outcome of an urban heritage conservation project were identified and categorised into six aspects: the physical, social, economic, cultural, political, and continuity aspects. Through an expert survey, the factors that constituted each aspect were filtered using the feature selection method of correlation to avoid factors that may seem related. The factors under each aspect were ranked using a weighted average ranking method to identify the most prioritised factors determining the outcome of an urban heritage conservation project. The priority weights of the aspects were calculated using Saaty’s analytic hierarchy process. The results show that the cultural aspect was the most important aspect, followed by the continuity aspect. The social and physical aspects were prioritised similarly, followed by the economic and political aspects. This study is distinctive because it identifies the influential factors that can help develop a conservation project performance assessment model for an urban heritage conservation project.

A novel intuitionistic fuzzy best-worst method for group decision making with intuitionistic fuzzy preference relations

This paper proposes a new intuitionistic fuzzy best-worst method (IFBWM) for group decision making (GDM) with intuitionistic fuzzy (IF) preference relations (IFPRs). IF values (IFVs) are used to express reference comparisons of criteria. Based on the additive consistency of IFPRs, this paper proposes the definition of additive consistency of IF reference comparisons (IFRCs). Based on the deviation minimization, a linear goal programming model is established to calculate the optimal IF priority weights. By the additive consistency, the consistency index in the closed form is computed by the score function of IFVs. A new approach is devised to enhance the additive consistency of IFRCs. Thus, an IFBWM with the additive consistency of IFRCs is proposed. For GDM with IFPRs, the best criterion and the worst criterion are identified for each decision maker by constructing the score matrix of the IFPR. The individual ranking order of the criteria is obtained by using the proposed IFBWM. Then, the collective ranking order of the criteria is obtained via building a 0–1 integer programming model. Therefore, a GDM method based on the developed IFBWM with IFPRs is proposed. Four examples are analyzed to demonstrate the effectiveness and the advantages of the proposed IFBWM for GDM.

Interaction and risk preference-based group decision-making with intuitionistic fuzzy preference relations

This research proposes a new method to solve group decision-making(GDM) problems with intuitionistic fuzzy preference relations(IFPRs). First, a new definition of multiplicative consistency of IFPR is presented to address the defects of the existing consistency definitions. Then, two programming models are established to obtain the most optimistic and pessimistic consistent IFPRs and corresponding intuitionistic fuzzy priority weights. Also, in order to improve the accuracy of aggregate information, a new method to determine the weights of decision-makers(DMs) is offered by considering the interaction among DMs. Subsequently, by combining the vagueness and non-vagueness of the aggregated information, a multiplicative consistency definition of the collective IFPR is provided. Moreover, to simplify the GDM process, a programming model for solving the priority weight is established, which effectively avoids the consistency test and correction of IFPRs. Finally, the values of the proposed method are illustrated by comparative analysis.