Consistent Fuzzy Preference Relations Research Articles

Hybrid multi-criteria decision-making model for assessing perceived significance of 23 potentially modifiable cancer risk factors among senior nursing officers

BackgroundPotentially modifiable cancer risk factors have been increasingly recognized among the Chinese population. In this study, we aimed to investigate the perceived significance of these risk factors among senior nursing officers, who play a crucial role in providing healthcare services. We also sought to determine senior nursing officers’ performance in reducing these risk factors.MethodsA questionnaire survey regarding 23 potentially modifiable cancer risk factors was conducted in November 2023 with 58 senior nursing officers at Taizhou Hospital in Zhejiang Province, China. The consistent fuzzy preference relation method and importance–performance analysis were used to determine the attribute weights and performance levels.ResultsThe senior nursing officers considered diabetes, ultraviolet radiation exposure, PM2.5 exposure, excess body weight, physical inactivity, alcohol consumption and secondhand smoking significant. However, performance in reducing secondhand smoking, physical inactivity, excess body weight, PM2.5 exposure, and ultraviolet radiation exposure required improvement.ConclusionsThe proposed hybrid multiple-criteria decision-making model enhances our understanding of the perceived significance of 23 modifiable cancer risk factors and performance in reducing them, which could facilitate improvements in health education efforts.

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.

Einstein consistency of fuzzy preference relations

Pairwise fuzzy preference matrices can be constructed using expert ratings. The number of pairwise preference values to be specified by the experts increases quadratically with the number of options. Consistency (transitivity) allows to reduce this quadratic complexity to linear complexity which makes this approach feasible also for large scale applications. Preference values are usually expected to be on a fixed finite interval. Additive preference is defined on such a finite interval. However, completing preference matrices using additive consistency may yield preferences outside this finite interval. Multiplicative preference is defined on an infinite interval and is therefore not suitable here. To overcome this problem we extend the concept of consistency beyond additive and multiplicative to arbitrary commutative, associative, and invertible operators. Infinitely many of such operators induce infinitely many types of consistency. As one example, we examine Einstein consistency, which is induced by the Einstein sum operator. Completing preference matrices using Einstein consistency always yields preferences inside the finite interval, which yields the first method that allows to construct large scale finite preference matrices using expert ratings. A case study with the real–world car preference data set indicates that Einstein consistency also yields more accurate preference estimates than additive or multiplicative consistency.

Analysis of obstacles to lowering demurrage at grain terminals in South Korea

This study aimed to analyze the obstacles to lowering demurrage at a grain terminal. After conducting in-depth interviews with working-level staff in grain terminal, 12 factors were selected to derive key factors for reducing demurrage. The Consistent Fuzzy Preference Relations methodology, which can extract experts’ knowledge and experiences, was employed to rank the obstacles in lowering demurrage. The analysis showed that the number one factor was “inventory requirements of grain shippers” (with an importance of 0.097), followed by “capacity of storage facilities” and “port congestion” as the second and third factors, respectively. As for sub-factors, “climate (e.g., typhoon, rain, etc.)” and “ship size” were ranked 11th and 12th, respectively. The analysis indicates that experts recognized factors that can be changed by shippers and that are difficult to control as more important obstacles than cost-related factors. The findings of this study provide the working-staff of ports, grain terminals, and the government implications for reducing demurrage.

항만 대기질 개선을 위한 정책 우선순위 분석에 관한 연구

The port areas in the metropolitan area maintain higher concentrations of air pollution throughout the year compared to other port areas, so air pollution management is urgently needed. Air pollutant emissions from ports in the metropolitan area were analyzed to be more than twice the national average. Despite these circumstances, analyses and studies on the sustainable and feasible port air quality improvement policy measures for the port areas in the metropolitan area are insufficient. In this respect, the purpose of this study was to derive policy priorities for improving air quality in the metropolitan ports. As for the method of the study, the CFPR (Consistent Fuzzy Preference Relation) method, which can integrate and analyze the opinions of experts, was used.
 As a result of the analysis, in the overall weight ranking, the first, second, and third places were shown to be the strengthening of the ship fuel sulfur content standard, the expansion of eco-friendly ships, and the expansion of support for ship emission reduction devices, respectively. That is, all of the first, second, and third detailed factors were analyzed as policies related to vessel-oriented air pollutant reduction. In particular, most of the policy items ranked at the top were analyzed as policy factors that require strong regulatory policies, large-scale financial inputs, and government facility subsidy policies. This study has implications in that it presents useful policy measures that can be applied practically to policy makers to improve port air quality.

Necessity of two normalities for the priority vectors of additively consistent fuzzy preference relations with application to group decision making

Deriving priority vectors for preference relations in group decision-making (GDM) is an interesting research topic. In this paper, this issue is examined for GDM with fuzzy preference relations (FPRs). It is proven that both the parameterized Tanino's normality and the usual k-normality are crucial to characterize additively consistent FPRs, based on which some existing results to derive priority vectors for additively consistent FPRs are improved by adding a suitable normality. Then, visible methods to check and modify the acceptably additive consistency and the consensus of FPRs in GDM are proposed without a preset threshold. Some examples are given to demonstrate how the proposed methods work, and comparisons to existing methods are also offered to indicate the advantages of the proposed methods.

Coupling synergy calculation between innovation and ethical responsibility for high-tech enterprises from the perspective of responsibility innovation

This paper aims to analyze the coupling coordination degree of technology, economy, social responsibility, and ethic of technological innovation in high-tech enterprises, and provide basis for the optimization of technological innovation system structure in high-tech enterprises. Using data of high-tech enterprises in China Statistical Yearbook and China Statistical Yearbook of Science and Technology in 2018, the authors applied Cloud model to index transformation, consistent fuzzy preference relations to determine index weights, coupling degree model to measure the coupling degree of responsible innovation system of high-tech enterprises in China. Research results show that the responsible innovation system of China’s high-tech enterprises in 2018 is in a low degree of coordination and coupling stage, and the high-tech enterprises in China invest relatively little in technical level, social development, and ethical innovation. This research contributes to the literature on responsible innovation, ethical responsibility in the high-tech enterprises, which is conducive to improving the quality of innovation activities. However, this research collected data from a single country at a single point in time. This paper studies from the perspective of responsible innovation and measures the coupling degree between innovation and ethical responsibility of high-tech enterprises. The establishment of coupling analysis model can not only effectively calculate the coupling degree of technological innovation system, but also deeply analyze the shortcomings of each subsystem of technological innovation system, and provide a basis for the formulation of promotion strategy.

Applying a multi-criteria decision-making approach to identify key satisfaction gaps in hospital nurses’ work environment

AimA decision analysis model was constructed to explore the key factors affecting the job satisfaction of hospital nurses and to analyze the key satisfaction gaps in the case hospital. BackgroundIn China, medical institutions are facing greater pressure and challenges in the normalization of epidemic prevention and control. Nurses play a critical role in the delivery of medical care services. Past studies have shown that improving job satisfaction among hospital nurses is important for both reducing nurse turnover and improving the quality of care. MethodsMcCloskey/Mueller satisfaction scale (MMSS-31) was used to survey 25 nursing specialists in a case hospital in Zhejiang. Then, the Consistent Fuzzy Preference Relation (CFPR) method was used to analyze the degree of importance of dimensions and corresponding sub-criteria. Finally, the importance-performance analysis method was applied to identify critical satisfaction gaps for the case hospital. ResultsIn terms of local weight for dimensions, “Control/Responsibility (C8)” ≻ “Praise/Recognition (C7)” ≻ “Extrinsic Rewards (C1)” are the top three key factors for nurses’ work environment satisfaction in the case of a hospital. In addition, the sub-criteria “Salary (C11)”, “Benefits (C13)”, “Child care (C33)”, “Recognition-peers (C73)”, “Encouragement/feedback (C74)”, and “Decision making (C85)” are the key factors for improving clinical nursing satisfaction in the case hospital. ConclusionThe issues that nurses care about but for which they have not attained expectations mainly involved extrinsic rewards, recognition/encouragement and control over their working process. The findings of this study could offer an academic reference for management and remind them to consider the above factors in exploring future reform, further improving nurses' job satisfaction and motivating them to provide better nursing services.

The Hybrid Multiple-Criteria Decision-Making Model for Home Healthcare Nurses' Job Satisfaction Evaluation and Improvement.

Objectives: To investigate and evaluate the key factors related to job satisfaction performance of home healthcare nurses (HHNs). Methods: A total of 31 HHNs from three community hospitals in Zhejiang province were invited to participate in the study. They completed a questionnaire survey based on the home healthcare nurse job satisfaction scale (HHNJS) from February to March 2022. Consistent fuzzy preference relation (CFPR) methods and important-performance analysis (IPA) were used to obtain the attribute weights and performance for HHNs job satisfaction. Results: The results showed that the attributes of C 13, C 14, C 15, C 23, C 24, C 42, C 51, and C 52 were key factors influencing HHNs job satisfaction. Conclusion: The hybrid multiple-criteria decision-making (MCDM) model can help home-healthcare-agency administrators better understand the key factors related to HHNs job satisfaction and establish reasonable improvement strategies.

An Alternative to Coping with COVID-19—Knowledge Management Applied to the Banking Industry in Taiwan

This study seeks to find an alternative strategy to cope with the impact of COVID-19. Though various measures have been adopted to respond to the threat of the pandemic, the problem remains unchanged. Undoubtedly, COVID-19 is also a crisis of knowledge, so this study explores whether the banking industry in Taiwan can apply knowledge management (KM) and fight the catastrophe of the century successfully and effectively. This study adopts an actual case to analyze the relationship between KM implementation and the banking industry; applies consistent fuzzy preference relations (CFPRs) to evaluate influential criteria including computational simplicity and guarantee the consistency of decision matrices; illustrates a decision-making model with seven criteria; and conducts pairwise comparisons, which are utilized to determine the priority weights of influential criteria amongst the outcome rankings and to formulate accurate KM strategies. The results show that predictions of success probabilities are higher than those of failure probabilities among the seven influential criteria and, in particular, the headquarters system and human resources are the most important priority indicators for implementing KM successfully during the pandemic or post-pandemic. The conclusion suggests significant policy implications for policymakers within other industries or countries in coping with COVID-19.

The service quality of travel service centers in international airports in Taiwan

This pioneering study aimed to analyze the quality of service at international airport travel service centers in Taiwan. A total of 250 customers filled the survey questionnaire and 11 panel experts provided views on service quality attributes. The Fuzzy Delphi Method was used to determine the leading service quality attributes and cluster them into reliability, proficiency, and empathy. Confirmatory factor analysis supported the factorial validity of these dimensions. Consistent fuzzy preference relations was applied to evaluate attributes’ weights. Moreover, importance-performance analysis was used to identify excellent service quality attributes and elicit marketing strategies. The results revealed 22 attributes that pleased consumers or required further development. The study builds a multi-criteria decision service quality assessment system, and through the empirical method, points out a direction for improvement to travel service centers in Taiwan. The findings provided solutions to improve marketing strategies through education, training, and managerial policy changes. The findings may serve as a guide for further cross-border or longitudinal research and practice.

Evaluation of key performance indicators of Internet of Things and Cloud Computing for infrastructure projects in Gujarat, India through Consistent Fuzzy Preference Relations approach

The construction industry is the world's most significant and dominant industry. Although it is a significant and dominant industry and utilizes the latest technology like the Internet of Things (IoT) and Cloud Computing, it is still considered as being weak. So, evaluating the Key Performance Indicators (KPIs) is crucial for adopting these technologies. The objective of this paper is to identify the KPIs of IoT and Cloud Computing for infrastructure projects based on the previous literature study and the authors' personal experience and evaluate them using the multi-criteria decision-making method like Consistent Fuzzy Preference Relation (CFPR). The case studies used for this research are 138 infrastructure projects of Gujarat, India. The finding of the study identified that all through 109 KPIs the “Nature and Willingness of the Organization” is the most severe KPI for the application of the Internet of Things (IoT) and Cloud Computing for the infrastructure project. The rank of all 109 KPIs has been obtained, and for the top ten KPIs' corrective and preventive mitigation measures have been proposed to implement future projects smoothly.

Deriving priorities based on representable uninorms from fuzzy preference relations

Deriving priorities of objects from preference relations is an important research topic for decision making with preference information. In this paper, this issue is solved for fuzzy preference relations using representable uninorms, with which several alternative definitions of representable uninorm-based consistency (⊗-consistency) are provided for complete and incomplete fuzzy preference relations. In particular, a procedure to evaluate incomplete fuzzy preference relations with arbitrary n−1 independent preference values is provided. Furthermore, a novel acceptable ⊗-consistency is defined by the proposed local priority vectors, and then a method to check and reach the ⊗-consistency of fuzzy preference relations by reducing the distances between the optimal priority vector and local ones is proposed. Specific examples are illustrated to show how the models work, and comparisons with the existing methods are also offered to show the advantages of the proposed method.

Using an Integrated Consistent Fuzzy Preference Relations and Interval Type-2 Fuzzy Topsis Methodology for Personnel Selection and Promotion

Promotion is an organizational procedure. Education, experience, and personal qualities are crucial requirements for individuals to be promoted. Their characteristics determine the promotion-eligible employee's approach to the job and compatibility with coworkers. This research employs an integrated Consistent Fuzzy Preference Relations (CFPR) - Interval Type-2 (IT2) Fuzzy TOPSIS methodology to identify the most competent individuals for promotion. Using this methodology, the ranking of individuals for a case study in Turkey is calculated. The CFPR technique calculates the weight of the criteria stated by experts in our most recent research [1]. Then, the IT2 Fuzzy TOPSIS method is used to determine the order of options using IT2 trapezoidal fuzzy numbers. Thus, the best competent candidate for promotion is selected. Thus, managers and human resources departments may assess and promote employees rapidly.

Measurement and evaluation of media user satisfaction in KM enterprises based on CFPR and cloud model

This research explores indicators and methods for an enterprise to measure and evaluate user satisfaction with enterprise social media for knowledge management. This paper presents qualitative indicators, including three service levels of enterprise social media for knowledge management (KM) from a techno-social perspective. This research puts forward a synthetic evaluation model mixed with linguistic variables, consistent fuzzy preference relations (CFPR) and cloud model for measuring and evaluating user satisfaction. The synthetic evaluation model can transform linguistic variables into quantitative data to obtain user satisfaction levels and determine the distance between the expected satisfaction level and actual performance. This research can help an enterprise to improve the service ability of its social media to meet users’ requirements for knowledge management.

Evaluating the Influence of Criteria Revitalization Strategy Implementation for the Hospitality Industry in the Post-Pandemic Era

This study applies consistent fuzzy preference relations (CFPR) to evaluate the influential criteria of revitalization strategies (RS) for the hospitality industry in the post-pandemic (COVID-19) era in Taiwan. A real case applies CFPR in order to analyze the relationship between governmental implementation and industrial expectations in Taiwan. The results indicate that “market revitalization”, such as the Taiwanese government’s implementation of various stimulus vouchers and coupons to encourage market consumption and revitalize the overall economy, is considered the most essential/important criteria for RS. This study strengthens the government sector by evaluating the heterogeneity of revitalization strategies best used to formulate the actions to pilot industries as a global contribution to fight the COVID-19 pandemic within a global crisis.

Performance evaluation of Asian major cruise terminals

Compared to cargo terminals, performance evaluation of cruise terminals sits in a backseat in the current port performance studies, particularly in the Northeast Asia. This paper aims to evaluate the performance of major cruise terminals in Asia, taking into account the important dimensions and criteria influencing cruise terminal development, and using primary data by experts' responses to a questionnaire survey. Complementary data are also acquired by field trips and interviews to find the hidden information that is not revealed from the set questions in the questionnaire survey. To tackle the multiplicity and uncertainty in the collected data, the hybrid of Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) and Consistent Fuzzy Preference Relation (CFPR) is employed to evaluate cruise terminal performance and highlight visual illustration of the test results. The findings provide useful insights for guiding cruise lines’ terminal selection and for aiding cruise terminals to benchmark their performance to effectively improve their service and maximize their overall performance.

Priority Weights for Predicting the Success of Hotel Sustainable Business Models

This study proposes the use of consistent fuzzy preference relations to evaluate the structure of hotel sustainable business model (HSBM) dimensions and the corresponding hierarchy of evaluation indicators, and predict the overall probability of success. As fuzzy preference relations require, a group of hotel professionals in Taiwan was asked to process pairwise comparisons using linguistic variables to determine the weights of dimensions and indicators. According to the results, finances were found to be the most important dimension, followed by human capital. The number of local cultural events in the hotel was identified as the most important indicator. The predictive values revealed the possibility for successful HSBM implementation, shedding light on the vision of sustainability for the hotel industry. The results of the present study contribute to the literature on sustainability by determining the importance and weights of dimensions and indicators for hotel business models, providing an example of the use of this strategic tool in generating and modifying sustainable business models for the hotel industry.

Evaluation of MVNO model implementation in remote and border areas using the consistent fuzzy preference relations method

Law No. 36 of 1999 concerning Telecommunication has brought many changes, especially in the development of telecommunications infrastructure in Indonesia. However, the penetration of telecommunications services in the forefront, outermost, and backward regions is still relatively low. The government has made various efforts in terms of minimizing the gap in telecommunication services between urban and rural areas through various programs. However, an acceleration is needed so that the service disparity can be immediately overcome. One of the telecommunications products that can be applied to overcome these barriers is the Mobile Virtual Network Operator (MVNO). This study evaluates the most appropriate type of MVNO model to be applied in Indonesia by implementing the Consistent Fuzzy Preference Relations (CFPR) method. This method is able to accommodate expert opinion through a series of scientific steps so as to produce weights for each alternative type of MVNO model. The results obtained are that the most appropriate model to be applied in Indonesia by taking into account the criteria given. The implementation of this model is expected to be able to encourage the optimization of BTS USO that has been declared by the government.

Resilient route selection of oversized cargo transport: the case of South Korea–Kazakhstan

PurposeResilient route selection for oversized cargoes, one of the general bulk cargoes, has not been adequately optimized in terms of using the Arctic route. This study solves the problem of selecting the optimal shipping routes for oversized cargoes from Busan (South Korea) to Balkhash (Kazakhstan).Design/methodology/approachThe study used the consistent fuzzy preference relation (CFPR) method, which is used to solve multi-criteria decision-making (MCDM) and uncertainty problems, to tackle the route selection. This method involves three procedures: first, the critical factors and alternative routes were obtained by the previous literature and an in-depth interview of experts of oversized cargo-handling with more than 20 years of working experience; second, the weightings for each critical factor were identified using the CFPR calculation process and third, alternative routes were evaluated using weighted critical factors.FindingsThe Northern Sea Route (NSR) combined with the inland waterways of Russia and Kazakhstan was first suggested for bulk carriers that handle oversized cargoes. The NSR could be a suitable route from Busan to Cape Kamenny of the Russian transshipment seaport, where oversized cargoes will be transferred to the river barge at Cape Kamenny, covering 4,913 km from the latter to Balkhash of Kazakhstan via the Ob/Irtysh River.Practical implicationsThis study equips stakeholders in route selection for cargoes with strategies and methods to improve transportation efficiently and enhance shipping routes between Asia and the Commonwealth of Independent States (CIS). In addition to oversized cargoes, coal and timber from Russia can be transported to Asia using inland waterways and the NSR, which can also be used to transport plant equipment for petroleum refineries among Asian countries.Originality/valueThis is the first study to evaluate the suitability of the Artic route for oversized cargoes from South Korea to Kazakhstan. It provides a comprehensive evaluation framework of multimodal shipping routes and offers references for decision-makers when dealing with similar problems.