Intuitionistic Fuzzy Cognitive Map Research Articles

An analysis of challenges to the low-carbon energy transition toward sustainable energy development using an IFCM-TOPSIS approach: A case study

As countries worldwide grapple with the urgent need to mitigate climate change, adopting low-carbon energy sources has become a top global priority. This priority is particularly emphasized in the European Union (EU), with various initiatives, policies, and regulations to promote renewable energy sources and reduce carbon emissions. Despite these efforts, the transition to a low-carbon energy future has faced several challenges, such as the high cost of renewable energy technologies, land use, and technical issues. These challenges require decision-makers to consider and address various factors to ensure sustainable and low-carbon energy development. In this context, the present study identified challenges to the low-carbon energy transition through a literature review from 2013 to 2023. The study then set out a novel intuitionistic fuzzy cognitive map method to map the interactions of identified challenges and analyze the case study performance in dealing with the challenges under three scenarios: people first, technology first, and duet. Subsequently, the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) method was applied to find the best scenario according to performance analysis. The results indicated that the most significant challenge is investment, followed by short-termism, and reformation, out of seventeen identified challenges. Results also indicated that the duet scenario was the best, and broad conclusions and policy implementations were provided according to the obtained results.

An integrated assessment of food waste model through intuitionistic fuzzy cognitive maps

In recent years, the waste management field has received substantial attention from policymakers, organizations, academics, and researchers due to increased focus on sustainability and carbon footprint reduction as well as concerns around rapid depletion of natural resources, public health, and environmental impact. Studies on food waste management have become especially important given the dramatic growth of the world's population and global hunger and malnutrition crisis. Considering the fact that one-third of the world's food supply is wasted or lost annually while hundreds of millions of people are living with food insecurity, it is easy to understand the importance of research studying appropriate food waste management actions for sustainability. Since the subject of food waste involves complicated linkages, the determination of a suitable model is pivotal. Integrated assessment models (IAMs) have been commonly used to uncover hidden patterns and present insights to policymakers. Furthermore, these models are well-designed to integrate data, information, and multidisciplinary knowledge into a single framework. This project presents a Fuzzy Cognitive Map (FCM) extended with intuitionistic fuzzy sets using the documentary coding method. This intuitionistic FCM (iFCM) is used to analyze the primary factors, explore the interactions between food waste factors, and prioritize some policies to reduce food waste by incorporating hesitancy weight factors representing the lack of information. Then several what-if scenario analyses are generated to review the interrelationships between factors in the developed model and facilitate a decision-making process for researchers. Eventually, it is concluded that food waste reduction is achievable with the implementation of the right policies, and this also improves the other concepts such as the intention not to food waste, shopping routines, and planning routines.

Time series prediction based on high-order intuitionistic fuzzy cognitive maps with variational mode decomposition

Time series prediction based on high-order intuitionistic fuzzy cognitive maps with variational mode decomposition

Analysis of Causal Relationships Effective Factors on the Green Supplier Selection in Health Centers Using the Intuitionistic Fuzzy Cognitive Map (IFCM) Method

The healthcare sector is one of the largest service industries with the highest potential to improve environmental performance. Hospitals as an important part of the healthcare system must act in a way that reduces their environmental consequences, which requires having a green supplier. The aim of this study was to identify the effective factors on the green supplier selection (GSS) in the hospital and to present an excellent model for analyzing the relationships between these factors. In this study, 14 concepts that effect the green supplier selection of a hospital have been extracted from in-depth literature and interviews entailing: financial capability, creativity and innovation, green technology, flexibility, organizational capability, commitment, trust on supplier, green quality, green transportation, environmental cooperation with customers, hazardous materials management, buy green, green warehouse and green packaging.In addition Intuitive fuzzy cognitive mapping approach was also used for data analysis and conclusion. The results showed that green technology index with 0.43 degree was the most influential and organizational capability index with 0.29 degree had the most influence over the other concepts. In addition, focusing on concepts like financial capability, trust on supplier and creativity and innovation process of green supplier selection of hospital. Taking these in consideration these factors should be given specific attention.

A novel integrated intuitionistic fuzzy decision aid for agile outsourcing provider selection: a COVID-19 pandemic-based scenario analysis.

In project management, agility concept has emerged for overcoming the limitations of traditional methodologies that are sequential and linear. An agile project contains planned processes as well as iterations. In global competitive areas, achieving agility in outsourcing processes enables the companies to cope with changes and dynamic issues. This paper introduces an integrated cognitive map-based intuitionistic fuzzy multiple criteria decision aid to rank agile outsourcing provider alternatives and then determine the best performing one. Intuitionistic fuzzy sets are employed for dealing with uncertain and vague data along with the loss of information that may be occurred in numerical operations with fuzzy numbers. Intuitionistic fuzzy cognitive map tool is used to weight the evaluation criteria that are interrelated with each other with causal links. The most appropriate agile outsourcing provider alternative is identified via intuitionistic fuzzy complex proportional assessment technique, which aims to obtain a solution relative to the ideal solution. In order to demonstrate the robustness of the proposed intuitionistic fuzzy decision aid, a case study is conducted in Turkish white goods industry. As a comparative study, a scenario analysis is provided to understand the impacts of COVID-19 pandemic on agile provider evaluation/selection decision. According to the results obtained from the scenario analysis, it is worth noting that home office procedures are appropriate for maintaining agility in outsourcing processes of project management.

The prediction of online time series with concept drift based on dynamic intuitionistic fuzzy cognitive map

Fuzzy cognitive maps (FCMs) have widely been applied for knowledge representation and reasoning. However, in real life, reasoning is always accompanied with hesitation, which is deriving from the uncertainty and fuzziness. Especially, when processing the online data, since the internal and external interference, the distribution and characteristics of sequence data would be considerably changed along with the passage of time, which further increase the difficulty of modeling. In this article, based on intuitionistic fuzzy set theory, a new dynamic intuitionistic fuzzy cognitive map (DIFCM) scheme is proposed for online data prediction. Combined with a novel detection algorithm of concept drift, the structure of DIFCM can be adaptively updated with the online learning scheme, which can effectively improve the representation of online information by capturing the real-time changes of sequence data. Moreover, in order to tackle with the possible hesitancy in the process of modeling, intuitionistic fuzzy set is applied in the construction of dynamic FCM, where hesitation degree as a quantitative index explicitly expresses the hesitancy. Finally, a series of experiments using public data sets verify the effectiveness of the proposed method.

Integrated approach of superiority and inferiority ranking method with interval-valued intuitionistic fuzzy cognitive map (IVIFCM) for analyzing the online resource too

Integrated approach of superiority and inferiority ranking method with interval-valued intuitionistic fuzzy cognitive map (IVIFCM) for analyzing the online resource too

Sustainable supply chain management framework in a higher education laboratory using intuitionistic fuzzy cognitive map

Purpose: This research aims to develop framework in the sustainable supply chain management (SCM) and to provide causal model of service industry specifically in higher education laboratory.Design/methodology/approach: The concepts of sustainable SCM in higher education laboratory were obtained by in-depth interviews and organized using Delphi method. While to identify the relationship between concepts, intuitionistic fuzzy cognitive map was utilized.Findings: As many as 15 concepts were identified to assess sustainability in the higher education laboratory SCM. These 15 concepts were classified into four categories according to its importance level, and there are two most important concepts: legal requirement and social responsibility. It is recommended for higher education laboratories to constantly obey national and regional government regulations and to satisfy current and prospective employers by providing work-ready graduates. This suggestion is expected to make higher education achieving its sustainability goals. Originality/value: This research is the first to develop a framework for sustainable SCM and to provide a causal model of the service industry especially in the education sector using intuitionistic fuzzy cognitive map.

A multi-criteria-based hazard and operability analysis for process safety

Multi-criteria Decision Making (MCDM) is widely used for problem-solving in various research fields and has shown good results in risk analysis as well. However, given the gaps found in previous studies in the context of risk analysis, this article proposes and conducts a novel MCDM-based HAZOP analysis: a hybrid methodology with an MCDM approach, Strategic Options Development and Analysis (SODA), and Intuitionistic Fuzzy Sets (IFS). We combine their advantages by using SODA with Intuitionistic Fuzzy Cognitive Maps and by reducing the inherent uncertainty and subjectivity of HAZOP with an IFS-based MCDM sorting algorithm. To demonstrate the usefulness and flexibility of this approach, a real application with a continuous pyrolysis system was conducted, and a comparative analysis was made of the main related studies to demonstrate the advantages of the approach now proposed. On applying it, 40 hazards were identified (seven more than a previous analysis conducted in 2016), and only 3 (7.5 %) of them required special attention. Among the positive points highlighted by the team, the most cited were the resource-saving, greater focus and objectivity, and a more realistic perception of hazards. In particular, we show that our methodology is more consistent and appropriate than those in previous related articles. To assist future users, we provide a framework for the application.

NON-LINEAR HEBBIAN LEARNING ALGORITHM FOR INTUITIONISTIC FUZZY COGNITIVE MAPS IN PREDICTIVE MODEL

Adv. Math. Sci. J. Vol. 9, No. 4 Open Access NON-LINEAR HEBBIAN LEARNING ALGORITHM FOR INTUITIONISTIC FUZZY COGNITIVE MAPS IN PREDICTIVE MODEL N. Martin and R. Priya DOI: https://doi.org/10.37418/amsj.9.4.66 Full Text Abstract Abstract Fuzzy cognitive map is a tool which establishes the causal relation between the factors of decision-making problem. It is … page AMSJ9-N4-66 Read More »

Intuitionistic fuzzy grey cognitive maps for forecasting interval-valued time series

In many real-world forecasting problems, the time series under investigation can be approximated. In that case, instead of dealing with its exact values, only their minima and maxima achieved in the predefined periods are considered. Such an approximation forms interval-valued time series (ITS). To forecast ITS, we propose a new method that relies on fuzzy cognitive maps (FCMs). We adapt standard FCMs to the forecasting of ITS using interval-valued intuitionistic fuzzy sets. In this way, we develop a forecasting model called the Intuitionistic Fuzzy Grey Cognitive Map (IFGCM). We validate our IFGCM using publicly available stock market data for 10 indexes for which the estimation of potential investment losses (minima) and gains (maxima) is crucial. The results of these experiments prove the high efficiency of the IFGCM, especially compared with state-of-the-art models.

Intuitionistic fuzzy cognitive map approach for the evaluation of supply chain configuration criteria

Because of the growing global competence and effectiveness concepts, supply chain becomes more important for organizations. Therefore, managers object to find best supply chain configuration for their firms. This study proposes a comprehensive configuration for supply chain management process, and it enables to understand relationships among supply chain integration, supply chain strategies, supply chain risk factors, and performance criteria. By reviewing the literature and using experts' knowledge, supply chain configuration criteria are determined. Intuitionistic fuzzy cognitive map methodology is employed to consider the interrelations between criteria. Intuitionistic fuzzy cognitive map methodology is a suitable tool due to the presence of causalities and relationships among criteria and the difficulty of expressing the interrelations with crisp numbers. It also deals with uncertain and vague data and allows representing hesitation. The application is conducted in an automobile factory, which is one of the largest manufacturers in Turkey. The results show that selection of proper supplier is the most significant supply chain configuration criteria. Thus, the importance of supplier selection criteria is also analyzed as the second phase of the study.

Multidrug-resistant tuberculosis risk factors assessment with intuitionistic fuzzy cognitive maps

Multidrug-resistant tuberculosis risk factors assessment with intuitionistic fuzzy cognitive maps

An Extended Intuitionistic Fuzzy Cognitive Map via Dempster-Shafer Theory

Fuzzy cognitive map has gradually emerged as a powerful paradigm for uncertain knowledge representation and a simulation mechanism that is applicable in dealing with complex artificial reasoning problems. To better model uncertain inference reasoning problems, we propose an extended intuitionistic fuzzy cognitive map via Dempster-Shafer theory. First of all, some new operations on IFSs are introduced from the perspective of Dempster-Shafer theory. Then, the extended intuitionistic fuzzy cognitive map is established via the proposed new operations. Next, we investigate the problem of modeling complex system from multiple decision makers using extended intuitionistic fuzzy cognitive maps and present a method to aggregate a number of maps. Particular emphases are put on defining the augmented connection matrices, determining the importance levels of different extended intuitionistic fuzzy cognitive maps and aggregating them. Finally, the performances of extended intuitionistic fuzzy cognitive maps have been validated through a number of simulations. The simulations indicate that the theory of extended intuitionistic fuzzy cognitive map not only provides much more choices to model complex system but also reduces the computational complexity by comparison with intuitionistic fuzzy cognitive map.

A cognitive map integrated intuitionistic fuzzy decision-making procedure for provider selection in project management

Organizations make use of project management methodologies, which provide an effective manner to achieve managerial goals, maintain the strength of the companies in increasing competition. Efficiency in planning, budgeting, and scheduling are provided so that high quality outputs are obtained through these processes. Agile project management methodology, which has been emerged from unpredictability of customer requirements and changeable business environment, is apt to cope with the failures of traditional project management tools. Besides, lean six-sigma project management methodology has become a combination of lean and six-sigma, which were opponent methodologies previously. This paper aims to determine the most suitable outsourcing provider alternative by presenting a novel cognitive maps-based intuitionistic fuzzy decision making procedure. Interrelationships among evaluation criteria are weighted employing intuitionistic fuzzy cognitive map technique because of the causal links among evaluation criteria, vagueness, fuzziness, and hesitation in data. Moreover, the most appropriate provider alternative for both agile and lean six-sigma project management methodologies is identified by utilizing intuitionistic fuzzy TOPSIS method, which aims for minimizing the closeness to the ideal solution while maximizing the distance from the anti-ideal solution in hesitative environment. The case study is carried out in a bank that performs in Turkish banking sector.

A new integrated approach of combined FCM and CODAS method in interval valued intuitionistic fuzzy cognitive map for multi criteria decision making to evaluate and prioritize the branded mobile

A new integrated approach of combined FCM and CODAS method in interval valued intuitionistic fuzzy cognitive map for multi criteria decision making to evaluate and prioritize the branded mobile

Time series prediction based on intuitionistic fuzzy cognitive map

Time series exist widely in either nature or society such that the research on analysis of time series has great significance. However, considering the nonlinearity and uncertainty, the prediction of time series is still an open problem. In this paper, by means of the intuitionistic fuzzy set theory, we proposed a novel time series prediction scheme based on intuitionistic fuzzy cognitive map. In the previous research, intuitionistic fuzzy cognitive map, as a kind of knowledge-based modeling tool, is mainly used in decision-making field, where concept structure and weight matrix are usually obtained from experience of experts. To tackle with the diversity of time series, the proposed algorithm constructs the conceptual structure of cognitive map and weight matrix directly from raw sequential data, which effectively enlarges the application range by reducing human participation. Moreover, in order to appropriately calculate the hesitation degree, which is the key role for the application of intuitionistic fuzzy sets, we propose a real-time adjustable hesitation degree calculation scheme. By using this proposed method, hesitation degree can be adaptively adjusted by combining Femi formula with dynamic membership degree. A number of experiments are implemented to reveal feasibility and effectiveness of the proposed schemes.

An Analysis on Barriers to Biomass and Bioenergy Development in Rural China Using Intuitionistic Fuzzy Cognitive Map

Biomass is viewed as one of the critical renewable energies and it widely exists in nature. Developing bioenergy has been promoted as a viable mean of dealing with environment issues that are related to the utilization of fossil fuel. However, due to many obstacles, the biomass and bioenergy technology has not won widespread support in developing countries, like China, with vast land area, particularly in rural area. Furthermore, most existing researches just focused on the description of the influence factors, along with the solution to the technical problems, while many social factors are overlooked. In fact, the process of developing biomass is indeed complicated due to the need for consensus and active participation of the various stakeholders, such as the government, the industry, and the local residents. Therefore, while integrating the intuitionistic fuzzy logic and fuzzy cognitive map, this study constructs an intuitionistic fuzzy cognitive map (IFCM) that is in line with experts’ suggestions and the current literature to investigate how to promote the development of biomass through enhancing public acceptance. We conduct several simulations from the perspective of different stakeholders, according to the IFCM. The analysis results reveal the influence mechanism in the system and illustrate the effect of various factors that are stressed by every stakeholder. The research design also provides a reference for future studies.

Integrating TOPSIS with interval-valued intuitionistic fuzzy cognitive maps for effective group decision making

Many real-life situations require ranking alternative decisions with respect to multiple criteria. The problem becomes more complicated when the knowledge of the considered criteria is vague and unreliable. In order to cope with the vagueness, the values of criteria have to be represented in an approximated way. To overcome the lack of reliability, many experts can be involved in the decision process and thus cooperatively elaborate more credible decisions. However, as it turns out, experts are usually unable to provide plausible information on interactions between vague decision criteria. Since those interactions substantially affect the ranking of alternative decisions, they should be taken into account in the decision process. To effectively cope with that issue, we propose a novel multi-criteria group decision-making method that integrates TOPSIS (technique for order of preference by similarity to ideal solution) with IVIFCMs (interval-valued intuitionistic fuzzy cognitive maps), a tool that is able to model interactions among highly imprecise criteria. We illustrate the application of the proposed IVIFCM-TOPSIS to the supplier selection task. Finally, we present the advantages derived from the use of our method compared to competitive approaches known from the literature. In particular, we show that our method is more consistent than the existing state-of-the-art methods used to solve the addressed decision problem.

High-order Intuitionistic Fuzzy Cognitive Map Based on Evidential Reasoning Theory

An intuitionistic fuzzy cognitive map (IFCM) is an extension of a fuzzy cognitive map (FCM) that forms a graph-oriented fuzzy map describing both causal relationships between pairs of concepts and the states of concepts via intuitionistic fuzzy sets (IFSs). In contrast with an FCM, an IFCM provides much more flexibility in system modeling. However, IFCMs may lead to confusing or unreasonable results in system modeling since they do not fully consider the negative influence from conventional operations on IFSs, the activation process of concepts, and the problem of aggregating knowledge with different importance levels. To solve the challenges of IFCMs, we propose a high-order IFCM based on evidential reasoning (ER) (IFCMR) theory in this study. First, we introduce an evidential intuitionistic fuzzy aggregation (EIFA) operator and a multiplication operation on IFSs using an ER theory. Second, we establish the theory of IFCMR based on the EIFA operator and the newly introduced multiplication operation on IFSs. Third, we propose a scheme of aggregating IFCMRs with different importance levels using the EIFA operator, which can also be utilized to aggregate conflict knowledge and to determine objective connections in terms of an evidential cognitive map (ECM). Finally, several numerical and practical examples are employed to test and verify the feasibility and validity of IFCMRs in comparison with both IFCMs and ECMs.