Fuzzy Cognitive Map

Usually, in real-world engineering problems, there are different types of uncertainties about the studied variables, which can be due to the specific variables under investigation or interaction between them. Fuzzy cognitive maps, which addresses the cause-effect relation between variables, is one of the most common models for better understanding of the problems, especially when the quantitative data are not available or the nature of the problem is qualitative. One of the significant issues of automatic construction of fuzzy cognitive maps is that it does not consider the experts' uncertainties because of their cognitive attitudes such as age, experience, etc., which affects the quality and validity of modeling of the complex problems. Therefore, in this paper, a Z-Cognitive maps approach is proposed based on the distance-based automatic construction approach and Z-numbers to capture uncertainty and ambiguity of experts' opinions on the variables and causality relationships. The suggested approach can act as decision support for cognitive maps problems considering the experts' cognitive information, which is tested by a numerical example.

The purpose of this study is to evaluate the drivers of bullwhip effect in supply chain of the pharmaceutical industry. This research is descriptive in terms of method and applied in terms of purpose. Conducting the research, first, based on the reviewing the literature on bullwhip effect in the supply chain, affecting drivers were extracted and were sent to 15 experts in form of a questionnaire. Then, using the fuzzy Delphi method, the final affecting criteria on bullwhip effect in the supply chain of the pharmaceutical industry were identified. Finally, in order to examine the relationships between the 13 basic drivers, another questionnaire was designed and asked the experts to fill it, where based on their answers and using DEMATEL and fuzzy cognitive map methods, critical drivers were determined. FCMapper software was used to conduct fuzzy cognitive map method and MATLAB was used for the DEMATEL method. In terms of centrality index in fuzzy cognitive map method, structured inventory control process, delivery time, inventory storage of chain elements, inventory policy and product return rate are 5 criteria of critical importance. In addition, the indicators of the number of echelons, forecasting (method) error and up-to-date demand forecast are in the eleventh to thirteenth ranks. Based on the results of DEMATEL method, inventory policy, price fluctuations, inventory storage of chain elements, structured inventory control process, differences with the desired inventory and information transparency were identified as the main drivers of bullwhip effect in the supply chain of the pharmaceutical industry.

Objective : The global increase in the elderly population has heightened the need for coordinated, tailored healthcare services that address the complex needs of older adults. This study aims to conceptualize the elderly healthcare ecosystem by identifying its key actors, classifying their roles, and examining the nature of their interactions. Methodology: A multi-stage methodological approach was employed. First, an extensive literature review—focusing on healthcare ecosystems and ageing studies—was conducted to develop an initial analytical framework. Based on this, ecosystem actors were identified and categorized using Mitchell et al.’s Stakeholder Salience Model. An expert panel was then consulted to validate actor attributes and refine classifications. To analyze interdependencies and determine influential actors, a Fuzzy Cognitive Map was constructed, enabling the assessment of causal relationships and the dynamic positioning of stakeholders within the ecosystem. Results : The analysis identified seven groups of actors within the healthcare ecosystem. FCM findings reveal that the elderly, families, and medical centers are the most influential actors. At the same time, the Ministry of Health and Medical Education, insurance and pension funds, and the Ministry of Cooperatives, Labor, and Social Welfare emerge as the most influential and central stakeholders in advancing ecosystem objectives. Conclusion : The study demonstrates that the elderly healthcare ecosystem is inherently dynamic, and stakeholder classifications should not be viewed as static. Attributes such as power, legitimacy, and urgency are fluid and context-dependent. The FCM results further highlight this dynamism by illustrating how shifts in causal relationships can reposition actors across stakeholder categories, underscoring the need for adaptive policymaking.