Fuzzy sets and their variants
Alireza Aliahmadi; Hamed Nozari; Javid Ghahremani-Nahr; Agnieszka Szmelter-Jarosz
Abstract
In recent years, the high complexity of the business environment, dynamism and environmental change, uncertainty and concepts such as globalization and increasing competition of organizations in the national and international arena have caused many changes in the equations governing the supply chain. ...
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In recent years, the high complexity of the business environment, dynamism and environmental change, uncertainty and concepts such as globalization and increasing competition of organizations in the national and international arena have caused many changes in the equations governing the supply chain. In this case, supply chain organizations must always be prepared for a variety of challenges and dynamic environmental changes. One of the effective solutions to face these challenges is to create a resilient supply chain. Resilient supply chain is able to overcome uncertainties and disruptions in the business environment. The competitive advantage of this supply chain does not depend only on low costs, high quality, reduced latency and high level of service. Rather, it has the ability of the chain to avoid catastrophes and overcome critical situations, and this is the resilience of the supply chain. AI and IoT technologies and their combination, called AIoT, have played a key role in improving supply chain performance in recent years and can therefore increase supply chain resilience. For this reason, in this study, an attempt was made to better understand the impact of these technologies on equity by examining the dimensions and components of the Artificial Intelligence of Things (AIoT)-based supply chain. Finally, using nonlinear fuzzy decision making method, the most important components of the impact on the resilient smart supply chain are determined. Understanding this assessment can help empower the smart supply chain.
Fuzzy sets and their variants
Taraneh Javanbakht; Shivanjan Chakravorty
Abstract
The present paper proposes a new application of the prediction of human behavior using TOPSIS as an appropriate tool for data optimization. Our hypothesis was that the analysis of the candidates with this method was influenced by the change of their behavior. We found that the behavior change could occur ...
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The present paper proposes a new application of the prediction of human behavior using TOPSIS as an appropriate tool for data optimization. Our hypothesis was that the analysis of the candidates with this method was influenced by the change of their behavior. We found that the behavior change could occur in more than one time span when the behavior of two candidates changed simultaneously. One of the advantages of this study is that the pattern of the behavior change with time is predicted with this method. Another advantage is that the modifications in the TOPSIS algorithm have made the predictions independent from the need of changing the fuzzy membership degrees of the candidates. This is the first time that these modifications in this technique with a new application including the numerical analysis of cognitive date are reported. Our results can be used in cognitive science, experimental psychology, cognitive informatics and artificial intelligence.