Applications of Fuzzy Systems in Management, Innovation and Sustainability
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D2: Operations Research and Fuzzy Decision Making".
Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 3324
Special Issue Editors
Interests: aggregation operators; fuzzy logic; decision making under uncertainty; innovation and finance
Special Issues, Collections and Topics in MDPI journals
Interests: uncertainty; decision making problems; strategy; entrepreneurship; aggregation operators
Interests: innovation management; competitiveness; fuzzy logic; regional development
Interests: fuzzy systems; uncertainty; decision making
Special Issue Information
Dear Colleagues,
This Special Issue presents the extended version of selected papers presented at the ICONIS 2021 congress. All accepted papers will follow a strict peer review process to reach the quality standards of an international journal.
The ICONIS 2021 congress is related to the Iberoamerican Network of Competitiveness, Innovation, and Development (REDCID), whose main objective is to quantify the multiplying effect of light innovation capabilities in the company and its impact on the progress and wellbeing of Ibero-America. More than 10 universities at the Ibero-American level are affiliated to this network.
The main themes of the Special Issue are focused on innovation because in order to have a knowledge-driven economy, knowledge-based organizations are needed (Yildirmaz et al. 2018; Merigo et al. 2016). To do that, a variety of disciplines such as engineering, management, economics, business, and many more are applied and combined in order to generate different types of innovation (Hashimoto, 2012). A common characteristic in the innovation process is the necessity to reduce uncertainties and risks (Koschatzky, 1998).
The other main topic of the conference is the use of fuzzy methodologies (Merigo et al. 2015; Zadeh, 1965) that allow improving decision making in organizations within different strategic areas (Blanco-Mesa et al., 2017; Gil-Lafuente, 2005). Within the fuzzy methodologies that help to incorporate the expertise, knowledge, and expectations of the decision maker is the ordered weighted average (OWA) operator developed by Yager (1988) several extensions of which have been developed (He et al. 2017; Yager et al., 2011).
(1) Conference name
- IV International Congress to the Innovation and Sustainability (ICONIS 2021)
(2) Conference venue and dates;
- October 28-29 Australia
- University of Technology Sydney
(3) Key topics of the conference;
- Soft computing
- Management innovation
- Sustainability
- Modelling and Simulation
- Business
- Finance
- Social innovation
- Fuzzy logic
- Computational intelligence
- Management
- Marketing
- Simulation
- Modeling
- Bibliometrics
References
Blanco-Mesa, F., Merigó, J.M., & Gil-Lafuente, A.M. (2017). Fuzzy decision making: A bibliometric-based review. Journal of Intelligent & Fuzzy Systems, 32, 2033–2050.
Gil-Lafuente, A.M. (2005). Fuzzy logic in financial analysis. Springer, Berlin.
Hashimoto, M., Kajikawa, Y., Sakata, I., Takeda, Y., & Matsushima, K. (2012). Academic landscape of innovation research and National Innovation System policy reformation in Japan and the United States. International Journal of Innovation and Technology Management, 9(06), 1250044.
He, X. R., Wu, Y. Y., Yu, D., & Merigó, J. M. (2017). Exploring the ordered weighted averaging operator knowledge domain: A bibliometric analysis, International Journal of Intelligent Systems, 32, 1151–1166.
Koschatzky, K. (1998). Firm innovation and region: the role of space in innovation processes. International Journal of Innovation Management, 2(04), 383-408.
Merigó, J. M., Cancino, C., Coronado, F., Urbano, D. (2016). Academic research in innovation: A country analysis. Scientometrics, 108, 559–593.
Merigó, J. M., Gil-Lafuente, A. M., & Yager, R. R. (2015). An overview of fuzzy research with bibliometric indicators. Applied Soft Computing, 27, 420–433.
Yager, R.R. (1988). On ordered weighted averaging aggregation operators in multi-criteria decision making. IEEE Transactions on Systems, Man and Cybernetics, B 18, 183-190.
Yager, R.R., Kacprzyk, J., & Beliakov, G. (2011). Recent developments in the ordered weighted averaging operators: Theory and practice. Springer-Verlag, Berlin.
Yildirmaz, H., Öner, M. A., & Herrmann, N. (2018). Impact of Knowledge Management Capabilities on New Product Development and Company Performance. International Journal of Innovation and Technology Management, 1850030.
Zadeh, L.A. (1965). Fuzzy sets. Information and Control, 8, 338-353.
Prof. Dr. Ernesto León-Castro
Dr. Fabio Blanco-Mesa
Dr. Victor Alfaro Garcia
Prof. Dr. Anna Maria Gil Lafuente
Prof. Dr. José M. Merigó Lindahl
Guest Editors
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