Fuzzy Sets, Simulation and Their Applications

A special issue of Axioms (ISSN 2075-1680). This special issue belongs to the section "Algebra and Number Theory".

Deadline for manuscript submissions: closed (31 October 2024) | Viewed by 1882

Special Issue Editors


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Guest Editor
Department of Industrial Engineering and Management, National Kaohsiung University of Applied Sciences, Kaohsiung 80778, Taiwan
Interests: random number; decision making; data envelopment analysis; fuzzy ranking

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Guest Editor
Department of Business Administration, Kao Yuan University, No.1821, Jhongshan Rd., Lujhu Dist., Kaohsiung City 821, Taiwan
Interests: quality management; statistical process control

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Guest Editor
Centro de Investigación y Desarrollo de Tecnología Digital, Instituto Politécnico Nacional, México City 07738, Mexico
Interests: intelligent systems; quantum computing; quantum intelligent systems; evolutionary computation; fuzzy systems; neural networks; deep learning; computational intelligence
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Special Issue Information

Dear Colleagues,

Various uncontrollable factors, such as a global pandemic, geopolitical instability, climate change, and others, have impacted contemporary business operations. With this complexity of the socioeconomic environment, fuzzy sets and simulation represent two fundamental methods to design and study complex systems in engineering, medicine, meteorology, manufacturing, economy, and management. Fuzzy sets could have the capability to represent and tackle vagueness and imprecise information. Modeling and simulation can avoid risks and reduce costs and failures associated with experimentation in the real system. Thus, fuzzy sets and simulations are welcomed more and more by both scholars and practitioners. This Special Issue aims to compile new and recent developments in methodologies, techniques, and applications of fuzzy sets and simulation for various practical problems and demonstrate the challenging issues of these concepts. We invite researchers and practitioners to submit original research and critical survey manuscripts in the fuzzy set and simulation techniques, methodologies, mixed approaches, and research directions pointing to unsolved issues.

Dr. Hui-Chin Tang
Dr. Shih-Chou Kao
Prof. Dr. Oscar Humberto Ross
Guest Editors

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Keywords

  • fuzzy set theory
  • extensions and generalizations of fuzzy sets
  • fuzzy multicriteria decision-making model
  • modeling and simulation
  • simulation and optimization
  • cryptography
  • random numbers

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Published Papers (2 papers)

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Research

21 pages, 342 KiB  
Article
Capital Asset Pricing Model and Ordered Weighted Average Operator for Selecting Investment Portfolios
by Cristhian R. Uzeta-Obregon, Tanya S. Garcia-Gastelum, Pavel A. Alvarez, Cristhian Mellado-Cid, Fabio Blanco-Mesa and Ernesto Leon-Castro
Axioms 2024, 13(10), 660; https://doi.org/10.3390/axioms13100660 - 25 Sep 2024
Viewed by 817
Abstract
The main objective of this article is to present the formulation of a Capital Asset Pricing Model ordered weighted average CAPMOWAand its extensions, called CAPM-induced OWA (CAPMIOWA), CAPM Bonferroni OWA (CAPMBon-OWA), and CAPM Bonferroni-induced OWA [...] Read more.
The main objective of this article is to present the formulation of a Capital Asset Pricing Model ordered weighted average CAPMOWAand its extensions, called CAPM-induced OWA (CAPMIOWA), CAPM Bonferroni OWA (CAPMBon-OWA), and CAPM Bonferroni-induced OWA CAPMBon-IOWA. A step-by-step process for applying this new proposal in a real case of formulating investment portfolios is generated. These methods show several scenarios, considering the attitude, preferences, and relationship of each argument, when underestimation or overestimation of the information by the decision maker may influence the decision-making process regarding portfolio investments. Finally, the complexity of the method and the incorporation of soft information into the modeling process lead to generating a greater number of scenarios and reflect the attitudes and preferences of decision makers. Full article
(This article belongs to the Special Issue Fuzzy Sets, Simulation and Their Applications)
12 pages, 573 KiB  
Article
Fuzzy Evaluation Model for Critical Components of Machine Tools
by Kuen-Suan Chen, Kai-Chao Yao, Chien-Hsin Cheng, Chun-Min Yu and Chen-Hsu Chang
Axioms 2024, 13(8), 555; https://doi.org/10.3390/axioms13080555 - 14 Aug 2024
Viewed by 561
Abstract
The rapid progression of emerging technologies like the Internet of Things (IoT) and Big Data analytics for manufacturing has driven innovation across various industries worldwide. Production data are utilized to construct a model for quality evaluation and analysis applicable to components processed by [...] Read more.
The rapid progression of emerging technologies like the Internet of Things (IoT) and Big Data analytics for manufacturing has driven innovation across various industries worldwide. Production data are utilized to construct a model for quality evaluation and analysis applicable to components processed by machine tools, ensuring process quality for critical components and final product quality for the machine tools. Machine tool parts often encompass several quality characteristics concurrently, categorized into three types: smaller-the-better, larger-the-better, and nominal-the-better. In this paper, an evaluation index for the nominal-the-better quality characteristic was segmented into two single-sided Six Sigma quality indexes. Furthermore, the process quality of the entire component product was assessed by n single-sided Six Sigma quality indexes. According to numerous studies, machine tool manufacturers conventionally base their decisions on small sample sizes (n), considering timeliness and costs. However, this often leads to inconsistent evaluation results due to significant sampling errors. Therefore, this paper established fuzzy testing rules using the confidence intervals of the q single-sided Six Sigma quality indices, serving as the fuzzy quality evaluation model for components of machine tools. Full article
(This article belongs to the Special Issue Fuzzy Sets, Simulation and Their Applications)
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