Advances in Fuzzy Systems and Decision Making Theory

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: 20 December 2026 | Viewed by 730

Editor


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Guest Editor
Shandong Key Laboratory of Design and Manufacturing for High-End Offshore Oil and Gas Equipment, China University of Petroleum (East China), Qingdao, China
Interests: complex systems; fuzzy theory; decision-making models; risk assessment; safety operations and maintenance

Special Issue Information

Dear Colleagues,

In an increasingly complex and uncertain world, the integration of fuzzy systems and decision-making theory has emerged as a powerful paradigm for tackling ambiguity and enhancing analytical capabilities. This Special Issue focuses on the latest advancements that harness the principles of fuzziness to improve decision-making processes across various domains, from artificial intelligence to operations research.

Fuzzy logic provides a robust framework for modeling imprecise data, enabling systems to emulate human reasoning and handle vague information effectively. By incorporating fuzzy sets, linguistic variables, and fuzzy inference mechanisms, decision-makers can evaluate alternatives more intuitively and make informed choices in uncertain environments. 

This issue highlights innovative research that explores novel fuzzy models, algorithms, and applications, including multi-criteria decision-making, fuzzy control systems, and fuzzy neural networks. We also encourage contributions that investigate the intersection of fuzzy systems with machine learning, big data analytics, and optimization techniques.

Topics of interest include the following:

- New fuzzy algorithms for real-time decision-making;

- Multi-criteria decision-making under uncertainty;

- Applications of fuzzy systems in finance, healthcare, and engineering;

- Hybrid approaches combining fuzzy logic with other computational techniques;

- Fuzzy optimization and large-scale data-driven fuzzy decision models;

- Theoretical advancements in fuzzy set theory and its implications for decision-making.

We invite researchers to share their insights and breakthroughs, contributing to the collective effort of refining decision-making under uncertainty and enhancing the practical applications of fuzzy systems.

Dr. Shibo Wu
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • fuzzy decision-making
  • multi-criteria decision analysis
  • fuzzy logic and inference systems
  • hybrid intelligent systems
  • fuzzy optimization
  • uncertainty modeling
  • fuzzy systems in artificial intelligence

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Published Papers (1 paper)

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Research

27 pages, 4782 KB  
Article
Failure Probability Assessment Method for Offshore Oil and Gas Systems Based on Interval-Valued T-Spherical Fuzzy Set and Credal Networks
by Shibo Wu, Changrun Chen, Zhaoyu Wang and Lin Song
Mathematics 2026, 14(12), 2151; https://doi.org/10.3390/math14122151 - 15 Jun 2026
Viewed by 216
Abstract
Probabilistic risk assessment of complex offshore oil and gas systems is often challenged by scarce statistical data and multiple uncertainties. Traditional point-value probability and standard Bayesian networks cannot fully represent and propagate these uncertainties, which may mislead high-risk security decision-making. To address this [...] Read more.
Probabilistic risk assessment of complex offshore oil and gas systems is often challenged by scarce statistical data and multiple uncertainties. Traditional point-value probability and standard Bayesian networks cannot fully represent and propagate these uncertainties, which may mislead high-risk security decision-making. To address this issue, this paper proposes a new hybrid risk assessment framework that combines interval-valued T-spherical fuzzy sets (IVTSFS) with credal networks (CN). First, IVTSFS is used to quantify the subjective risk perception of multiple experts, effectively capturing hesitancy, fuzziness, and group disagreement. An improved probability mapping mechanism is introduced to align linguistic evaluations with objective failure frequency spaces, thereby avoiding systemic transformation biases. Subsequently, the interval conditional probability table is constructed using the imprecise leakage noise-OR model, which alleviates the problem of parameter dimension explosion in complex causal structure and explicitly retains the parameter uncertainty. The 2U algorithm is then applied to perform accurate interval inference in CN. The feasibility and comparative advantages of the method are illustrated in the actual case of the single-point mooring system. The results clearly output the upper and lower bounds of the system failure risk, and identify the key vulnerable nodes through diagnostic reasoning and sensitivity analysis. This study has theoretical contributions in fuzzy decision-making and uncertainty modeling. By unifying advanced fuzzy cognitive quantification and imprecise probability propagation, it provides a structured uncertainty representation tool for expert-informed risk screening under data scarcity. Full article
(This article belongs to the Special Issue Advances in Fuzzy Systems and Decision Making Theory)
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