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Applied Sciences
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Applications of Fuzzy Systems and Fuzzy Decision Making, 2nd Edition
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Applications of Fuzzy Systems and Fuzzy Decision Making, 2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 August 2026 | Viewed by 808

Special Issue Editor

Prof. Dr. Diana Kalibatiene

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Guest Editor
Department of Information Systems, Faculty of Fundamental Sciences, Vilnius Gediminas Technical University, LT-10223 Vilnius, Lithuania
Interests: business rules and ontology based information systems development and conceptual modelling; knowledge-based multi-criteria dynamic business process modelling and simulation; multi-criteria decision making methods application in different fields; fuzzy theory application in quality planning and prediction
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Special Issue Information

Dear Colleagues,

Over the years, significant developments have been made in fuzzy systems. Fuzzy logic can be applied in areas such as fuzzy clustering in image processing, classification, regression, and decision making; fuzzy control to map expert knowledge to control systems; fuzzy modeling to combine expert knowledge; and fuzzy optimization to solve development problems.

An advanced fuzzy system is a flexible method of combining multiple conflicting, cooperative, and collaborative sets of knowledge. Combined with the features of artificial intelligence and decision-making systems, a number of studies have focused on the many applications of fuzzy decision making. Those intelligent systems, together with other technologies, have opened up a new way of thinking, as well as new approaches to research, development, and application.

This Special Issue aims to present the latest results on advances in fuzzy sets, fuzzy systems, decision making, and related applications.

The main areas include, but are not limited to, intelligent systems, sustainable development, socio–cyber–physical systems, e-administration, environmental engineering, smart cities, healthcare, security, visualization, business process automation, manufacturing systems, logistics, telecommunication, infrastructure, and transportation.

Prof. Dr. Diana Kalibatiene
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 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 sets
  • fuzzy systems
  • decision making

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Related Special Issue

Published Papers (2 papers)

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Research

39 pages, 912 KB  
Article
An Explainable Fuzzy Multi-Criteria Decision-Making Framework with SHAP-Guided Rule Extraction for Transparent Decision Support Under Uncertainty
by Jesús Alberto Rodríguez-Flores, Alexander Sánchez-Rodríguez, Yandi Fernández-Ochoa, Gelmar García-Vidal, Alexis Cordovés-García and Reyner Pérez-Campdesuñer
Appl. Sci. 2026, 16(10), 5169; https://doi.org/10.3390/app16105169 - 21 May 2026
Abstract
Conventional fuzzy multi-criteria decision-making (MCDM) methods support ranking under uncertainty but often provide limited explanation of why alternatives are preferred. This study proposes an explainable fuzzy decision-making framework that integrates the Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy TOPSIS with surrogate modeling, SHAP-based [...] Read more.
Conventional fuzzy multi-criteria decision-making (MCDM) methods support ranking under uncertainty but often provide limited explanation of why alternatives are preferred. This study proposes an explainable fuzzy decision-making framework that integrates the Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy TOPSIS with surrogate modeling, SHAP-based analysis, and linguistic rule extraction. The main contribution is an explanation layer that preserves the original FAHP–FTOPSIS ranking structure while decomposing ranking scores into criterion-level contributions and transforming recurrent attribution patterns into IF–THEN rules. The framework is evaluated through a supplier-selection case study using expert fuzzy evaluations, local perturbation analysis, leave-one-supplier-out cross-validation, and a synthetic benchmark. The results show that the fuzzy MCDM layer produces discriminative rankings and that the top-ranked supplier remains comparatively stable under perturbations. Among the tested surrogates, the Random Forest Regressor achieved the strongest local fidelity, outperforming linear regression and a shallow decision tree. SHAP analysis showed ordinal alignment between FAHP weights and global criterion importance, while the extracted rules achieved high coverage, consistency, and threshold stability. The framework is useful for researchers, decision analysts, procurement managers, and supply chain professionals who require transparent, interpretable, and auditable multicriteria decisions under uncertainty. Full article
(This article belongs to the Special Issue Applications of Fuzzy Systems and Fuzzy Decision Making, 2nd Edition)
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36 pages, 2365 KB  
Article
A Novel Method to Investigate the Effect of Normalization Techniques on Fuzzy Multi-Criteria Decision-Making in Web Service Quality Assessments
by Diana Kalibatienė and Rūta Simanavičienė
Appl. Sci. 2026, 16(6), 2940; https://doi.org/10.3390/app16062940 - 18 Mar 2026
Viewed by 441
Abstract
Fuzzy multi-criteria decision-making (MCDM) methods remain popular for addressing decision-making problems involving uncertainty and explainability. However, decisions are usually made using data with different dimensions or even modalities. Therefore, existing MCDM methods incorporate various normalization techniques in order to transform attribute values into [...] Read more.
Fuzzy multi-criteria decision-making (MCDM) methods remain popular for addressing decision-making problems involving uncertainty and explainability. However, decisions are usually made using data with different dimensions or even modalities. Therefore, existing MCDM methods incorporate various normalization techniques in order to transform attribute values into dimensionless quantities, ensuring the robustness and reliability of the decision-making results. Nevertheless, these normalization techniques may affect the ranking of alternatives. This study therefore proposes a novel method to investigate the effect of various normalization techniques on fuzzy MCDM methods. The study introduces a novel method for creating a fuzzy decision-making matrix using Tukey’s fences method, enabling the evaluation of alternatives using attributes under uncertain conditions. This method was evaluated in the context of web service quality assessments involving multi-dimensional and random variable attributes. The study demonstrated that Vector and Linear normalization techniques yield similar alternative rankings when using fuzzy MCDM methods, whereas rankings differ when Non-linear normalization techniques are applied. We believe that the current study will allow researchers and practitioners to address various practical uncertain decision-making problems with multi-dimensional attributes, thus promoting the digital transformation of complex, real-world decision-making issues. Full article
(This article belongs to the Special Issue Applications of Fuzzy Systems and Fuzzy Decision Making, 2nd Edition)
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