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Advances in Multi-Criteria Decision Making Methods with Applications
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Advances in Multi-Criteria Decision Making Methods with Applications

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 (10 December 2025) | Viewed by 5502

Special Issue Editors

Dr. Gabriella Marcarelli

E-Mail Website
Guest Editor
Department of Law, Economics, Management and Quantitative Methods, University of Sannio, Italy
Interests: multi-criteria decision-making methods; analytic hierarchy process; consistency and transitivity measures for pairwise comparison matrices; group decisions
Dr. Pietro Amenta

E-Mail Website
Guest Editor
Department of Law, Economics, Management and Quantitative Methods, University of Sannio, Italy
Interests: AHP; multi-group decision-making problem; multivariate data analysis; linear algebra; statistical analysis of preferences
Dr. Antonio Lucadamo

E-Mail Website
Guest Editor
Department of Law, Economics, Management and Quantitative Methods, University of Sannio, Italy
Interests: multidimensional data analysis; multicollinearity problem in logistic regression; robustness in classification techniques and regression models; consistency of pairwise comparison matrices and aggregation of judgments in the analytic hierarchy process; structural equation modeling in tourism; circular data

Special Issue Information

Dear Colleagues,

Multi-criteria decision-making (MCDM) approaches help decision-makers face problems characterized by multiple conflicting criteria. They include analytical tools and methods that have been widely used over the past few decades to solve complex decision-making problems in various fields, such as economics, finance, logistics, environmental remediation, business, engineering, medicine, law, etc.

Over the past 40 years, numerous multiple-criteria methods have been developed. The software available has made MCDM methods more accessible, increasing their use amongst researchers and the user community. Recently, there have been suggestions for combining two or more methods.

We invite researchers and practitioners to submit original research and critical survey manuscripts that propose MCDM approaches and their applications in real-life-related problems.

This Special Issue focuses on, but is not limited to, the following topics:

  • Decision analysis;
  • Decision support systems;
  • Group decision-making;
  • Integrated approaches for modeling decision-making;
  • Soft-computing techniques for MCDM;
  • Consistency measures;
  • Pairwise comparisons.

Dr. Gabriella Marcarelli
Dr. Pietro Amenta
Dr. Antonio Lucadamo
Guest Editors

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. Mathematics 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 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

  • multi-criteria decision-making (MCDM)
  • group decision-making
  • pairwise comparisons
  • decision analysis
  • decision support systems
  • group decision-making
  • integrated approaches for modeling decision-making
  • soft-computing techniques for MCDM
  • consistency measures
  • pairwise comparisons

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

26 pages, 1507 KB  
Article
A Novel ANP-DEMATEL Framework for Multi-Criteria Decision-Making in Adaptive E-Learning Systems
by Maja Gligora Marković, Nikola Kadoić and Božidar Kovačić
Mathematics 2025, 13(22), 3714; https://doi.org/10.3390/math13223714 - 19 Nov 2025
Viewed by 317
Abstract
E-learning systems that support personalized learning require sophisticated decision-making methods to adapt content to students optimally. This paper deals with applying multi-criteria decision-making methods in assigning learning objects in an e-learning system to students based on relevant customization criteria. The novelty of this [...] Read more.
E-learning systems that support personalized learning require sophisticated decision-making methods to adapt content to students optimally. This paper deals with applying multi-criteria decision-making methods in assigning learning objects in an e-learning system to students based on relevant customization criteria. The novelty of this study lies in the application of ANP and DEMATEL to improve content adaptation for students. Structuring the decision-making problem according to the DEMATEL and using ANP for prioritization has made the entire selection of learning objects better with respect to cognitive and learning styles and Bloom’s taxonomy levels. The method consists of various forms. In the first, DEMATEL has identified dependencies between criteria and clusters, mentioning their influence values on a 0–4 scale. A linear transformation model quantified the compatibility level of a student profile to a learning material. The transformed DEMATEL results were incorporated in all the interdependencies among criteria. The unweighted supermatrix was normalized by cluster weights assigned by experts before the iterative computation led to the converging weighted supermatrix. The outcome was that the individual students made these final priority rankings for learning materials. A pilot experiment was carried out to validate the system, and the results revealed that in the experimental group, the personalized learning environment showed the maximum statistical improvement over the control group. The research was conducted in Croatia, and the participants were students (N = 77) from two public universities and one polytechnic. Ultimately, the newly developed combined ANP-DEMATEL approach was effective in an instantaneous result-optimized dynamic learning path generation, ensuring knowledge acquisition. This research further contributes to developing intelligent educational systems by demonstrating how ANP and DEMATEL can be used synergistically to improve e-learning personalization. Future work could include optimizing weight assignment strategies or using new learning contexts to further adaptivity. Full article
(This article belongs to the Special Issue Advances in Multi-Criteria Decision Making Methods with Applications)
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22 pages, 1472 KB  
Article
Industrial Palletizing Robots: A Distance-Based Objective Weighting Benchmarking
by Nhat-Luong Nhieu, Hoang-Kha Nguyen and Nguyen Truong Thinh
Mathematics 2025, 13(20), 3313; https://doi.org/10.3390/math13203313 - 17 Oct 2025
Viewed by 530
Abstract
In the context of increasingly strong digital transformation and production automation, choosing the right palletizing robot plays a key role in optimizing operational efficiency in industrial chains. However, the wide variety of robot types and specifications complicates decision-making and increases the risk of [...] Read more.
In the context of increasingly strong digital transformation and production automation, choosing the right palletizing robot plays a key role in optimizing operational efficiency in industrial chains. However, the wide variety of robot types and specifications complicates decision-making and increases the risk of biased judgments. To overcome this challenge, this study develops an objective multi-criteria decision-making (MCDM) framework that integrates two complementary methods for selecting the optimal industrial pal-letizing robot in the context of modern manufacturing that is increasingly dependent on intelligent automation solutions. Specifically, an improved CRITIC approach is employed to determine objective criteria weights by refining the measurement of contrast intensity and inter-criteria conflict, while normalization ensures comparability of heterogeneous robot parameters. CRADIS is then applied to rank the alternatives based on their relative closeness to the ideal solution. The contributions of this study are twofold: methodological, enhancing the objectivity and robustness of weighting through refined CRITIC and normalization, and practical, offering a reproducible evaluation framework for managers when choosing industrial robots. Application to eight palletizing robots demonstrates that “repeatability” and “power consumption” significantly influence rankings. Sensitivity analysis further confirms the model’s stability and reliability. These findings not only support evidence-based investment decisions but also provide a foundation for extending the method to other industrial technology selection problems. Full article
(This article belongs to the Special Issue Advances in Multi-Criteria Decision Making Methods with Applications)
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19 pages, 1825 KB  
Article
Exploration of the Critical Factors Influencing the Development of the Metaverse Industry Based on Linguistic Variables
by Chen-Tung Chen and Chen-Hao Wu
Mathematics 2025, 13(11), 1860; https://doi.org/10.3390/math13111860 - 2 Jun 2025
Viewed by 713
Abstract
Recently, the development of the Metaverse has emerged as a pivotal concern within both industrial and academic realms. The future development of the Metaverse industry is shrouded in uncertainty, complexity, and a dearth of technical and economic information. To address these challenges, this [...] Read more.
Recently, the development of the Metaverse has emerged as a pivotal concern within both industrial and academic realms. The future development of the Metaverse industry is shrouded in uncertainty, complexity, and a dearth of technical and economic information. To address these challenges, this paper integrates the fuzzy Delphi method and fuzzy DEMATEL based on linguistic variables to explore the critical factors of the Metaverse industry. In accordance with the proposed methodology, a case study is presented to explore the critical factors of the Metaverse industry in Taiwan. The results of the empirical analysis demonstrated that the order of importance for the three principal dimensions is as follows: “infrastructure”, “consumer behavior”, and “user experience”. From the perspective of causality, “infrastructure” can be considered a driving dimension, whereas “user experience” can be regarded as a passive dimension. Regarding the critical factors, it can be observed that “virtual and real integration”, “equipment lightweight”, and “network communication” act as driving factors, exhibiting a high degree of correlation with the advancement of the Metaverse industry. Therefore, the proposed method not only possesses a robust theoretical foundation but also offers tangible practical value in the real world. Full article
(This article belongs to the Special Issue Advances in Multi-Criteria Decision Making Methods with Applications)
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20 pages, 3731 KB  
Article
Fuzzy Clustering with Uninorm-Based Distance Measure
by Evgeny Kagan, Alexander Novoselsky and Alexander Rybalov
Mathematics 2025, 13(10), 1661; https://doi.org/10.3390/math13101661 - 19 May 2025
Viewed by 987
Abstract
In this paper, we suggest an algorithm of fuzzy clustering with a uninorm-based distance measure. The algorithm follows a general scheme of fuzzy c-means (FCM) clustering, but in contrast to the existing algorithm, it implements logical distance between data instances. The centers [...] Read more.
In this paper, we suggest an algorithm of fuzzy clustering with a uninorm-based distance measure. The algorithm follows a general scheme of fuzzy c-means (FCM) clustering, but in contrast to the existing algorithm, it implements logical distance between data instances. The centers of the clusters calculated by the algorithm are less dispersed and are concentrated in the areas of the actual centers of the clusters that result in the more accurate recognition of the number of clusters and of data structure. Full article
(This article belongs to the Special Issue Advances in Multi-Criteria Decision Making Methods with Applications)
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29 pages, 1042 KB  
Article
Macro-Scale Temporal Attenuation for Electoral Forecasting: A Retrospective Study on Recent Elections
by Alexandru Topîrceanu
Mathematics 2025, 13(4), 604; https://doi.org/10.3390/math13040604 - 12 Feb 2025
Viewed by 1947
Abstract
Forecasting election outcomes is a complex scientific challenge with notable societal implications. Existing approaches often combine statistical analysis, machine learning, and economic indicators. However, research in network science has emphasized the importance of temporal factors in the dissemination of opinions. This study presents [...] Read more.
Forecasting election outcomes is a complex scientific challenge with notable societal implications. Existing approaches often combine statistical analysis, machine learning, and economic indicators. However, research in network science has emphasized the importance of temporal factors in the dissemination of opinions. This study presents a macro-scale temporal attenuation (TA) model, which integrates micro-scale opinion dynamics and temporal epidemic theories to enhance forecasting accuracy using pre-election poll data. The findings suggest that the timing of opinion polls significantly influences opinion fluctuations, particularly as election dates approach. Opinion “pulse” is modeled as a temporal function that increases with new poll inputs and declines during stable periods. Two practical variants of the TA model, ETA and PTA, were tested on datasets from ten elections held between 2020 and 2024 around the world. The results indicate that the TA model outperformed several statistical methods, ARIMA models, and best pollster predictions (BPPs) in six out of ten elections. The two TA implementations achieved an average forecasting error of 6.92–6.95 percentage points across all datasets, compared to 7.65 points for BPP and 14.42 points for other statistical methods, demonstrating a performance improvement of 10–83%. Additionally, the TA methods maintained robust performance even with limited poll availability. As global pre-election survey data become more accessible, the TA model is expected to serve as a valuable complement to advanced election-forecasting techniques. Full article
(This article belongs to the Special Issue Advances in Multi-Criteria Decision Making Methods with Applications)
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