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: 10 December 2025 | Viewed by 1168

Special Issue Editors


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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

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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

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

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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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Published Papers (2 papers)

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Research

20 pages, 702 KiB  
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
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)
29 pages, 1042 KiB  
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 714
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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