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Multi-criteria Decision Making and Data Mining, 2nd Edition

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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 (31 March 2026) | Viewed by 28641

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Prof. Dr. James Liou

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Guest Editor

Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei 10608, Taiwan
Interests: multiple criteria decision making (MCDM); decision support; data mining
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Prof. Dr. Artūras Kaklauskas

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Guest Editor

Department of Construction Economics and Property Management, Vilnius Gediminas Technical University, Vilnius, Lithuania
Interests: sustainable development; multiple-criteria decision-making; intelligent decision-support systems; environmental, economic, political, and social sustainability dimensions; Industry 4.0; Industry 5.0; Society 5.0; cognitive data mining
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Given the complexity of the socioeconomic environment, decision making is one of the most notable ventures, whose mission is to decide the best alternative under numerous qualitative and quantitative factors/criteria. Multiple-criteria decision-making (MCDM) methods and hybrid models are quickly emerging as useful methods for evaluating and improving alternatives. Through the gradual maturation of information technology and the growth of the data analysis environment, large amounts of data within organizations could be accumulated. Therefore, some data-driven MADM models which integrate machine learning/data mining and MCDM methods to help decision-makers select the best alternative in various industries have been developed. Data mining or machine learning techniques are primarily concerned with discovering hidden patterns and relationships in data to assist decision-makers making judgements. MCDM is mainly concerned with problems which require ranking, classification, and sorting based on multiple criteria or attributes. Combining data mining with MCDM methodologies to establish new or hybrid decision-making models involves combining the advantages of both methods in management sciences.

This Special Issue aims to collate original research papers that offer the latest developments and applications of MCDM, data mining, or hybrid models across various fields.

Prof. Dr. James Liou
Prof. Dr. Artūras Kaklauskas
Guest Editors

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Keywords

multiple-criteria decision-making (MCDM)
data mining
data driven
machine learning
knowledge-based systems
hybrid multiple-criteria decision-making methods
intelligent decision support systems
optimization techniques
soft computing
application of mcdm methods
site selection
resource allocation
supply chain management
production management
quality management
risk management
decision analysis for sustainable production and consumption
group decision making
MCDM theories
MCDM in strategic management
decision making
hybrid decision-making analysis
information technologies in decision making
innovative applications of MCDM methods
weighting approach
technologies and techniques
sustainability assessment data mining models and tools
data mining result validation
privacy concerns and ethics
practical applications (government, international development, culture healthcare, education, media, insurance, Internet of Things, agriculture, industry)
case studies
impacts of data science
quality of city life and data mining
smart city and data mining
behavioral change and data mining
neuromarketing and data mining

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Published Papers (12 papers)

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Research

33 pages, 892 KB

Open AccessArticle

A Novel Spherical Distance Measure for SF-TOPSIS: A Generalized MCDM Framework via Application to Municipal Solid Waste Landfill Site Selection

by Ezgi Güler

Mathematics 2026, 14(9), 1416; https://doi.org/10.3390/math14091416 - 23 Apr 2026

Viewed by 94

Abstract

Municipal solid waste (MSW) landfill site selection is a complex multi-criteria decision-making (MCDM) problem involving uncertainty and conflicting criteria. Although spherical fuzzy extensions of the Technique for Order Preference by Similarity to Ideal Solution (SF-TOPSIS) are widely used, existing studies rely on conventional distance measures that do not fully capture the geometric structure of spherical fuzzy sets. To address this limitation, this study proposes an enhanced SF-TOPSIS framework incorporating a novel spherical distance measure to improve consistency, discrimination capability, and structural compatibility. The framework integrates Spherical Fuzzy Weighted Arithmetic Mean (SWAM) and Spherical Fuzzy Weighted Geometric Mean (SWGM) operators and evaluates robustness using Spearman rank correlation. Additionally, a coefficient of variation (CV)-based analysis is conducted to examine the dispersion of closeness coefficients. The applicability of the approach is demonstrated through a landfill site selection case; however, the main contribution lies in a generalized distance-based formulation applicable to various MCDM problems. Results show that the proposed distance improves agreement between aggregation operators, increasing correlation values from 0.905 to 0.976, while producing a more stable distribution of closeness coefficients. Overall, the study advances spherical fuzzy MCDM by introducing a geometrically consistent distance formulation. Full article

(This article belongs to the Special Issue Multi-criteria Decision Making and Data Mining, 2nd Edition)

27 pages, 3974 KB

Open AccessFeature PaperArticle

An Assessment of Indifference Threshold Values to Achieve Full Objective Indifference Threshold-Based Attribute Ratio Analysis

by Sarfaraz Hashemkhani Zolfani and Alireza Nemati

Mathematics 2026, 14(2), 235; https://doi.org/10.3390/math14020235 - 8 Jan 2026

Viewed by 674

Abstract

Multi-criteria decision-making (MCDM) models are moving toward being data-oriented. Meanwhile, MCDM models’ totalitarian reliance on experts’ preferences may reduce the accuracy of results in real-world challenges. Therefore, there is a huge gap in refining MCDM models to be data-structured rather than relying on experts’ and decision-makers’ ideas. In this research article, the primary indifference threshold values of the Indifference Threshold-based Attribute Ratio Analysis (ITARA) model, which is one of the popular objective weighting MCDM techniques, have been investigated and improved to achieve the goal of a full-objective MCDM model. ITARA utilizes decision-makers’ and experts’ opinions to set the indifference threshold values, which are integral to obtaining criteria weights, and since this step is not data-based, unlike the whole technique, it is prone to deficiencies. Three critical frameworks based on the minimum value, standard deviation, and max–min distance are designed to assess the sensitivity of the indifference threshold values and optimize the initialization values to start the model. Two case studies based on actual data are considered in this research to observe the frameworks’ outcomes and the rank reversal phenomenon. The results demonstrated that the assigning weights procedure is deeply sensitive to a max–min framework, while the standard deviation framework illustrated more stable results and a slight change in criteria rankings. The min framework moderately fluctuated between the max–min and standard deviation frameworks. Full article

(This article belongs to the Special Issue Multi-criteria Decision Making and Data Mining, 2nd Edition)

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62 pages, 4507 KB

Open AccessArticle

Integration Modes Between MCDM Methods and Machine Learning Algorithms: A Structured Approach for Framework Development

by Hatice Kocaman and Umut Asan

Mathematics 2026, 14(1), 33; https://doi.org/10.3390/math14010033 - 22 Dec 2025

Viewed by 1810

Abstract

Decision-making is increasingly guided by the integration of Multi-Criteria Decision-Making (MCDM) and Machine Learning (ML) approaches. Despite their complementary strengths, the literature lacks clarity on which forms of integration exist, what contributions they offer, and how to determine the most effective form for a given decision problem. This study systematically investigates integration modes through a methodology that combines a literature review, expert judgment, and statistical analyses. It develops a novel categorization of integration modes based on methodological characteristics, resulting in five distinct modes: sequential approaches (ML → MCDM and MCDM → ML), hybrid integration (MCDM + ML), and performance comparison approaches, including ML vs. MCDM and ML vs. ML evaluated through MCDM. In addition, new evaluation criteria are introduced to ensure rigor, comparability, and reliability in assessing integration forms. By applying correspondence, cluster, and discriminant analyses, the study reveals distinctive patterns, relationships, and gaps across integration modes. The primary outcome is a novel evidence-based framework designed to guide researchers and practitioners in selecting the appropriate integration modes based on problem characteristics, methodological requirements, and application context. The findings reveal that sequential approaches (ML → MCDM and MCDM → ML) are most appropriate when efficiency, structured decision workflows, bias reduction, minimal human intervention, and the management of complex multi-variable decision problems are key objectives. Hybrid integration (MCDM + ML) is better suited to dynamic and data-rich environments that require flexibility, continuous adaptation, and a high level of automation. Performance comparison approaches are most appropriate for validation-oriented studies that evaluate outputs (MCDM[ML vs. ML]) and benchmark alternative methods (ML vs. MCDM), thereby supporting reliable method selection. Furthermore, the study underscores the predominance of integration modes that combine value-based MCDM methods with classification-based ML algorithms, particularly for enhancing interpretability. Environmental science and healthcare emerge as leading domains of adoption, primarily due to their high data complexity and the need to balance diverse, multi-criteria stakeholder requirements. Full article

(This article belongs to the Special Issue Multi-criteria Decision Making and Data Mining, 2nd Edition)

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21 pages, 1538 KB

Open AccessFeature PaperArticle

A Hybrid Fuzzy DEMATEL–DANP–TOPSIS Framework for Life Cycle-Based Sustainable Retrofit Decision-Making in Seismic RC Structures

by Paola Villalba, Antonio J. Sánchez-Garrido, Lorena Yepes-Bellver and Víctor Yepes

Mathematics 2025, 13(16), 2649; https://doi.org/10.3390/math13162649 - 18 Aug 2025

Cited by 2 | Viewed by 1940

Abstract

Seismic retrofitting of reinforced concrete (RC) structures is essential for improving resilience and extending service life, particularly in regions with outdated building codes. However, selecting the optimal retrofitting strategy requires balancing multiple interdependent sustainability criteria—economic, environmental, and social—under expert-based uncertainty. This study presents a fuzzy hybrid multi-criteria decision-making (MCDM) approach that combines DEMATEL, DANP, and TOPSIS to represent causal interdependencies, derive interlinked priority weights, and rank retrofit alternatives. The assessment applies three complementary life cycle-based tools—cost-based, environmental, and social sustainability analyses following LCCA, LCA, and S-LCA frameworks, respectively—to evaluate three commonly used retrofitting strategies: RC jacketing, steel jacketing, and carbon fiber-reinforced polymer (CFRP) wrapping. The fuzzy-DANP methodology enables accurate modeling of feedback among sustainability dimensions and improves expert consensus through causal mapping. The findings identify CFRP as the top-ranked alternative, primarily attributed to its enhanced performance in both environmental and social aspects. The model’s robustness is confirmed via sensitivity analysis and cross-method validation. This mathematically grounded framework offers a reproducible and interpretable tool for decision-makers in civil infrastructure, enabling sustainability-oriented retrofitting under uncertainty. Full article

(This article belongs to the Special Issue Multi-criteria Decision Making and Data Mining, 2nd Edition)

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30 pages, 1040 KB

Open AccessArticle

The Problem of Assigning Patients to Appropriate Health Institutions Using Multi-Criteria Decision Making and Goal Programming in Health Tourism

by Murat Suat Arsav, Nur Ayvaz-Çavdaroğlu and Ercan Şenyiğit

Mathematics 2025, 13(10), 1684; https://doi.org/10.3390/math13101684 - 21 May 2025

Cited by 1 | Viewed by 2519

Abstract

Health tourism is an increasingly vital sector for both Kayseri and Türkiye, contributing significantly to exports and foreign currency inflows. Recent investments in health tourism infrastructure have positioned Kayseri as one of the leading cities in the country, particularly due to its strong healthcare facilities. This study explores Kayseri’s potential in health tourism, with a focus on bariatric surgery, by employing Multi-Criteria Decision Making (MCDM) and optimization methods. The study first provides an extensive literature review to identify the key factors influencing patients’ selection of health institutions for bariatric surgery. Subsequently, the Group Best-Worst Method (G-BWM) is applied using expert input from managers of bariatric surgery centers to determine the relative importance of these factors. Based on the G-BWM findings, nine health institutions in Kayseri offering obesity surgery services are evaluated and ranked using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), which generates institutional performance scores. Building on these results, a Goal Programming model is developed to assign patients to suitable health institutions while simultaneously considering the health institution’s revenue and patient satisfaction. This study offers several novel contributions. It integrates MCDM techniques with goal programming in the context of health tourism—a combination not widely explored in the literature. Additionally, it provides a comparative assessment of the factors influencing health tourists’ decision-making processes, offering policymakers a strategic framework for resource allocation. Lastly, by presenting a mathematical model for patient-institution assignment, the study offers practical guidance for health tourism organizations aiming to enhance both health institution revenue and patient satisfaction in the health tourism sector. Full article

(This article belongs to the Special Issue Multi-criteria Decision Making and Data Mining, 2nd Edition)

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25 pages, 2236 KB

Open AccessFeature PaperArticle

New Perspectives on the Causes of Stagnation and Decline in the Sharing Economy: Application of the Hybrid Multi-Attribute Decision-Making Method

by Hsu-Hua Lee, Chien-Hua Chen, Ling-Ya Kao, Wen-Tsung Wu and Chu-Hung Liu

Mathematics 2025, 13(7), 1051; https://doi.org/10.3390/math13071051 - 24 Mar 2025

Cited by 3 | Viewed by 1644

Abstract

Against the backdrop of global economic changes and rapid technological innovation, the sharing economy model is gradually transforming the operational mechanisms of traditional industries. However, some industries have experienced stagnation and recession during this transition, leading to market development constraints. The necessity of this study lies in filling the gap in the existing literature by conducting an in-depth analysis of the critical factors contributing to industrial stagnation and recession in the sharing economy. This study aims to provide concrete countermeasures for businesses and policymakers. The novelty of this research study lies in integrating multiple key variables affecting industrial development, including green production concepts, the circular economy, large-scale production, high-quality product demand driven by industrial automation, the sharing economy, and smart production. By employing multi-criterion decision-making methods, we quantitatively assess the impact of these factors more accurately. This study employs the Multi-Attribute Decision-Making (MADM) model, integrating the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and the Analytic Network Process (ANP) to form D&ANP for analytical research. Highly automated industries are selected as the research subjects. The DEMATEL technique is used to construct the Influential Network Relationship Map (INRM), while the ANP concept is incorporated to develop the D&ANP model. Through the D&ANP method, influential weights are calculated and combined with industry-specific assessments of the suitability of potential causes (or attributes) contributing to economic stagnation and recession to determine the average performance values for each industry. These values are further compared with benchmark suitability performance values to distinguish ideal and non-ideal conditions across industries facing economic stagnation and recession. The analysis results indicate that different industries are influenced by varying factors, requiring strategic adjustments based on their unique development environments. Accordingly, this study provides industry-specific recommendations to optimize business models and resource allocation, mitigate the risks of economic stagnation and recession, and promote sustainable industrial development and economic recovery. The findings of this study not only contribute to empirical research on the impact of the sharing economy on industrial development but also serve as a decision-making reference for businesses. By offering strategic insights, enterprises can better respond to market dynamics, enhance competitiveness, and ensure long-term stable growth. Full article

(This article belongs to the Special Issue Multi-criteria Decision Making and Data Mining, 2nd Edition)

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22 pages, 3727 KB

Open AccessArticle

A Multicriteria Customer Classification Method in Supply Chain Management

by Felipe Barrera, Marina Segura and Concepción Maroto

Mathematics 2024, 12(21), 3427; https://doi.org/10.3390/math12213427 - 31 Oct 2024

Cited by 1 | Viewed by 2516

Abstract

Since Kraljic’s strategic matrix was applied to supply chain management, classification of items, suppliers, and customers has become of increasing interest to research and companies. The aim of this research is to develop an easily interpretable multicriteria classification matrix method and validate it in real-world scenarios with a robustness analysis. This method assigns alternatives to one of four classes defined by critical dimensions that integrate several evaluation criteria. Initially, a global search pre-classifies the alternatives using the PROMETHEE net flows. Then, two local searches are carried out that make use of the discriminant properties of the net flow signs to improve the quality of the assignments. This approach is specifically applied to pre-classified alternatives near the boundary between two or more categories. The method has been validated by segmenting thousands of customers. Four customer segments were identified: strategic, collaborative, transactional, and non-preferred. A comparison was made between the results and those derived from an alternative method. Through an extensive sensitivity analysis, the proposed method was shown to be robust to parameter variation, highlighting its reliability in real dynamic contexts. The method provides valuable, easily interpretable information, which constitutes the basis for developing personalised strategies to enhance customer relationship management. Full article

(This article belongs to the Special Issue Multi-criteria Decision Making and Data Mining, 2nd Edition)

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10 pages, 257 KB

Open AccessArticle

Fuzzy–Rough Analysis of ESG Ratings and Financial and Growth Ratios on the Stock Returns of Blue-Chip Stocks in Taiwan

by Kao-Yi Shen

Mathematics 2024, 12(16), 2511; https://doi.org/10.3390/math12162511 - 14 Aug 2024

Viewed by 3147

Abstract

This study uses fuzzy–rough analysis to investigate the influence of Environmental, Social, and Governance (ESG) ratings, along with critical financial and growth ratios, on the stock returns of blue-chip companies in Taiwan. The growing importance of ESG factors in investment decisions underscores the need to understand their impact on stock performance. By integrating the fuzzy–rough set theory, which accommodates uncertainty and imprecision in data, we analyze the complex relationships between ESG ratings, traditional financial metrics (such as ROE, return on equity), and stock returns. Our findings provide insights into how ESG considerations, alongside financial indicators, drive the returns of Taiwan’s blue-chip stocks. Three public-listed companies were evaluated using this approach, and the results are consistent with the actual stock performance. This research contributes to the field by offering a robust methodological approach to assess the nuanced effects of ESG factors on financial performance, thus aiding investors and management teams in making informed decisions. Full article

(This article belongs to the Special Issue Multi-criteria Decision Making and Data Mining, 2nd Edition)

31 pages, 1978 KB

Open AccessArticle

A Multi-Criteria Assessment Model for Cooperative Technology Transfer Projects from Universities to Industries

by Rui Xiong, Hongyi Sun, Shufen Zheng and Sichu Liu

Mathematics 2024, 12(12), 1894; https://doi.org/10.3390/math12121894 - 18 Jun 2024

Cited by 2 | Viewed by 2789

Abstract

Cooperative Technology Transfer (CTT) is a technology transfer model where universities and enterprises jointly participate throughout the entire process of technology transfer activities. Most discussions focus on its mechanisms and influencing factors, yet a framework to guide CTT projects in practice is still lacking. This study proposes an assessment model based on the life-cycle of CTT projects, covering the initial cooperation relationship, project management during the mid-term, and technological achievements at the end. The model was evaluated by 14 experts first and then validated through two CTT projects in China. Gray Relation Analysis was employed to calculate the weights of different factors based on their relative importance, while the Dempster–Shafer theory was utilized to combine evidence from various sources and address the uncertainty in the assessment. The results of the case analysis indicate that the attitudes of universities and enterprises are considered critical in influencing the success of CTT projects, while management issues that arise during the projects can pose potential risks. This research serves as an applied exploration and has three functions. Firstly, the model can be used as a feasibility study before the project commences. Secondly, it can be utilized to analyze and improve potential issues during the project. Finally, it can be used for a post-project experience summary. Full article

(This article belongs to the Special Issue Multi-criteria Decision Making and Data Mining, 2nd Edition)

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21 pages, 1646 KB

Open AccessArticle

A Hybrid Model to Explore the Barriers to Enterprise Energy Storage System Adoption

by James J. H. Liou, Peace Y. L. Liu and Sun-Weng Huang

Mathematics 2023, 11(19), 4223; https://doi.org/10.3390/math11194223 - 9 Oct 2023

Cited by 1 | Viewed by 2490

Abstract

Using green energy is an important way for businesses to achieve their ESG goals and ensure sustainable operations. Currently, however, green energy is not a stable source of power, and this instability poses certain risks to normal business operations and manufacturing processes. The installation of energy storage equipment has become an indispensable accompaniment to facilitating green energy use for an enterprise. However, businesses may encounter significant barriers during the process of installing energy storage equipment. This study aims to explore and discern the key barrier factors that influence the assessment and decision-making process of installing energy storage equipment. A hybrid approach combining the Decision-making and Trial Evaluation Laboratory (DEMATEL) and Interpretive Structural Modeling (ISM) is developed to explore the causality relationships and degrees of influence among these key factors. The Z-number and Rough Dombi Weighted Geometric Averaging (RDWGA) methods are also utilized to integrate the experts’ varied opinions and uncertain judgements. Finally, recommendations are provided based on the results to assist businesses to make informed decisions while evaluating the installation of energy storage equipment, to ensure a stable and uninterrupted supply of green energy for use in normal operations. Full article

(This article belongs to the Special Issue Multi-criteria Decision Making and Data Mining, 2nd Edition)

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19 pages, 2956 KB

Open AccessArticle

A Large-Scale Reviews-Driven Multi-Criteria Product Ranking Approach Based on User Credibility and Division Mechanism

by Wenzhi Cao, Xingen Yang and Yi Yang

Mathematics 2023, 11(13), 2952; https://doi.org/10.3390/math11132952 - 1 Jul 2023

Cited by 2 | Viewed by 2712

Abstract

Massive online reviews provide consumers with the convenience of obtaining product information, but it is still worth exploring how to provide consumers with useful and reliable product rankings. The existing ranking methods do not fully mine user information, rating, and text comment information to obtain scientific and reasonable information aggregation methods. Therefore, this study constructs a user credibility model and proposes a large-scale user information aggregation method to obtain a new product ranking method. First, in order to obtain the aggregate weight of large-scale users, this paper proposes a consistency modeling method of text comments and star ratings by mining the associated information of user comments, including user interaction information and user personalized characteristics information, combined with sentiment analysis technology, and then constructs a user credibility model. Second, a double-layer group division mechanism considering user regions and comment time is designed to develop the large-scale group ratings aggregation approach. Third, based on the user credibility model and the large-scale ratings aggregation approach, a product ranking method is developed. Finally, the feasibility and effectiveness of the proposed method are verified through a case study for automobile ranking and a comparative analysis is furnished. The analysis results of the application case of automobile ranking show that there is a significant difference between the ranking results obtained by the ratings aggregation method based on the arithmetic mean and the ranking results obtained by this method. The method in this study comprehensively considers user credibility and group division, which can be reflected in user aggregation weights and the group aggregation process, and can also obtain more scientific and reasonable decision results. Full article

(This article belongs to the Special Issue Multi-criteria Decision Making and Data Mining, 2nd Edition)

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18 pages, 688 KB

Open AccessArticle

Key Factors for a Successful OBM Transformation with DEMATEL–ANP

by Tien Son Nguyen, Jen-Ming Chen, Shih-Hsien Tseng and Li-Fen Lin

Mathematics 2023, 11(11), 2439; https://doi.org/10.3390/math11112439 - 25 May 2023

Cited by 3 | Viewed by 3451

Abstract

Production costs and global competition have increased sharply in recent years, forcing manufacturers to upgrade to the original brand manufacturer (OBM) to survive and thrive and capture more profit margins. However, studies that explore key factors that affect the success of such an important transition are lacking. Therefore, this study aims to investigate the key factors that will influence the success of contract manufacturers to upgrade to the OBM on the basis of a decision-making trial and evaluation laboratory with an analytic network process. Our results identify six key factors that exhibit a cause-and-effect relationship among the key criteria. Moreover, organizational innovation will determine the difference between the success and the failure of an OBM transition apart from material and component stability. Our findings can help researchers, policy makers, and practitioners increase their understanding of how to upgrade manufacturers successfully in global value chains. Full article

(This article belongs to the Special Issue Multi-criteria Decision Making and Data Mining, 2nd Edition)

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