Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.5
2.4
Journals
Mathematics
Special Issues
Multi-Criteria Decision Making and Data Mining
share announcement

Multi-Criteria Decision Making and Data Mining

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Fuzzy Sets, Systems and Decision Making".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 74587

Special Issue Editors

Prof. Dr. James Liou

E-Mail Website1 Website2
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
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Artūras Kaklauskas

E-Mail Website1 Website2
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,

With 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 growth of the data analysis environment comes the accumulation of large amounts of data within organizations. 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 can combine 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 in the broad fields.

Prof. Dr. James Liou
Prof. Dr. Artūras Kaklauskas
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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • 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

Published Papers (30 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

10 pages, 1411 KiB  
Article
Explainable Deep-Learning-Based Depression Modeling of Elderly Community after COVID-19 Pandemic
by Hung Viet Nguyen and Haewon Byeon
Mathematics 2022, 10(23), 4408; https://doi.org/10.3390/math10234408 - 23 Nov 2022
Cited by 6 | Viewed by 1325
Abstract
The impact of the COVID-19 epidemic on the mental health of elderly individuals is causing considerable worry. We examined a deep neural network (DNN) model to predict the depression of the elderly population during the pandemic period based on social factors related to [...] Read more.
The impact of the COVID-19 epidemic on the mental health of elderly individuals is causing considerable worry. We examined a deep neural network (DNN) model to predict the depression of the elderly population during the pandemic period based on social factors related to stress, health status, daily changes, and physical distancing. This study used vast data from the 2020 Community Health Survey of the Republic of Korea, which included 97,230 people over the age of 60. After cleansing the data, the DNN model was trained using 36,258 participants’ data and 22 variables. We also integrated the DNN model with a LIME-based explainable model to achieve model prediction explainability. According to the research, the model could reach a prediction accuracy of 89.92%. Furthermore, the F1-score (0.92), precision (93.55%), and recall (97.32%) findings showed the effectiveness of the proposed approach. The COVID-19 pandemic considerably impacts the likelihood of depression in later life in the elderly community. This explainable DNN model can help identify patients to start treatment on them early. Full article
(This article belongs to the Special Issue Multi-Criteria Decision Making and Data Mining)
►▼ Show Figures

Figure 1

19 pages, 910 KiB  
Article
A Novel MCDM Approach Based on OPA-WINGS for Policy Making in Undergraduate Elective Courses
by Sarfaraz Hashemkhani Zolfani, Alireza Nemati, Pedro J. Reyes-Norambuena and Carlos A. Monardes-Concha
Mathematics 2022, 10(22), 4211; https://doi.org/10.3390/math10224211 - 11 Nov 2022
Cited by 5 | Viewed by 1588
Abstract
This research develops a novel MCDM approach that combines the ordinal priority approach (OPA) and a weighted influence nonlinear gauge system (WINGS), for policy making about undergraduate programs and specifically elective courses. We interviewed eight professors at the School of Engineering, Universidad Catolica [...] Read more.
This research develops a novel MCDM approach that combines the ordinal priority approach (OPA) and a weighted influence nonlinear gauge system (WINGS), for policy making about undergraduate programs and specifically elective courses. We interviewed eight professors at the School of Engineering, Universidad Catolica del Norte, who are highly engaged in organizing elective courses to obtain their prioritization criteria for offering them to undergraduate students. We proposed and applied an MCDM approach based on OPA-WINGS to rank criteria that make the process of planning future electives courses to offer more straightforward. We found that scientific thinking, Industries’ needs, and the School’s research lines are the main criteria for designing a new elective class. We conducted a sensitivity analysis to demonstrate de robustness of the suggested measures. This work illustrates how OPA-WINGS can improve decision-making for offering elective courses. The results indicate that Industries’ needs and School’s research lines strongly impact undergraduate programs’ direction. Full article
(This article belongs to the Special Issue Multi-Criteria Decision Making and Data Mining)
►▼ Show Figures

Figure 1

16 pages, 1089 KiB  
Article
A Multi-Criteria Decision-Making Framework for Prioritizing and Overcoming Sectoral Barriers in Converting Agricultural Residues to a Building Material
by Dragan Pamučar, Masoud Behzad, Miljojko Janosevic and Claudia Andrea Aburto Araneda
Mathematics 2022, 10(21), 4003; https://doi.org/10.3390/math10214003 - 28 Oct 2022
Cited by 2 | Viewed by 1412
Abstract
Biological products utilization are increasingly encouraged in different sectors such as building construction to facilitate moving towards a circular economy. However, this task is facing several barriers in supply chain and construction sectors. This study identified common barriers in converting agricultural residues to [...] Read more.
Biological products utilization are increasingly encouraged in different sectors such as building construction to facilitate moving towards a circular economy. However, this task is facing several barriers in supply chain and construction sectors. This study identified common barriers in converting agricultural residues to building materials and products in the agriculture sector, transportation, and manufacturing, as well as construction and operation phases in the building sector. The feasibility level to overcome the barriers has been scored. In addition, the barriers and sectors have been prioritized through ordinal priority approach. The results ranked the priority of the barriers as technology (0.3083), policy (0.2211), knowledge (0.1972), cost (0.1500), social and cultural (0.0739), and infrastructure (0.0494). Sectors were ranked in feasibility level to overcome the barriers from lowest to highest as operating, construction, manufacturing, transport, and agriculture. It is recommended to local communities to give priority to the building sector rather than supply chain and work under an integrated framework to enhance the feasibility level, which should include localization, prevention, collaboration, and digitalization. In particular, Chile should promote converting agricultural residues to building products as the project aligns with several initiatives existing in its circular economy roadmap. Full article
(This article belongs to the Special Issue Multi-Criteria Decision Making and Data Mining)
►▼ Show Figures

Figure 1

22 pages, 928 KiB  
Article
An Intelligent System for Patients’ Well-Being: A Multi-Criteria Decision-Making Approach
by Fabián Silva-Aravena, Jimmy H. Gutiérrez-Bahamondes, Hugo Núñez Delafuente and Roberto M. Toledo-Molina
Mathematics 2022, 10(21), 3956; https://doi.org/10.3390/math10213956 - 25 Oct 2022
Cited by 1 | Viewed by 1206
Abstract
The coronavirus pandemic has intensified the strain on medical care processes, especially waiting lists for patients under medical management. In Chile, the pandemic has caused an increase of 52,000 people waiting for care. For this reason, a high-complexity hospital (HCH) in Chile devised [...] Read more.
The coronavirus pandemic has intensified the strain on medical care processes, especially waiting lists for patients under medical management. In Chile, the pandemic has caused an increase of 52,000 people waiting for care. For this reason, a high-complexity hospital (HCH) in Chile devised a decision support system (DSS) based on multi-criteria decision-making (MCDM), which combines management criteria, such as critical events, with clinical variables that allow prioritizing the population of chronic patients on the waiting list. The tool includes four methodological contributions: (1) pattern recognition through the analysis of anonymous patient data that allows critical patients to be characterized; (2) a score of the critical events suffered by the patients; (3) a score based on clinical criteria; and (4) a dynamic–hybrid methodology for patient selection that links critical events with clinical criteria and with the risk levels of patients on the waiting list. The methodology allowed to (1) characterize the most critical patients and triple the evaluation of medical records; (2) save medical hours during the prioritization process; (3) reduce the risk levels of patients on the waiting list; and (4) reduce the critical events in the first month of implementation, which could have been caused by the DSS and medical decision-making. This strategy was effective (even during a pandemic period). Full article
(This article belongs to the Special Issue Multi-Criteria Decision Making and Data Mining)
►▼ Show Figures

Figure 1

25 pages, 932 KiB  
Article
Analysis of Innovation Drivers of New and Old Kinetic Energy Conversion Using a Hybrid Multiple-Criteria Decision-Making Model in the Post-COVID-19 Era: A Chinese Case
by Chun-Chieh Tseng, Jun-Yi Zeng, Min-Liang Hsieh and Chih-Hung Hsu
Mathematics 2022, 10(20), 3755; https://doi.org/10.3390/math10203755 - 12 Oct 2022
Cited by 1 | Viewed by 1241
Abstract
To overcome the continuous decline in its gross domestic product growth rate, China has advocated new and old kinetic energy conversion (NOKEC) as a policy for sustainable economic development in the post-COVID-19 era. The innovation drivers of NOKEC are the key to promoting [...] Read more.
To overcome the continuous decline in its gross domestic product growth rate, China has advocated new and old kinetic energy conversion (NOKEC) as a policy for sustainable economic development in the post-COVID-19 era. The innovation drivers of NOKEC are the key to promoting sustainable economic development. However, the innovation drivers have various orientations, and their selection requires multiple-criteria decision-making (MCDM). This study proposes a modified Delphi method combined with the best–worst method (BWM) as a research framework for selecting and ranking innovation drivers. Our results show the validity of this integrated research framework on a case based in China in the post-COVID-19 era. The results reveal 21 innovation-driven factors of NOKEC with varying levels of relative importance. These results may provide a basis for policymakers and researchers with a useful further understanding of the importance and prioritizing of innovation drivers. In this study, BWM uses 4% fewer pairwise comparisons than AHP, and the consistency ratio is in the range of 0.00 to 0.24. Full article
(This article belongs to the Special Issue Multi-Criteria Decision Making and Data Mining)
►▼ Show Figures

Figure 1

18 pages, 936 KiB  
Article
The Suitability-Feasibility-Acceptability Strategy Integrated with Bayesian BWM-MARCOS Methods to Determine the Optimal Lithium Battery Plant Located in South America
by Sarfaraz Hashemkhani Zolfani, Ramin Bazrafshan, Fatih Ecer and Çağlar Karamaşa
Mathematics 2022, 10(14), 2401; https://doi.org/10.3390/math10142401 - 08 Jul 2022
Cited by 15 | Viewed by 3146
Abstract
This study aims to help managers develop a proper strategy and policy for their company’s future. After the global COVID-19 pandemic, developed countries decided to change their production and relocate and re-industrialize. The U.S.’s big electronics and automobile companies are not an exception [...] Read more.
This study aims to help managers develop a proper strategy and policy for their company’s future. After the global COVID-19 pandemic, developed countries decided to change their production and relocate and re-industrialize. The U.S.’s big electronics and automobile companies are not an exception to this rule. The lithium batteries are the main instrument of mobile phone and electric vehicles. The leading lithium battery supplier for the U.S mobile phone companies is China. Argentina, Bolivia, and Chile (in South America) have some of the largest lithium mines in the world; these countries are known as the lithium triangle. Among the 86 million tonnes of lithium resources worldwide, 49.9 million tonnes exist in this area. The researchers in this study surveyed the best country for constructing a battery for companies in the U.S. Because of the growth of electric vehicles and their use of the lithium battery, the world is facing astronomical prices for lithium. To emphasize this issue and help managers create good policy, this study combined multiple methods. The improved suitability-feasibility-acceptability (SFA) strategy is integrated with the Bayesian best-worst method (BBWM) and measurement of alternatives and rankings according to compromise solution (MARCOS) multicriteria methods to determine the best destination. For comparison, based on the SFA strategy, seven criteria are introduced: commercially viable reserves, national minimum wage, corporate income tax, accessibility to mining companies, accessibility to the waterway, population, and political stability index. The Bayesian BWM analysis reveals that the foremost factor is corporate income tax, whereas MARCOS’s findings indicate that Chile is the best country to construct the lithium battery industry. To verify the proposed approach, a comparison analysis also is performed. Full article
(This article belongs to the Special Issue Multi-Criteria Decision Making and Data Mining)
►▼ Show Figures

Figure 1

30 pages, 4771 KiB  
Article
Efficient Prediction of Court Judgments Using an LSTM+CNN Neural Network Model with an Optimal Feature Set
by Daniyal Alghazzawi, Omaimah Bamasag, Aiiad Albeshri, Iqra Sana, Hayat Ullah and Muhammad Zubair Asghar
Mathematics 2022, 10(5), 683; https://doi.org/10.3390/math10050683 - 22 Feb 2022
Cited by 20 | Viewed by 4138
Abstract
As the amount of historical data available in the legal arena has grown over time, industry specialists are driven to gather, compile, and analyze this data in order to forecast court case rulings. However, predicting and justifying court rulings while using judicial facts [...] Read more.
As the amount of historical data available in the legal arena has grown over time, industry specialists are driven to gather, compile, and analyze this data in order to forecast court case rulings. However, predicting and justifying court rulings while using judicial facts is no easy task. Currently, previous research on forecasting court outcomes using small experimental datasets yielded a number of unanticipated predictions utilizing machine learning (ML) models and conventional methodologies for categorical feature encoding. The current work proposes forecasting court judgments using a hybrid neural network model, namely a long short-term memory (LSTM) network with a CNN, in order to effectively forecast court rulings using historic judicial datasets. By prioritizing and choosing features that scored the highest in the provided legal data set, only the most pertinent features were picked. After that, the LSTM+CNN model was utilized to forecast lawsuit verdicts. In contrast to previous related experiments, this composite model’s testing results were promising, showing 92.05 percent accuracy, 93 percent precision, 94 percent recall, and a 93 percent F1-score. Full article
(This article belongs to the Special Issue Multi-Criteria Decision Making and Data Mining)
►▼ Show Figures

Figure 1

23 pages, 5226 KiB  
Article
An Improved Intellectual Capital Management Method for Selecting and Prioritizing Intangible-Related Aspects: A Case Study of Small Enterprise in Thailand
by Ratapol Wudhikarn and Danaitun Pongpatcharatorntep
Mathematics 2022, 10(4), 626; https://doi.org/10.3390/math10040626 - 17 Feb 2022
Cited by 6 | Viewed by 1648
Abstract
This study develops a new integrated approach for improving deficiencies relating to executives’ intuitive or illogical decisions, mainly found in past intellectual capital management (ICM) methods. To simultaneously rectify several flaws, the process model of intellectual capital (IC), a traditional ICM method, is [...] Read more.
This study develops a new integrated approach for improving deficiencies relating to executives’ intuitive or illogical decisions, mainly found in past intellectual capital management (ICM) methods. To simultaneously rectify several flaws, the process model of intellectual capital (IC), a traditional ICM method, is integrated using decision science methods—the analytic network process (ANP) and quality function deployment (QFD). The process model of IC is adopted as a core procedure of the proposed ICM approach. ANP is integrated to improve the ability to consider relationships among the IC critical factors and their impacts, while QFD is included to facilitate the systematic consideration and identification of correlations, linkages, and impacts between all IC-related elements from the business concept to strategic plans. The proposed method was applied to two case studies in one real enterprise in Thailand. The results of the implementation reveal the priorities of all IC-related aspects, and the first priority of key success factors (KSFs), key performance indicators (KPIs), and action plans (APs) are all associated with the organization in the structural capital dimension. The results demonstrate that the method may offer advantages with respect to the conceptual expectations and may prioritize critical IC factors and identify their weights. Furthermore, the improved method could indicate the correlations and impacts between related elements, such as critical factors and associated indicators. This study proposes a new comprehensive and systematic management framework by integrating different concepts—decision science methods and the ICM method. To the best of the authors’ knowledge, this improved approach has not been explored or proposed in earlier studies. Full article
(This article belongs to the Special Issue Multi-Criteria Decision Making and Data Mining)
►▼ Show Figures

Figure 1

24 pages, 1886 KiB  
Article
A Combined Interval Type-2 Fuzzy MCDM Framework for the Resilient Supplier Selection Problem
by Seyed Amirali Hoseini, Sarfaraz Hashemkhani Zolfani, Paulius Skačkauskas, Alireza Fallahpour and Sara Saberi
Mathematics 2022, 10(1), 44; https://doi.org/10.3390/math10010044 - 23 Dec 2021
Cited by 21 | Viewed by 3209
Abstract
Selecting the most resilient supplier is a crucial problem for organizations and managers in the supply chain. However, due to the inherited high degree of uncertainty in real-life projects, developing a decision-making framework in a crisp or fuzzy environment may not present accurate [...] Read more.
Selecting the most resilient supplier is a crucial problem for organizations and managers in the supply chain. However, due to the inherited high degree of uncertainty in real-life projects, developing a decision-making framework in a crisp or fuzzy environment may not present accurate or reliable results for the managers. For this reason, it is better to evaluate the potential suppliers in an Interval Type-2 Fuzzy (IT2F) environment for better dealing with this ambiguity. This study developed an improved combined IT2F Best Worst Method (BWM) and IT2F technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model “Atieh Sazan” Co. as a case study, such that the IT2FBWM was employed for obtaining the weight of criteria. The IT2FTOPSIS was utilized for ranking the potential suppliers based on Hamming distance measure. In both phases, the opinions of experts as IT2F linguistic terms were employed for weighting the criteria and obtaining the relative importance of the alternatives in terms of the evaluative criteria. After obtaining the final results, the proposed model was validated by replacing Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW) approaches separately instead of BWM for weighting the criteria. After executing both new models, it was found that the final ranking was similar to the final ranking of the proposed model, representing the reliability and accuracy of the obtained results. Moreover, it was concluded that the resilient criteria of “Reorganization” and “Redundancy” are the most determinant measures for selecting the best supplier rather than measures in the Iranian Construction Industry. Full article
(This article belongs to the Special Issue Multi-Criteria Decision Making and Data Mining)
►▼ Show Figures

Figure 1

16 pages, 3340 KiB  
Article
A Novel Trading Strategy Framework Based on Reinforcement Deep Learning for Financial Market Predictions
by Li-Chen Cheng, Yu-Hsiang Huang, Ming-Hua Hsieh and Mu-En Wu
Mathematics 2021, 9(23), 3094; https://doi.org/10.3390/math9233094 - 30 Nov 2021
Cited by 7 | Viewed by 3854
Abstract
The prediction of stocks is complicated by the dynamic, complex, and chaotic environment of the stock market. Investors put their money into the financial market, hoping to maximize profits by understanding market trends and designing trading strategies at the entry and exit points. [...] Read more.
The prediction of stocks is complicated by the dynamic, complex, and chaotic environment of the stock market. Investors put their money into the financial market, hoping to maximize profits by understanding market trends and designing trading strategies at the entry and exit points. Most studies propose machine learning models to predict stock prices. However, constructing trading strategies is helpful for traders to avoid making mistakes and losing money. We propose an automatic trading framework using LSTM combined with deep Q-learning to determine the trading signal and the size of the trading position. This is more sophisticated than traditional price prediction models. This study used price data from the Taiwan stock market, including daily opening price, closing price, highest price, lowest price, and trading volume. The profitability of the system was evaluated using a combination of different states of different stocks. The profitability of the proposed system was positive after a long period of testing, which means that the system performed well in predicting the rise and fall of stocks. Full article
(This article belongs to the Special Issue Multi-Criteria Decision Making and Data Mining)
►▼ Show Figures

Figure 1

22 pages, 964 KiB  
Article
An Intuitionistic Fuzzy Approach for Smart City Development Evaluation for Developing Countries: Moroccan Context
by Mohamed Hanine, Omar Boutkhoum, Fatima El Barakaz, Mohamed Lachgar, Noureddine Assad, Furqan Rustam and Imran Ashraf
Mathematics 2021, 9(21), 2668; https://doi.org/10.3390/math9212668 - 21 Oct 2021
Cited by 9 | Viewed by 2163
Abstract
Rapid urbanization to meet the needs of the growing population has led to several challenges such as pollution, increased and congested traffic, poor sustainability, and impact on the ecological environment. The conception of smart cities comprising intelligent convergence systems has been regarded as [...] Read more.
Rapid urbanization to meet the needs of the growing population has led to several challenges such as pollution, increased and congested traffic, poor sustainability, and impact on the ecological environment. The conception of smart cities comprising intelligent convergence systems has been regarded as a potential solution to overcome these problems. Based on the information, communications, and technology (ICT), the idea of a smart city has emerged to decrease the impact of rapid urbanization. In this context, important efforts have been made for making cities smarter and more sustainable. However, the challenges associated with the implementation and evaluation of smart cities in developing countries are not examined appropriately, particularly in the Moroccan context. To analyze the efficacy and success of such efforts, the evaluation and comparisons using common frameworks are significantly important. For this purpose, the present research aims to investigate and evaluate the most influential dimensions and criteria for smart city development (SCD) in the Moroccan context. To reach this goal, this study proposes a new integrated Multi-Criteria Decision-Making (MCDM) model based on Intuitionistic Fuzzy Analytical Hierarchy Process (IF-AHP) and Intuitionistic Fuzzy Decision-Making Trial and Evaluation Laboratory (IF-DEMATEL). In the given context, the IF-AHP is employed to analyze the structure of the problem and calculate the weights of the qualitative and quantitative dimensions/criteria by incorporating the uncertainty values provided by the experts. Later, IF-DEMATEL is used to construct the structural correlation of dimensions/criteria in MCDM. The use of intuitionistic fuzzy set theory helps in dealing with the linguistic imprecision and the ambiguity of experts’ judgment. Results reveal that ‘Smart Living and Governance’ and ‘Smart Economy’ are major dimensions impacting the SCD in the Moroccan context. The proposed model focuses on enhancing the understanding of different dimensions/criteria and situations in smart cities compared to traditional cities and elevates their decision-making capability. Moreover, the results are discussed, as are the managerial implications, conclusions, limitations, and potential opportunities. Full article
(This article belongs to the Special Issue Multi-Criteria Decision Making and Data Mining)
►▼ Show Figures

Figure 1

11 pages, 278 KiB  
Article
A New Ranking Method for Interval-Valued Intuitionistic Fuzzy Numbers and Its Application in Multi-Criteria Decision-Making
by Jeevaraj Selvaraj and Abhijit Majumdar
Mathematics 2021, 9(21), 2647; https://doi.org/10.3390/math9212647 - 20 Oct 2021
Cited by 6 | Viewed by 1916
Abstract
Ranking of interval-valued intuitionistic fuzzy numbers (IVIFNs) is an important task for solving real-life Decision-Making problems. It is a potential area of research that has attracted the researchers working in fuzzy mathematics. Researchers worldwide are looking for a unique ranking principle that can [...] Read more.
Ranking of interval-valued intuitionistic fuzzy numbers (IVIFNs) is an important task for solving real-life Decision-Making problems. It is a potential area of research that has attracted the researchers working in fuzzy mathematics. Researchers worldwide are looking for a unique ranking principle that can be used to discriminate any two arbitrary IVIFNs. Various ranking functions on the set of IVIFNs have been proposed. However, every method has some drawbacks in ranking arbitrary IVIFNs due to the partial ordering. This paper introduces a new ranking principle for comparing two arbitrary IVIFNs by defining a new score function based on the non-membership value of IVIFNs. In this paper, firstly, the limitations of a few well-known and existing ranking methods for IVIFNs have been discussed. Secondly, a new non-membership score on the class of IVIFNs has been introduced. Thirdly, the superiority of the proposed score function in ranking arbitrary IVIFNs over the existing methods has been demonstrated. Finally, the proposed non-membership score function has been utilized in interval-valued intuitionistic fuzzy TOPSIS (IVIF-TOPSIS) using numerical examples. Full article
(This article belongs to the Special Issue Multi-Criteria Decision Making and Data Mining)
17 pages, 1609 KiB  
Article
A Self-Learning Based Preference Model for Portfolio Optimization
by Shicheng Hu, Danping Li, Junmin Jia and Yang Liu
Mathematics 2021, 9(20), 2621; https://doi.org/10.3390/math9202621 - 17 Oct 2021
Cited by 2 | Viewed by 1970
Abstract
An investment in a portfolio can not only guarantee returns but can also effectively control risk factors. Portfolio optimization is a multi-objective optimization problem. In order to better assist a decision maker to obtain his/her preferred investment solution, an interactive multi-criterion decision making [...] Read more.
An investment in a portfolio can not only guarantee returns but can also effectively control risk factors. Portfolio optimization is a multi-objective optimization problem. In order to better assist a decision maker to obtain his/her preferred investment solution, an interactive multi-criterion decision making system (MV-IMCDM) is designed for the Mean-Variance (MV) model of the portfolio optimization problem. Considering the flexibility requirement of a preference model that provides a guiding role in MV-IMCDM, a self-learning based preference model DT-PM (decision tree-preference model) is constructed. Compared with the present function based preference model, the DT-PM fully considers a decision maker’s bounded rationality. It does not require an assumption that the decision maker’s preference structure and preference change are known a priori and can be automatically generated and completely updated by learning from the decision maker’s preference feedback. Experimental results of a comparison show that, in the case that the decision maker’s preference structure and preference change are unknown a priori, the performances of guidance and fitness of the DT-PM are remarkably superior to function based preference models; in the case that the decision maker’s preference structure is known a priori, the performances of guidance and fitness of the DT-PM is approximated to the predefined function based model. It can be concluded that the DT-PM can agree with the preference ambiguity and the variability of a decision maker with bounded rationality and be applied more widely in a real decision system. Full article
(This article belongs to the Special Issue Multi-Criteria Decision Making and Data Mining)
►▼ Show Figures

Figure 1

18 pages, 1274 KiB  
Article
Integrating Cluster Analysis into Multi-Criteria Decision Making for Maintenance Management of Aging Culverts
by Francesca Marsili and Jörg Bödefeld
Mathematics 2021, 9(20), 2549; https://doi.org/10.3390/math9202549 - 12 Oct 2021
Cited by 4 | Viewed by 1540
Abstract
Negligence in relation to aging infrastructure systems could have unintended consequences and is therefore associated with a risk. The assessment of the risk of neglecting maintenance provides valuable information for decision making in maintenance management. However, infrastructure systems are interdependent and interconnected systems [...] Read more.
Negligence in relation to aging infrastructure systems could have unintended consequences and is therefore associated with a risk. The assessment of the risk of neglecting maintenance provides valuable information for decision making in maintenance management. However, infrastructure systems are interdependent and interconnected systems of systems characterized by hierarchical levels and a multiplicity of failure scenarios. Assessment methodologies are needed that can capture the multidimensional aspect of risk and simplify the risk assessment, while also improving the understanding and interpretation of the results. This paper proposes to integrate the multi-criteria decision analysis with data mining techniques to perform the risk assessment of aging infrastructures. The analysis is characterized by two phases. First, an intra failure scenario risk assessment is performed. Then, the results are aggregated to carry out an inter failure scenario risk assessment. A cluster analysis based on the k-medoids algorithm is applied to reduce the number of alternatives and identify those which dominate the decision problem. The proposed approach is applied to a system of aging culverts of the German waterways network. Results show that the procedure allows to simplify the analysis and improve communication with infrastructure stakeholders. Full article
(This article belongs to the Special Issue Multi-Criteria Decision Making and Data Mining)
►▼ Show Figures

Figure 1

20 pages, 4231 KiB  
Article
Sustainability Ranking of the Iranian Major Ports by Using MCDM Methods
by Ali Majidi, Seyed M. J. Mirzapour Al-e-Hashem and Sarfaraz Hashemkhani Zolfani
Mathematics 2021, 9(19), 2451; https://doi.org/10.3390/math9192451 - 02 Oct 2021
Cited by 8 | Viewed by 2532
Abstract
The maritime industry is moving towards a sustainable supply chain (SSC), intending to increase the quality of logistics and make more profit. The sustainability of the maritime supply chain (MSC) in Iran is one of the topics that has not been widely studied. [...] Read more.
The maritime industry is moving towards a sustainable supply chain (SSC), intending to increase the quality of logistics and make more profit. The sustainability of the maritime supply chain (MSC) in Iran is one of the topics that has not been widely studied. Ports play a crucial role in promoting Iran’s position in the international transit of products, strengthening economic, social, and environmental connections with neighboring eastern and northeastern countries, improving GDP, and promoting the role of the free zone in the national development of the country. Port development is one of the essential elements in the government’s strategic planning in developing and activating the East axis. It has a special priority in line with government policies based on deprivation elimination and improving people’s living standards. This paper considers five significant ports of Iran that are part of special economic zones and studies the sustainability of those ports. In this research, different multi-criteria decision-making methods were applied to solve a sustainability-ranking problem of major Iranian ports. First of all, by using the SWARA method, sub-criteria of loading and unloading oil, pier length, and population obtained the highest scores in economic, environmental, and social aspects of sustainability, respectively, which shows that they had the greatest impact on the sustainability assessment of Iranian ports. Finally, the MARCOS and CoCoSo techniques were the most similar in all three dimensions of sustainability, and both seemed to be suitable methods for evaluating the sustainability of ports. Furthermore, the implementation of sensitivity analysis and definition of different scenarios for port evaluation and the high efficiency of the MARCOS method were determined in solving the port-ranking problem. According to the MARCOS method results, economic, environmental, and social criteria were all effective criteria in the sustainable development of major Iranian ports and have been largely applied in Astara, Bushehr, and Imam Khomeini ports. Based on the analysis of the results, several managerial insights to make better industry decisions are also revealed. Full article
(This article belongs to the Special Issue Multi-Criteria Decision Making and Data Mining)
►▼ Show Figures

Figure 1

30 pages, 5932 KiB  
Article
On the Modelling of Emergency Ambulance Trips: The Case of the Žilina Region in Slovakia
by Ľuboš Buzna and Peter Czimmermann
Mathematics 2021, 9(17), 2165; https://doi.org/10.3390/math9172165 - 05 Sep 2021
Cited by 5 | Viewed by 2593
Abstract
The efficient operation of emergency medical services is critical for any society. Typically, optimisation and simulation models support decisions on emergency ambulance stations’ locations and ambulance management strategies. Essential inputs for such models are the spatiotemporal characteristics of ambulance trips. Access to data [...] Read more.
The efficient operation of emergency medical services is critical for any society. Typically, optimisation and simulation models support decisions on emergency ambulance stations’ locations and ambulance management strategies. Essential inputs for such models are the spatiotemporal characteristics of ambulance trips. Access to data on the movements of ambulances is limited, and therefore modelling efforts often rely on assumptions (e.g., the Euclidean distance is used as a surrogate of the ambulance travel time; the closest available ambulance is dispatched to a call; or the travel time estimates, offered by application programming interfaces for ordinary vehicles, are applied to ambulances). These simplifying assumptions are often based on incomplete data or common sense without being fully supported by the evidence. Thus, data-driven research to model ambulance trips is required. We investigated a unique dataset of global positioning system-based measurements collected from seventeen emergency ambulances over three years. We enriched the data by exploring external sources and designed a rule-based procedure to extract ambulance trips for emergency cases. Trips were split into training and test sets. The training set was used to develop a series of statistical models that capture the spatiotemporal characteristics of emergency ambulance trips. The models were used to generate synthetic ambulance trips, and those were compared with the test set to decide which models are the most suitable and to evaluate degrees to which they fit the statistical properties of real-world trips. As confirmed by the low values of the Kullback–Leibler divergence (0.004–0.229) and by the Kolmogorov–Smirnov test at the significance level of 0.05, we found a very good fit between the probability distributions of spatiotemporal properties of synthetic and real trips. A reasonable modelling choice is a model where the exponential dependency on the population density is used to locate emergency cases, emergency cases are allocated to hospitals following empirical probabilities, and ambulances are routed using the fastest paths. The models we developed can be used in optimisations and simulations to improve their validity. Full article
(This article belongs to the Special Issue Multi-Criteria Decision Making and Data Mining)
►▼ Show Figures

Figure 1

12 pages, 506 KiB  
Article
Decision-Making under Group Commitment
by Meir Kalech
Mathematics 2021, 9(17), 2080; https://doi.org/10.3390/math9172080 - 27 Aug 2021
Viewed by 1271
Abstract
Coordination is essential for establishing and sustaining teamwork. Agents in a team must agree on their tasks and plans, and thus, group decision-making techniques are necessary to reach agreements in teams. For instance, to agree on a joint task, the agents can provide [...] Read more.
Coordination is essential for establishing and sustaining teamwork. Agents in a team must agree on their tasks and plans, and thus, group decision-making techniques are necessary to reach agreements in teams. For instance, to agree on a joint task, the agents can provide their preferences for the alternative tasks, and the best alternative could be selected by majority. Previous works assumed that agents only provide their preferences for the alternatives. However, when selecting a joint task for teamwork, it is essential to consider not only the preferences of the agents, but also the probability of the agents being able to execute the task if it is selected. In this paper, we propose a novel model, the decIsion-MAking under Group commItmeNt modEl (IMAGINE), for computing the optimal decision for a team considering several parameters. Each agent provides: (1) the utility of each alternative for the team, (2) the associated cost for the agent by executing the alternative, and (3) the probability that the agent will be able to execute the alternative task. The IMAGINE gathers these data from the agents, as well as the requisite quorum for each alternative task, which is the minimum number of agents required to complete the task successfully. Given this information, the IMAGINE determines the optimal decision for the group. We evaluated the IMAGINE by comparing it to a baseline method that does not consider the quorum requirement. We show that the IMAGINE generally comes up with a better decision than the baseline method and that the higher the quorum, the better the decisions the IMAGINE makes are. Full article
(This article belongs to the Special Issue Multi-Criteria Decision Making and Data Mining)
►▼ Show Figures

Figure 1

21 pages, 786 KiB  
Article
A Novel Framework for Mining Social Media Data Based on Text Mining, Topic Modeling, Random Forest, and DANP Methods
by Chi-Yo Huang, Chia-Lee Yang and Yi-Hao Hsiao
Mathematics 2021, 9(17), 2041; https://doi.org/10.3390/math9172041 - 25 Aug 2021
Cited by 4 | Viewed by 2470
Abstract
The huge volume of user-generated data on social media is the result of the aggregation of users’ personal backgrounds, past experiences, and daily activities. This huge size of the generated data, the so-called “big data,” has been studied and investigated intensively during the [...] Read more.
The huge volume of user-generated data on social media is the result of the aggregation of users’ personal backgrounds, past experiences, and daily activities. This huge size of the generated data, the so-called “big data,” has been studied and investigated intensively during the past few years. In spite of the impression one may get from the media, a great deal of data processing has not been uncovered by existing techniques of data engineering and processing. However, very few scholars have tried to do so, especially from the perspective of multiple-criteria decision-making (MCDM). These MCDM methods can derive influence relationships and weights associated with aspects and criteria, which can hardly be achieved by traditional data analytics and statistical approaches. Therefore, in this paper, we aim to propose an analytic framework to mine social networks, feed the meaningful information via MCDM methods based on a theoretical framework, derive causal relationships among the aspects of the theoretical framework, and finally compare the causal relationships with a social theory. Latent Dirichlet allocation (LDA) will be adopted to derive topic models based on the data retrieved from social media. By clustering the topics into aspects of the social theory, the probability associated with each aspect will be normalized and then transformed to a Likert-type 5-point scale. Afterwards, for every topic, the feature importance of all other topics will be derived using the random forest (RF) algorithm. The feature importance matrix will be transformed to the initial influence matrix of the decision-making trial and evaluation laboratory (DEMATEL). The influence relationships among the aspects and criteria and influence weights can then be derived by using the DEMATEL-based analytic network process (DANP). The influence weight versus each criterion can be derived by using DANP. To verify the feasibility of the proposed framework, Taiwanese users’ attitudes toward air pollution will be analyzed based on the value–belief–norm (VBN) theory by using social media data retrieved from Dcard (dcard.tw). Based on the analytic results, the causal relationships are fully consistent with the VBN framework. Further, the mutual influences derived in this work that were seldom discussed by earlier works, i.e., the mutual influences between altruistic concerns and egoistic concerns, as well as those between altruistic concerns and biosphere concerns, are worth further investigation in future. Full article
(This article belongs to the Special Issue Multi-Criteria Decision Making and Data Mining)
►▼ Show Figures

Figure 1

19 pages, 1677 KiB  
Article
Using DANP-mV Model to Improve the Paid Training Measures for Travel Agents amid the COVID-19 Pandemic
by Chui-Hua Liu and Bochner Liu
Mathematics 2021, 9(16), 1924; https://doi.org/10.3390/math9161924 - 12 Aug 2021
Cited by 5 | Viewed by 2097
Abstract
Due to the COVID-19 pandemic bringing travel to a standstill, an initiative of paid training for travel agencies was launched by the government. The purpose of this research is to improve the effectiveness of this measure, which is fundamental to the next tourism [...] Read more.
Due to the COVID-19 pandemic bringing travel to a standstill, an initiative of paid training for travel agencies was launched by the government. The purpose of this research is to improve the effectiveness of this measure, which is fundamental to the next tourism crisis management. Based on the related theories of tourism crisis management, organizational learning, and behavior adaption, the DANP-mV model developed an evaluation system for examining the training measure. The result of an influential network relationship map (INRM) shows that using influential effects, the “policy object” dimension and its criterion “subsidiary” should be the first improvement priority. To effectively achieve the aspiration level, the gap values point out “training courses” dimension and its criterion “excellent services” are first to improve. An action plan is produced containing all the findings and made available for easy indexing. It may contribute to the current measure improvements. Therefore, this innovative approach could help decision makers with tourism crisis policy making and help the sector with evolving learning readiness to remain sustainable. Full article
(This article belongs to the Special Issue Multi-Criteria Decision Making and Data Mining)
►▼ Show Figures

Figure 1

16 pages, 947 KiB  
Article
Evaluating the Application of CSR in the High-Tech Industry during the COVID-19 Pandemic
by Shih-Chia Chang, Ming-Tsang Lu, Mei-Jen Chen and Li-Hua Huang
Mathematics 2021, 9(15), 1715; https://doi.org/10.3390/math9151715 - 21 Jul 2021
Cited by 8 | Viewed by 2778
Abstract
Since its conception, corporate social responsibility (CSR) has seen continuous growth and become a highly discussed issue. In this paper, we propose an evaluation of how the COVID-19 pandemic could impact CSR applications. The pandemic has provided an opportunity for commerce to move [...] Read more.
Since its conception, corporate social responsibility (CSR) has seen continuous growth and become a highly discussed issue. In this paper, we propose an evaluation of how the COVID-19 pandemic could impact CSR applications. The pandemic has provided an opportunity for commerce to move on to being more authentic, to offer genuine CSR applications and to contribute toward dealing with pressing environmental and social issues. Hence, this purpose of the research is to obtain a better understanding of whether the integration of environment, social, corporate governance and economic (ESGE) aspects into CSR strategies can support sustainable development toward more sustainable growth during the COVID-19 pandemic. To meet this challenge, we offer a mixture multiple-criteria decision making (MCDM) model. Very few empirical studies have discussed CSR in the high-tech industry and proposed strategies and planning for ESGE efficiency. Using interviews with experts and a literature review, we identify the elements related to actual practices of the high-tech industry’s appraisal and the integrated MCDM techniques to suggest efficient enhancement models. The best worst method (BWM) and modified VIKOR are implemented to estimate the strategic weights and the gaps of the aspiration value. The results are valuable for classifying the priorities of CSR and are therefore helpful for those who are associated with high-tech industry management, practices and implementation. Full article
(This article belongs to the Special Issue Multi-Criteria Decision Making and Data Mining)
►▼ Show Figures

Figure 1

22 pages, 4103 KiB  
Article
An Analysis of Usage of a Multi-Criteria Approach in an Athlete Evaluation: An Evidence of NHL Attackers
by Roman Vavrek
Mathematics 2021, 9(12), 1399; https://doi.org/10.3390/math9121399 - 16 Jun 2021
Cited by 4 | Viewed by 2028
Abstract
The presented research focuses on the commonly used Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), which is applied to an evaluation of a basic set of 581 national hockey league (NHL) players in the 2018/2019 season. This is used [...] Read more.
The presented research focuses on the commonly used Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), which is applied to an evaluation of a basic set of 581 national hockey league (NHL) players in the 2018/2019 season. This is used in combination with a number of objective methods for weighting indicators for identifying differences in their usage. A total of 11 indicators with their own testimonial values, including points, hits, blocked shots and more, are selected for this purpose. The selection of a method for weighting indicators has a major influence on the results obtained and the differences between them, and maintains the internal links within the ranked set of players. Of the evaluated methods, we prefer the Mean Weight method, and we recommend that the input indicators be considered equivalent when evaluating athletes. Full article
(This article belongs to the Special Issue Multi-Criteria Decision Making and Data Mining)
►▼ Show Figures

Figure 1

14 pages, 2286 KiB  
Article
An Improved Machine Learning-Based Employees Attrition Prediction Framework with Emphasis on Feature Selection
by Saeed Najafi-Zangeneh, Naser Shams-Gharneh, Ali Arjomandi-Nezhad and Sarfaraz Hashemkhani Zolfani
Mathematics 2021, 9(11), 1226; https://doi.org/10.3390/math9111226 - 27 May 2021
Cited by 17 | Viewed by 4342
Abstract
Companies always seek ways to make their professional employees stay with them to reduce extra recruiting and training costs. Predicting whether a particular employee may leave or not will help the company to make preventive decisions. Unlike physical systems, human resource problems cannot [...] Read more.
Companies always seek ways to make their professional employees stay with them to reduce extra recruiting and training costs. Predicting whether a particular employee may leave or not will help the company to make preventive decisions. Unlike physical systems, human resource problems cannot be described by a scientific-analytical formula. Therefore, machine learning approaches are the best tools for this aim. This paper presents a three-stage (pre-processing, processing, post-processing) framework for attrition prediction. An IBM HR dataset is chosen as the case study. Since there are several features in the dataset, the “max-out” feature selection method is proposed for dimension reduction in the pre-processing stage. This method is implemented for the IBM HR dataset. The coefficient of each feature in the logistic regression model shows the importance of the feature in attrition prediction. The results show improvement in the F1-score performance measure due to the “max-out” feature selection method. Finally, the validity of parameters is checked by training the model for multiple bootstrap datasets. Then, the average and standard deviation of parameters are analyzed to check the confidence value of the model’s parameters and their stability. The small standard deviation of parameters indicates that the model is stable and is more likely to generalize well. Full article
(This article belongs to the Special Issue Multi-Criteria Decision Making and Data Mining)
►▼ Show Figures

Figure 1

15 pages, 983 KiB  
Article
Self-Management Portfolio System with Adaptive Association Mining: A Practical Application on Taiwan Stock Market
by Jia-Hao Syu, Yi-Ren Yeh, Mu-En Wu and Jan-Ming Ho
Mathematics 2021, 9(10), 1093; https://doi.org/10.3390/math9101093 - 12 May 2021
Cited by 5 | Viewed by 1880
Abstract
A well-established financial trading system should well perform in resource allocation, risk management, and sustainability. In this paper, we propose a self-management portfolio system with adaptive association mining for practical applications. The system allocates funds into independent units for risk management, and utilizes [...] Read more.
A well-established financial trading system should well perform in resource allocation, risk management, and sustainability. In this paper, we propose a self-management portfolio system with adaptive association mining for practical applications. The system allocates funds into independent units for risk management, and utilizes association mining and adaptive closing mechanism for resource allocation and sustainability, and adopts a self-management module for monitoring positions. The proposed system boosts the annual return and Sharpe ratio to 9.1% and 0.578 (increased to 2.28 and 2.48 times), and reduces the drawdown risk to 34.6% (decreased to almost half). Furthermore, the system rapidly closes the stock positions to avoid drawdown risk in the bear markets, and gradually increases the stock positions when the market turns into bull. Compared with benchmarks, proposed system outperforms all benchmarks in all measurements and on randomly sampled dataset. Full article
(This article belongs to the Special Issue Multi-Criteria Decision Making and Data Mining)
►▼ Show Figures

Figure 1

23 pages, 3097 KiB  
Article
Risk Evaluation of Electric Power Grid Enterprise Related to Electricity Transmission and Distribution Tariff Regulation Employing a Hybrid MCDM Model
by Wenjin Li, Bingkang Li, Rengcun Fang, Peipei You, Yuxin Zou, Zhao Xu and Sen Guo
Mathematics 2021, 9(9), 989; https://doi.org/10.3390/math9090989 - 28 Apr 2021
Cited by 5 | Viewed by 1650
Abstract
In China, a new-round marketization reform of electricity industry is in progress, and the electricity transmission and distribution tariff reform is the core and important task. Currently, the electricity transmission and distribution tariff regulation has gone to the second round in China, and [...] Read more.
In China, a new-round marketization reform of electricity industry is in progress, and the electricity transmission and distribution tariff reform is the core and important task. Currently, the electricity transmission and distribution tariff regulation has gone to the second round in China, and the electric power grid enterprises are facing a closed-loop regulatory system and an increasingly strict regulatory environment. Therefore, it is urgent to evaluate the risk of electric power grid enterprise that is related to electricity transmission and distribution tariff regulation, which can aid the electricity regulators and electric power grid enterprise operators to manage risk and promote the sustainable development of electric power industry. In this paper, a hybrid novel multi-criteria decision making (MCDM) method combining the fuzzy Best-Worst method (FBWM) and improved fuzzy comprehensive evaluation method based on a vague set is proposed for the risk evaluation of electric power grid enterprise related to electricity transmission and distribution tariff regulation. The risk evaluation index system is built. Subsequently, the FBWM is utilized to determine the optimal weights of electric power grid enterprise risk criteria, and the improved fuzzy comprehensive evaluation method that is based on vague set is employed to rank the comprehensive risk grade of electric power grid enterprise related to electricity transmission and distribution tariff regulation. The risk of a province-level electric power grid enterprise that is located in Northern China is empirically evaluated using the proposed MCDM method, and the result indicates that the overall risk of this province-level electric power grid enterprise belongs to ‘High’ grade, but it is very close to ‘Very High’ grade. The results indicate that the proposed hybrid novel MCDM method in this paper is effective and practical. Meanwhile, it provides a new view for the risk evaluation of electric power grid enterprise that is related to electricity transmission and distribution tariff regulation. Full article
(This article belongs to the Special Issue Multi-Criteria Decision Making and Data Mining)
►▼ Show Figures

Figure 1

27 pages, 16362 KiB  
Article
Visualization Method for Decision-Making: A Case Study in Bibliometric Analysis
by Roozbeh Haghnazar Koochaksaraei, Frederico Gadelha Guimarães, Babak Hamidzadeh and Sarfaraz Hashemkhani Zolfani
Mathematics 2021, 9(9), 940; https://doi.org/10.3390/math9090940 - 23 Apr 2021
Cited by 4 | Viewed by 2619
Abstract
Data and information visualization have drawn an increasingly wide range of interest from several academic fields and industries. Concurrently, exploring a huge set of data to support feasible decisions needs an organized method of Multi-Criteria Decision Making (MCDM). The dramatic increasing of data [...] Read more.
Data and information visualization have drawn an increasingly wide range of interest from several academic fields and industries. Concurrently, exploring a huge set of data to support feasible decisions needs an organized method of Multi-Criteria Decision Making (MCDM). The dramatic increasing of data producing during the past decade makes visualization necessary as a presentation layer on the top of MCDM process. This study aims to propose an integrated strategy to rank the alternatives in the dataset, by combining data, MCDM methods, and visualization layers. In fact, the well designed combination of Information Visualization and MCDM provides a more user-friendly approach than the traditional methods. We investigate a case study in bibliometric analyses, which have become an important dimension and tool for evaluating the impact and performance of researchers, departments, and universities. Hence, finding the best and most reliable papers, authors, and publishers considering diverse criteria is one of the important challenges in science world. Therefore, this text is presenting a new strategy on the bibliometric dataset as a case study and it demonstrates that this strategy can be more meaningful for the end users than the current tools. Finally, the presented simulations illustrate the performance and utilization of this combination. In other words, the researchers of this study could design and implement a tool that overcomes the biggest challenges of data analyzing and ranking via a combination of MCDM and visualization methodologies that can provide a tremendous amount of insight and information from a massive dataset in an efficient way. Full article
(This article belongs to the Special Issue Multi-Criteria Decision Making and Data Mining)
►▼ Show Figures

Figure 1

20 pages, 2232 KiB  
Article
Systematic Evaluation Model for Developing Sustainable World-Class Universities: An East Asian Perspective
by Meng-Chen Zhang, Bo-Wei Zhu, Chao-Meng Huang and Gwo-Hshiung Tzeng
Mathematics 2021, 9(8), 837; https://doi.org/10.3390/math9080837 - 12 Apr 2021
Cited by 8 | Viewed by 2252
Abstract
Due to the unbalance between Asian and Western countries in terms of higher education development and pressure from global competition, universities in several East Asian countries have striven to become world-class universities (WCUs) by actively assessing themselves using various global ranking systems and [...] Read more.
Due to the unbalance between Asian and Western countries in terms of higher education development and pressure from global competition, universities in several East Asian countries have striven to become world-class universities (WCUs) by actively assessing themselves using various global ranking systems and subsequently investing in key performance indicators. Numerous scholars have suggested that for these East Asian catch-up universities (EACUs), independently improving the elements related to high-weight indicators could produce short-term increases in ranking performance; however, this approach is not conducive to sustainable development. In addition, little is currently understood regarding sustainable development strategies for developing EACUs into WCUs. This study proposes a systematic evaluation model for self-assessment and the creation of strategies to transform EACUs into sustainable WCUs. The fuzzy Delphi method was used to determine criteria for a new evaluation framework, and the decision-making trial and evaluation laboratory method was employed to construct the influential relationships among the criteria. Two cases were then selected to demonstrate the superiority of the model for creating sustainable development strategies for EACUs. This study provides a systematic perspective and a useful tool for decision-makers at EACUs to achieve sustainable development goals. Full article
(This article belongs to the Special Issue Multi-Criteria Decision Making and Data Mining)
►▼ Show Figures

Figure 1

19 pages, 1903 KiB  
Article
A Hybrid MCDM Model for Evaluating Open Banking Business Partners
by Alexander Kuan Daiy, Kao-Yi Shen, Jim-Yuh Huang and Tom Meng-Yen Lin
Mathematics 2021, 9(6), 587; https://doi.org/10.3390/math9060587 - 10 Mar 2021
Cited by 12 | Viewed by 3850
Abstract
Open banking (OB) is an emerging business field in the financial sector, which relies on intensive collaboration between banks and non-banking service providers. However, how to evaluate OB business partners from multiple perspectives for banks is underexplored. Therefore, this study proposed a hybrid [...] Read more.
Open banking (OB) is an emerging business field in the financial sector, which relies on intensive collaboration between banks and non-banking service providers. However, how to evaluate OB business partners from multiple perspectives for banks is underexplored. Therefore, this study proposed a hybrid decision model with supports from seasoned domain experts. This study also adopts a domestic bank from Taiwan and four non-banking service providers to illustrate the hybrid approach with the confidence-weighted fuzzy assessment technique. The proposed model might be the first attempt to explore the OB adoption strategy by the novel approach. However, its limitations are the presumed independent relationship among the factors of this hybrid model. Additionally, the results hinge upon domain experts’ knowledge. In practice, the research findings identify the relative importance of banks’ crucial factors to select OB strategic partners, which provide managerial insights and valuable guidance for the banking sector. Full article
(This article belongs to the Special Issue Multi-Criteria Decision Making and Data Mining)
►▼ Show Figures

Figure 1

22 pages, 4303 KiB  
Article
Risk Evaluation of Electric Power Grid Investment in China Employing a Hybrid Novel MCDM Method
by Yana Duan, Yang Sun, Yu Zhang, Xiaoqi Fan, Qinghuan Dong and Sen Guo
Mathematics 2021, 9(5), 473; https://doi.org/10.3390/math9050473 - 25 Feb 2021
Cited by 9 | Viewed by 2002
Abstract
Socio-economic development is undergoing changes in China, such as the recently proposed carbon peak and carbon neutral targets, new infrastructure development strategy and the Coronavirus disease 2019 (COVID-19) pandemic. Meanwhile, the new-round marketization reform of the electricity industry has been ongoing in China [...] Read more.
Socio-economic development is undergoing changes in China, such as the recently proposed carbon peak and carbon neutral targets, new infrastructure development strategy and the Coronavirus disease 2019 (COVID-19) pandemic. Meanwhile, the new-round marketization reform of the electricity industry has been ongoing in China since 2015. Therefore, it is urgent to evaluate the risk of electric power grid investment in China under new socio-economic development situation, which can help the investors manage risk and reduce risk loss. In this paper, a hybrid novel multi-criteria decision making (MCDM) method combining the latest group MCDM method, namely, Bayesian best–worst method (BBWM) and improved matter-element extension model (IMEEM) is proposed for risk evaluation of electric power grid investment in China under new socio-economic development situation. The BBWM is used for the weights’ determination of electric power grid investment risk criteria, and the IMEEM is employed to rank risk grade of electric power grid investment. The risk evaluation index system of electric power grid investment is built, including economic, social, environmental, technical and marketable risks. The risk of electric power grid investment under new socio-economic development situation in Inner Mongolia Autonomous Region of China is empirically evaluated by using the proposed MCDM method, and the results indicate that it belongs to “Medium” grade, but closer to “High” grade. The main contributions of this paper include: (1) it proposes a hybrid novel MCDM method combining the BBWM and IMEEM for risk evaluation of electric power grid investment; and (2) it provides a new view for risk evaluation of electric power grid investment including economic, social, environmental, technical and marketable risks. The proposed hybrid novel MCDM method for the risk evaluation of electric power grid investment is effective and practical. Full article
(This article belongs to the Special Issue Multi-Criteria Decision Making and Data Mining)
►▼ Show Figures

Figure 1

21 pages, 2181 KiB  
Article
Evaluating Industry 4.0 Technology Application in SMEs: Using a Hybrid MCDM Approach
by Shih-Chia Chang, Hsu-Hwa Chang and Ming-Tsang Lu
Mathematics 2021, 9(4), 414; https://doi.org/10.3390/math9040414 - 20 Feb 2021
Cited by 27 | Viewed by 2834
Abstract
Evaluating Industry 4.0 technology application in small and medium-sized enterprises (SMEs) is an issue that requires a multi-criteria strategy comprising quantitative and qualitative elements. The purpose of this study is to integrate performance estimation of Industry 4.0 technology application using the technology–organization–environment (TOE) [...] Read more.
Evaluating Industry 4.0 technology application in small and medium-sized enterprises (SMEs) is an issue that requires a multi-criteria strategy comprising quantitative and qualitative elements. The purpose of this study is to integrate performance estimation of Industry 4.0 technology application using the technology–organization–environment (TOE) framework. Relating TOE to Industry 4.0 technology application evaluation is more multifaceted than other methods and it requires comprehensive analysis. In this study, we applied a multiple-criteria decision-making (MCDM) approach to develop a model which integrates MCDM to perform an assessment that prioritizes the influence weights of Industry 4.0 technology application to SMEs’ factors. Firstly, we carried out a review of the literature and the TOE framework was selected to generate nine elements, along with three aspects used to measure Industry 4.0 technology application in SMEs. Secondly, the approach of the decision-making trial and evaluation laboratory (DEMATEL) was set up using an influence network relations digraph (INRD). The DEMATEL-based analytic network process (DANP) was used to indicate the influence weights linking the above aspects and elements. Lastly, the modified VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) technique applied influence weights to assess the aspects/elements in the gaps identified and to investigate how to reduce the gaps so as to estimate the application of Industry 4.0 technology by SMEs. The results show that the technology aspect is the most influential factor. Full article
(This article belongs to the Special Issue Multi-Criteria Decision Making and Data Mining)
►▼ Show Figures

Figure 1

18 pages, 1901 KiB  
Article
Analysis of Key Factors for Supplier Selection in Taiwan’s Thin-Film Transistor Liquid-Crystal Displays Industry
by Jung-Fa Tsai, Chin-Po Wang, Ming-Hua Lin and Shih-Wei Huang
Mathematics 2021, 9(4), 396; https://doi.org/10.3390/math9040396 - 17 Feb 2021
Cited by 11 | Viewed by 2978
Abstract
With the advent of science and technology, smart devices have become ubiquitous; since the display unit is a vital component in many smart devices, the Thin-Film Transistor Liquid-Crystal Displays (TFT-LCD) industry has been one of the most rapidly growing industries. Taiwanese manufacturers play [...] Read more.
With the advent of science and technology, smart devices have become ubiquitous; since the display unit is a vital component in many smart devices, the Thin-Film Transistor Liquid-Crystal Displays (TFT-LCD) industry has been one of the most rapidly growing industries. Taiwanese manufacturers play a critical role in this industry. This study investigates key factors for supplier selection in Taiwan’s TFT-LCD industry. TFT-LCD is a technology-intensive industry. However, few studies in the past considered the technological abilities dimension in supplier selection. Therefore, this study discusses the factors related to the technological abilities dimension in supplier selection. Most research considered supplier selection based on the traditional criteria such as cost and quality. This study discusses the importance of the resilience criteria such as agility and flexibility. A method combining DEMATEL (Decision Making Trial and Evaluation Laboratory) and ANP (Analytic Network Process) is applied to analyze key factors for supplier selection in Taiwan’s TFT-LCD industry. The analytical results indicate that the technological abilities dimension and resilience criteria are at the forefront of the ranking in prominence. The influential weights of criteria and the causal diagram among all criteria derived from this study can offer guidance for suppliers on improving various factors to become desirable partners in the TFT-LCD industry supply chain. Full article
(This article belongs to the Special Issue Multi-Criteria Decision Making and Data Mining)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-30
Back to TopTop