Special Issue "Multiple Criteria Decision Making"

A special issue of Mathematics (ISSN 2227-7390).

Deadline for manuscript submissions: 31 July 2021.

Special Issue Editors

Assoc. Prof. Dr. Violeta Kersuliene
E-Mail Website1 Website2
Guest Editor
Department of Law, Business Management faculty, Vilnius Gediminas Technical University, LT-10223 Vilnius, Lithuania
Interests: operations research; optimization and decision analysis; multicriteria decision making; MCDM; multiple-criteria optimization; multiattribute decision making (MADM); multiobjective optimization (MODM); approximations; mathematics for decision making; decision support systems; evaluation sustainable development; civil engineering; management; knowledge management; game theory and economical computing; finance engineering; algorithms and software engineering; energy; fuzzy set theory; negotiations; the consensus in groups
Prof. Dr. Zenonas Turskis
grade E-Mail Website1 Website2 Website3
Guest Editor
Laboratory of Operations Research, Institute of Sustainable Construction, Vilnius Gediminas Technical University LT-10223 Vilnius, Saulėtekio al. 11, Lithuania
Interests: operations research; optimization and decision analysis; multicriteria decision making; MCDM; multiple-criteria optimization; multiattribute decision making (MADM); multiobjective optimization (MODM); approximations; mathematics for decision making; decision support systems; evaluation sustainable development; civil engineering; management; knowledge management; game theory and economical computing; finance engineering; algorithms and software engineering; energy; fuzzy set theory; negotiations; the consensus in groups
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Special Issue Information

Dear Colleagues,

Decision making is one of the most critical topics in various areas of human activity and shows how to make the right decisions to reach the end goal. Decision makers over the past few decades have successfully used multicriteria decision-making (MCDM) approaches to solve complex decision-making problems in a variety of fields, such as economics, finance, logistics, environmental remediation, business, engineering, medicine, law, and more. Decision makers need MCDM tools that help to balance conflicting goals with multiple choices and limited resources and time for decision makers to match conflicting goals.

Therefore, the use of the MCDM methods for solving problems in different fields is prevalent and helps in making significant decisions. MCDM is an effective systematic and quantitative way to deal with issues in the presence of several alternatives and several usually different criteria. Decision making regarding very complex problems, including business-related decisions and real-life decisions, requires an appropriate and reliable decision support system. Optimization may be considered as a decision-making process to get the most out of available resources for the best attainable results. Many real-world problems are multifaceted or multiattribute problems, which naturally involve multiple competing goals that need to be optimized at the same time within certain constraints or by choosing from the available discrete alternatives. In contrast to single-goal optimization, solutions to multiobjective and multiattribute problems correspond to a set of solutions with trade-offs, each expressing a peculiar trade-off between different goals or attributes. Optimization can be considered as a decision-making process to maximize the effectiveness of the available resources used to achieve the best results possible.

 Considering, planning, and appropriate decision making requires the use of analytical methods that examine trade-offs, consider multiple scientific, political, economic, ecological and social dimensions, and reduces possible conflicts in an optimizing framework. MCDM techniques fall into two major groups. The first group is discrete MCDM, including multiattribute utility theory (MAUT), analytic hierarchy process/analytic network process (AHP/ANP), and outranking methods, where the decision maker has to evaluate a finite set of alternatives to a) select the best option, b) rank alternatives from the best to worst, and c) classify alternatives into predefined classes or the described options, i.e., multiattribute decision-making (MADM) methods. The second is continuous MCDM, including multiobjective programming and goal programming, where there is an infinite set of alternatives, i.e., multiobjective decision-making (MODM) methods.

MCDM approaches are considered the established methods to aid decision makers in taking suitable decisions, and their applications are growing in popularity in many fields that include but are not limited to business management, logistics, supply chains, energy, urban development, waste management, and others. In decision-making theories as well as in business practice, decision makers encounter many imprecise concepts. Imprecise data are the premises which serve the specification of economic models and, consequently, the decision-making process. All this requires the utilization of the vague interference rule. In the second half of the 20th century, we witnessed the development of the behavioral economy. The world of economic concepts and models became even more imprecise. In addition, the design, planning, and operations management rely on mathematical models, the complexity of which depends on the detail of models and complexity/characteristics of the problem they represent. It is thus no surprise that with the ever-increasing complexity of the issues, optimization comes with an inherent facet of uncertainty conveyed in different formal ways and calls for innovative approaches to produce optimal and interpretable solutions. Today’s real-world problems involve multiple data sets, some precise or objective and some uncertain or subjective. Many decision problems manage linguistic information assessed through several ordered qualitative scales. In these contexts, the main question arising is how to aggregate this qualitative information. This issue welcomes MCDM procedures that rank a set of alternatives assessed using a specific non-uniform ordered qualitative scale for each criterion. Therefore, ordinal models to manage the ordinal degree of proximity from different ordered qualitative sizes are essential to this issue. Moreover, decision makers often make decisions in the face of the unknown.

A wide range of statistical and nonstatistical decision-making techniques have been proposed in the literature to model complex business processes. Unfortunately, decision making by humans is often suboptimal in ways that can be reliably predicted. Fuzzy set theory laid the foundations for significant modeling uncertainty, vagueness, and imprecision. The method of fuzzy sets noted substantial progress in economics in both theoretical and practical studies. MCDM has considerably expanded beyond classical and formal methodologies and has also involved intuitive and informal processes. Therefore, in addition to conventional MCDM methods, this Special Issue also welcomes their integration with uncertainty theory, such as fuzzy sets, rough sets, neutrosophic sets, etc.

Hence, different mathematical models of real-life multicriteria optimization problems can be applied to various uncertain frameworks, with particular emphasis on real-life optimization problems. Neutrosophic logic, set, probability, statistics, and others are, respectively, generalizations of fuzzy and intuitionistic fuzzy logic and set, classical and imprecise probability, classical statistics, and others. Furthermore, the process industry seeks not only to minimize cost but also to minimize adverse environmental and social impacts. On the other hand, to give an appropriate response to the new challenges raised, the decision-making process can be done by applying different methods and tools, as well as using different objectives. In real-life problems, the formulation of decision-making problems and application of optimization techniques to support decisions are particularly complex, and a wide range of optimization techniques and methodologies are used to minimize risks or improve quality in making concomitant decisions. The importance of strategic behavior in the human and social world is increasingly recognized in theory and practice. As a result, multicriteria optimization models and applications have emerged as a fundamental tool in pure and applied research. Multicriteria optimization models and applications strongly support decision-making processes in an interactive environment. They draw on mathematics, economics, statistics, engineering, biology, political science, operations research, and other subjects. A multioptimization occurs when multiple criteria considered by a decision maker are concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously. The decision maker finds a set of objectives in a situation in which each goal is possibly conflicting, possibly equally important, or perhaps overlapping. The problem is then to determine the trade-off between objectives to support the decision-making process. Additionally, a sensitivity analysis should be done to validate/analyze the influence of uncertainty regarding decision making.

In this Special Issue, researchers from academia and industries are invited to submit papers that explore aspects of multiobjective or multiattribute modeling and optimization in a crisp or uncertain environment and will elaborate on the state-of-the-art case studies in selected areas of application related to sustainable development decision aiding. Analytical models, empirical studies, and case-based studies are all welcome, as long as the research work provides new insights and implications for the practice of decision making.

In recent years, new mathematical developments have been applied in the context of financial economics. Thus, a relevant challenge is to provide a bridge between, on the one hand, new mathematical tools and, on the other, economics and finance issues.

Articles are welcome on this issue where systemic solutions in practical decision making that bring economic, social, and environmental benefits are offered through a variety of methodologies and tools (e.g., information technologies and multiple criteria decision-making methods). Articles that propose new methods dealing with multifaceted issues are also welcome.

Since the pioneering paper of Zadeh, many extensions of the fuzzy set theory with practical applications in different areas have also been proposed, including intuitionistic fuzzy sets, interval-valued fuzzy sets, interval-valued intuitionistic fuzzy sets, rough sets, bipolar fuzzy sets, grey sets, hesitant fuzzy sets, fuzzy numbers, and fuzzy oriented sets, among others. The multivalued logic and the lattice are the theoretical background of fuzzy set theory. MCDM methods for handling imprecision and vagueness in real decision-making problems are used in several different areas. The fuzzy set theory allows capturing uncertainty, imprecision, inaccurate definition of a decision problem and, as a consequence, fuzzing the issue.

As editors, we invite original research papers in this Special Issue that report on state-of-the-art and recent advancements in multicriteria decision making using the fuzzy or vague determined environment to computing, group decision-making problems, pattern recognition, information processing, and many other practical achievements.

The objective of this Special Issue is to gather a collection of papers reflecting the latest developments in practical applications of the MCDM mathematical tools and the latest developments in the mathematical programming methods of operations research for multicriteria optimization for different fields of multicriteria optimization approaches, models, applications and techniques. The use of some factor models to manage potential risks and other applications in economic theory and modeling are also of interest.

The scope of this issue is MCDM in a broad sense, focusing on recent advances in both discrete and continuous techniques and significant applications in different fields.

This Special Issue focuses on the science and art of multicriteria decisions, especially in multidisciplinary settings. We expect to publish high-quality papers in the categories of discovery, integration, application, and teaching of multicriteria decisions.

We invite authors to submit original research and review articles which give a more in-depth insight into the applications of MCDM theories in real-life problem-solving.

We hope that this Special Issue will stimulate both theoretical and applied research on the MCDM and related fields. It is undoubtedly impossible in this short editorial to provide a more comprehensive description of all potential articles in this Special Issue.

We invite authors to submit original research articles which propose novel MCDM optimization models for solving real-life-related problems.

  • Decent work and economic growth
  • Industry, innovation, and infrastructure
  • Sustainable development
  • Responsible resource consumption and production
  • Climate action
  • Peace, justice and strong institutions

The proposed papers should present the advanced MCDM systems related to the following directions of quantified decision making:

  • Applications of MCDM
  • Modeling of MCDM
  • Decision analysis for sustainable production and consumption
  • Decision support systems
  • Discrete and continuous MCDM
  • Economic diagnosis and forecasting
  • Fuzziness in MCDM
  • Granular computing-based multi-objective or multiattribute optimization
  • Group decision making
  • Integrated approaches for modeling decision making
  • Intuitiveness in MCDM
  • MCDM methodologies
  • MCDM theories
  • MCDM in strategic management
  • MCDM design issues
  • Multigoal decisions
  • MODM intelligence problem
  • Multistage multiobjective or multiattribute problem
  • MCDM negotiation and group decisions
  • Neural-network-based multiobjective or multiattribute optimization
  • Risk management
  • Soft-computing techniques for MCDM
  • Survey and theoretical articles, as well as application papers
  • The development of MCDM Methods capable of capturing sustainability
  • The development of MCDM methods capable of capturing sustainability and fuzziness (uncertainty, imprecision and inaccurate definition) of the decision problem
  • Tools for multicriteria decisions

We are motivated by the overriding aim to indicate the connections between MCDM systems and real-life problems.

Assoc Prof. Dr. Violeta Kersuliene
Prof. Dr. Zenonas Turskis
Guest Editors

Manuscript Submission Information

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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 1600 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

  • Application of MCDM methods
  • Application of the interactive MCDM methods
  • Application of uncertainty MCDM approaches
  • Applications of fuzzy MCDM methods
  • Applications of outranking MCDM methods
  • Conditional value-at-risk
  • Conflict resolution
  • Data mining tools
  • Decision making
  • Fuzzy (uncertain, imprecise, ill-determined) decision problems
  • Fuzzy reasoning
  • Fuzzy sets
  • Grey systems
  • Group decision making
  • Hybrid decision-making analysis
  • Information technologies in decision making
  • Innovative applications of MCDM methods
  • Interval-valued fuzzy sets
  • Intuitionistic fuzzy sets
  • Lexicographic approach
  • Life-cycle analysis
  • mathematical programming in MCDM
  • MCDM in strategic management
  • Multicriteria decision aid
  • Multicriteria decision making
  • Multidimensional measurement framework
  • Multiobjective decision making
  • Multiple-criteria analysis
  • Multiple-criteria decision making
  • MCDM in Negotiations
  • Neutrosophic decision-making theories and methods
  • New trends in multicriteria evaluation
  • Optimization
  • Optimization techniques
  • Ordinal proximity measures
  • Outranking MCDM methods
  • Pareto frontier
  • Portfolio optimization
  • Procurement
  • Reference point method
  • Rough set theory
  • Variational analysis
  • Weighting approach

Published Papers (12 papers)

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Research

Open AccessArticle
Evaluation of Human Resources in Transportation Companies Using Multi-Criteria Model for Ranking Alternatives by Defining Relations between Ideal and Anti-Ideal Alternative (RADERIA)
Mathematics 2021, 9(9), 976; https://doi.org/10.3390/math9090976 - 27 Apr 2021
Viewed by 217
Abstract
Multi-criteria decision-making methods (MCDM) represent a very powerful tool for making decisions in different areas. Making a rational and reliable decision, while respecting different factors, is a challenging and difficult task; MCDM models have a great impact on achieving this goal. In this [...] Read more.
Multi-criteria decision-making methods (MCDM) represent a very powerful tool for making decisions in different areas. Making a rational and reliable decision, while respecting different factors, is a challenging and difficult task; MCDM models have a great impact on achieving this goal. In this paper, a new MCDM technique is presented—ranking alternatives by defining relations between the ideal and anti-ideal alternative (RADERIA), which was tested for the evaluation of human resources (HR) in a transportation company. The RADERIA model has three key advantages that recommend it for future use: (1) the RADERIA model has a new approach for data normalization that enables defining the normalization interval according to the judgments of a decision-maker; (2) an adaptive model for data normalization of the RADERIA model allows tough conversion into various forms of decreasing functions (linear, quadratic equation, etc.); and (3) the resistance of the RADERIA model to the rank reversal problem. Furthermore, in many simulations, the RADERIA method has shown stability when processing a larger number of datasets. This was also confirmed by a case study with 36 alternatives, as considered in this paper. The results and verification of the proposed new method were acquired through a comprehensive verification of the complexity of the results. The complexity of the results was executed through (1) comparison with four other multi-criteria methods, (2) checking the resistance of the RADERIA model to the rank reversal problem, and (3) the analysis of the impact of changes in the measurement scale on the ranking results. Full article
(This article belongs to the Special Issue Multiple Criteria Decision Making)
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Open AccessArticle
Fuzzy and Interval AHP Approaches in Sustainable Management for the Architectural Heritage in Smart Cities
Mathematics 2021, 9(4), 304; https://doi.org/10.3390/math9040304 - 04 Feb 2021
Cited by 1 | Viewed by 385
Abstract
For the past four decades, the methodology of fuzzy analytic hierarchy process based on fuzzy trapezoidal or triangular numbers with the linear type of membership functions has witnessed an expanding development with applicability to a wide variety of areas, such as industry, environment, [...] Read more.
For the past four decades, the methodology of fuzzy analytic hierarchy process based on fuzzy trapezoidal or triangular numbers with the linear type of membership functions has witnessed an expanding development with applicability to a wide variety of areas, such as industry, environment, education, government, economics, engineering, health, and smart city leadership. On the other hand, the interval gray analytic hierarchy process is a more practical method when a significant number of professionals have large variations in preferences and interests in complex decisions. The paper examines the management of architectural heritage in smart cities, using methods of multi-criteria decision making. Two appropriate methods generally recommended by the scientific literature have been applied: fuzzy and interval grey analytic hierarchy process. By using both techniques, there is an opportunity to analyze the consensual results from the aspect of two different stakeholder groups: architectural heritage experts and smart city development experts. Trapezoidal fuzzy analytical hierarchical process shows better stability than a triangular one. Both approaches assign priority to the strategy, but the interval approach gives a more significant rank to architectural heritage factors. The similarity of the proposed methods has been tested, and the similarity factor in the ranking indicates a high degree of similarity in comparing the reference rankings. Full article
(This article belongs to the Special Issue Multiple Criteria Decision Making)
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Open AccessArticle
A Novel Integrated Interval Rough MCDM Model for Ranking and Selection of Asphalt Production Plants
Mathematics 2021, 9(3), 269; https://doi.org/10.3390/math9030269 - 29 Jan 2021
Viewed by 329
Abstract
Asphalt production plants play an important role in the field of civil engineering, but also in the entire economic system since the construction of roads enables uninterrupted functioning within it. In this paper, the ranking of asphalt production plants on the territory of [...] Read more.
Asphalt production plants play an important role in the field of civil engineering, but also in the entire economic system since the construction of roads enables uninterrupted functioning within it. In this paper, the ranking of asphalt production plants on the territory of the Autonomous Province of Vojvodina has been performed. The modern economy needs contemporary models and methods to solve complicated MCDM problems and, for these purposes, it has been developed an original Interval Rough Number (IRN) Multi-criteria decision-making (MCDM) model that implies an extension of two methods belonging to the field with interval rough numbers. After forming a list of eight most significant criteria for assessing the efficiency of asphalt production plants, the Interval Rough Number PIvot Pairwise RElative Criteria Importance Assessment (IRN PIPRECIA) method was developed to determine the significance of the criteria. A total of 21 locations with asphalt mixture installation were considered. For that purpose, seven asphalt production plants were included, and for their ranking, the IRN EDAS (Evaluation based on Distance from Average Solution) method was created. The aim of this paper is to develop a novel interval rough model that can be useful for determining the efficiency of asphalt production plants. Averaging in group decision-making (GDM) for both methods was performed using an IRN Dombi weighted geometric averaging (IRNDWGA) aggregator. The obtained results show that (A15) Ruma (SP)–Mačvanska Mitrovica–Zasavica has the best characteristics out of the set of locations considered in this study. However, Alternatives A6 and A19 are also variants with remarkably good characteristics since there is very little difference in values compared to the first-ranked alternative. Also, the obtained results have shown that the developed model is applicable, which is proven through a comparative analysis. Full article
(This article belongs to the Special Issue Multiple Criteria Decision Making)
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Open AccessArticle
An ITARA-TOPSIS Based Integrated Assessment Model to Identify Potential Product and System Risks
Mathematics 2021, 9(3), 239; https://doi.org/10.3390/math9030239 - 26 Jan 2021
Viewed by 364
Abstract
This is a forward-looking approach that uses a multiple-criteria decision analysis (MCDA) model as an assessment tool for risk identification. This study proposes an indifference threshold-based attribute ratio analysis and technique for order preference by similarity to an ideal solution (ITARA-TOPSIS)-based assessment model [...] Read more.
This is a forward-looking approach that uses a multiple-criteria decision analysis (MCDA) model as an assessment tool for risk identification. This study proposes an indifference threshold-based attribute ratio analysis and technique for order preference by similarity to an ideal solution (ITARA-TOPSIS)-based assessment model to identify critical failure modes in products and systems. The improved indifference threshold-based attribute ratio analysis (ITARA) method can generate more reliable weights for risk factors. In addition, the modified technique for order preference by similarity to an ideal solution (TOPSIS) is used to obtain the risk levels of the failure modes. The gray correlation coefficient is applied to replace the conventional Euclidean distance, and a new index is used to determine the priority of failure modes. The determination of risk factors is based on the failure mode and effect analysis (FMEA) theory, including severity, occurrence, and detection. An important indicator, the expected cost, is also included in the framework. The case of a steam turbine for a nuclear power plant is used to demonstrate the approach, and the analysis results show that the proposed model is practical and effective. Moreover, the advantages of our integrated model are illustrated through model comparisons and sensitivity analysis. This paper can help decision-makers, risk engineers, and related researchers to better understand how a systematic risk assessment can be conducted. Full article
(This article belongs to the Special Issue Multiple Criteria Decision Making)
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Open AccessArticle
How to Influence the Results of MCDM?—Evidence of the Impact of Cognitive Biases
Mathematics 2021, 9(2), 121; https://doi.org/10.3390/math9020121 - 07 Jan 2021
Viewed by 452
Abstract
Multi-criteria decision-making (MCDM) methods aim at dealing with certain limitations of human information processing. However, cognitive biases, which are discrepancies of human behavior from the behavior of perfectly rational agents, might persist even when MCDM methods are used. In this article, we focus [...] Read more.
Multi-criteria decision-making (MCDM) methods aim at dealing with certain limitations of human information processing. However, cognitive biases, which are discrepancies of human behavior from the behavior of perfectly rational agents, might persist even when MCDM methods are used. In this article, we focus on two among the most common biases—framing and loss aversion. We test whether these cognitive biases can influence in a predictable way both the criteria weights elicited using the Analytic Hierarchy Process (AHP) and the final ranking of alternatives obtained with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). In a controlled experiment we presented two groups of participants with a multi-criteria problem and found that people make different decisions when presented with different but objectively equivalent descriptions (i.e., frames) of the same criteria. Specifically, the results show that framing and loss aversion influenced the responses of decision makers during pairwise comparisons, which in turn caused the rank reversal of criteria weights across groups and resulted in the choice of a different best alternative. We discuss our findings in light of Prospect Theory and show that the particular framing of criteria can influence the outcomes of MCDM in a predictable way. We outline implications for MCDM methodology and highlight possible debiasing techniques. Full article
(This article belongs to the Special Issue Multiple Criteria Decision Making)
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Open AccessArticle
Application of Multi-Objective Evolutionary Algorithms for Planning Healthy and Balanced School Lunches
Mathematics 2021, 9(1), 80; https://doi.org/10.3390/math9010080 - 31 Dec 2020
Viewed by 608
Abstract
A multi-objective formulation of the Menu Planning Problem, which is termed the Multi-objective Menu Planning Problem, is presented herein. Menu planning is of great interest in the health field due to the importance of proper nutrition in today’s society, and particularly, in school [...] Read more.
A multi-objective formulation of the Menu Planning Problem, which is termed the Multi-objective Menu Planning Problem, is presented herein. Menu planning is of great interest in the health field due to the importance of proper nutrition in today’s society, and particularly, in school canteens. In addition to considering the cost of the meal plan as the classic objective to be minimized, we also introduce a second objective aimed at minimizing the degree of repetition of courses and food groups that a particular meal plan consists of. The motivation behind this particular multi-objective formulation is to offer a meal plan that is not only affordable but also varied and balanced from a nutritional standpoint. The plan is designed for a given number of days and ensures that the specific nutritional requirements of school-age children are satisfied. The main goal of the current work is to demonstrate the multi-objective nature of the said formulation, through a comprehensive experimental assessment carried out over a set of multi-objective evolutionary algorithms applied to different instances. At the same time, we are also interested in validating the multi-objective formulation by performing quantitative and qualitative analyses of the solutions attained when solving it. Computational results show the multi-objective nature of the said formulation, as well as that it allows suitable meal plans to be obtained. Full article
(This article belongs to the Special Issue Multiple Criteria Decision Making)
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Open AccessArticle
MULTIMOORA under Interval-Valued Neutrosophic Sets as the Basis for the Quantitative Heuristic Evaluation Methodology HEBIN
Mathematics 2021, 9(1), 66; https://doi.org/10.3390/math9010066 - 30 Dec 2020
Cited by 1 | Viewed by 684
Abstract
During the last decade, researchers put a lot of effort into the development of the multi-criteria decision methods (MCDM) capable of dealing with the uncertainty and vagueness of the initial information. MCDM approaches that work under the environment of the interval-valued neutrosophic sets [...] Read more.
During the last decade, researchers put a lot of effort into the development of the multi-criteria decision methods (MCDM) capable of dealing with the uncertainty and vagueness of the initial information. MCDM approaches that work under the environment of the interval-valued neutrosophic sets (IVNS) demonstrate credibility for the analysis of different opinions as well as for the inconsistency of the criteria evaluation data. The novel multicriteria decision-making approach MULTIMOORA-IVNS (multi-objective optimisation by ratio analysis under interval-valued neutrosophic sets) is presented in this paper. A novel heuristic evaluation methodology HEBIN (heuristic evaluation based on interval numbers) that exploits MULTIMOORA-IVNS for the processing of the evaluation results is also presented in this research. HEBIN is able to increase the accuracy of the checklists-based heuristic evaluation and to diminish the impact of the inconsistencies caused by the evaluators. A comparison of six e-commerce websites is introduced to reveal the practicalities of the proposed multicriteria decision-making application. Full article
(This article belongs to the Special Issue Multiple Criteria Decision Making)
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Open AccessArticle
A Multiple Criteria Decision Making Approach to Designing Teaching Plans in Higher Education Institutions
Mathematics 2021, 9(1), 9; https://doi.org/10.3390/math9010009 - 22 Dec 2020
Viewed by 648
Abstract
The involvement of competences in the teaching–learning planning process in Higher Education is essential for their success in the European Higher Education Area. This study presents a participatory multi-criteria model based on Voting Analytic Hierarchy Process (VAHP) analysis, focusing on the attainment of [...] Read more.
The involvement of competences in the teaching–learning planning process in Higher Education is essential for their success in the European Higher Education Area. This study presents a participatory multi-criteria model based on Voting Analytic Hierarchy Process (VAHP) analysis, focusing on the attainment of competences that permits consensus between lecturers and students in the design of teaching plans using two assessments: the assessment of competences by students and the lecturers’ assessment of the contribution of teaching strategies to the attainment of each competence. To validate the methodology, a survey was carried out to determine the preferences of 211 students on the competences of a quantitative subject in several business degrees and a survey of 11 lecturers to assess the contribution of the teaching strategies in the acquisition of each competence. The results show that practical lessons and group work should receive more importance in the teaching plans of the subject Financial Management according to the defined competences, the students’ preferences and the criterion of each lecturer for each teaching strategy. The results show the applicability of the participatory methodology proposed to formally agree on the design of teaching plans in higher education organizations between lecturers and students. Full article
(This article belongs to the Special Issue Multiple Criteria Decision Making)
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Open AccessArticle
The Influence of Criteria Selection Method on Consistency of Pairwise Comparison
Mathematics 2020, 8(12), 2200; https://doi.org/10.3390/math8122200 - 10 Dec 2020
Viewed by 413
Abstract
The more criteria a human decision involves, the more inconsistent the decision. This study experimentally examines the effect on the degree of pairwise comparison inconsistency by using the (im)possibility of selecting the criteria for the evaluation and the size of the decision-making problem. [...] Read more.
The more criteria a human decision involves, the more inconsistent the decision. This study experimentally examines the effect on the degree of pairwise comparison inconsistency by using the (im)possibility of selecting the criteria for the evaluation and the size of the decision-making problem. A total of 358 participants completed objective and subjective tasks. While the former was associated with one possible correct solution, there was no single correct solution for the latter. The design of the experiment enabled the acquisition of eight groups in which the degree of inconsistency was quantified using three inconsistency indices (the Consistency Index, the Consistency Ratio and the Euclidean distance) and these were analysed by the repeated measures ANOVA. The results show a significant dependence of the degree of inconsistency on the method of determining the criteria for pairwise evaluation. If participants are randomly given the criteria, then with more criteria, the overall inconsistency of the comparison decreases. If the participants can themselves choose the criteria for the comparison, then with more criteria, the overall inconsistency of the comparison increases. This statistical dependence exists only for males. For females, the dependence is the opposite, but it is not statistically significant. Full article
(This article belongs to the Special Issue Multiple Criteria Decision Making)
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Open AccessArticle
A New Hybrid MCDM Model for Personnel Selection Based on a Novel Grey PIPRECIA and Grey OCRA Methods
Mathematics 2020, 8(10), 1698; https://doi.org/10.3390/math8101698 - 03 Oct 2020
Cited by 2 | Viewed by 705
Abstract
People represent one of the most significant resources of an organization, and therefore, personnel selection is one of the problems that organizations have increasingly been facing. The criteria that influence the final decision are usually opposing, so the application of multiple-criteria decision-making methods [...] Read more.
People represent one of the most significant resources of an organization, and therefore, personnel selection is one of the problems that organizations have increasingly been facing. The criteria that influence the final decision are usually opposing, so the application of multiple-criteria decision-making methods (MCDM) represents a suitable way for the facilitation of the given process. Additionally, the decision environment is characterized by the vagueness and uncertainty and, because of that, it is very hard to express the criteria over the exact crisp numbers. To acknowledge the unpredictability and obscurity of the available information important for the selection of the optimal candidate, a hybrid grey MCDM model for personnel selection is proposed in this paper. As an extension of the PIPRECIA method, the novel Grey Pivot Pairwise Relative Criteria Importance Assessment—the PIPRECIA-G method—is proposed and used for the determination of criteria importance. The PIPRECIA-G method preserved the good features of the PIPRECIA, but its superiority is reflected in its ability to deal with input data that are vague and grey. For the final ranking of the considered alternative candidates, the OCRA-G method is used. Basing the decision process and candidate selection on the two grey extended MCDM methods contributes to the increase of the reliability and confidence in the performed selection. Full article
(This article belongs to the Special Issue Multiple Criteria Decision Making)
Open AccessArticle
Financial Performance Assessment of Construction Firms by Means of RAM-Based Composite Indicators
Mathematics 2020, 8(8), 1347; https://doi.org/10.3390/math8081347 - 12 Aug 2020
Viewed by 562
Abstract
This paper aims to provide a novel construct that is based on data envelopment analysis (DEA) range adjusted measure (RAM) of efficiency and demonstrate its practical implementation by evaluating the financial performance of a sample of three upper-class contracting license (Classes 5–7) Greek [...] Read more.
This paper aims to provide a novel construct that is based on data envelopment analysis (DEA) range adjusted measure (RAM) of efficiency and demonstrate its practical implementation by evaluating the financial performance of a sample of three upper-class contracting license (Classes 5–7) Greek construction firms. In a two-step framework, firm efficiency (i.e., composite indicators (CIs)) is produced firstly by means of RAM using single financial ratios, which are selected by grey relational analysis (GRA), and then Tobit regression is employed to model the CIs. In light of the results, only 4% of the sampled firms are efficient, and the firm ranking is consistent with the ranking of Grey Relational Grande (GRG) values produced by GRA. Moreover, the firms with a contracting license of the highest level (Class 7) appear not to be superior in efficiency to their counterparts that belong to Classes 5–6. Full article
(This article belongs to the Special Issue Multiple Criteria Decision Making)
Open AccessArticle
A Revised Inverse Data Envelopment Analysis Model Based on Radial Models
Mathematics 2020, 8(5), 803; https://doi.org/10.3390/math8050803 - 15 May 2020
Cited by 2 | Viewed by 540
Abstract
In recent years, there has been an increasing interest in applying inverse data envelopment analysis (DEA) to a wide range of disciplines, and most applications have adopted radial-based inverse DEA models. However, results given by existing radial based inverse DEA models can be [...] Read more.
In recent years, there has been an increasing interest in applying inverse data envelopment analysis (DEA) to a wide range of disciplines, and most applications have adopted radial-based inverse DEA models. However, results given by existing radial based inverse DEA models can be unreliable as they neglect slacks while evaluating decision-making units’ (DMUs) overall efficiency level, whereas classic radial DEA models measure the efficiency level through not only radial efficiency index but also slacks. This paper points out these disadvantages with a counterexample, where current inverse DEA models give results that outputs shall increase when inputs decrease. We show that these unreasonable results are the consequence of existing inverse DEA models’ failure in preserving DMU’s efficiency level. To rectify this problem, we propose a revised model for the situation where the investigated DMU has no slacks. Compared to existing radial inverse DEA models, our revised model can preserve radial efficiency index as well as eliminating all slacks, thus fulfilling the requirement of efficiency level invariant. Numerical examples are provided to illustrate the validity and limitations of the revised model. Full article
(This article belongs to the Special Issue Multiple Criteria Decision Making)
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