Multi-criteria Decision-making Problem Research Articles

A group stability-based consensus model for multi-criteria group decision-making problems with linguistic distribution assessments

In multi-criteria group decision-making (MCGDM) problems with linguistic distribution assessments (LDAs), this study proposes a group stability-based consensus model to facilitate the generation of solutions that satisfy predefined group stability and group consensus requirements. The group stability and group consensus measures are constructed at the criteria, alternative, expert, and global levels. In terms of different degrees of group stability and group consensus in MCGDM problems, it can be categorized into four situations by comparing with predefined thresholds, which are: 1) group stability measure is satisfied, and group consensus measure is satisfied; 2) group stability measure is not satisfied, and group consensus measure is satisfied; 3) group stability measure is satisfied, and group consensus measure is not satisfied; 4) group stability measure is not satisfied, and group consensus measure is not satisfied. To accelerate the group stability-based group consensus reaching process, optimization problems with group stability and group consensus requirements are constructed to obtain suggested intervals and recommended intervals for LDAs. This contributes to the feedback mechanisms corresponding to the four different situations. The proposed method is applied to an ultrasonic medical imaging workstation selection problem to demonstrate its effectiveness. The superiority of the proposed method is validated through comparative experiments.

Automatic and Semi-automatic Analytic Hierarchy Process (AHP)

The Analytic Hierarchy Process (AHP) is the most commonly used method for solving multi-criteria decision-making problems worldwide. Although AHP offers many advantages for problems with several alternatives and criteria, the pairwise comparisons require considerable effort. State-of-the-art methodologies have demonstrated that AHP is suitable for automating decisions on tabular data. Considering it, this paper proposes a new approach to decision-making in both automated and semi-automated ways. For the automated approach, formulas are proposed for the normalization and computation of criteria weights. For the semi-automated approach, functions are proposed to convert normalized tabular data into values on the Saaty scale. These values are then used to automatically construct pairwise comparison matrices. This approach allows decision-makers to generate such matrices when necessary, thereby minimizing or even eliminating the effort required for pairwise comparisons. Simulations demonstrate the differences obtained depending on the use of the conversion function. Comparisons with state-of-the-art methods reveal that the proposed approach is compatible with existing methodologies. The viability of the proposed methodology is also explored through problems of selecting genotypes/varieties of agricultural crops, showing its viability in real problems. The obtained results show that the proposed approach produces results similar to other decision-making methods.

How Can Risk-Averse and Risk-Taking Approaches be Considered in a Group Multi-Criteria Decision-Making Problem?

We propose an alternative decision-making methodology based on adopting a mixed risk-averse and risk-taking behavior, improving the objectivity of decision-making. We demonstrate the methodology by prioritizing Iranian tourism centers’ activity under pandemic conditions, providing insights to policymakers on those to keep active or reduce the activity of – hence, those worth developing ahead of future disease outbreaks. This research follows a three-step methodology. First, criteria for evaluation are identified and categorized into tourist attractions, infrastructure, and healthcare dimensions. Second, criterion weights are calculated based on expert opinions, collected using a best-worst method-based questionnaire. Third, tourism centers are evaluated by employing risk-averse and risk-taking best-worst methods. We identify popular attractions, general services, and drugstore accessibility as the primary indicators of tourist attractions, infrastructure, and healthcare, respectively. By clustering tourism centers using K-means algorithm, we find that, in order, the cities of Semnan, Kerman and Zahedan are the tourism centers most suited to staying active during disease outbreaks. For multi-criteria decision-making problems that rely on experts’ evaluations, the proposed methodology can improve the reliability of decision-making. The methodology and framework presented can be used to support various types of decision-making, including evaluation, ranking, selection or sorting.

Analysis of the Multi Objective Optimization by Ratio Analysis (MOORA) Method in Determining Pilot Areas at PT. XYZ

This research analyzes the application of the Multi Objective Optimization by Ratio Analysis (MOORA) method model in determining the Pilot Area at PT XYZ. This method is used to evaluate various performance criteria, including customer satisfaction, productivity, service quality, and operational efficiency. Currently, the Pilot Area assessment and selection process at PT XYZ is still done manually, which causes a lack of accuracy and efficiency. MOORA was chosen for its ability to handle multi-criteria decision-making problems more systematically and objectively. The analysis results showed that Alternative Area 7 obtained the highest final score of 0.39, placing it as an area with superior performance. The application of MOORA is proven to improve accuracy and efficiency in the Pilot Area determination process, providing a more objective basis for decision-making. By using MOORA, PT XYZ can evaluate area performance more comprehensively and accountably. This research recommends that PT XYZ implement the MOORA method thoroughly and conduct periodic evaluations of the methods used. For theory development, PT XYZ can add specific evaluation criteria according to company needs. The implementation of these suggestions is expected to improve the quality of service and competitiveness of PT XYZ in the global market. Further research is expected to compare MOORA with other methods to strengthen the validity of the results. Thus, this research not only provides a practical contribution to PT XYZ but also adds academic insight into the application of multi-criteria optimization methods in the context of performance management and service improvement.

The Deck-of-cards-based Ordinal Regression method and its application for the development of an ecovillage

This paper presents the deck-of-cards-based Ordinal Regression (DOR), a new multicriteria decision-aiding procedure that conjugates the deck-of-cards method with an ordinal regression approach to define a multicriteria value function representing the preferences of the decision maker (DM). The deck-of-cards method allows the DM to express the ranking order of a set of reference alternatives along with the intensity of preferences between reference alternatives. An ordinal regression procedure is then used to define a multicriteria value function that represents the ranking of the reference alternatives as well as the preference intensity. This approach can be applied to define value functions with different formulations, such as weighted sum, additive value, or Choquet integral. The value function thus obtained can be used to comprehensively evaluate alternatives of a multi-criteria decision problem. The value function provided by DOR can also be applied to a multi-objective optimisation problem. In this study, we applied DOR to handle urban and regional planning decisions in which facilities are required to be selected, located, and planned. In particular, we consider the interactions between criteria and synergies between facilities in an enriched version of the so-called space–time model. We applied this methodology to a real-world problem to plan the development of a sustainable ecovillage in the province of Turin (Italy), thus supporting the president of the cooperative owning the ecovillage in his decisions regarding which structures to select, where to locate them, and when to plan their realisation.

Optimizing microbial strain selection for pyrethroid biodegradation in contaminated environments through a TOPSIS-based decision-making system

Improved and contemporary agriculture relies heavily on pesticides, yet some can be quite persistent and have a stable chemical composition, posing a significant threat to the ecology. Removing harmful effects is upon their degradability. Biodegradation must be emphasized to lower pesticide degradation costs, especially in the soil. Here, a decision-making system was used to determine the best microbial strain for the biodegradation of the pyrethroid-contaminated soil. In this system, the criteria chosen as: pH (C1), Temp (C2), RPM (C3), Conc. (C4), Degradation (%) (C5) and Time required for degradation(hrs) (C6); and five alternatives were Bacillus (A1), Acinetobacter (A2), Escherichia (A3), Pseudomonas (A4), and Fusarium (A5). The best alternative was selected by applying the TOPSIS (technique for order performance by similarity to ideal solution) method, which evaluates based on their closeness to the ideal solution and how well they meet specific requirements. Among all the specified criteria, Acinetobacter (A2) was the best and optimal based on the relative closeness value ((Ri∗\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${R}_{i}^{*}$$\\end{document}) = 0.740 (A2) > 0.544 (A5) > 0.480 (A1) > 0.403 (A4) > 0.296 (A3)). However, the ranking of the other alternatives is also obtained in the order Fusarium (A5), Bacillus (A1), Pseudomonas (A4), Escherichia (A3). Hence this study suggests Acinetobacter is the best microbial strain for biodegradation of pyrethroids; while least preference should be given to Escherichia. Acinetobacter, versatile metabolic nature with various xenobiotic compounds' degradation ability, is gram-negative, aerobic, coccobacilli, nonmotile, and nonspore forming bacteria. Due to less study about Acinetobacter it is not in that much frame as the other microorganisms. Hence, considering the Acinetobacter strain for the biodegradation study will give more optimal results than the other microbial strains. Novelty of this study, the TOPSIS method is applied first time in selecting the best microbial strain for the biodegradation of pyrethroid-contaminated soil, considering this selection process as multi-criteria decision-making (MCDM) problem.

A new quadrant constellation graph-based water quality index and map

Water quality (WQ) evaluation is very important issue in human health and environment management. Many WQ evaluation (WQE) methods based on statistical methods and multi-criteria decision making (MCDM) methods have been developed and used. The common drawback is that they lack in visualization/intuitiveness of the WQE results. They evaluate the WQ level only quantitatively. To intuitively represent the WQ level, the previous WQE methods have applied some additional graphical methods such as concentration/distribution/contour maps combined with complicated interpolation techniques based on geographic information system. The objective of this research is to develop a new effective approach to evaluate the WQ level not only quantitatively but also intuitively without requiring additional complicated graphical technique. To do this, we proposed a new quadrant constellation graph-based water quality index (QCG-WQI) and map (QCG-WQM). The QCG-WQI evaluates the WQ level quantitatively, and QCG-WQM evaluates the WQ level intuitively. To demonstrate the effectiveness, three illustration cases were considered and compared with the results from some well-known MCDM methods. The results demonstrate that the new approach is very effective intuitive and quantitative WQE tool. It could be widely applied to not only WQE and environmental evaluation but also MCDM problems in many physical research works.

Two Weighting Methods for Software-Defined Network Controllers Ranking as a Multicriteria Decision-Making Problem

Two Weighting Methods for Software-Defined Network Controllers Ranking as a Multicriteria Decision-Making Problem

Offshoring Location Decision in Fuzzy Environment

Offshoring location selection is a crucial decision for firms in terms of competitiveness, flexibility, productivity, and profitability. Determining an efficient and appropriate location for offshoring has been a substantial multicriteria decision-making (MCDM) problem. Considering that the outcome of an MCDM method alone can be misleading, a novel hybrid approach is presented in this study. Thus, five MCDM methods are utilized to solve the problem, and the results of four MCDM methods are integrated to assure an optimal offshoring location. A Fuzzy-AHP (analytical hierarchy process) integrated with the technique for order preference by similarity to ideal solution (TOPSIS), additive ratio assessment (ARAS), elimination et choix traduisant la realité (ELECTRE), and weighted sum method (WSM) methodology is proposed for the appraisal and selection of the optimal offshoring location. In this context, fifteen alternative locations are determined based on the attractiveness of the locations in terms of offshoring. Fuzzy-AHP is implemented to analyze the problem's structure and find the weights of the quantitative and qualitative criteria. Consistency tests are implemented to assess the quality of inputs of an expert. Then, TOPSIS, WSM, ARAS, and ELECTRE are used to evaluate and rank the candidate locations and present a comparative analysis. By considering fifteen countries and using real data, offshoring location selection is conducted through the proposed methodology. Moreover, sensitivity analysis is made to diminish the subjectivity and assess the robustness of the techniques. The results demonstrated that giving more weights to the labor characteristics and proximity to market criteria might improve the quality of the best offshoring country index.

A holistic approach to introducing a light electric freight vehicle (LEFV) system in a historic urban environment: The case of Quito

In this paper we propose a comprehensive approach for introducing a first/last mile system based on Light Electric Freight Vehicles (LEFVs) in the historical centre of Quito (Ecuador). The approach includes four crucial steps for successful launching and establishment of a LEFVs system. The first step considers organizational aspects by proposing a number of alternative cooperative business models. In the second step, the LEFVs fleet mix problem is defined as a Multicriteria Decision Making Problem (MCDM) and solved by the Analytic Network Process (ANP). The third step includes the problem of optimal micro hub location as well as the problem of optimal routing of LEFVs. The micro hub location problem is modelled as a capacitated p-median problem and solved by an exact solution algorithm. The problem of optimal LEFVs routing is defined as the capacitated pickup and delivery with time windows addressed by a web-based optimization tool. The fourth step includes an assessment of the existing policy framework and proposing a set of additional legal instruments for facilitating the LEFVs market uptake. Based on the proposed four-step approach, the first/last mile system is currently being implemented in the historical centre of Quito. The initial results appear promising regarding the success of the new LEFVs first/last mile system.

A new integrated rough multi-criteria decision-making model for enterprise resource planning software selection.

Enterprise resource planning (ERP) is widely used to boost the total market power of businesses. The wrong selection is one of the key reasons why ERP installations fail. Due to the complexity of the business environment and the range of ERP systems, choosing an ERP system is a complex and time-consuming procedure. ERP alternatives may be assessed using several criteria, so the ERP selection process may be considered a multi-criteria decision-making (MCDM) problem. In this study, the rough best worst method (BWM) was used to determine criteria weights, while the newly developed rough integrated simple weighted sum product (WISP) was used to rank ERP alternatives. Results suggest that the SFT-4 coded software is regarded as the best option, followed by SFT-5, SFT-6, SFT-2, SFT-3, and SFT-1. Results of the newly developed rough WISP method are compared to those of existing rough techniques in the sensitivity analysis. The differences between them have been found to be negligible. The outcomes show how effectively developed rough BWM and WISP integrated method performs in terms of ERP selection with usability, accuracy, ease of use, and consistency. This study will help decision-makers in a context where ERP is implemented choose the best ERP software for different sectors.

Covering-Based Intuitionistic Hesitant Fuzzy Rough Set Models and Their Application to Decision-Making Problems

In this paper, we present four categories of covering-based intuitionistic hesitant fuzzy rough set (CIHFRS) models using intuitionistic hesitant fuzzy β-neighborhoods (IHF β-neighborhoods) and intuitionistic hesitant fuzzy complementary β-neighborhoods (IHFC β-neighborhoods. Through theoretical analysis of covering-based IHFRS models, we propose the intuitionistic hesitant fuzzy TOPSIS (IHF-TOPSIS) technique for order of preference by similarity to an ideal solution, addressing multicriteria decision-making (MCDM) challenges concerning the assessment of IHF data. A compelling example aptly showcases the suggested approach. Furthermore, we address MCDM problems regarding the assessment of IHF information based on CIHFRS models. Through comparison and analysis, it is evident that addressing MCDM problems by assessing IHF data using CIHFRS models proves more effective than utilizing intuitionistic fuzzy data with CIFRS models or hesitant fuzzy information with CHFRS models. IHFS emerges as a unique and superior tool for addressing real-world challenges. Additionally, covering-based rough sets (CRSs) have been successfully applied to decision problems due to their robust capability in handling unclear data. In this study, by combining CRSs with IHFS, four classes of CIFRS versions are established using IHF β-neighborhoods and IHFC β-neighborhoods. A corresponding approximation axiomatic system is developed for each. The roughness and precision degrees of CBIHFRS models are specifically talked about. The relationship among these four types of IHFRS versions and existing related versions is presented based on theoretical investigations. A method for MCDM problems through IHF information, namely, IHF-TOPSIS, is introduced to further demonstrate its effectiveness and applicability. By conducting a comparative study, the effectiveness of the suggested approach is evaluated.

EXPERT ASSESSMENTS IN DECISION-MAKING PROBLEMS: THE METHOD OF MULTIPLICATIVE FUNCTIONS

Expert judgments and fuzzy set theory are two different concepts of systems analysis, but their combination improves the mathematical tool of multi-criteria analysis of decision-making problems. The method of expert evaluations belongs to the class of partially formalized methods based on the use of intuition and experience of decision-makers. The multi-criteria nature of the decision-making problem is manifested in the search for an alternative that simultaneously satisfies the constraint and optimizes the vector quantity, the elements of which correspond to the objective functions of the problem. The choice of the optimal alternative, in most cases, is also ambiguous and is based on subjective, undefined information about the object of decision-making, or selection criteria that do not have quantitative assessments. In this case, it can be said that the decision-making process is fuzzy. Multiplicative Function Method is one of the approaches to modeling fuzzy systems, problems related to fuzzy inference, or fuzzy model description. The purpose of the work is to study the possibility of combining the theory of fuzzy sets with a deterministic algorithm for a multi-criteria decision-making problem.

Evaluation of extracting biomass energy using a strategic decision support system

Electricity significantly influences the long-term development of society. Traditional power generation technologies have encountered numerous challenges. The rising demand for power generation and the scarcity of fossil fuel resources have prompted the adoption of renewable energy sources. Biomass is a renewable energy source that can be utilized to generate power and promote a more sustainable environment. This paper presents improved Pythagorean Probabilistic Hesitancy Fuzzy Set (PPHFS) multi-criteria information fusion processes. The development of connection numbers (CN) and the proposal of a distance measure between CNs have been initiated. The suggested solution involves a distance measure based on the CN score function. Set-pair analysis (SPA) is a prior uncertainty theory that incorporates three elements of CN and overlaps with the PPHFS. Biomass energy generation poses a fuzzy multi-criteria decision-making (MCDM) problem that requires a wide range of resources for evaluation. The study introduces a novel Projection Ranking by Similarity to the Referencing Vector (PRSRV) and Simultaneous Evaluation of Criteria and Alternatives (SECA) to effectively handle data ambiguity and generate weights for all criteria in the MCDM problem. The criteria’s weights were found to fall within the range of 0.2122 and 0.0232. The proposed method indicates that animal residues are the most desirable resource for bioenergy production, followed by municipal solid residues, agricultural residues, forest and crop residues, industrial residues, and algae. The research aims to assist decision-makers and organizations in evaluating and prioritizing various energy sources on a sustainable scale. The method presented exhibits superior data adaptability compared to other MCDM approaches.

A novel fuzzy scenario-based stochastic general best-worst method

The best-worst method (BWM) is a popular multi-criteria decision-making (MCDM) method known for the low number of pairwise comparisons and high consistency. General BWM (GBWM) is a new version of BWM that considers the interdependencies between interwind factors in MCDM problems. This study proposes a fuzzy stochastic GBWM for weighting intertwined factors using a scenario-based approach in complex intertwined or hierarchical networks under uncertainty. Fuzzy stochastic GBWM provides decision-makers with a wide range of weights, from fuzzy-stochastic weights to stochastic weights (defuzzified weights), fuzzy weights, and deterministic weights to use with different assumptions in the decision-making process. We demonstrate the efficacy and applicability of the proposed method with a well-known car-buying problem in the literature and a real-world problem in the transportation industry.

Modified VIKOR-GP approach by considering the Leopold Matrix for optimal water reuse allocation

ABSTRACT The global water resource crisis caused by population growth, and increasing water demand has led to using treated wastewater in many regions. Effective distribution of wastewater treatment across various consumer sectors is crucial for sustainable environmental practices and resource management. Wastewater Allocation (WA) is a multi-criteria decision-making problem, and this study used modified VIKOR (VIekriterijumsko KOmpromisno Rangiranje) method in the combination with GP (Goal Programming) technique to optimize WA in Iran. The environmental effects were evaluated using Leopold Matrix results. The proposed approach resulted in an allocation of 6912 m3/day for urban green space irrigation 10,368 m3/day for the agricultural irrigation, and 2592 m3/day for industrial water demand at the Shahreza wastewater treatment plant. The research shows that better recommendations for resource allocation may be developed by combining quantitative assessment, optimization, and mathematical models. This approach has proven successful in maximizing the reuse of wastewater for a variety of uses.

Novel picture fuzzy power partitioned Hamy mean operators with Dempster-Shafer theory and their applications in MCDM

In some multi-criteria decision-making (MCDM) scenarios, decision makers must address challenges like handling uncertain and incomplete information, managing biases in criteria values, and assessing interrelationships among criteria based on their partitioning as per their characteristics. To tackle these challenges, a picture fuzzy set (PFS) can be utilized to quantify vague information, Hamy mean (HM) can be used to consider criteria interrelationships while the power average (PA) mitigates any kind of biasness. Also, to overcome the limitations of persistence and invariantess in algebraic operations, Dempster-Shafer theory (DST) is employed. By integrating the conventional HM with the traditional PA under partitioning, this paper first introduced the novel power partitioned Hamy mean (PPtHMq\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$PPtHM^q$$\\end{document}) operator. Then, this operator is extended for picture fuzzy numbers (PFNs) with DST and two novel operators are introduced, which are named as picture fuzzy power partitioned Hamy mean (PFPPtHMDSTq)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(PFPPtHM^q_{DST})$$\\end{document} and picture fuzzy weighted power partitioned Hamy mean (PFWPPtHMDSTq)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(PFWPPtHM^q_{DST})$$\\end{document} with some desirable properties. Moreover, based on these operators, a new method for MCDM in the PFS environment has been designed. The paper illustrates their application in selecting the best hotel among four alternatives (B1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$B_1$$\\end{document}, B2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$B_2$$\\end{document}, B3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$B_3$$\\end{document}, B4\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$B_4$$\\end{document}) based on five criteria, which are partitioned into two sets. Results indicate that the best and worst alternatives under these operators are hotels B1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$B_1$$\\end{document} and B4\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$B_4$$\\end{document}, respectively. Sensitivity analysis explores the impact of granularity parameter variations, and comparative analysis demonstrates the effectiveness of the presented operators. Overall, the study concludes that these operators offer flexibility, generality, and consistency for analyzing MCDM problems in PFS environments.

A Robust Decision-Making Model for Medical Supplies via Selecting Appropriate Unmanned Aerial Vehicle

Recently, Unmanned Aerial Vehicles (UAVs) have been used in many fields, including the field of health care, especially in delivering the necessary medical equipment and supplies, due to the many advantages they have compared to other traditional methods and the presence of different types of UAVs, to improve healthcare and provide it with the medical supplies and equipment necessary to save the lives of patients. Choosing the appropriate UAV for a specific situation represents a problem facing decision-makers, which is considered a multi-criteria decision-making problem. Since the decision-making process is cumbersome and complex, and deals with uncertainty and ambiguity. In this research, we proposed multi-criteria decision-making (MCDM) model using CRITERIA (Criteria Importance through Intercriteria Correlation) and MARICA (Multi-Attribute Rating Analysis with Ideal Concepts) methods integrated with neutrosophic logic, which is considered a powerful tool in dealing with uncertainty and ambiguity. The CRITIC method calculates the weight of criteria, whereas it takes into account the correlations and relationships between the criteria, whether they are positive or negative, unlike other methods that consider the criteria separately, which allows for a more accurate and comprehensive analysis of the decision problem. The MARICA method is used also to rank the alternatives. It allows decision-makers to evaluate alternatives according to how well they perform across multiple criteria by considering several factors at once. This helps increase the effectiveness of judgments by taking into account all relevant factors. Moreover, MARICA is a user-friendly method that doesn't require complex mathematical calculations, making it accessible to anyone who wants to make sound choices. The UAV with the highest ranking is the one that will be chosen and represents the best among the alternatives. The proposed model proved its effectiveness by applying it to an experimental case.

A study of entrepreneur and innovative university index by entropy-based grey relational analysis and PROMETHEE

In recent years, the concept of entrepreneurial and innovative universities has gained widespread prominence. Many universities have been paying more attention to being entrepreneurial and innovative by improving their organizational systems, advancing their infrastructure, and increasing financial support. Since numerous criteria with different weights exist, ranking universities based on entrepreneurial and innovative performance can be considered a multi-criteria decision-making (MCDM) problem. This article aims to investigate how different multi-criteria decision-making methods with different criterion weights can affect university rankings and to highlight the reasons that contribute to these differences. In this scope, Grey Relational Analysis (GRA) and Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) methods were used to rank and compare the universities in Türkiye according to the 2022 Entrepreneur and Innovative University Index (EIUI). In addition to the current weights of each EIUI dimension, entropy-based weights and equal weights were used in MCDM methods. Three ranking approaches with varying weights provided different rankings for universities. The effect of criterion weights was found to be more important in the ranking difference than the method used. The ranks for universities coded U1 and U2 as the most entrepreneurial and innovative universities remained the same. In addition, the performance of each university according to each dimension was evaluated graphically using the GAIA plane to enable them to identify areas for improvement in their rankings.

A Decision Model for High School Students' Summer job Choices: a Study Combining Hierarchical Analysis and Entropy Power Methods

As summer vacation approaches, high school students face an annual opportunity: choosing a summer job. These job opportunities not only provide a way to earn money, but also help students explore their career interests, develop key skills, and prepare them for future educational and career paths. However, high school students often have difficulty in finding the right job. To solve this, we have constructed a decision model that combines the hierarchical analysis of hierarchy (AHP) and entropy weighting method, aiming to find the best match among the many choices for high school students. The problem is essentially a multi-criteria decision-making problem that involves a number of decision-making factors, which need to be taken into account, along with the job characteristics and the applicant's profile and preferences. In our research, summer jobs are evaluated on a total of eight criteria: income, safety risk, job purpose, working conditions, and work. The model takes into account the job characteristics and the user's profile/preferences, and is therefore able to recommend the best job that matches the user's profile/preferences. In this paper, the AHP is used to determine the relative importance of these criteria for each individual, while the entropy weighting method is used to determine the relative importance of these criteria from an objective perspective. The model works as expected by validating it against nineteen types of job and ten fictional characters. Not only can we recommend the best jobs with the highest scores, but we can also rank all candidate jobs according to the calculated priority, providing users with a wider range of job options.