Preference Ranking Organization Method For Enrichment Evaluation Research Articles

Spatial analysis of antimicrobial resistance in the environment. A systematic review

Antimicrobial resistance (AMR) is a global major health concern. Spatial analysis is considered an invaluable method in health studies. Therefore, we explored the usage of spatial analysis in Geographic Information Systems (GIS) in studies on AMR in the environment. This systematic review is based on database searches, a content analysis, ranking of the included studies according to the preference ranking organization method for enrichment evaluations (PROMETHEE) and estimation of data points per km2. Initial database searches resulted in 524 records after removal of duplicates. After the last stage of full text screening, 13 greatly heterogeneous articles with diverse study origins, methods and design remained. In the majority of studies, the data density was considerably less than one sampling site per km2 but exceeded 1,000 sites per km2 in one study. The results of the content analysis and ranking showed a variation between studies that primarily used spatial analysis and those that used spatial analysis as a sec ondary method. We identified two distinct groups of GIS methods. The first was focused on sample collection and laboratory testing, with GIS as supporting method. The second group used overlay analysis as the primary method to combine datasets in a map. In one case, both methods were combined. The low number of articles that met our inclusion criteria highlights a research gap. Based on the findings of this study we encourage application of GIS to its full potential in studies of AMR in the environment.

Analysis of knowledge evolution in PROMETHEE: A longitudinal and dynamic perspective

Since the preference ranking organization method for enrichment evaluations (PROMETHEE) was proposed, thousands of papers have already been published in journals indexed by Web of Science (WoS) addressing applications across various areas. This paper aims to dynamically present the knowledge evolution concerning the cognitive structure and transmission trajectories from a longitudinal perspective. To this end, we retrieved 1351 documents covering years from 1982 to 2021 and detected the interrelationships and characteristics of essential themes, as well as some key thematic areas formed by their evolution. The most critical and noteworthy milestone documents were also identified and illustrated in chronological sequence to provide a good entry point for newcomers in this domain.

Multi-method analysis to identify criteria interrelations for sustainability assessment of urban transportation services

To comply with the European Green Deal, decarbonization of transportation is decisive, especially a shift towards more sustainable transportation services is essential. Due to the tripartite nature of sustainability (society, environment and economy), multiple interrelated criteria are part of the sustainability assessment of transportation services. Discovering these interrelations between criteria to anticipate conflicts and integrate them appropriately into multi-criteria analysis is important for ranking urban transportation services. This paper presents a novel, two-folded methodology for assessing the sustainability of different urban transportation services. First, integrative structural modeling with polarity (ISM-P) is used for universally identifying sustainability criteria interrelationships and weighting them. Second, the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) method is applied to a sustainability assessment of five specific urban transportation services (public and sharing). In conclusion, applying the methodology to the example of urban transportation services in the Ruhr region, the importance of public transportation as the most sustainable alternative is proven.

Multi-criteria decision support systems for electric vehicle charging schedules in a smart secondary distribution system

The operation of a smart secondary distribution network (SDN) has gained significance due to the penetration of chargers for electric vehicle (EV) and in-situ generation. The SDN operators (SDNOs) need dedicated decision support systems (DSS) for scheduling the EVs for charging without overloading the distribution transformer. Multi-criteria DSS(MCDSS) are proposed for the first time to balance the conflicting constraints stemming from the perspectives of both EV users and grid operators. This new approach has also incorporated a contribution-based fairness value (CFV) for each prosumer. MCDSS formulations employing analytical hierarchy process (AHP), VIšekriterijumsko KOmpromisno Rangiranje (VIKOR), and Preference Ranking Organisation METHod for Enrichment Evaluation (PROMETHEE) have been developed for generating fair charging schedules. Performance metrics have also been proposed to compare the three MCDSS techniques. The developed scheduling approaches have been tested in an Australian 19-bus SDN with Ausgrid datasets, and the schedules obtained are presented to bring out the significance of the proposed MCDSS.

Algorithm for the formation of a system of multi-criteria metrics for evaluating the activity of energy companies taking into account ESG criteria

The article emphasizes the challenges facing Ukraine's energy sector, in particular, the energy crisis caused by Russian strikes on critical infrastructure facilities and their wear and tear. To overcome them, Ukraine needs significant investments in the energy sector to restore, modernize and expand this infrastructure, developing new energy sources and implementing energy-efficient measures. By disclosing ESG criteria and aligning their activities with the Sustainable Development Goals in their reports, companies can demonstrate their commitment to sustainability initiatives, the achievement of global goals and responsible business conduct, thereby increasing their own investment attractiveness and attracting more investment for the energy sector. The work also emphasizes the importance of energy efficiency and green energy for increasing Ukraine’s energy independence and reducing dependence on imports, which can be achieved with the help of the proposals of the UN General Assembly and the Marshall Plan for post-war recovery of Ukraine. The peculiarities, advantages and disadvantages of the methodologies of scientists are considered, in particular: Analytic Hierarchy Process (AHP), Data Envelopment Analysis (DEA), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE). Transparency and commitment to sustainable development at the corporate level is covered by various ratings. The methodologies of Energy Transparency Index 2021 (II international edition), DJSI, MSCI ESG Ratings, FTSE4Good Index and Corporate Knights’ index 100 most sustainable corporations were analyzed. The article highlights the differences in approaches and sets of criteria used by different rating agencies, as well as the advantages and disadvantages of each approach. A multi-criteria metric system is proposed for evaluating the activity of energy companies in Ukraine taking into account ESG criteria and the stages of development of such a system are outlined, namely the selection of research objects, criteria for analysis, data collection, scoring of companies, comparison of indices, consideration of quality factors, analysis, verification and generalization of research results.

A Multi-Criteria Decision Aid Tool for Radiopharmaceutical Selection in Tau PET Imaging.

The accumulation of pathologically misfolded tau is a feature shared by a group of neurodegenerative disorders collectively referred to as tauopathies. Alzheimer's disease (AD) is the most prevalent of these tauopathies. Immunohistochemical evaluation allows neuropathologists to visualize paired-helical filaments (PHFs)-tau pathological lesions, but this is possible only after death and only shows tau in the portion of brain sampled. Positron emission tomography (PET) imaging allows both the quantitative and qualitative analysis of pathology over the whole brain of a living subject. The ability to detect and quantify tau pathology in vivo using PET can aid in the early diagnosis of AD, provide a way to monitor disease progression, and determine the effectiveness of therapeutic interventions aimed at reducing tau pathology. Several tau-specific PET radiotracers are now available for research purposes, and one is approved for clinical use. This study aims to analyze, compare, and rank currently available tau PET radiotracers using the fuzzy preference ranking organization method for enrichment of evaluations (PROMETHEE), which is a multi-criteria decision-making (MCDM) tool. The evaluation is based on relatively weighted criteria, such as specificity, target binding affinity, brain uptake, brain penetration, and rates of adverse reactions. Based on the selected criteria and assigned weights, this study shows that a second-generation tau tracer, [18F]RO-948, may be the most favorable. This flexible method can be extended and updated to include new tracers, additional criteria, and modified weights to help researchers and clinicians select the optimal tau PET tracer for specific purposes. Additional work is needed to confirm these results, including a systematic approach to defining and weighting criteria and clinical validation of tracers in different diseases and patient populations.

Multicriteria Decision Making in Supply Chain Management Using FMEA and Hybrid AHP-PROMETHEE Algorithms.

In today's global environment, supplier selection is one of the critical strategic decisions made by supply chain management. The supplier selection process involves the evaluation of suppliers based on several criteria, including their core capabilities, price offerings, lead times, geographical proximity, data collection sensor networks, and associated risks. The ubiquitous presence of internet of things (IoT) sensors at different levels of supply chains can result in risks that cascade to the upstream end of the supply chain, making it imperative to implement a systematic supplier selection methodology. This research proposes a combinatorial approach for risk assessment in supplier selection using the failure mode effect analysis (FMEA) with hybrid analytic hierarchy process (AHP) and the preference ranking organization method for enrichment evaluation (PROMETHEE). The FMEA is used to identify the failure modes based on a set of supplier criteria. The AHP is implemented to determine the global weights for each criterion, and PROMETHEE is used to prioritize the optimal supplier based on the lowest supply chain risk. The integration of multicriteria decision making (MCDM) methods overcomes the shortcomings of the traditional FMEA and enhances the precision of prioritizing the risk priority numbers (RPN). A case study is presented to validate the combinatorial model. The outcomes indicate that suppliers were evaluated more effectively based on company chosen criteria to select a low-risk supplier over the traditional FMEA approach. This research establishes a foundation for the application of multicriteria decision-making methodology for unbiased prioritization of critical supplier selection criteria and evaluation of different supply chain suppliers.

Tribological and machining characteristics of AA7075 hybrid composites and optimizing utilizing modified PROMETHEE approach

In this research work an attempt was made to reinforce AA7075 composites with B4C and SiC particles through stir casting route. SEM with EDS mapping revealed that the reinforcement were uniformly over the composites and hardness reduces with the addition of SiC particles owing to the inverse hall petch effect. The results revealed that wear rate reduces with addition of SiC particles owing to the formation of mechanically mixed layer and protective oxide layer confirmed through SEM with EDAX mapping. Three distinct cracks were formed, when slides at different temperature as confirmed through worn surface morphology, pits cracks and plasticization of material were the other features observed. The used motor oil properties were analyzed and results divulged that the oil suitable for dielectric fluid. Increase in Material Removal Rate (MRR), reduction in Tool Wear Ratio (TWR) and Surface Roughness (Ra) was observed with the incorporation of powder particles owing to the bridging effect. Black spots, craters, micro pits, globules and micro crack are the distinct observed on the machined surface topography. The modified Preference Ranking Organization METHod for Enrichment of Evaluations (PROMETHEE) optimization technique was utilized to find the optimal parametric combination.

Optimal routing for mass transit systems using multicriteria methodologies

Transportation consumes approximately 29% of the world's energy and is responsible for approximately 25% of CO2 emissions worldwide. In Cuenca, Ecuador, fossil fuels for transportation represent 59.9% of total energy consumption. Ecuadorian legislation has specified gradually replacing traditional public bus transport units with electric units. Because the relevant costs include those associated with replacing equipment and improving route networks, alternatives that prioritize incorporating the first units on the best routes are being sought. This work establishes a methodological process with a set of tools to prioritize the gradual migration from conventional to electric buses according to the characteristics of each route. The Preference Ranking Organization Method for Enrichment of Evaluation (PROMETHEE) multicriteria technique is used to detect the best routes according to technical, social, environmental, economic and location criteria. A final rank of routes is obtained using Visual PROMETHEE software. After the values defining each criterion and its corresponding weight were processed, the final ranking makes it possible to prioritize one route before others. Given 23 lines and 475 bus units, the method enabled us to determine that in an initial phase, 68 electric buses are required to be used on five lines to reduce fuel consumption. The factors that most influence decision making are the number of passengers served, the length of the trip and the energy required by the route.

Hybrid Neutrosophic- Promethee Method for Selection of Relay Node in WBAN

A wireless body area network (WBAN) is made up of small health-monitoring sensors that are implanted or placed on the human body. These sensors collect and transmit human medical and physiological data . WBANs are linked to medical servers that observe the health of patients. Because of its ability to continuously and remotely monitor patients' health, this type of network can save critical patients.WBAN is being used to establish communication links between these sensors and provide feedback to users. To provide mobility, sensor nodes must perform on electric power. As a result, the most crucial problem in WBAN is low energy consumption. It is difficult to replace the batteries in in-body sensors. To resolve this problem, we present an Neutrosophic -Promethee (Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE)method for choosing the selection of relay node, in which AHP calculates the weight of different criteria and and forwards them to the promethee technique which used for outranking them. The experimental outcomes of this study indicate that energy utilization and overall network reliability are significantly reduced. Keywords:WBAN, MCDM method, Neutrosophic method, Promethee method.

A Combined AHP-PROMETHEE Approach for Portfolio Performance Comparison

Comparing portfolio performance is complex due to the fact that each model is dominant in its own risk space. Since there is no single dominant performance measure, the research problem is how to incorporate several different measures into a performance evaluation model that allows portfolios to be ranked. In this regard, the objective of this study was to develop a new comprehensive method for comparing portfolio performance based on multiple-criteria decision-making (MCDM). This paper proposes an integrated approach for stock market decision making that combines the Analytic Hierarchy Process (AHP) and the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE), which allow hierarchical evaluation of a finite number of alternatives according to different criteria. This hybrid approach is especially advantageous, utilizing the strengths of both individual methods. AHP enables the decomposition of a complex problem into its constituent parts and the determination of weights for criteria, while the PROMETHEE method allows the investor to determine the preference function, complete ranking, and analysis of the robustness of the results. For the MCDM model in this study, different dimensions of performance measures are considered criteria: return measures, risk measures, stability measures, and predictability measures. The methodology has been applied in comparing real portfolios selected on the basis of different risk measures. For this purpose, weekly return data were used for a sample of stocks that are components of the STOXX Europe 600 Index for the period 2000–2020. In addition, a sensitivity analysis is performed to investigate the strength of the results of this method. It suggests that the simultaneous consideration of different performance measures and the investor’s attitude towards the importance of these measures are notably important in the portfolio efficiency estimation process.

IMPLEMENTASI METODE PROMETHEE PADA PEMILIHAN REGISTRAR DOMAIN DI INDONESIA

A domain registrar is a commercial company whose job is to register domain names that have been registered with ICANN (Internet Corporation for Assigned Names and Numbers) or maintain domain ccTLDs that involve certain country codes. Domain registration is the first step in website development as an effort to the existence of a business entering the digital economy era. To ensure the continuity of a business in the internet world, the selection of a domain registration service is very important. Because the domain is the identity as well as the address of a business on the internet. Domain registration service organizations must meet various important criteria needed as a professional domain registration service. These criteria must be able to answer the crucial characteristics of a domain registrar. This study uses PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation). The PROMETHEE method is used to rank among all alternatives based on predetermined criteria. The stages of problem solving in this study are determining alternatives, determining criteria, to ranking alternatives, so that will produce the best alternative output. In this study the PROMETHEE calculation results obtained with the best alternative output being the Domainesia registrar with a net flow value of 0.592.

Fuzzy PROMETHEE model for public transport mode choice analysis

The importance of public transportation service quality research is significantly increasing in recent years, it is the key to understanding and analyzing passengers’ preferences. Different approaches are utilized to explore users’ preferences however, dominantly these apply merely subjective scoring of the attributes and alternatives of the mobility. In this paper, we design a specific model for public transportation mode choice which is capable of integrating subjective scoring with scoring by objective measures such as distance or time. Owing to this purpose, we combine the outranking Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE) as a method to evaluate passengers’ preferences for tangible and intangible criteria with the fuzzy theory, and the Graphical Analysis for Interactive Aid (GAIA) plane to visualize the interactions between attributes as well as to test the robustness of the results via sensitivity analysis. The contribution of this paper is the constructed integrative method that is less subjective than the well-known models but also keeps the freedom of individual evaluators in expressing their preferences. Moreover, another significant issue of mode choice analysis is the group consideration, which is also refined in the new methodology by taking into account not only the mean of group preferences but also their range. A common characteristic of public surveys, the possible vague responses of the layman pattern is solved with the fuzzy approach to reduce the risk of uncertain scoring. The proposed model acts as a great base for the fuzzy inference system that can facilitate mode choice for passengers within a changing environment. The efficiency of the new methodology is demonstrated through a real-world case study of Budapest city, the obtained results are supporting underground mode service quality and highlighting its impact on citizens’ behavior in favor of public transport.

Evaluation of logistics centers in terms of sustainability via MCDM methods

PurposeThe purpose of the study is the evaluation of choosing the determined logistics centers’ (LCs’) locations via Multi-Criteria Decision-Making (MCDM) methods.Design/methodology/approachSince there is more than one criterion in the evaluation of LC location, MCDM methods are used. The Entropy and CRiteria Importance Through Intercriteria Correlation (CRITIC) methods are used to determine the main criteria and sub-criteria weights. The Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) method is used for ranking and evaluating 12 alternatives in Turkey and sensitivity analysis according to the threshold values of criteria and preference functions.FindingsPossible LC alternatives in Turkey and 12 locations ranked. Changing the criteria weights at a significant level did not affect the ranking results much. In the sensitivity analysis of the criteria for which market data could not be obtained (scored by the experts) it is found that they either made no difference or made little difference in the ranking of the alternatives.Practical implicationsIn terms of sustainability, economic factors along with environmental and social dimensions are important criteria for logistics companies and LC. The use of these variables in the LC location selection makes a difference.Originality/valueThis study focuses on LC location selection, via PROMETHEE with evaluated preference functions for criteria, taking into account nine scenarios that use variable criteria weight calculated via different MCDM methods. The results are evaluated by using sensitivity analysis and taking into account the sustainability aspect for LC. Multiple methods have been used effectively together in a way that support each other.

SELECTION OF SOCIAL MEDIA PLATFORMS USING FUZZY PROMETHEE METHOD WITH DIFFERENT SCENARIO TYPES

Social media are widely and mostly utilized in many areas to increase advertising, client relations, tourism, journey and many others by means of members or organizations. Social Media Platforms (SMPs) are becoming increasingly popular in the world. The selection of SMPs, which might be widely and mostly utilized by members or organizations, becomes a critical and complex problem. Moreover, some problems that need to be investigated which consist of the reason for the use of different SMPs, purpose, and frequency of use. In this study, we have investigated to explain these problems via questionnaire which evaluates by undergraduate pupils (N = 173). Therefore, we have created twelve different scenarios that are different weights and thresholds and handled the selection and ranking of SMPs by fuzzy Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) I and II. Consequently, we able to conclude that "Which SMPs were more preferred?", "What are the reasons for selection of SMPs? and "Which criteria are more important for this selection?". In addition, this study can be guiding an infrastructure for the content of new SPMs in the future.

Mathematical Assessment of Machine Learning Models Used for Brain Tumor Diagnosis

The brain is an intrinsic and complicated component of human anatomy. It is a collection of connective tissues and nerve cells that regulate the principal actions of the entire body. Brain tumor cancer is a serious mortality factor and a highly intractable disease. Even though brain tumors are not considered a fundamental cause of cancer deaths worldwide, about 40% of other cancer types are metastasized to the brain and transform into brain tumors. Computer-aided devices for diagnosis through magnetic resonance imaging (MRI) have remained the gold standard for the diagnosis of brain tumors, but this conventional method has been greatly challenged with inefficiencies and drawbacks related to the late detection of brain tumors, high risk in biopsy procedures, and low specificity. To circumvent these underlying hurdles, machine learning models have recently been developed to enhance computer-aided diagnosis tools for advanced, precise, and automatic early detection of brain tumors. This study takes a novel approach to evaluate machine learning models (support vector machine (SVM), random forest (RF), gradient-boosting model (GBM), convolutional neural network (CNN), K-nearest neighbor (KNN), AlexNet, GoogLeNet, CNN VGG19, and CapsNet) used for the early detection and classification of brain tumors by deploying the multicriteria decision-making method called fuzzy preference ranking organization method for enrichment evaluations (PROMETHEE), based on selected parameters, in this study: prediction accuracy, precision, specificity, recall, processing time, and sensitivity. To validate the results of our proposed approach, we performed a sensitivity analysis and cross-checking analysis with the PROMETHEE model. The CNN model, with an outranking net flow of 0.0251, is considered the most favorable model for the early detection of brain tumors. The KNN model, with a net flow of -0.0154, is the least appealing option. The findings of this study support the applicability of the proposed approach for making optimal choices regarding the selection of machine learning models. The decision maker is thus afforded the opportunity to expand the range of considerations which they must rely on in selecting the preferred models for early detection of brain tumors.

Multi-criteria decision making and project portfolio management for transport infrastructure investment in Thailand

Transport project selection is always critical in government planning due to limited resources and diverse demands. Project portfolio management (PPM) was proposed in this study to suggest an optimal set of projects under predetermined constraints. With multiple conflicting criteria, comprehensive steps of multi-criteria decision making (MCDM) procedures were proposed in this study. The process begins with a step of pre-screening the projects and selecting the criteria for project evaluation. Sequential steps of: 1) weight determination process using the proportion method; 2) MCDM process using preference ranking organisation method for enrichment evaluations (PROMETHEE) II; 3) constraint consideration process using PROMETHEE V, was proposed to evaluate and select an optimal project portfolio. The study showed that decision making could be successfully done for the transport infrastructure investment with minimal complex calculation steps and questionnaire input from experts.

Comparison of multi-criteria decision aiding methods in the problem of employee recruitment

One of the most important categories of decisions made in the company are decisions regarding human resource management, in particular decisions related to the proper selection of personnel and recruitment. Wrong recruitment decisions can directly and indirectly lead to financial losses, while the right personnel selection decisions reduce job turnover, increase morale and eliminate the cost of subsequent recruitment process. The decision-making methods used in the literature in the recruitment of employees are mostly complicated, which may make their results unreliable for the management. Therefore, the article addresses the problem of verifying the applicability of relatively easy-to-use decision support methods, which were AHP (Analytic Hierarchy Process) and PROMETHEE (Preference Ranking Organization METHod for Enrichment Evaluations). Although in the considered problem of staff selection, the same candidate for employment was indicated as the best one, the conducted research allowed to conclude that these methods may lead to slightly different recommendations. Therefore, it is worth using both methods to confirm the results and obtain a more complete picture of possible decisions.

ЗАСТОСУВАННЯ МЕТОДУ БАГАТОКРИТЕРІАЛЬНОЇ ОПТИМІЗАЦІЇ ДЛЯ ПРИЙНЯТТЯ РІШЕНЬ У СФЕРІ ФІНАНСОВОГО ЗАБЕЗПЕЧЕННЯ ВІЙСЬК

The article considers the theoretical basis for the use of optimisation methods for managerial decision-making and search for an optimal solution. The article describes the methods of multi-criteria optimisation, which, based on the analysis of the relevant literature, are often used in various spheres of human activity, namely: simple additive weighting (SAW), elimination and choice expressing the reality (ELECTRE), preference ranking organisation method for enrichment evaluation (PROMETHEE), technique for order preference by similarity to ideal solution (TOPSIS), analytical hierarchy process (AHP), and analytical network process (ANP). The key characteristics of the decision-making environment in the field of military financial support are identified, based on the conditions and characteristics of such a decision-making environment, namely: a significant number of criteria to be taken into account, the presence of subjectivity in decision-making and the efficiency of such decisions, TOPSIS was chosen as the main method. In the methodological part of the article, the main characteristics, procedure of use and indicators of this method are considered. For practical application of the method, the article develops a case of decision-making by the military organisation A0001 regarding the optimal use of a limited financial resource out of 4 proposed options. In the article, a list of criteria was compiled, they were grouped, and a matrix was formed. In the final part of the article, a matrix of weighting coefficients for each of the 4 options is formed, its normalisation is carried out and the distance/closeness of the proposals of further solutions to the ideal solution is calculated. Based on the calculations, the author chose the optimal option for the use of financial resources, which, according to the TOPSIS method, had the smallest distance to the ideal solution, i.e., was the closest of the proposed options to the ideal option for the use of financial resources.

Non-Pharmaceutical Intervention Strategies to Respond to the COVID-19 Pandemic: Preference Ranking Method

One of the hot topics of discussion today is coronavirus disease 2019 (COVID-19).  The disease is easily transmitted from one person to another person. However, there are no specific drugs that can alleviate the disease thus non-pharmaceutical intervention strategies is a good option. This paper aims to apply the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) method to outrank the intervention strategies. A case study is presented where five experts were invited to rate ten alternatives and ten criteria using linguistic scales. Spreadsheet software and PROMETHEE-GAIA software were employed to establish outranking results and to provide evidence on the vigorousness of the outranking results. The final outranking indicates that the most and the least preferred intervention strategies are alternative A1 (lockdown/quarantine) and alternative A10 (Practice of hand hygiene) respectively. The outranking results are further analyzed with distribution analysis and weights sensitivity analysis where these analyses provide evidence on the vigorous of the outranking results. It is found that these analyses confirm the position of A1 as the most preferred intervention strategy to curtail the COVID-19 transmissions.  The findings would be beneficial for public health authorities to deal with multiple challenges to curb the spread of COVID-19.