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Aim: Annually, millions of people die from kidney diseases making it a serious public health concern. Patients with renal insufficiency have hemostatic defects that enhance their risk of thrombosis and bleeding. Evaluating the advantages and hazards of anticoagulant thromboprophylaxis is challenging. Material and Methods: By using the fuzzy preference ranking organization method for enrichment evaluation (fuzzy PROMETHEE) and fuzzy multi-criteria optimization and compromise solution (fuzzy VIKOR), which are analytical multi-criteria decision-making (MCDM) techniques, this study aims to analyze diverse types of anticoagulants; specifically, Vitamin K-Antagonist (warfarin) and direct oral anticoagulants namely; Dabigatran, Rivaroxaban, Apixaban, and Edoxaban, by considering various criteria. Results: Direct oral anticoagulants outperformed warfarin in terms of safety and effectiveness. Among all the analyzed anticoagulants, Apixaban was found to be the best-ranked anticoagulant with both fuzzy PROMETHEE and fuzzy VIKOR, with a net flow of 0.0064 and the lowest Qj value of 0.0000, respectively, compared with other alternatives depending on the selected criteria while warfarin was found to be the least ranked anticoagulant with a net flow of -0.0043 and the highest number of Qj being 1, respectively. Conclusion: The MCDM techniques used were effective, highly accurate, and sensitive in the comparison of anticoagulant therapy for venous thrombosis in renal insufficiency based on the chosen criteria. The proper use of a safe and effective anticoagulant will boost better performance not only in patients' survival and well-being but also in the health care center. [Turk J Vasc Surg 2023; 32(1.000): 42-50]

ObjectivesIn this study, a learning-based optimization method is proposed and implemented for determining new monitoring sites when expanding the roadside air pollution monitoring network. Utilizing the bigdata available in Seoul, this decision-making tool is developed that takes into account the objectives of selecting new monitoring sites and incorporates social, economic, and environmental characteristics. The optimized results can suggest potential locations for new roadside air pollution monitoring sites. Additionally, the capability of this tool to facilitate objective decision-making processes is evaluated by determining the influence range providing reliable air pollution information with the addition of the new monitoring sites MethodsThe proposed learning-based optimization algorithm is a new approach for selecting the new optimal monitoring sites by comprehensively considering social, economic, and environmental factors aligned with the installation purpose of the monitoring system in Seoul. The algorithm starts with genetic algorithms to select candidate locations for new monitoring sites that maximize the influence area of the expanded monitoring network compared to the existing monitoring network, capture a high overall level of air pollution, and do not overlap with the existing monitoring network. After that, PROMETHEE method is applied to evaluate the solutions generated by the genetic algorithm and choose the final solution that best fits six evaluation factors (Information entropy, number of new monitoring sites, distance from point sources, wind speed, traffic volume, and population) to be considered when installing new monitoring sites Results and DiscussionThe learning-based optimization algorithm selects 10 potential new monitoring sites adding to the existing roadside air pollution monitoring network having 15 monitoring sites. The explainable spatiotemporal range of the air pollution information that can be expected after the installation of the new monitoring sites is quantified to cover 84.33% of Seoul, reducing the uncertainty of the air pollution information of existing monitoring network by 26.15%. The final solution, selected from several solutions, can get new optimal roadside air pollution monitoring sites reflecting the regional characteristics of Seoul and the installation purpose of the monitoring system by having a small number of newly established monitoring locations, being close to air pollution emissions facilities, and having a high population and traffic volume ConclusionThe proposed learning-based optimization method, using relevant variables for the installation purpose of the monitoring system, can derive the objective solution for deciding new monitoring locations of the roadside air pollution monitoring network, considering additional social factors as opposed to urban air pollution monitoring network. The final solution obtained through the optimization algorithm has great potential for future use, as it can guide to determine practical and feasible new monitoring sites with additional on-site verification. Furthermore, this optimized approach can be applied widely during the decision-making process for the expansion of other environmental monitoring networks.

The aim of this study is to present criteria to evaluate the resilience of sewer networks related to ground collapse and urban flooding likely to occur in a specific region and then to determine the ranks of the sewer networks resilience of the selected regions to show the applicability of the analytic hierarchy process (AHP) and the Preference Ranking Organization Method for Enriching Evaluations (PROMETHEE II) method. Fourteen evaluation criteria representing resistance, reliability, redundancy, and response and recovery are presented and their weights are estimated by the AHP by asking questionnaires to 10 sewer experts, leading to the result that the sub-criteria of reliability showed the highest importance, followed by the length ratio of good pipelines (under resistance) and adequacy of the flow capacity of the bypass pipelines (under redundancy). Four separate small blocks of drainage areas (total area of 3.57 km2; sewer length of 50.6 km) in Seoul are chosen for the case study. Using appropriate preference functions and thresholds for each evaluation criterion for PROMETHEE II application yields the resilience rankings of four blocks as Block III > Block IV > Block I > Block II. A sensitivity analysis was also carried out by changing the weights. HIGHLIGHTS The factors that affect the sewer network resilience were identified and evaluated.; 4Rs (Resistance, Reliability, Redundancy, Response and recovery) were considered.; The AHP was used to determine the weights of the criteria.; PROMETHEE II was used to obtain the final ranking of the sewer network resilience.; Sensitivity analysis was carried out by changing the weights.;

Fungal chitosan is one of the piezoelectric biomaterials that can be used in energy harvesting. Fungal biomass offers a sustainable source for chitosan, as an alternative to crustacean-based chitosan. However, the fungal chitosan yield is relatively lower than other conventional sources of chitosan. Therefore, this study aims to review different experimental procedures for the chemical extraction of chitosan from biomass. The selection of the most effective extraction method using Preference Ranking Organization Method for the Enrichment of Evaluations and Geometrical Analysis for Interactive Decision Aid (PROMETHEE-GAIA), a systematic multiple criteria analysis based on ranking and section of alternatives (i.e. different extraction methods). PROMETHEE-GAIA was performed as the critical and comprehensive discussion in this paper. The PROMETHEE-GAIA analysis showed that the microwave-assisted organic acid-alkaline treatment extraction method showed the highest ranking for the most effective fungal chitosan method due to positive response from the chitosan yield, degree of deacetylation, toxicity and temperature.

The selection of mining methods is a challenging and complicated concept in mining engineering. It depends on various and different factors such as geotechnical, geological and economic properties and characteristics. Kodakan Gold Mine in Iran is currently mined using the open pit method. However, due to the special conditions of this mine and the increase in waste removal costs, it is inevitable to decide to select an underground mining method in the future. The purpose of this research is to select the most proper underground mining method for this mine. The shape, dip, and depth of the deposit, the thickness of the ore, grade distribution, recovery, skilled manpower, output per worker, and strength specifications of the ore, hanging-wall, and footwall are considered as the main decision attributes. Since there are different parameters in selecting the appropriate mining method using the multi-attribute decision making approach, therefore hybrid multi-attribute decision-making method was employed in this paper to enhance the strength of the decision model and eliminate the weaknesses of the classical methods. Regarding the results of this study, rock quality designation of the hanging-wall and deposit shape have the highest weight value in selecting the underground mining method. Moreover, the shrinkage mining method is proposed as the most appropriate method.

Various decontamination technologies have been developed for removing contaminated areas in industries. Although it is important to consider parameters such as safety, cost, and time when selecting the decontamination technology, till date their comparative study is missing. Furthermore, different decontamination technologies influence the decontamination effects in different ways. Therefore, this study compares different decontamination techniques for the steam generator using a multicriteria decision-making method. A steam generator is a large device comprising both low- and very low-level waste (LLW, VLLW) and reflects the difference in weights of the standards according to the classification of the waste. For LLW and VLLW decontaminations, chemical oxidizing reduction decontamination (CORD) and decontamination grit blasting were used as the preferred techniques, respectively, considering the purpose of decontamination differs based on the initial state of waste. An expert survey revealed that safety in LLW and waste minimization in VLLW exhibited high preference. This evaluation method can be applied not only to the comparison between each process, but also to the creation of process scenarios. Therefore, determining the decontamination approach using logical decision-making methods may improve the safety and economic feasibility of each step in the decommissioning process and ensure a public acceptance.

The recognition of rights associated with members of society with special characteristics is a reality in the management of governments at a global level. Making these rights effective and respected is a permanent task for the states, among which is Ecuador. That is why the objective of this research is to identify the main factors that affect the violation of the rights of priority attention groups in Ecuador. To meet the proposed objective, methods such as PESTEL analysis, Pareto's technique, Promethee, and TOPSIS were used. As a result, the factors that favor the violation of rights in each area analyzed were identified. Once the most important ones were found, actions were proposed based on them, and the most appropriate actions to mitigate them were obtained.

The application of indigenous justice in Ecuador is a topic of interest given its implications for the social life of the nation. The problem focuses on the fact that this constitutes a part of the country's cultural heritage, but in turn, sometimes contradicts the so-called ordinary justice. Due to the, it is proposed as the objective of the investigation to propose the most appropriate actions to mitigate the limitations of the application of indigenous justice in Ecuador. Empirical methods such as the interview, the cause-effect diagram, and multi-criteria decision methods such as Promethee and Vikor were used for this. It was obtained as a result that the main actions must strengthen the communication system through dialogues and conferences of judicial institutions, with the objective of Indigenous Justice, to increase the knowledge on that topic among the members of the society, and encourage the protection of the ethnical minorities and, in turn, set the necessary limits in each territory, to prevent the violation of rights and impunity, and to achieve that the indigenous communities comply with all the stipulated on the Constitution, the international treaties that Ecuador has signed, considering the indigenous justice as an immediate source of right.

Identifying and selecting the best cotton fabric from a series of available samples is a challenging multicriteria decision-making (MCDM) problem that includes fuzziness and uncertainty. Pythagorean fuzzy sets (PFSs) are widely used to manage complex and uncertain MCDM issues. The Preference Ranking Organization Method for Enrichment of Evaluation (PROMETHEE) is a widely used classical MCDM to assess and rank alternatives. In this study, we use the PROMETHEE approach to solve a real case of ranking cotton fabrics in a PFS environment, where alternatives are compared based on the PFS linguistic scales, and a score function is used as a defuzzification function. A comparative analysis is also performed with different score functions. The ranking results of the proposed PF-PROMETHEE method correlate strongly with other score functions, which indicates that the PF-PROMETHEE method is feasible and effective. The salient contributions of the PF-PROMETHEE method are as follows: (1) the method can manage uncertainty better than other methods; (2) uncertainty is evaluated by linguistic scales in the PF environment; (3) the method can effectively select cotton woven fabric; and (4) the method is applicable to a wide variety of MCDM problems in the textile industry.

Amaç: Yapılan çalışmada, raylı ulaşım sistemlerinde kullanılan bakım yöntemlerini verimlilik açısından değerlendirebilmek için literatürdeki çalışmalar, raylı sistemler verimlilik hesaplama kriterleri incelenerek çok kriterli karar verme metotları (ÇKKV) ile önceliklendirme yapılması amaçlanmıştır.Yöntem: Bakımın verimlilik üzerindeki etkisi belirlenmiş, uygulanan bakım metotları ve bakım metotlarının ulaşım verimliliğini etkilediği kriterler belirlenmiştir. Belirlenen kriterler ÇKKV metotları ile hesaplanmıştır.Bulgular: Çalışmamızda, bakımın arıza oluşumu ilişkisi nedeniyle verimliliği etkilediği ve verimliliği etkileyen 10 ayrı bakım kriteri olduğu, AHP uygulaması sonucunda, 10 kriter arasında %23,96 etki ağırlığı ile bakım maliyetinin en yüksek etkiye sahip olduğu tespit edilmiştir. PROMETHEE uygulamasında, tercih edilmesi gereken bakım yöntemleri olarak izleme yöntemi ile bakımın 0,8634 ortalama akım değeri ile ilk sıradaki yöntem olduğu, bu yöntemi aktif bakım (0,2437), periyodik bakım (0,1377), önleyici bakım (0,0685) ve fırsat bakım (0,0396) uygulamalarının takip ettiği tespit edilmiştir. Negatif yönlü ortalama akım değerine sahip operatör bakımı (-0,4353), yerinde bakım (-0,4549) ve düzeltici bakım (-0,4627) uygulamalarının tercih önceliği bulunmadığı tespit edilmiştir.Özgünlük: Çalışmada PROMETHEE uygulamasının, raylı ulaşım sistemlerinde kullanılan bakım yöntemlerinin verimliliği üzerinde kullanılmadığı görülmüştür. Çalışmanın raylı ulaşım sistemlerinde verimliliği etkileyen diğer başlıklar üzerinde uygulanabileceği ve literatüre katkı sağlayacağı düşünülmektedir.

Due to increasing throughput and expansion in extended hinterlands, container ports worldwide are witnessing the establishment of dry ports in their hinterlands to ensure the smooth flow of containerised cargo and maintain efficient port–city relationships. Since the dry port has become a key node in maritime logistics, connecting all involved parties of the network, the proper selection of dry port location contributes to cost and lead time reductions, as well as better logistics performance. Moreover, being a coordinating component at port and hinterland interfaces, the selection of dry port locations has a significant impact on the urban and natural environment of the port city. This study proposes a framework for identifying the best location for a new dry port using three multi-criteria decision analysis (MCDA) techniques, contextualizing the case study of Chittagong port, the premier port in Bangladesh. Fuzzy Analytic Hierarchy Process (FAHP) and Best Worst Method (BWM) MCDAs have been applied to find the weights of the dry port location selection decision criteria. The derived outcomes have been used to outrank the initially identified five locations, based on the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) I and II. We find that the proximity of the dry port to the exporter and importer, accessibility of the high-capacity road network, proximity of the dry port to seaport, and availability of rail network are the crucial criteria for dry port establishment in Bangladesh. Savar has been identified as the best location for establishing new dry ports in Bangladesh, followed by Mirsharai. This research will benefit government and private investors for making their investment decisions on the selection of dry ports. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Today, the number of shopping malls, which have an important place in the retail sector, which is the return of social and modern life, is increasing day by day. This situation leads the administrations to various and innovative searches. Analyzing the current performances of shopping centers and offering appropriate solutions are also a solution to these pursuits. Due to this newly emerged need and, in order to contribute to the development and performance measurement of shopping centers, in this study, a performance index measurement model is developed for shopping malls. In the proposed performance index model, 6 main criteria and 135 sub-criteria were determined. In order to evaluate the performance criteria and the performance of the shopping malls, Analytic Hierarchy Process (AHP) and Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) methods were preferred. In the application phase of the developed model, the sample of the research was applied to four shopping malls in Istanbul. The analysis of the results, were evaluated comparatively with 2 methods namely SWOT analysis and radar diagram analysis. A far as we know, this study presents, a new and comprehensive model for evaluating the performance of shopping malls first time in the literature. It is hoped that, with the proposed model and proposed solutions, competitive advantage in the retail sector will be achieved and will make a positive contribution to the sector. [ABSTRACT FROM AUTHOR]

Market segmentation is a key concept in marketing that groups consumers by their needs, characteristics, or purchasing behavior. The objectives of this research are to develop outranking multicriteria models based on preference ranking organization method for enrichment evaluation (PROMETHEE) in order to segment food consumers and apply them to a survey of healthy and sustainable meat. The models consider two categories of purchasing criteria; one related to product and another to the distributor process. One model generates ordered segments of consumers, while the other obtains four segments according to consumer performance in both criteria categories. An extension of FlowSort method for the sorting problems with Likert scale data is also contributed. The profile of segments shows the significance level of variables such as gender, but mainly those related to food‐related lifestyles, when characterizing the consumer groups. This proposal represents a robust approach, which is useful in the effective design of marketing campaigns and policies. [ABSTRACT FROM AUTHOR]

Multicriteria decision aid consists of helping decision makers to compare (rank, choose, sort, etc.) different alternatives which are evaluated on conflicting criteria. In the last decades, numerous decision aid methods have been developed. Three main categories of decision aid methods are usually considered: the aggregating, interactive and outranking methods. While aggregation methods produce a complete ranking of the set of alternatives, outranking methods usually allow some pairs of alternatives to remain incomparable. This happens either if the two alternatives present some strong conflicting information or if there are not enough elements in the decision problem to state the preference or indifference between them. A well‐known family of outranking procedures are the Promethee methods. The aim of this work is first to provide an analysis of the incomparability relation produced by Promethee I. From our point of view, some shortcomings of this incomparability relation are presented. Then, a new method based on the comparison of weighted coalitions of mono‐criterion net flow score differences, called Prometheeγ, is proposed. Prometheeγ and Promethee i are then further compared. [ABSTRACT FROM AUTHOR]

Multi-Criteria Decision Making (MCDM) methods help researchers in solving many problems in terms of numerical analysis. However, MCDM methods have not been very popular in the health sector. In this study, five ones of Turkey's most intense and highly populated cities were selected and the risk of the spread of Covid-19 disease was evaluated on the basis of seven criteria. The PROMETHEE and the ELECTRE methods were conducted to rank the cities in terms of the spread of Covid-19. The PROMETHEE method correctly ranked the most risky city as Istanbul, but ELECTRE ranked Istanbul the second most risky. The results of the methods are compared with real data. PROMETHEE gave more convenient results than ELECTRE. Also, this paper offers a new field of study to the literature. [ABSTRACT FROM AUTHOR]

Copper ore is a non-renewable resource with lower ore grades, which means that the extraction of more rock material is required to produce the same amount of copper, implying a greater consumption of materials, reagents, water, and energy. Since there is a greater amount of copper sulfide present in nature, concentration using the bubble flotation method will generate a greater number of tailings. This article discusses the environmental issues resulting from tailings dams and how multi-criteria decision analysis can help prioritize those sites in order to promote circular economy measures to compensate for and reduce the impacts of this type of waste generated by the copper mining industry. This work aims to contribute to this purpose by taking information from abandoned and non-active tailings, which are currently present as a result of the lack of regulations in times prior to environmental obligations and because they are metallurgical waste from old operations that had metal recovery rates that were much lower than the current rates. We propose a model based on the multi-criteria Promethee method to prioritize the tailings dams according to the commercial value of the existing materials in the deposits. A case study with an application of the model to 103 dams in the mining region of Coquimbo in northern Chile is shown. [ABSTRACT FROM AUTHOR]

A methodology that can support the decision process for selecting an Electric Vehicle (EV) is crucial. Considering the quantity, diversity, and complexity of existing EV models, their advantages and disadvantages, and people's increasing concern for sustainability, selecting and buying a personal EV appropriate to one's needs and requirements is not a simple task. Currently, criteria such as price, consumption per 100 km, range, comfort, brand, safety, and technology can be used to analyse available EV models. In this context, the decision-making process can be supported by the Multicriteria Decision Analysis (MCDA), "a method that clearly structures and evaluates complex problems with several alternatives, conflicting criteria, and complex scenarios. The novelty of this paper is to present a hybrid MCDA approach, combining two powerful methods (AHP and PROMETHEE) into a single framework to assist EV selection by any given person who has no multicriteria knowledge. A survey was conducted to define relevant criteria for a private person selecting an EV. The approach was validated for EV selection, considering the available offer within a target value, based solely on available public data, and excluding qualitative criteria. The paper discusses a case study whereby the proposed approach, using both quantitative and qualitative criteria, assists a decision-maker in selecting an EV from a set of alternatives, validated by the decision-maker, proving the importance and relevance of supporting the decision-making process. [ABSTRACT FROM AUTHOR]

Construction industry workers; are exposed to serious safety and health risks, hazardous work environments, and intense physical work. This situation causes fatal and non-fatal accidents, reduces productivity, and causes a loss of money and time. Construction safety management can use wearable sensors to improve safety performance. Since there are many types of sensors and not all sensors can be used in construction applications, it is necessary to identify suitable and reliable sensors. This requirement causes a sensor selection problem. The study aims to determine the priority order of physiological and kinematic sensors in preventing risks in the construction industry. Within the scope of this purpose, five criteria and seven alternatives were determined in line with the literature research and expert opinions. The criteria weights were calculated with the AHP method, and the alternatives were ranked with PROMETHEE and AHP. Providing a proactive approach to the use of sensors in the construction industry will provide safer working conditions, identify workers at risk, and help identify and predict potential health and safety risks. It will contribute to the literature on improving construction health and safety management.

This study endeavoured to investigate the machining of Waspaloy using Electric Discharge Machining (EDM), utilizing copper-graphite composites as the tool material. Given the limited existing research on machining Waspaloy with composite tools, this study aims to address this gap by employing a copper-graphite composite tool. In this work, pure copper electrode and three composite electrodes with varying graphite percentages in copper, viz. copper containing 5%, 10%, and 15% graphite (CuGr-5, CuGr-10, and CuGr-15), are utilized for experimentation. Composite electrodes are fabricated by the stir-casting process. The scanning electron microscope reveals that the graphite specks are homogeneously disseminated over the matrix material. The Taguchi mixed orthogonal array was used for developing experimental runs. By varying the current, polarity, pulse on and off times, tool materials, and gap, machining performance was measured in terms of Material Removal Rate (MRR), Tool Wear Rate (TWR), and Surface Roughness (Ra). It was observed that CuGr-5 provides an enhancement in MRR due to the improved electric conductivity, bridging effect, and increased energy concentration at the spark gap. Diverse characteristics witnessed on the surface morphology include black dots, globules, remelted layers, micro-cracks, and scratches. When machined with a CuGr5 electrode, the surface quality improved owing to the completed flushing and uniform distribution of generated heat as confirmed through worn surface morphology. The parameters were optimized utilizing the PROMETHEE optimization technique; it was found that the CuGr-5 electrode with the assessment value 0.02458 was optimal for machining of Waspaloy.

The combination of MCDM and fuzzy sets offers new potential ways to solve the challenges posed by complex image contents, such as selecting the optimal segmentation algorithm or evaluating the segmentation quality based on various parameters. Since no single segmentation algorithm can achieve the best results on satellite image datasets, it is essential to determine the most appropriate segmentation algorithm for each satellite image, the content of which can be characterized by relevant visual features. In this research, we proposed a set of visual criteria representing the fundamental aspects of satellite image segmentation. The segmentation algorithms chosen for testing were evaluated for their performance against each criterion. We introduced a new method to create a decision matrix for each image using fuzzy fusion, which combines the image content vector and the evaluation matrix of the studied segmentation algorithms. An extension of the Preference Ranking Organization Method Enrichment Evaluation (PROMETHEE) using intuitive fuzzy sets (IFSs) was applied to solve this problem. The results acquired by the proposed methodology were validated by comparing them with those obtained in expert ratings and by performing a sensitivity analysis.

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.

A methodology that can support the decision process for selecting an Electric Vehicle (EV) is crucial. Considering the quantity, diversity, and complexity of existing EV models, their advantages and disadvantages, and people's increasing concern for sustainability, selecting and buying a personal EV appropriate to one's needs and requirements is not a simple task. Currently, criteria such as price, consumption per 100 km, range, comfort, brand, safety, and technology can be used to analyse available EV models. In this context, the decision-making process can be supported by the Multicriteria Decision Analysis (MCDA), “a method that clearly structures and evaluates complex problems with several alternatives, conflicting criteria, and complex scenarios. The novelty of this paper is to present a hybrid MCDA approach, combining two powerful methods (AHP and PROMETHEE) into a single framework to assist EV selection by any given person who has no multicriteria knowledge. A survey was conducted to define relevant criteria for a private person selecting an EV. The approach was validated for EV selection, considering the available offer within a target value, based solely on available public data, and excluding qualitative criteria. The paper discusses a case study whereby the proposed approach, using both quantitative and qualitative criteria, assists a decision-maker in selecting an EV from a set of alternatives, validated by the decision-maker, proving the importance and relevance of supporting the decision-making process.

Fungal chitosan is one of the piezoelectric biomaterials that can be used in energy harvesting. Fungal biomass offers a sustainable source for chitosan, as an alternative to crustacean-based chitosan. However, the fungal chitosan yield is relatively lower than other conventional sources of chitosan. Therefore, this study aims to review different experimental procedures for the chemical extraction of chitosan from biomass. The selection of the most effective extraction method using Preference Ranking Organization Method for the Enrichment of Evaluations and Geometrical Analysis for Interactive Decision Aid (PROMETHEE-GAIA), a systematic multiple criteria analysis based on ranking and section of alternatives (i.e. different extraction methods). PROMETHEE-GAIA was performed as the critical and comprehensive discussion in this paper. The PROMETHEE-GAIA analysis showed that the microwave-assisted organic acid-alkaline treatment extraction method showed the highest ranking for the most effective fungal chitosan method due to positive response from the chitosan yield, degree of deacetylation, toxicity and temperature. [ABSTRACT FROM AUTHOR]

The Preference Ranking Organization Method for Enrichment of Evaluations (PROMETHEE) is an outranking series of multi‐attribute decision making used to assess a finite collection of alternatives based on conflicting criteria. One of its significant benefits is its adjustability in response to a set of acceptable preference functions that may quantify the differences between alternatives conditional on the kind and structure of the criteria. This article offers the novel approach of the PROMETHEE method, named the 2TLmF PROMETHEE method, which combines the multi‐polarity with linguistic information of decision problems. The customary procedure of the offered technique begins with the normalized weights of the attributes assigned by decision‐makers. After that, Gaussian and the usual preference functions are utilized to determine the preferences of the alternatives. The ultimate choice is based on the alternative's positive and negative outranking flow. The positive outranking demonstrates how one alternative outranks all other options, while the negative preference flow implies an alternative is superior to all other options. There are several modifications to the PROMETHEE approach, two of which are discussed in this article. The first is PROMETHEE I, which uses positive and negative preference flows to generate a partial ranking. Secondly, PROMETHEE II gives a comprehensive rating list based on the net flow, representing the balance of positive and negative preference flows. Further, a comprehensive flowchart is used to show the entire picture of the proposed methodology. As an application, a real‐life situation related to selecting an elliptical machine for a home gym is chosen to illustrate the transparency and reliability of the presented work. Finally, a comparative study is carried out to illustrate the strength and applicability of the proposed method. [ABSTRACT FROM AUTHOR]

Artificial intelligence development and research leaders in business, industry, and nations gain a major competitive edge. Additionally, it is clear that nations with a well-established national artificial intelligence policy have an edge over others, both technologically and economically. To further their artificial intelligence capability, nations also seek to develop a strategy, vision, structure, and working environment that encourages collaboration between the public sector, private industry, and educational institutions. Artificial intelligence is thought to be a tool that will help bridge the gap between powerful and developing countries growing in the future. Using data from "The Global AI Index" for 2021, this study aims to examine and analyze the present state of artificial intelligence management in 62 nations in terms of talent, infrastructure, business environment, development and research government policy, and commercial efforts. The research used PROMETHEE, which is widely used in multi-criteria decision-making evaluations, and its geometric representation, the GAIA plane. This study also found that the United States of America is the world leader in artificial intelligence (AI) research and development as well as AI investment. The United Kingdom, China, Israel, Canada, the Netherlands, South Korea, and Germany all rank well. China is rapidly catching up to the USA. At the very bottom of the list are nations such as Armenia, Kenya, Egypt, South Africa, and Pakistan. Turkey's values are more similar to those of nations towards the bottom of the list than of those in the top half. There is a significant gap between the top three countries and the rest of the world in all parameters included in the survey. Except for the 'State Strategy' category, Turkey scores quite low compared to the top-performing countries. Decision makers are expected to address the identified global challenges of the study by creating a more comprehensive national AI strategy, both financially and in terms of content. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Aim: Annually, millions of people die from kidney diseases making it a serious public health concern. Patients with renal insufficiency have hemostatic defects that enhance their risk of thrombosis and bleeding. Evaluating the advantages and hazards of anticoagulant thromboprophylaxis is challenging. Material and Methods: By using the fuzzy preference ranking organization method for enrichment evaluation (fuzzy PROMETHEE) and fuzzy multi-criteria optimization and compromise solution (fuzzy VIKOR), which are analytical multi-criteria decision-making (MCDM) techniques, this study aims to analyze diverse types of anticoagulants; specifically, Vitamin K-Antagonist (warfarin) and direct oral anticoagulants namely; Dabigatran, Rivaroxaban, Apixaban, and Edoxaban, by considering various criteria. Results: Direct oral anticoagulants outperformed warfarin in terms of safety and effectiveness. Among all the analyzed anticoagulants, Apixaban was found to be the best-ranked anticoagulant with both fuzzy PROMETHEE and fuzzy VIKOR, with a net flow of 0.0064 and the lowest Qj value of 0.0000, respectively, compared with other alternatives depending on the selected criteria while warfarin was found to be the least ranked anticoagulant with a net flow of -0.0043 and the highest number of Qj being 1, respectively. Conclusion: The MCDM techniques used were effective, highly accurate, and sensitive in the comparison of anticoagulant therapy for venous thrombosis in renal insufficiency based on the chosen criteria. The proper use of a safe and effective anticoagulant will boost better performance not only in patients' survival and well-being but also in the health care center. [ABSTRACT FROM AUTHOR]

Multi-attribute group decision making is widely used in the real world, and many scholars have done a lot of research on it. The public's focus on emergencies can provide an important reference for emergency handling decision making in the social media big data environment. Due to the complexity of emergency handling decision making, the asymmetry of user evaluation information is easy to cause the loss of important information. It is very important to mine valuable information for decision making through online reviews. Then, a generalized extended hybrid distance measure method between the probabilistic linguistic term sets is proposed. Based on this, an extended GDMD-PROMETHEE large-scale multi-attribute group decision-making method is proposed as well, which can be used to decision making under symmetric information and asymmetric information. Firstly, web crawler technology is used to explore the topics of public concern of emergency handling on social media platforms, and use k -means cluster analysis to classify the crawling variables, then the attributes and subjective weights of emergency handling plans are obtained by TF-IDF and Word2vec technology. Secondly, in order to better retain the linguistic evaluation information from decision-makers, a new generalized probabilistic hybrid distance measure method based on Hamming distance is proposed. Considering the difference of decision makers' evaluation, the objective weight of decision makers is calculated by combining the maximum deviation method with the new extended hybrid Euclidean distance. On this basis, the comprehensive weights of the attributes are calculated by combining subjective and objective factors. Meanwhile, this paper realizes the distance measures and information fusion of probabilistic linguistic term sets under cumulative prospect theory, and the ranking results of the emergency handling plans based on the extended GDMD-PROMETHEE algorithm are given. Finally, the feasibility and effectiveness of the extended GDMD-PROMETHEE algorithm are verified by the case study of the explosion accident handling decision making of Shanghai "6.18" Petrochemical, and the comparative analyses between the several traditional algorithms demonstrate the extended GDMD-PROMETHEE algorithm is more scientific and superior in this paper. [ABSTRACT FROM AUTHOR]

The aim of this study was to rank several chemical hazards present in one food item, namely infant formula. We first identified the substances potentially present in infant foods according to the results of the French infant Total Diet Study and to the available scientific literature. Second, we built three criteria to rank the hazards: severity, contribution to the total exposure, and risk characterisation. Each criterion was scored using quantitative or semi-quantitative scales. Third, in order to rank the chemical hazards, two approaches of aggregation of the three criteria were deployed. On the one hand, a multi-criteria decision analysis outranking method and on the other hand a semi-quantitative risk-matrix type method. We then tested these approaches on follow-on formulae for the 7–12 months population, for which contamination data from the French infant Total Diet study were available. The results of both methods showed that the six prioritised substances are the same even if not in the exact same order (acrylamide, inorganic arsenic, furan, chromium VI, lead and PCDD/Fs) demonstrating the robustness of these approaches. [ABSTRACT FROM AUTHOR]

Measuring and assessing the degree of technological readiness at macro level is crucial in guiding the overall technological development for the countries. The problem is multi-criteria in nature, involving assessment and consolidation over multiple dimensions. This study develops a composite index to measure basic technological maturity from supply chain integration perspective and utilizes multicriteria decision-making method PROMETHEE to obtain a ranking among 28 OECD countries. Based on OECD data and metrics set availability, a composite index is developed using 10 relevant metrics. Then a comparative study is performed for 28 countries for the year 2019 under six different scenarios using Visual PROMETHEE. The study is original in developing a composite index with corresponding metrics, providing a comparative evaluation across OECD countries, and offering a comprehensive analysis utilizing almost all major visual analysis tools of the Visual PROMETHEE. The results indicate that the metric set selected and the methodology applied with the visual tools of PROMETHEE provide a systematic which enables country-based benchmarking under different dimensions and enable policymakers to identify the improvement opportunities for each dimension. [ABSTRACT FROM AUTHOR]

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Background: Network meta-analysis exploits randomized data to compare multiple interventions and generate rankings. Selecting an optimal treatment may be complicated when multiple conflicting outcomes are evaluated in parallel. Design: The present study suggested the incorporation of multicriteria decision-making methods in network meta-analyses to select the best intervention when multiple outcomes are of interest by creating partial and absolute rankings with the TOPSIS, VIKOR, and PROMETHEE algorithms. The TOPSIS and VIKOR techniques represent distance-based methods for compromise intervention selection, whereas the PROMETHEE analysis method allows the definition of preference and indifference thresholds. In addition, the PROMETHEE technique allows a variety of modeling options by selecting alternative preference functions. Different weights may be applied to outcomes objectively with the entropy method as well as subjectively with the analytic hierarchy process, enabling the individualization of treatment choice depending on the clinical scenario. Results: Visualization of decision analysis may be performed with multicriteria score-adjusted scatterplots, while league tables may be constructed to depict the PROMETHEE I partial ordering of interventions. A simulated study was performed assuming equal weights of outcomes, and the TOPSIS, VIKOR, and PROMETHEE II methods were compared using a similarity coefficient, indicating a high degree of agreement among methods, especially with higher numbers of interventions. Conclusions: Multicriteria decision analysis provides a flexible and computationally direct way of selecting compromise interventions and visualizing treatment selection in network meta-analyses. Further research should provide empirical data about the implementation of multicriteria decision analysis in real-world network meta-analyses aiming to define the most suitable method depending on the clinical question. Highlights: Multicriteria decision-making methods can be implemented in network meta-analysis to indicate compromise interventions. The TOPSIS, VIKOR, and PROMETHEE methods can be used for optimal treatment selection when conflicting outcomes are evaluated. The weights of outcomes can be defined objectively or subjectively, reflecting the priorities of the decision maker. Graphical Abstract This is a visual representation of the abstract. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to provide insight into the operational factors of big data. The operational indicators/factors are categorized into three functional parts, namely synthesis, speed and significance. Based on these factors, the organization enhances its big data analytics (BDA) performance followed by the selection of data quality dimensions to any organization's success. Design/methodology/approach: A fuzzy analytic hierarchy process (AHP) based research methodology has been proposed and utilized to assign the criterion weights and to prioritize the identified speed, synthesis and significance (3S) indicators. Further, the PROMETHEE (Preference Ranking Organization METHod for Enrichment of Evaluations) technique has been used to measure the data quality dimensions considering 3S as criteria. Findings: The effective indicators are identified from the past literature and the model confirmed with industry experts to measure these indicators. The results of this fuzzy AHP model show that the synthesis is recognized as the top positioned and most significant indicator followed by speed and significance are developed as the next level. These operational indicators contribute toward BDA and explore with their sub-categories' priority. Research limitations/implications: The outcomes of this study will facilitate the businesses that are contemplating this technology as a breakthrough, but it is both a challenge and opportunity for developers and experts. Big data has many risks and challenges related to economic, social, operational and political performance. The understanding of data quality dimensions provides insightful guidance to forecast accurate demand, solve a complex problem and make collaboration in supply chain management performance. Originality/value: Big data is one of the most popular technology concepts in the market today. People live in a world where every facet of life increasingly depends on big data and data science. This study creates awareness about the role of 3S encountered during big data quality by prioritizing using fuzzy AHP and PROMETHEE. [ABSTRACT FROM AUTHOR]

Shipyard 4.0 refers to the application of Industry 4.0 principles to shipyards. To keep up with the challenges, the shipyard industry, like other industries, strives to realize Shipyard 4.0 in its industry. The goal of this study is the creation of an indoor positioning system (IPS) to determine the positions of all assets in the shipyards, such as employees, welding machines, cranes, and carriers, and track their movements using an IPS. Users can use positioning systems to find and track the location of a particular object. The most well-known position tracking system is the Global Positioning System, which is widely used in determining the location and position of objects in the external environment. The GPS for locating and tracking an object indoors is not recommended for indoor use, as indoor signals transmitted from a satellite to a device are weakened by indoor obstructions. Many different IPSs are in development that track and position objects indoors. Choosing the most suitable IPS for a shipyard is a multicriteria decision problem. A combined AHP and PROMETHEE method is proposed in this study to determine which IPS technologies would be most suitable in shipyards. In the literature review, it is shown that the AHP and PROMETHEE methods are used separately or together in solving problems in many areas. However, no study has been conducted in which the AHP and PROMETHEE methods are used together for the IPS selection problem. For this purpose, an application of the proposed method in IPS selection and evaluation was carried out at the SEDEF shipyard, and the most suitable technology was determined by evaluating different IPS technology options.

Decision makers increasingly operate in real-time information-rich environments, where limited time is available for interpreting data to inform decisions. These environments are driven by static or mobile sensing devices that can provide numerous dynamic data points. A prominent approach in this space is to utilise drones, which can be deployed to gather targeted information. However, deciding how best to deploy available drones is nontrivial, and stands to benefit from decision support aids that plan routes. Such a system must operate under time constraints created by the changing attributes of routes as the situation unfolds. This study describes a dynamic decision support system (DSS) for situational awareness with drones. The system applies Multi-Criteria Decision Making (MCDM) methods within a dynamic genetic algorithm to provide a continuously revised ranking of routes. Five desiderata for dynamic decision support are presented. It is shown how a dynamic DSS can be equipped with declarative specification of preferences (Desiderata 1), dynamic revision of recommendations (Desiderata 2), and high diversity of options (Desiderata 3). The study then compares four MCDM methods, namely the Weighted Product Model (WPM), the Analytic Hierarchy Process (AHP), the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and the Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE), with regards to how consistently they trade-off between criteria (Desiderata 4) and the stability of results under small changes to criteria values (Desiderata 5). To evaluate the trade-offs between criteria we analyse the smoothness of change in criteria outcomes as criteria weightings increase for each algorithm. The outcomes are calculated by automating the selection of routes in a case study that applies drones to the task of harbour management. The stability of results for the different MCDM methods are compared. Perturbations were applied to sets of routes ranked by each algorithm then each algorithm was reapplied and the magnitude of the changes in ranking was assessed. Overall, TOPSIS was found to be the algorithm which made the most consistent trade-offs between criteria, only under-performing another algorithm with respect to a single criterion. AHP and WPM were the next most consistent algorithms and PROMETHEE was the least consistent algorithm. TOPSIS was also found to be the most stable method under small changes to criteria values. AHP was the second most stable, followed by PROMETHEE and WPM respectively. The results show that TOPSIS achieves the best result for both Desiderata 4 and 5 and consequently the study finds TOPSIS to be an appropriate MCDM method for dynamic decision support. • Desired features for dynamic decision support systems are introduced. • A dynamic decision support system for situational awareness with drones is outlined. • TOPSIS identified as most suitable MCDM method for dynamic decision support. [ABSTRACT FROM AUTHOR]

In the last decade, shipping decarbonization has accelerated rapidly in response to the regulatory framework. Shifting toward alternative marine fuel options is the subject of extensive study from stakeholders and researchers. This study attempts to propose a decision support model for alternative fuel evaluation. The decision-making process is multidimensional, comprising economic, technical, environmental, and social aspects, and has been carried out with the aid of the outranking multicriteria methodology, Promethee II. The approach is based on a comprehensive list of 11 criteria and 25 sub-criteria, covering all the crucial aspects. The weighting criteria process postulates the viewpoints of six stakeholder categories, including all the stakeholders’ preferences: shipowners, fuel suppliers, industry and engine manufacturers, academics, banks and the public. The results demonstrated that although LNG, MGO and HFO are classified in the highest positions, there are renewable options that also appear in high-ranking positions in most categories and especially among academics, banks, the public and in the combined case scenario. The commercially available options of drop-in biofuels, bio and e-LNG, fossil and bio methanol were ranked in these high positions. This approach offers insight into the assessment and selection of alternative marine fuel options, providing an incentive for strategic planning.

This work proposes a methodology based on multicriteria decision aid (MCDA) and a cluster analysis to identify ideal locations for the installation of police facilities or vehicle parking and policing around stadiums in Recife, Brazil, during potential violent sports events (criminal occurrences from football supporters or fanbases). A K-means unsupervised clustering algorithm is used to group criminal data into homogeneous clusters based on their characteristics. Each type of criminal occurrence is linked to a single cluster. The optimal location is addressed based on the PROMETHEE method (Preference Ranking Organization Method for Enrichment Evaluation), allowing clusters to be organized into a hierarchy based on the number of facilities (N), the average distance (D) from the criminal occurrence to the associated cluster, and the coverage level (C), which is the proportion of crime occurring in a location less than 500 m from the associated cluster. Through a data analysis of crimes and violence in the region, this study seeks to identify patterns of criminal behaviour and high-risk areas to determine the most strategic locations for police units and enhance the public security decision-making process. The choice for the k parameters ranged from 1 to 30, incorporating all regions of the analysis, with a computational cost of 43 min of running time using an Intel Core i3-3217U (1800 GHz and 10 GB of RAM). This approach and methodology can be useful for supporting public security policies in the region and can contribute to reducing violence around stadiums. The empirical application can help guide public managers’ decisions regarding resource allocation and the implementation of more effective security policies, with the aim of ensuring a safer environment for fans and residents in the areas near stadiums.

The decision-making process is part of everyday life for people and organizations. When modeling the solutions to problems, just as important as the choice of criteria and alternatives is the definition of the weights of the criteria. This study will present a new hybrid method for weighting criteria. The technique combines the ENTROPY and CRITIC methods with the PROMETHE method to create EC-PROMETHEE. The innovation consists of using a weight range per criterion. The construction of a weight range per criterion preserves the characteristics of each technique. Each weight range includes lower and upper limits, which combine to generate random numbers, producing “t” sets of weights per criterion, allowing “t” final rankings to be obtained. The alternatives receive a value corresponding to their position with each ranking generated. At the end of the process, they are ranked in descending order, thus obtaining the final ranking. The method was applied to the decision support problem of choosing policing strategies to reduce crime. The model used a decision matrix with twenty criteria and fourteen alternatives evaluated in seven different scenarios. The results obtained after 10,000 iterations proved consistent, allowing the decision maker to see how each alternative behaved according to the weights used. The practical implication observed concerning traditional models, where a single final ranking is generated for a single set of weights, is the reversal of positions after “t” iterations compared to a single iteration. The method allows managers to make decisions with reduced uncertainty, improving the quality of their decisions. In future research, we propose creating a web tool to make this method easier to use, and propose other tools are produced in Python and R.