Preference Ranking Organization Method For Enrichment Evaluation Research Articles

Analysis of the Optimum Performance for Polymer and Polymer–Nanocomposite-modified Asphalt by Using Multicriteria Decision Analysis

The influence of Acrylate Styrene Acrylonitrile (ASA) and ASA/nanosilica (ASA/Si) additives was investigated by using a dynamic shear rheometer (DSR). Firstly, an ASA polymer was blended with the virgin asphalt binder at two different concentrations (3% ASA and 5% ASA). After observing that 5% ASA was the optimum concentration for modification, nanosilica particles were further incorporated into the 5% ASA-modified asphalt binder with two different percentages (5% ASA 3%Si; 5% ASA 5%Si). Frequency sweep tests were conducted across various frequencies at elevated temperatures. The experimental outcomes were analyzed using master curves, rutting, and fatigue resistance parameter plots. Additionally, to provide a more holistic analysis, two different multicriteria decision analysis (MCDA) techniques, namely the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) and the Technique for the Order of Preference by a Similarity to Ideal Solution (TOPSIS), were conducted to identify the best-performing asphalt binder by considering three different parameters: workability, performance under different conditions, and cost. The frequency sweep tests showed that the 5% ASA 5%Si asphalt worked best in terms of resistance to rutting. On the other hand, the virgin binder performed better than all modified binders when it failed to resist fatigue. On the other hand, the PROMETHEE analysis identified the 5% ASA-modified asphalt binder as the optimal choice, while the TOPSIS analysis determined that the 5% ASA 3%Si-modified binder provided the best performance. The differences between the experimental results and the MCDA were due to using more than one evaluation parameter and looking at how well the asphalt binder worked at different temperature ranges at the same time.

STATISTICAL INTEGRATION OF QGIS AND MULTI-CRITERIA DECISION ANALYSIS FOR SUSTAINABLE RURAL DEVELOPMENT IN BELSAR DEVELOPMENT BLOCK

The definition of “rural areas” varies significantly across regions, making the determination of rurality levels crucial for sustainable development and effective policy design. This study introduces a comprehensive rurality index to rank and categorize villages within the Belsar Development Block of Gonda District, India. Utilizing Multiple-criteria Decision-making Methods (MCDMs) such as Fuzzy Analytic Hierarchy Process (F-AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Weighted Product Model (WPM), and Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE), we developed a robust methodology for this assessment. A thorough sensitivity analysis was also conducted to ensure the reliability of the results. The study identified fourteen critical factors, grouped into five domains: (1) socioeconomic status, (2) education, (3) land use, (4) employment, and (5) healthcare. Based on these factors, villages were ranked and classified into four categories according to their rurality levels, and a detailed rurality map was created with QGIS. The study revealed that the modified F-AHP emerged as the most effective MCDM method for this rurality assessment. Given the systematic execution of this assessment, it can serve as a model for comparative studies in other regions of India and globally, promoting sustainable rural development through informed decision-making.

A novel multicriteria web‐based decision support system to enhance resource allocation in energy companies: a portfolio selection with c‐optimal PROMETHEE

AbstractIn an uncertain world, decision support systems (DSSs) are essential for effective portfolio management. Electricity companies often face challenges in allocating limited resources among competing projects, influenced by sustainability and regulatory requirements. To address these challenges, multicriteria approaches have been developed, focusing on complex issues involving outranking methods. This paper introduces a methodological advance with a novel algorithm that enhances the c‐optimal PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations) approach, resolving scaling issues inherent in the PROMETHEE V method. Our key contribution is a refined procedure for efficiently identifying and comparing c‐optimal solutions while preserving the method's axiomatic integrity, ensuring the selection of the “true best” portfolio. Additionally, we present a web‐based DSS designed to implement this methodology, featuring interactive visualization and sensitivity analysis tools. Freely available online, both the algorithm and DSS were validated through a case study with a Brazilian electricity company, demonstrating their practical benefits and applicability across various business contexts.

A systematic framework for the design and material selection of composite for tennis racket upon impact

The present study explores the comprehensive composite material selection methodology of a tennis racket involving coupled Multi-Criteria Decision-Making (MCDM) and numerical analysis. Initially all the possible material alternatives from the composite family were shortlisted using CES Selector 2016. A design space was developed incorporating MCDM technique naming VICKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje), PROMETHEE (Preference Ranking Organisation Method for Enrichment Evaluation) and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) to rank the most suitable composite material based on critical mechanical properties to identify the most suitable composite materials for applications requiring optimal combinations of rigidity, strength, and structural performance. Using finite element analysis, the structural integrity of each of the materials was evaluated on static and dynamic stress analysis modules of ANSYS. Out of all material alternatives, T300 carbon fiber epoxy was considered as the most suitable for tennis rackets. Further, a comparison between the selected material was also made with conventional materials and were found in good agreement with selected material.

Evaluating Multi-Criteria Decision-Making Methods for Sustainable Management of Forest Ecosystems: A Systematic Review

Multi-criteria decision-making (MCDM) methods provide a framework for addressing sustainable forest management challenges, especially under climate change. This study offers a systematic review of MCDM applications in forest management from January 2010 to March 2024. Descriptive statistics were employed to analyze trends in MCDM use and geographic distribution. Thematic content analysis investigated the appearance of MCDM indicators supplemented by Natural Language Processing (NLP). Factorial Correspondence Analysis (FCA) explored correlations between models and publication outlets. We systematically searched Web of Science (WoS), Scopus, Google Scholar, Semantic Scholar, CrossRef, and OpenAlex using terms such as ‘MCDM’, ‘forest management’, and ‘decision support’. We found that the Analytical Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) were the most commonly used methods, followed by the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE), the Analytic Network Process (ANP), GIS, and Goal Programming (GP). Adoption varied across regions, with advanced models such as AHP and GIS less frequently used in developing countries due to technological constraints. These findings highlight emerging trends and gaps in MCDM application, particularly for argan forests, emphasizing the need for context-specific frameworks to support sustainable management in the face of climate change.

Multicriteria decision support under uncertainty: combining outranking methods with Bayesian networks

AbstractAssessing alternative solutions that have uncertain evaluations in conflicting multiple criteria is not straightforward. Probabilistic models such as Bayesian networks (BNs) can effectively model and represent the uncertainty in such problems, but they do not include built-in mechanisms to guide different decision makers (DMs) with varying preferences toward the final decision. We propose a systematic approach to combine outranking methods with BNs to provide decision support for solutions with multiple and conflicting criteria under uncertainty. The proposed approach is based on Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE), and it offers different levels of precision and flexibility to the DMs in assessing the solutions. Our approach includes graphical tools and summary metrics to enhance the presentation of its results to the DMs. We test our approach with a case study on supplier selection where the uncertainty in supplier performances is modeled with a BN. We demonstrate that the proposed approach can enable the joint use of multiple criteria techniques with probabilistic modeling techniques like BNs to provide decision support in complex environments including uncertainty.

Influence of Cu distribution in thermally sprayed Cu-bearing coatings on the prevention of Desulfovibrio vulgaris corrosion

Thermally sprayed Cu-based coatings show promise for combating microbiologically influenced corrosion (MIC) within natural gas pipelines. However, the efficacy of Cu-bearing coatings against MIC over prolonged exposure to bacterial fluids remains unclear. To examine the effect of Cu distribution on the prevention of MIC, two types of Cu-containing coatings were prepared: stainless steel (SS)-Cu composite coatings and Monel 400 coatings with Cu in solid solution. These coatings were deposited using high velocity oxy-fuel (HVOF) and wire arc spray (WAS) techniques. Dynamic flow corrosion testing was utilized to examine the influence of Cu concentration and distribution on biofilm attachment and pit formation. The results show that SS-Cu composite coatings exhibit preferential Cu dissolution when exposed to Desulfovibrio vulgaris solution, significantly reducing biofilm attachment over the short-term period (7 days) which correlated with the release rate of Cu ions. However, accelerated Cu ion release led to Cu2S deposition on sample surfaces, diminishing anti-biofilm properties with prolonged exposure (28 days). Pit depth measurements indicated a correlation between pit formation and Cu ion release throughout the exposure period, with bio-generic H2S likely contributing to increased pit corrosion. Utilizing the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE), the anti-MIC performance of coatings were ranked based on biofilm thickness, biofilm coverage, pit depth, antimicrobial effect, and splat thickness. The ranking placed WAS Monel with Cu in solid solution as the most effective coating, attributed to its thin laminar structure, enhanced corrosion resistance, and antimicrobial properties. Cu in solid solution was concluded to be more effective in resisting MIC of Desulfovibrio vulgaris under dynamic flow conditions compared to Cu existing as segregated phase within composite coatings.

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

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

Dynamic decision making for situational awareness using drones: Requirements, identification and comparison of decision support methods

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.

Combined compromise for ideal solution (CoCoFISo): A multi-criteria decision-making based on the CoCoSo method algorithm

Each decision-making tool should be tested and validated in real case studies to be practical and fit to global problems. The application of multi-criteria decision-making methods (MCDM) is currently a trend to rank alternatives. In the literature, there are several multi-criteria decision-making methods according to their classification. During our experimentation on the Combined Compromise Solution (CoCoSo) method, we encountered its limits for real cases. The authors examined the applicability of the CoCoFISo method (improved version of combined compromise solution), by a real case study in a university campus and compared the obtained results to other MCDMs such as Preference Ranking Organisation Method for Enrichment Evaluations (PROMETHEE), Weighted Sum Method (WSM) and Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS). Our research finding indicates that CoCoSo is an applied method that has been developed to solve complex multi variable assessment problems, while CoCoFISo can improve the shortages observed in CoCoSo and deliver stable outcomes compared to other developed tools.The findings imply that application of CoCoFISo is suggested to decision makers, experts and researchers while they are facing practical challenges and sensitive questions regarding the utilization of a reliable decision-making method. Unlike many prior studies, the current version of CoCoSo is unique, original and is presented for the first time. Its performance was approved using several strategies and examinations.

Multi-attribute decision-making analysis based on the bipolar N-soft PROMETHEE method

The multi-attribute decision-making (MADM) methods can deeply mine hidden information in data and make a more reliable decision with actual needs and human cognition. For this reason, this paper proposes the bipolar N-soft PROMETHEE (preference ranking organization method for enrichment of evaluation) method. The method fully embodies the advantages of the PROMETHEE method, which can limit the unconditional compensation between attribute values and effectively reflect the priority between attribute values. Further, by introducing an attribute threshold to filter research objects, the proposed method not only dramatically reduces the amount of computation but also considers the impact of the size of the attribute value itself on decision-making. Secondly, the paper proposes the concepts of attribute praise, attribute popularity, total praise, and total popularity for the first time, fully mining information from bipolar N-soft sets, which can effectively handle situations where attribute values have different orders of magnitude. In addition, this paper presents the decision-making process of the new method, closely integrating theoretical models with real life. Finally, this paper analyses and compares the proposed method with the existing ones, further verifying the effectiveness and flexibility of the proposed method.

Woven fabric selection and grading for ladies’ winter shawls using multicriteria decision-making approach

Purpose Quality assessment of textile products is of prime concern to intimately meet consumer demands. The dilemma faced by textile producers is to figure out the stability among quality criteria and efficiently deal with target specifications. Hence, the basic devotion is to attain the optimum value product which entirely satisfies the views and perceptions of consumers. Selection of best fabric among several alternatives in the presence of contradictory measures is a disputing problem in multicriteria decision-making. Design/methodology/approach In the current study, the analytic hierarchy process (AHP) and preference ranking organization method for enrichment evaluation (PROMETHEE) are proficiently used to solve the problem in selection of branded woven shawls. AHP method verifies comparative weights of the criteria selection, while the ranking of fabric alternatives grounded on specific net-outranking flows is executed through PROMETHEE II method. Findings The collective AHP and PROMETHEE approaches are applied for the useful accomplishment of grading of branded shawls based on multicriteria weights, used for effective selection of fabric materials in the textile market. Practical implications In the apparel industry, fabric and garment manufacturers often rely on hit-and-trial methods, leading to significant wastage of valuable resources and time, in achieving the desirable fabric qualities. The implementation of the findings can assist apparel manufacturers in streamlining their fabric selection processes based on multiple criteria. By adopting this method, industry players can make informed decisions, ensuring a balance between quality standards and consumer expectations, thereby enhancing both product value and market competitiveness. Originality/value The methods of Visual PROMETHEE and AHP are assimilated to offer a complete method for the selection and grading of fabrics with reference to multiple selection criteria.

Enhancing Polybutylene Terephthalate (PBT) Composites: A PROMETHEE-Based Comparative Study of Reinforcement Materials

Polybutylene terephthalate (PBT) composites hold significant potential across various industrial applications due to their desirable mechanical, thermal, and electrical properties. In this study, we utilized the PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations) method to evaluate six alternative reinforcement materials for PBT composites: Glass Fibers (GF), Carbon Fibers (CF), Natural Fibers (NF), Carbon Nanotubes (CN), Nano-clay Particles (NP), and Aramid Fibers (AF). The evaluation parameters considered were Tensile Strength (MPa), Flexural Strength (MPa), Thermal Conductivity (W/mK), Electrical Conductivity (S/m), and Cost ($). Through comprehensive analysis, Carbon Nanotubes emerged as the top-ranked reinforcement material, exhibiting exceptional performance across all evaluation parameters. With high Tensile Strength, Flexural Strength, and Thermal Conductivity, combined with significant Electrical Conductivity, Carbon Nanotubes demonstrated their suitability for demanding applications. Additionally, while the Cost factor was comparatively higher, its superior performance justifies the investment. Conversely, Natural Fibers received the lowest rank among the alternatives. Despite potential advantages in cost-effectiveness and environmental sustainability, Natural Fibers exhibited inferior mechanical and thermal properties compared to other materials. Their low Tensile Strength, Flexural Strength, and negligible Electrical Conductivity highlight limitations in performance for many industrial applications. This study provides valuable insights for engineers and material scientists in selecting suitable reinforcement materials for PBT composites based on specific performance criteria. The PROMETHEE method offers a systematic approach to decision-making, facilitating informed choices in material selection for diverse applications. Future research could explore optimization strategies and further investigate the properties and potential applications of emerging reinforcement materials for PBT composites.

Evaluation of new scintillation crystals with MCDM methods for brain PET

Brain Positron Emission Tomography (PET) imaging has been increasingly important in recent decades for improved detection and staging of brain cancer and other brain illnesses. Many elements, including the choice of radiation detecting medium, contribute to the overall image quality used to characterize a PET system's performance. In our previous study, novel transparent optical scintillator crystals for brain PET system were simulated. Therefore, there is a need to evaluate these materials using advanced computational techniques. Hence, in this study, we evaluated and compared them using two Multi-Criteria Decision-Making (MCDM) techniques, namely Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) and Visekriterijumska Optimizacija I Kompromisno Resenje (VIKOR). The crystals used in this study were strontium hafnate (SHO), gadolinium aluminum gallium garget (GAGG), gadolinium yttrium gallium aluminum garget (GYGAG), gadolinium lutetium gallium aluminum garget (GLuGAG), and lastly lutetium oxyorthosilicate (LSO) for comparison. Density, effective atomic number, energy resolution, light output, and decay time were selected as important criteria. Importance weights of each criteria were then assigned by considering the high resolution and high sensitivity detectors. With both MCDM methods, the results showed that SHO outranked the other scintillator materials followed by LSO and GLuGAG. GYGAG, and GAGG is revealed as the least favorable crystals, in agreement with the previous simulation studies. This study can be extended by including more scintillators as they become available in the future.

Optimizing Drug Delivery Vehicle with Multi-Criteria Decision Making (MCDM) - Based Excipient Selection

Excipients are used in drug delivery systems as a means of effectively delivering drugs to their target site. Multi-criteria decision-making (MCDM) methods are tools for decision-making that consider multidimensional factors. Such methods are a comparative technology used in medicine that combines individual criteria into the total assessment of selected alternatives. This study aims to enhance the solubility and bioavailability of drugs through the application of MCDM-based excipient selection. By incorporating the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE), various excipients can be evaluated and ranked based on their suitability for specific applications, considering parameters related to drug solubility and bioavailability. The results highlight the potential of cyclodextrins (net flow: 0.0023) and Eudragit polymers (net flow: 0.0016) as preferred options for drug carriers, while Poloxamer 188 (P188) (net flow: -0.0030) is identified as the least preferred option. This study demonstrates the effectiveness of the PROMETHEE method in improving the performance of poorly soluble and bioavailable drugs, ultimately contributing to the development of new drug delivery systems. The findings have significant implications for therapeutic outcomes in the treatment of diseases.

An integrated multi-polar fuzzy N-soft preference ranking organization method for enrichment of evaluations of the digitization of global economy

Digitization represents the ultimate expression of globalization that has revolutionized every facet of global existence, enhancing connectivity, financial terms, trade opportunities, and public services. To further broaden or fortify this digital realm and advance global progress, various strategies for multi-criteria group decision-making (MCGDM) have been developed. This research article extends the literature study on the sensational MCGDM method known as Preference Ranking Organization Method for Enrichment of Evaluations (PROMETHEE), which shares the burden of selecting the best alternative under many terms and conditions. This article aims to make a positive contribution to the development of the digital world. The main principle of the PROMETHEE technique is to calculate positive and negative flows of the selected options according to the divergence of the alternatives’ scores. It provides us with partial and full rankings of options by determining score degrees, suitable preference functions, and a multi-criteria preference index. To deal with the multi-polarity of the modern world and assign N-ordered levels, this paper proposes the multi-polar fuzzy N-soft (mFNS) PROMETHEE technique. Prior to this, an Analytic Hierarchy Process (AHP) technique is applied to guarantee the credibility of the criterion weights for each criterion. After that, the whole strategy of m-polar fuzzy N-soft PROMETHEE approach to order the selected options is explained, and all the course of action of this remarkable (MCGDM) technique are synchronized in an extensive flowchart, helping us to learn more about the technology keenly. Moreover, the utility of our discussed technology is illustrated by considering two applications of generating electricity through human movement and choosing the best digital currency. The most suitable choice is extracted with the help of the outranked directed graph. The results obtained by mFNS PROMETHEE technique benefits us with the selection of most reliable source to digitized the global economy, in addition, It enables us to rank the alternatives from most preferable to least one, that not only saves time but also produces the better outcomes. Subsequently, the eminence of mFNS PROMETHEE technology is checked by comparability with the prior art. Lastly, the advantages and disadvantages of our proposed technique are supplemented to demonstrate its productivity and shortcomings.

Finding the Nation with the Highest Rate of Deaths due to Cancer Using PROMETHEE

Cancer is a growing problem in today’s world. Cancer is considered to be the leading cause of death in many countries. The incidence rates for various cancer forms vary by nation and can depend on many factors such as the lifestyle of the people, obesity rates, exposure to certain chemicals, family history, and others. As preventing cancer is a significant public health challenge, it is important to identify which country is leading in death rates due to the disease. This study uses a Multi-criteria decision-making (MCDM) method named Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) to perform this test and rank the countries on this basis. The outcome of the decision-making process is to create awareness among people to reduce the effects caused by cancer in that particular country

INTEGRATION OF TAGUCHI AND PROMETHEE FOR CNC MILLING MACHINING PARAMETER OPTIMIZATION ON AA6061

In the manufacturing industry, machining has developed quite rapidly from the use of conventional machines to unconventional machines. Unconventional machines that are often used today are optimize computer numerically controlled (CNC), the use of CNC in the manufacturing industry provides many benefits in product quality and productivity. One of them is CNC milling, this type is one of the main machines on the production floor. Machining optimization becomes the main goal to achieve the ideal response in order to produce products with good and consistent quality and productivity. Surface quality leads to surface roughness, while productivity leads to material removal rate. This study aims to optimize CNC milling machining parameters on AA6061 with Taguchi experimental design and preference ranking organization method for enrichment evaluation (PROMETHEE) method. Machining was controlled using wet machining conditions to maintain temperature during machining. Experiments were conducted nine times with three factors and levels. These factors included spindle speed, feed rate, and depth of cut. The result of this research is the ideal value of the combination of surface roughness and material burning rate which is 0.565 (experiment 3). This best experiment is influenced by spindle speed 2600 rpm, feed rate 65 mm/min, and depth of cut 2.5 mm. Feed rate has the largest contribution in influencing the response which is 43.23%, followed by depth of cut 25.24%, and spindle speed 15.91%.

Evaluation of personal protective equipment to protect health and safety in pesticide use

IntroductionAgriculture emerges as one of the most dangerous industries in the world, considering injury and illness rates. After the service sector in Turkey, the next large-scale sector is the agricultural sector, which constitutes 20% of the general employment. The exposure of farmers to pesticides, used to increase the quality and productivity of agricultural products, causes health risks via the mouth, respiration, skin, and eyes. Pesticide use in Turkey is increasing; the annual average increase is estimated at 1.2%. Exposure to pesticides can be reduced by wearing personal protective equipment to protect against health and safety hazards.ObjectiveThis study aimed to determine the importance of personal protective equipment using the multi-criteria decision-making method to prevent the risk of injury and disease resulting from pesticide use.Materials and methodsThe Analytical Hierarchy Process (AHP) method was used to find the weights of the criteria determined by expert opinion and a literature review. The Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) was used to rank personal protective equipment.ResultsPersonal protective equipment includes masks, gloves, overalls, safety shoes, glasses, and hats. The use of multi-criteria decision-making methods in health and safety in the agricultural sector will contribute to the literature.ConclusionEmphasizing the use of personal protective equipment, especially when using pesticides, will increase the rate of use of protective measures.

Optimization of micropollutant removal from wastewaters using the PROMETHEE multicriteria decision method

This article explores the use of multicriteria decision methods (MCDMs) to identify the optimum treatment for removing micropollutants from wastewater. With the increasing complexity of industrial and urban wastewater, which contains a wide range of micropollutants, it is becoming crucial to develop effective and sustainable treatment strategies. MCDMs provide a framework for evaluating a variety of treatments, considering several criteria, such as efficiency, cost, energy consumption, environmental impact and technical feasibility. This article examines different processing technologies and compares those using MCDM methods such as the preference ranking organization method for enrichment evaluation (PROMETHEE). Using this approach, this study proposes a systematic and objective method for identifying the most appropriate treatment options, facilitating more efficient wastewater management and protection of the aquatic environment.