Fuzzy Analytic Hierarchy Process Method Research Articles

Comparative analysis of hybrid geothermal-solar systems and solar PV with battery storage: Site suitability, emissions, and economic performance

Renewable energy integration has become a critical focus in the global effort to reduce carbon emissions and diversify energy sources. In regions with distinct geographic features, such as Türkiye, combining different renewable technologies can offer enhanced energy security. This study investigates the site suitability and economic and environmental performance of hybrid geothermal-solar systems and solar PV systems with battery storage across the provinces of Osmaniye, Hatay, and Kilis, of Türkiye. Using the fuzzy-AHP method, site suitability is evaluated, addressing a key gap in comparing these systems' adaptability to varying geographic conditions. This study is the first to directly compare these two renewable energy technologies in terms of site suitability. The findings reveal significant differences in site suitability, with solar PV systems with battery storage demonstrating broader applicability across the region. The suitable sites (20–100 % suitability) cover 1260.82 km² for solar PV systems with battery storage and only 122.18 km² for hybrid geothermal-solar systems. In terms of environmental impact, hybrid geothermal-solar systems exhibit significantly lower carbon emissions, averaging 44.6 kg CO₂/MWh, compared to 123.8 kg CO₂/MWh for solar PV systems with battery storage. Economically, hybrid geothermal-solar systems also outperform with a lower levelized cost of electricity of $0.091 kWh versus $0.254 kWh for solar PV systems. These results highlight the environmental and economic advantages of hybrid geothermal-solar systems, while also emphasizing their limited scalability to regions with geothermal activity. Conversely, solar PV systems, despite their higher emissions and costs, offer greater flexibility and potential for widespread deployment.

Gradation optimization of AC-20 asphalt mixture based on the fuzzy analytic hierarchy process and comprehensive evaluation method

The aggregate gradation of asphalt mixture is one of the most important factors affecting the service life of asphalt pavement. In order to study the gradation of asphalt mixture with the best comprehensive performance, this study puts forward a new method of mineral aggregate gradation optimization based on the fuzzy analytic hierarchy process and comprehensive evaluation method, aiming at the multi-level and multi-index evaluation of the road performance of asphalt mixture. First, the orthogonal test design method is applied to design nine target gradations with coarse aggregate (>4.75 mm) and fine aggregate (<4.75 mm) of AC-20 (asphalt concrete) mixture serving as two factors and the upper, middle, and lower positions of the gradation curve as three levels, and then the road performance test research is carried out. Second, a comprehensive model for evaluation of road performance of mineral aggregate gradation is established. A fuzzy complementary judgment matrix is constructed, and the index weights of each level and the hierarchical total ranking weight are calculated. Then, the membership function is introduced into the comprehensive evaluation model for the road performance of mineral aggregate gradation, and the membership values of each index of asphalt mixture road performance are obtained. Finally, the fuzzy comprehensive evaluation method is used to find out the comprehensive evaluation value of the road performance of the nine graded asphalt mixtures, and the mineral aggregate gradation is optimized. The research results show that the 1# gradation of the asphalt mixture has the highest comprehensive road performance evaluation value, and the combination of the fuzzy hierarchical analysis process and comprehensive evaluation method can more objectively and comprehensively evaluate the comprehensive road performance of the asphalt mixture and provide a useful reference for the optimal selection of asphalt mixture mineral gradation.

Integrating machine learning algorithms and fuzzy AHP for assessing livelihood vulnerability in Southwestern Coastal Bangladesh

The people living in the coastal regions of Bangladesh rely significantly on nature for their livelihoods, which renders them sensitive to climate change. The aim of this study is to determine the key indicators which contribute more to livelihood vulnerability of disaster-prone Gabura union in southwestern coastal Bangladesh. To achieve this goal, three machine learning algorithms are employed for determining the key indicators of livelihood vulnerability. Subsequently, a livelihood vulnerability index (LVI) is constructed using these key indicators with the weighting of indicators facilitated by Fuzzy AHP method. And finally, a livelihood vulnerability map (LVM) is generated to visualize the spatial distribution and analysis of livelihood vulnerability within the union. The study employs a mixed-methods approach, including questionnaire surveys, focus group discussions, key informant interviews, and remote sensing image analysis. A household survey of 950 households tracked livelihood vulnerability using 25 indicators across three domains of vulnerability, e.g., exposure, sensitivity, and adaptive capacity. ArcGIS and Google Earth Engine facilitated spatial data analysis. The Gabura Union exposed high vulnerability (LVI 0.63), driven by elevated exposure (0.61) and sensitivity (0.59) and lower adaptive capacity (0.30). The Livelihood Vulnerability Map (LVM) illustrates vulnerability across wards of the union, emphasizing high vulnerability zones on the periphery of the union, along the Kopothakho and Kholpetua rivers while central part of the union shows a moderate vulnerability level. The study's novelty lies in effectively integrating multiple methods for livelihood vulnerability assessment. Policymakers should target interventions focusing on areas along the Kopothakho River, to enhance community resilience.

Analysis of solution strategies for the transition to renewable energy in Saudi Arabia

The shift from fossil fuels to low-carbon energy solutions is crucial to mitigate the effects of climate change. The Kingdom of Saudi Arabia (KSA) has formulated a policy framework to accelerate the development of renewable energy (RE) as part of Vision 2030. Therefore, the transition to RE is vital for reducing dependence on fossil fuels and mitigating climate change. Nevertheless, critical barriers hinder RE adoption in the KSA. This study identified several main barriers, sub-barriers, and solution strategies for the analysis. Therefore, the fuzzy Analytical Hierarchy Process (AHP) method was incorporated to evaluate seven barriers and twenty-eight sub-barriers. Later, the fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method was used to prioritize the nine solution strategies for adopting RE technologies in the KSA. The fuzzy AHP results emphasize that technological, environmental, and market barriers obstruct RE adoption. Fuzzy TOPSIS indicates that public awareness campaigns, energy subsidy reforms, and modernization of the energy grid are the most suitable solutions for the adoption of RE technologies in the KSA. Consequently, this research will help governments, stakeholders, and policymakers expedite RE adoption by determining these barriers and strategies to achieve the Saudi Vision 2030 goal.

Research on Service Quality for China’s Ceramic Product Design Industry

As a part of the service industry, product design practitioners should possess an understanding of the multifaceted factors and solutions that contribute to delivering exceptional service quality. However, related research on the service quality factors and solutions for China’s ceramic product design industry is still an important research gap. In view of this, an integrated approach based on multi-criteria decision making (MCDM), combining the fuzzy analytic hierarchy process (FAHP) and the measurement of alternatives and ranking according to compromise solution (MARCOS), was proposed in this research to analyse and evaluate the service quality factors and solutions for China’s ceramic product design industry. Initially, the FAHP method determined the significance of the service quality dimensions and indicators. Subsequently, the MARCOS method ranked the alternatives based on their performance against these criteria. This research focuses on the growing subject of service quality in China’s ceramic product design industry. The proposed model identifies essential service quality factors and solutions for China’s ceramic product design industry. The findings of this study may assist ceramic product design practitioners in China in making strategic decisions to provide excellent service quality.

Analysis of PM10 Substances via Intuitionistic Fuzzy Decision-Making and Statistical Evaluation

Air pollution is a situation that negatively affects the health of humans and all living things in nature and causes damage to the environment. The most important cause of air pollution is the amount and density of substances called “particulate matter” above guidelines. Particulate matter (PM) are mixed liquid droplets and solid particles with advective diameters less than 2.5 μm (PM2.5—fine particles) and between 2.5 and 10 μm (PM2.5–10—coarse particles). PM10 is defined as one that can remain in the air for a long time and settle in the respiratory tract, damaging the lungs. It is important to identify the underlying causes of air pollution caused by PM10. In this context, these criteria need to be evaluated to minimize the negative effects of PM10. In the study, monthly average PM10 data obtained from the Air Quality Monitoring Station in Kocaeli, Türkiye, between 2017 and 2023 are used. After determining the criteria for PM10, the criteria are prioritized with the Intuitionistic Fuzzy AHP (IF-AHP) method by taking decision-maker opinions. The proposed decision-making model aims to guide obtaining and focusing on the important causes of out-of-limit and dangerous PM10 concentrations in the air. Additionally, PM10 data is analyzed in the context of COVID-19 and a statistical analysis is conducted. One-way Analysis of Variance (ANOVA) is used to evaluate whether there is a significant difference in average monthly data over the years. The Games–Howell test, one of the post-hoc tests, is used for determining differences between groups (years). In addition, monthly PM10 values for the future are estimated using the Expert Modeler tool in the software IBM® SPSS® Statistics 22. The study is important in that it provides a focus on the criteria affecting PM10 with an intuitionistic fuzzy perspective, along with statistical analysis.

How sustainable are agricultural practices in European Union countries? A spherical fuzzy logic based evaluation

ABSTRACT Climate change and population growth have heightened the need for a comprehensive framework to analyse the environmental impacts of agricultural practices. While traditional agricultural activities have detrimental environmental effects such as soil degradation, water pollution, and greenhouse gas emissions, sustainable agriculture is an innovative approach to minimizing humanity’s significant problems, such as depletion of natural resources, climate change, and biodiversity loss. In this study, a model is developed to evaluate the agri-environmental performance of countries. This model evaluates and ranks countries using the spherical fuzzy analytical hierarchy process and grey relational analysis methods within the European Union’s agri-environmental indicators framework. This study is among the pioneering studies to utilise the spherical fuzzy logic approach for determining the relative importance of agri-environmental performance criteria. The findings reveal that crucial factors such as the irrigable utilised agricultural area, the sales of pesticides per utilised agricultural area, and the share of main livestock types in total livestock units are critical to agri-environmental performance. In addition, in the implementation phase of the study, the performances of the European Union member countries are evaluated using the proposed model. This constitutes another contribution of the research. The results of this study indicate that Finland, Lithuania, and Sweden are the frontrunners in agri-environmental performance, while Italy, the Netherlands, and Malta rank lower. The model developed in this study has far-reaching implications for developing targeted environmental protection policies and advancing sustainable agricultural practices on a global scale.

Managing uncertainties in urban junction design: a case study of the FAHP method in Chettia

The essay delves into how an urban crossroads in Chettia, Algeria was reconstructed using the Fuzzy Analytic Hierarchy Process (FAHP) method to assess and choose the best design from multiple alternatives. The inherent uncertainties in this type of project can be effectively managed by using fuzzy algorithms, which mimic human reasoning in decision-making. A comprehensive and organised study is made possible by the methodology's foundational premise on a hierarchical problem classification into 32 criteria and sub-criteria. To establish the weightings of these criteria and lay the groundwork for comparing alternatives, the FAHP approach is employed, which is well-known in the area of multi-criteria decision-making. The feasibility and efficacy of this method are illustrated in the case study pertaining to the reconstruction of the Chettia intersection. A global performance indicator was used to rate the four solutions that the experts reviewed. When it comes to building and maintaining rural road networks, this strategy is invaluable for making educated decisions. In this case, the FAHP method provides a rigorous and methodical way to choose the optimal design. It enables the management of the uncertainties inherent in this type of urban infrastructure project while considering various complicated and sometimes contradicting aspects. Urban planners and transportation engineers may benefit much from this concept if it were applied to other comparable projects.

Advanced digital competency assessment of vocational teachers': A new approach based on fuzzy-analytical hierarcy process

<p>Teachers need digital competence to adapt easily to the current digital era. This study tries to discover the perceptions of vocational high school (VHS) teachers in Indonesia related to advanced digital competencies, which include information, communication, content creation, digital security, and problem-solving competencies. The multi-criteria Analytical Hierarchy Process (AHP) problem-solving method is used to rank the priority digital competencies that are most mastered by the respondents, and then their performance is validated by the fuzzy AHP artificial intelligence-based method. A poll was conducted with 392 respondents, with the research instrument adopting the digital competency measurement platform from DigComp. The study's results show that the fuzzy AHP method has proven that the classical AHP method is a very good way to prioritize VHS teachers' digital skills based on several factors. The two methods gave almost identical results in determining the priority order of VHS teacher digital competencies. The survey results reveal that VHS teachers in Indonesia must immediately develop their skills in terms of digital content creation and digital security. Teachers, teacher professional organizations, and decision-makers are expected to use the findings of this study as a reference in implementing VHS teacher digital competency improvement trainings.</p>

Ranking the effective non-pharmaceutical prevention strategies against COVID-19 using fuzzy analytic hierarchy process method

Ranking the effective non-pharmaceutical prevention strategies against COVID-19 using fuzzy analytic hierarchy process method

Locating Emergency Stations Using Multi-Criteria Decision-Making (MCDM) Methods: Application of Ankara Province

An earthquake ranks first among natural disasters in Turkey, with a rate of 70%. Minimizing the effects of a disaster after it occurs is possible by establishing effective disaster management. Especially the earthquakes that occurred on February 6, 2023, showed how important it is to deliver aid to the disaster area. In this study, the problem of determining the location of emergency stations to be established in order to deliver aid to the disaster area in case of a disaster was examined. There are studies in the literature to determine the locations of emergency stations to be established in provincial centers. In this study, the locations of emergency stations to be established in the non-central districts of Ankara were tried to be determined using multi-criteria decision-making (MCDM) methods. In the study, firstly, the criteria that would be effective in the location selection of emergency stations were determined, and the weights of these criteria were calculated with the Pythagorean Fuzzy Analytical Hierarchy Process (PFAHP) method. In the second stage of the study, 16 districts outside the center of Ankara were compared with the Pythagorean Fuzzy Technique for Order Preference by Similarity To An Ideal Solution (PFTOPSIS) method, and the districts where emergency stations should be opened first were evaluated.

Landslide Risk Assessments through Multicriteria Analysis

Natural risks comprise a whole range of disasters and dangers, requiring comprehensive management through advanced assessment, forecasting, and warning systems. Our specific focus is on landslides in difficult terrains. The evaluation of landslide risks employs sophisticated multicriteria models, such as the weighted sum GIS approach, which integrates qualitative parameters. Despite the challenges posed by the rugged terrain in Northern Algeria, it is paradoxically home to a dense population attracted by valuable hydro-agricultural resources. The goal of our research is to study landslide risks in these areas, particularly in the Mila region, with the aim of constructing a mathematical model that integrates both hazard and vulnerability considerations. This complex process identifies threats and their determining factors, including geomorphology and socio-economic conditions. We developed two algorithms, the analytic hierarchy process (AHP) and the fuzzy analytic hierarchy process (FAHP), to prioritize criteria and sub-criteria by assigning weights to them, aiming to find the optimal solution. By integrating multi-source data, including satellite images and in situ measurements, into a GIS and applying the two algorithms, we successfully generated landslide susceptibility maps. The FAHP method demonstrated a higher capacity to manage uncertainty and specialist assessment errors. Finally, a comparison between the developed risk map and the observed risk inventory map revealed a strong correlation between the thematic datasets.

A comprehensive perspective on local drivers in community-based health tourism industry development: a qualitative study of Mazandaran Province, Iran

BackgroundHealth tourism is an important component that may influence the direction of development in a region. Mazandarn, Iran, is recognized as a highly sustainable market in the region because of its abundant natural resources, temperate temperature, and strategic geographical location. Therefore, considering the importance of health tourism and its existing potential in Mazandaran, Iran, this study was conducted with the aim of exploring a comprehensive perspective on local drivers in community-based health tourism industry development and factors affecting the attraction of health tourism.MethodsWe conducted this study in Mazandarn, Iran, using a qualitative approach. Participants included a sample of Iranian people, aged 34–54 years, with previous history of health tourism or expert in it. Participants were selected from three different categories of the community: academic professionals in health tourism, managers in health tourism, and health tourists. Data were obtained via semi-structured in-depth interviews and focus group discussions. Inductive qualitative content analysis was used to converge and compare themes through participant data. The interviews kept going until data saturation was achieved.ResultsBased on our findings, we distilled local drivers in community-based health tourism industry development into five main categories and 30 subcategories: (Sharifabadi AM, Ardakani FA. A model for health tourism development using fuzzy TOPSIS and interpretive structural modeling in Yazd province. J Health Adm (JHA). 2014;17:55.) infrastructure and resources; (Hemmati F, Dabbaghi F, Mahmoudi G. Investigating the impact of Information Technology on the status of Health Tourism in Mashhad, Iran. Revista Publicando. 2018;5(15):54–65.) tourist attractions; (Sarabi Asiabar A, Rezapour A, Raei B, Tahernezhad A, Alipour V, Behzadifar M. Economic, Cultural, and Political Requirements for Medical Tourism Development in Iran: Insights from a Fuzzy Analytical Hierarchy Process Method. Med J Islamic Repub Iran. 2022;35:199.) socio-cultural contexts; (Mosadeghrad AM, Sadeghi M. Medical tourism: Reasons for choosing Iran. Payesh (Health Monitor). 2021;20(2):145–66.) economic-financial factors; and (Manna R, Cavallone M, Ciasullo MV, Palumbo R. Beyond the rhetoric of health tourism: shedding light on the reality of health tourism in Italy. Curr Issues Tourism. 2020;23(14):1805–19.) political-communicative factors. The findings of the study showed that, from the participants’ point of view, although there are several strategies, such as the development of public service and tourism infrastructure, increasing tourist attractions, and formulating appropriate policies and procedures for the development of health tourism, they are also faced with many challenges, especially political, economic, and cultural challenges.ConclusionThis study showed that improving infrastructure and resources, promoting tourist attractions, informing socio-cultural contexts, improving economic and financial capacity, and developing political and communicative contexts might increase the attraction of health tourists. The suggested components are not contextually driven, although empirical outcomes may differ based on the level of service offerings in health tourism locations.

A Social Assessment Framework to Derive a Social Score for Green Material Selection: A Case Study from the Sri Lankan Cement Industry

Assessing the sustainability of material-based products now encompasses social sustainability, a vital aspect often overlooked. Even though the existing frameworks provide a starting point, they do not often differentiate between the assessment criteria when making comparisons within one specific material category, which has made sustainability assessments more focused on environmental and economic aspects. This study addresses this critical gap by pioneering a social assessment framework curated to help practitioners to choose the most sustainable cement type out of the standard cement types used in the industry. Utilizing the Fuzzy Analytic Hierarchy Process (FAHP) and linear-scoring method, criteria weights were systematically assigned based on scoring by industry and academic experts. The findings highlight the importance of integrating social sustainability with environmental and economic factors in cement selection. Unlike traditional material selection, which primarily considers cost and performance, green material selection emphasizes the holistic impact of materials, including social factors. Variations in weightage decisions among experts highlight the influence of practical experience, research interests, and context. Functionality emerges as a crucial criterion. The ranking of cement types based on social scores places CEM II/B-M at the top, followed by CEM IV/A, CEM II/A-S, CEM II/A-V, CEM I, and CEM II/A-LL. The evolving nature of sustainability necessitates ongoing research to refine and expand existing frameworks for a more sustainable construction industry.

Integrated remote sensing and geographic information system overlay analysis for groundwater potential evaluation using AHP and fuzzy AHP: Southern sections of the western Afar rift margin and associated rift floor

A persistent drought commonly affects the southern sections of the western Afar rift margin and the associated rift floor, leaving the populace concerned about their access to food. A comprehensive assessment and usage of the local groundwater resource is therefore essential to overcoming the ongoing drought and meeting the area's increasing demand for sustainable water. With the aid of RS, GIS, AHP, and Fuzzy AHP methods, this study aims to assess the area's groundwater potential. Lineament density, Geology,Topographic Wetness Index (TWI), Recharge, Slope, Land Use and Land Cover, Soil, and Drainage Density were created using GIS and RS techniques and are the thematic layers taken into consideration for this purpose. The weight of these themes for their relative significance to groundwater occurrence was determined using AHP and Fuzzy AHP (FAHP). Lineament density is the primary factor influencing the presence of groundwater, followed by geology, TWI, and recharge. The Very low (221.48 km2), Low (9084.46 km2), Moderate (8281.96 km2), High (1441.65 km2), and Very High (102.45 km2) groundwater potential zones (GWPZs) were delineated using the GIS-overlay analysis technique. The validation of the groundwater potential zone map was completed using the well discharge, transmissivity, and specific capacity, and the scatter plot demonstrated a strong connection with R2 values of 0.81, 0.79, and 0.80, respectively. The sensitivity index (SI) shows that lineament density and geology, which have SI values of 2.08% and 1.74%, respectively, have the greatest influence on the distributions of groundwater potentiality in the catchment, while drainage density has the least influence, with a SI value of −0.89%. By ensuring sustainable management and development, the study's findings will help with decision-making for the long-term supply of groundwater. Additionally, the approach utilized in the study can be applied to other comparable arid to semiarid regions.

A fuzzy analytic hierarchy process model to enhance energy security: the case of Morocco

Energy security has become increasingly important because of its impact on all sectors of the economy, and even more because of the scarcity of primary energy resources and price fluctuations caused by geopolitical tensions. This has prompted all countries to assess their energy situation and take strategic measures to mitigate energy risks. Accordingly, the aim of this study is to identify the dimensions of energy security with the greatest impact on Morocco's energy security, as well as the priority strategic measures for mitigating energy risk. To this end, we consider the fuzzy analytic hierarchy process (F-AHP) method, which is a multiple criteria decision analysis method, which has been used to obtain results based on a structured evaluation process. The obtained results, following the considered comparison matrix, underscore that availability and resilience dimensions have the greatest impact on the Moroccan energy security, with respective weights of 48% and 28%. Besides, mitigating energy risk in Morocco primarily involves the development of renewable energies compared with seven other proposed measures, along with their degree of priority.

Key process identification method for aircraft skin milling based on quality characteristics and process correlation analysis

As a key aircraft component, the machining quality of aircraft skin is difficult to control. This paper proposes a key process identification method for aircraft skin milling, which comprehensively considers the influence of key quality features and the correlation between features and processes. The Fuzzy Analytic Hierarchy Process and Entropy Weight Method are used to calculate the influence weights of quality features, and the key quality features are comprehensively identified by the improved Fuzzy Borda Method. The improved algorithm reduces the non-linearity of calculation and the weakening of evaluation, solving the problem of low discrimination in the recognition results of a single evaluation method; At the same time, combining factors such as process difficulty, the relationship between quality characteristics and processes was analyzed, and key processes were identified by calculating relational grade. The experimental results show that the key processes of skin milling are the previous procedures of key quality features and the process located in the middle of the skin. The recognition results are more accurate and scientific compared to the recognition methods that do not consider the relationship between features and processes. This has certain guiding significance for practical aircraft skin machining tools and has good generalizability.

Selection model of chemical tanker ships for cargo types using fuzzy AHP and fuzzy TOPSIS

There are many chemical tanker ships on the market with very different characteristics. However, their operational performance for cargo types remains uncertain. Also, it is unclear which criteria should be taken into consideration primarily in the selection of chemical tanker ships. Therefore, choosing the most suitable ones for the cargo to be transported is an important problem for maritime companies. In this respect, the study aims to determine suitable chemical tanker ships for the four main cargo groups transported by them: animal and vegetable oils, corrosives (abrasives), organic chemicals, and petroleum products. A methodology that combines the fuzzy AHP, fuzzy TOPSIS, and clustering methods is used. 30 alternative chemical tanker ships are determined to represent the characteristics of all ships with a tonnage of 1–30 k DWT on the market. They are clustered into performance levels according to their fuzzy TOPSIS ranking scores. Consequently, a chemical tanker selection manual is developed based on the common characteristics of high-performance ships as well as the order of importance of criteria. It is envisaged that it would be a reference for ship operators and shipowners in the selection of suitable ships for the type of cargo.

A study of appropriate wave energy technology for sustainable development in Australia

The deployment and development of wave energy systems to increase energy sector efficiency is essential for governments. To develop and maximize the exploitation of wave energy sources, applying appropriate technologies is extremely important. The design of decision-making tools to identify the best technology for developing energy resources optimally is one of the primary decision-making issues in the energy sector. In this article, we have proposed a Multi-Criteria Decision Making (MCDM) model to select suitable technologies among eleven wave energy harvesting technologies: OPT PowerBuoy, AquaBuoy, Archimedes Wave Swing, Salter’s Duck, Aquamarine PowerOyster, Bio Wave, SEAREV, Weptos, Mighty Whale, Wave dragon. To handle conflicting objectives during evaluation, the Fuzzy AHP method is used to calculate the weight of the criteria. Then, wave energy harvesting technology is ranked using the Fuzzy TOPSIS approach. An actual case study from Australia was examined in order to show the viability of the suggested decision-making methodology. This study applies a valuable reference to the issue of wave technology selection; Therefore, managers involved in wave energy can use the problem-solving approach in this study to identify the most suitable wave energy technology based on their criteria. Research results show that the most suitable technologies for optimal development of wave energy sources in Australia are WET-05 and WET-03 with coefficients of 0.852 and 0.806, respectively. Meanwhile, two technologies considered unsuitable for developing wave energy sources in this study are WET-06 and WET-11 with scores of 0.375 and 0.381, respectively. In this study, the combined application of the two FAHP-FTOPSIS methods is considered more appropriate because of its theoretical ease of understanding as well as the simplicity of application and robustness of the results.

A novel spherical fuzzy AHP method to managing waste from face masks and gloves: an Istanbul-based case study

A novel spherical fuzzy AHP method to managing waste from face masks and gloves: an Istanbul-based case study