Fuzzy Analytic Hierarchy Process Research Articles

Geospatial modeling of wildfire susceptibility on a national scale in Montenegro: A comparative evaluation of F-AHP and FR methodologies

Abstract Wildfires pose a significant ecological, environmental, and socioeconomic challenge in southeastern Europe. The preservation of wildlands is not only essential but also a foremost priority for Montenegro, a country recognized as the world’s first ecological state. Consequently, the development of optimal methodologies and models is of paramount importance to enhance fire protection measures. With this objective in mind, this study strives to create a wildfire susceptibility model on a national scale for Montenegro. The study employed seven natural and anthropogenic causative criteria: vegetation type; aspect; slope; elevation; climate classification; distance from road; and population. The modeling process integrates both natural and anthropogenic causal criteria, employing the Fuzzy Analytic Hierarchy Process (F-AHP) and Frequency Ratio (FR) within geoinformatics environment. The outcomes of the F-AHP model reveal that 72.84% of the total area is categorized as having high to very high susceptibility. Conversely, based on the FR model, only 29.07% of the area falls within these susceptibility levels. In terms of validation, the area under curvature values indicates good performance of the F-AHP model. In contrast, the FR model demonstrates poor performance. These novel findings, pertaining to Montenegro at a national scale, offer valuable insights for preemptive wildfire safeguarding efforts. Moreover, the methodologies employed, with necessary modifications, hold potential for application in geographically diverse regions.

Bringing the queen mother of the west to life: Digital reconstruction and analysis of Taoist Celestial Beings Worshiping mural’s apparel

Abstract Painted during the Yuan Dynasty, Taoist Celestial Beings Worshiping is a Taoist propaganda painting. It is one of the largest surviving ancient murals worldwide, featuring a variety of images of these beings dressed in costumes that vividly illustrate the blend of religious and artistic values in Chinese culture. To record and present the mural more intuitively and improve the study of Taoist Celestial Beings Worshiping, this article takes the example of five goddesses centered Queen Mother of the West in the mural and analyzes the styles, structures, colors, and patterns of the costumes from the perspective of costume engineering. Human models are established and the costumes at multiple levels are reconstruction by means of 3D virtual simulation technology. The display images are accompanied by QR codes, which can be scanned to view the 3D model. Finally, the Fuzzy Analytic Hierarchy Process was used to comprehensively evaluate the reconstruction effect of clothing, and the result was “good.” The resulting digital figure can realize the “revitalization of cultural relics” and provides a new perspective for the digital exhibition of murals, which is conducive to the development of digital tourism and promotes the development of traditional culture.

Selection of data analytic techniques by using fuzzy AHP TOPSIS from a healthcare perspective

The healthcare industry has been put to test the need to manage enormous amounts of data provided by various sources, which are renowned for providing enormous quantities of heterogeneous information. The data are collected and analyzed with different Data Analytic (DA) and machine learning algorithm approaches. Researchers, scientists, and industrialists must manage or select the best approach associated with DA in healthcare. This scientific study is based on decision analysis between the DA factors and alternatives. The information affects the whole system in a rational manner. This information is very important in healthcare sector for appropriate prediction and analysis. The evaluation discusses its benefits and presents an analytic framework. The Fuzzy Analytic Hierarchy Process (Fuzzy AHP) approach is used to address the weight of the factors. The Fuzzy Techniques for Order Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) address the rank of the data analytic alternatives used in healthcare sector. The models used in the article briefly discuss the challenges of DA and approaches to address those challenges. The assorted factors of DA are capture, cleaning, storage, security, stewardship, reporting, visualization, updating, sharing, and querying. The DA alternatives include descriptive, diagnostic, predictive, prescriptive, discovery, regression, cohort and inferential analyses. The most influential factors of the DA and the most suitable approach for the DA are evaluated. The ‘cleaning’ factor has the highest weight, and ‘updating’ is achieved at least by the Fuzzy-AHP approach. The regression approach of data analysis had the highest rank, and the diagnostic analysis had the lowest rank. Decision analyses are necessary for data scientists and medical providers to predict diseases appropriately in the healthcare domain. This analysis also revealed the cost benefits to hospitals.

Comfort evaluation of carrier-based aircraft missile handling operation based on fuzzy analytic hierarchy process

The analysis and evaluation of missile handling operation are crucial for ensuring safety and reliability during these processes. This paper proposes a novel assessment method for missile handling motions based on the Fuzzy Analytic Hierarchy Process (FAHP). The methodology comprises several stages: Initially, an analysis of missile handling motions is conducted. Subsequently, joint evaluation indicators influencing operational comfort are selected. Following this, membership functions and a comprehensive evaluation matrix are constructed. Finally, a fuzzy comprehensive evaluation model for the comfort level of carrier-based aircraft ammunition handling operations is established. A typical case study of carrier-based aircraft missile handling operation is presented to illustrate the fuzzy comprehensive evaluation process. The evaluation results are then compared with those obtained from the Rapid Upper Limb Assessment (RULA) and Ovako Working Posture Analysis System (OWAS) models. Comparative analysis indicates that the results derived from the fuzzy comprehensive evaluation model demonstrate high reliability.

Identifying hidden groundwater reserves: GIS advances and multi-criteria decision analysis for enhanced potential assessment

The aquifer system may become susceptible as a result of excessive groundwater extraction without regard for scientific principles. This study evaluates the Groundwater Potential (G.P.) using multicriteria decision analysis (MCDA) and geospatial modelling. Here in MCDA, the G.P. is measured using Remote Sensing (R.S.) and Geographic Information System (GIS), together with Analytical Hierarchical Process (AHP), Fuzzy Analytical Hierarchical Process (FAHP), and Best Worst Method (BWM). Analysis and model creation took into account 15 groundwater affecting elements that affect the G.P. Groundwater Potential Zones (GPZs) in Cachar District, India varied by the following percentages: 46.97%, 37.41%, and 15.62% for AHP; 46.13%, 40.92%, and 12.95% for FHP; and 50.65%, 36.51%, and 12.84% for BWM, respectively, for low, medium, and high zones. The FAHP and BWM models outperformed the AHP model, according to the results of the qualitative validation. The concerned authorities might identify vulnerable locations and plan sustainable groundwater management with the use of these MCDA approaches combined with GIS.

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>

Development of a rating model for assessing the condition of steel railway bridges

Steel railway bridges play a crucial role in global transportation networks, yet their operational lifespan is often compromised by factors such as corrosion, aging, exposure conditions, and environmental challenges. Structural Health Monitoring (SHM) systems are widely employed to extend the longevity of these bridges. However, existing SHM models typically have limitations, as they tend to focus on a limited set of parameters, and their reliance on qualitative definitions introduces subjectivity into the evaluation process. To address these shortcomings, this research aims to develop an innovative priority weight-based condition rating model. Casual Factors (CF), laboratory and Non-Destructive Tests (NDT), Visual Inspection (VI), Environmental Conditions (EC), and Type of Element (TOE), were identified as main parameters to the condition assessment. Then, the research gathered opinions from 100 experts to establish the importance of these parameters. By averaging expert opinions and ensuring a high level of consistency (i.e. below 10%), the study aimed to minimize subjectivity. Numerical definitions were introduced to the model to enhance objectivity. The Fuzzy Analytical Hierarchy Process (FAHP) was employed to calculate appropriate weightings for the identified parameters, resulting in a comprehensive equation that encapsulates the main factors influencing the condition of steel railway bridges.To validate the developed rating model, a case study was conducted, analyzing seventeen railway bridges located in various regions of Sri Lanka. Notably, the model takes into account physical, geographical, and environmental conditions, making it applicable to diverse locations worldwide. This innovative approach contributes to the enhancement of steel railway bridge maintenance strategies, promoting their sustained and reliable operation on a global scale.

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

Assessing the impact of cleaner bonds on clean energy expenditure for sustainable economic recovery: A fuzzy analytical approach

This research evaluates the impact of investing in cleaner bonds on funding clean energy initiatives, pivotal for sustainable economic recovery. The study seeks to assess the efficacy of cleaner bonds in fostering environmentally friendly projects and propelling economic growth and applies fuzzy analytical hierarchy process to examine the findings. Findings suggest that cleaner bonds potentially increase clean energy expenditures by an average growth rate of 4.9 % per year, significantly enhancing economic resilience. Additionally, these bonds are shown to accelerate the revitalization of green economies by approximately 17 % annually. The research underscores the role of energy performance contracts as viable alternatives to conventional project financing, through the application of various modelling tools and fuzzy analyses. It also discusses the strategic allocation of public and private funds into cleaner bond investments to accelrate economic sustainability and energy conservation. This study highlights the essential nature of cleaner bonds for entities and environmental groups aiming to secure funding for long-term projects, stressing the importance of expanding the sustainable economy through green bond issuances for eco-friendly and energy-efficient developments. Furthermore, the research recommends implications for stakeholders to diligently consider the legal aspects associated with these investments.

Prioritizing Vulnerability Factors of Global Food Supply Chains by Fermatean Fuzzy Analytical Hierarchy Process

Abstract In response to heightened competition arising from globalization, companies are crafting strategies to sustain their operations. However, these strategies also introduce risks that require meticulous management. The onset of the COVID-19 pandemic has exacerbated disruptions in supply chains, including the vulnerable food supply chain (FCS), strained further by escalating food prices and resource depletion in recent times. Within this context, the vulnerability of global FSCs has escalated significantly due to government-imposed lockdowns during the pandemic. This study aims to comprehensively investigate the multifaceted disruptions in global FSCs caused by the COVID-19 pandemic. By delving deep into the complexities of these disruptions, it seeks to uncover the key factors contributing to the vulnerability of supply chains. Employing a blend of literature review and expert opinions, the study identifies and prioritizes factors using the Fermatean Fuzzy Analytical Hierarchy Process (FF-AHP). A two-level criteria framework consisting of three main criteria and eleven sub-criteria has been developed, taking into account expert recommendations and previous studies. According to the results obtained, it has been revealed that the Managerial factors within the main criteria are the most significant factors in the fragility of the FSC. Among these factors, it has been observed that Technology, Corporation, and Inventory Management are the leading criteria causing to the vulnerability of the FSC. This is the first study to investigate the vulnerabilities of FSC using fuzzy logic. The research underscores the imperative of comprehensive risk management strategies that encompass all stakeholders within the supply chain, particularly during unanticipated crises like pandemics.

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.

Research on Contract Risk Management of Hospital Construction Project based on Fuzzy Analytic Hierarchy Process

With Covid-2019 outbreak in December 2019, the number of medical infrastructure projects in infrastructure projects has increased. Although contract management is an important link of medical infrastructure project management but there is little quantitative research on the risk analysis. in this paper, firstly, three first-level risk indicators of contract risk management evaluation system are extracted by combing literature: contract drafting, contract signing and contract performance. Then, taking the contract management of a 3A hospital in Chengdu as an example, 12 second-level risk indicators are determined by fault tree method. Then, by experts scoring and statistically analyzing the risk points of 100 construction project contracts signed by the hospital from 2016 to 2020, the weight of secondary risk indicators is determined and the risk matrix is established. Then, the evaluation matrix of contract risk management evaluation system is established by 10 experts. Finally, the evaluation index system of contract risk management is obtained by combining the risk matrix with the evaluation matrix through fuzzy analytic hierarchy process, and the maximum membership degree method is adopted to complete the risk assessment. The research results show that there are high risks in the contract drafting stage and medium risks in the contract signing and contract performance stages in the contract management of a Grade A hospital in Chengdu. The author puts forward reasonable suggestions to improve the contract management level of hospital construction projects.

Fuzzy Analytic Hierarchy Process–Technique for Order Preference by Similarity to Ideal Solution: A Hybrid Method for Assessing Vegetation Management Strategies under Electricity Distribution Lines to Prevent Deforestation Based on Ecosystem Service Criteria

This study evaluated vegetation management (VM) strategies under electricity distribution lines (EDLs) through ecosystem service (ES) criteria. Deforestation, worsened by insufficient VM practices, poses a threat to ecosystem stability. Using a hybrid FAHP (Fuzzy Analytic Hierarchy Process) and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) approach, ten VM strategies were assessed based on 15 ES criteria. The FAHP results identified biodiversity, timber resources, and erosion control as the most crucial criteria due to their significant weights. The TOPSIS analysis determined that VM6 (creation and restoration of scrub edges) was the most effective strategy, achieving a value of 0.744 for reducing deforestation and enhancing energy security. VM6 helps preserve forest cover and protect infrastructure by creating a “V”-shaped structures within the EDLs corridor. This study underscores the importance of ES-oriented VM strategies for sustainable vegetation management and deforestation mitigation. It also highlights the need for incorporating scientific, ES-based decision support mechanisms into VM strategy development. Future research should expand stakeholder perspectives and conduct a comprehensive assessment of ESs to ensure that VM strategies align with ecological and socio-economic sustainability. This study provides a framework for improving VM practices and offers directions for future sustainable energy management research. This study focuses exclusively on ecological criteria for evaluating VM strategies, neglecting other dimensions. Future research should use methods like ANP and fuzzy cognitive maps to explore inter-dimension relationships and their strengths. Additionally, employing SWARA, PIPRECIA, ELECTRE, and PROMETHEE for ranking VM strategies is recommended.

Temporal-spatial-fusion-based risk assessment on the adjacent building during deep excavation

Foundation pit excavation will inevitably cause uneven ground settlement to pose potential risks to adjacent structures and infrastructures. To better perceive the risk status of adjacent buildings using multi-source information, a temporal-spatial-fusion-based risk assessment (TSFRA) model under the consideration of uncertainty and causality is developed by integrating FAHP (Fuzzy Analytic Hierarchy Process), DGDT (DAG-GNN, DEMATEL method, and topological analysis), and CM (cloud model). More specifically, FAHP handles temporal weights of excavation conditions based on expert knowledge. DGDT determines spatial weights for monitoring points, with DAG-GNN constructing causal graphs in a data-driven manner, DEMATEL handling global interactions, and topological analysis calculating node importance. CM can finally fuse the temporal (excavation conditions) and spatial (monitoring points) weights with settlement monitoring data, resulting in qualitative assessments of the overall risk status of adjacent buildings in uncertain environments. The proposed TSFRA is verified in the case of a Shanghai metro station construction project. Results indicate that the perceived risk of the project is at a relatively low level, consistent with the actual condition. High-risk excavation conditions and locations can be easily identified. There is a trend toward higher risk during the excavation of soft soil layers, and thus some risk control measures can be formulated to avoid potential risk events. In short, the weight determination and fusion method in TSFRA contribute to handling uncertainties in expert judgments and causalities in collected data, which can practically provide more reliable decision-making in excavation-induced risk assessment and control for ensuring the safety of adjacent buildings.

Mô hình lựa chọn nhà cung cấp: Ứng dụng phương pháp phân tích thứ bậc mờ

It has been argued that supplier selection is of paramount importance for production companies. In particular, selecting appropriate suppliers may improve production outputs’ quality for firms. Until date, some methods have been developed to select suppliers; yet, some weaknesses still exist. To fill the literature gap, this paper aims to set up a model for judging and selecting potential suppliers from manufactures’ perspectives by the fuzzy Analytic Hierarchy Process (fuzzy AHP). It is posulated that fuzzy AHP is not only applicable for supplier selection, but also helpful in assessing staff’s operational efficiency, evaluating product quality, and appraising the types of technologies to use in the production line.

Assessing groundwater contamination risk in industrial zone of Ranipet district, Southern India: A modified DRASTIC and Fuzzy-AHP approach

Groundwater plays a substantial function in sustaining the water requirements of Ranipet district, Tamil Nadu, India, across a variety of domains, including drinking water, agriculture, and industry. However, rising demand and industrial activities have caused concern about groundwater pollution and susceptibility in the region. So, this study employed a modified DRASTIC model to find the regions that are most vulnerable to groundwater contamination. The model includes a novel parameter, "proximity to industry,” in addition to the seven traditional factors (depth to water level, net recharge, aquifer media, soil media, topography, impact of vadose zone and hydraulic conductivity) to account for the industrial nature of the study region. The fuzzy-analytical hierarchy process (FAHP) was used to compute parameter weights and ratings. All the parameters are overlayed in ArcGIS software to create the groundwater vulnerability map (GWVM). The map revealed that the central area along the Palar River is highly vulnerable to contamination. The southern and northern parts have medium to extremely low groundwater contamination risks. The depth of the water table was the most significant component impacting the vulnerability, as per the sensitivity analysis. The model is further validated by chromium concentrations, which were shown to be high in highly sensitive zones and low in low-risk zones. This study also suggests the need for extensive and periodic groundwater quality assessments in highly vulnerable zones for sustainable water resource management.

Evaluating music education interventions for mental health in Chinese university student: a dual fuzzy analytic method

This study addresses the growing anxiety and depression among Chinese university students by evaluating and ranking music education strategies to alleviate these issues. We integrates Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). FAHP was utilized to determine the weight of factors such as academic pressures, social relationships, and cultural norms, while fuzzy TOPSIS ranked the effectiveness of music education interventions based on these weights. The results revealed that ‘Mental health stigma’ and ‘Academic Pressures and Rigidity’ are among the highest weighted factors, significantly impacting student anxiety. ‘Music Appreciation and Music-Based Self-Care’ emerged as the most effective strategy. These results highlight the importance of direct involvement in music-related activities for improving student mental health.

Analysis of barriers to adopting Industry 4.0 to achieve agri‐food supply chain sustainability: A group‐based fuzzy analytic hierarchy process

AbstractAgri‐food supply chains (AFSCs) need to become more sustainable, and Industry 4.0 (I4.0) is a crucial enabler. However, various barriers make adopting I4.0 technologies to achieve AFSC sustainability challenging. Few previous studies have examined China's agri‐food industry. Through a literature review and consultation of Chinese experts, we identify 27 barriers in six categories and prioritize these using a group‐based fuzzy analytic hierarchy process to produce novel results. First, we identify six new I4.0 adoption barriers closely linked with China's economic, social and cultural environments, including acquisition of intelligent agricultural equipment subsidies based on guanxi. Second, our prioritization of barriers reveals that the key challenges to applying I4.0 are the increased cost of terminal logistics, acquisition of intelligent agricultural equipment subsidies based on guanxi, low compatibility of I4.0 technologies with existing agricultural equipment, and problems with the government subsidy model. These results have managerial implications for governments and knowledge dissemination organizations.

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.

Assessing travel time performance of multimodal transportation systems using fuzzy-analytic hierarchy process: A case study of Bhopal City

Insufficient multimodal transportation infrastructure in Indian cities increases travel time, waiting time, traffic congestion, traffic accidents, travel costs, fast-growing energy consumption, and walking distance. The multimodal transportation system's journey time performance must be analysed to tackle these transportation concerns. Thus, this research provides a fundamental method for assessing multimodal transportation system trip time performance. This study proposes three steps for assessing travel time performance: identifying factors, generating individual indices, and evaluating the Multimodal Transport System Travel-Time Performance Index. The travel-time performance index of Bhopal city was 0.79 after evaluating the results of the individual indices (Access 0.49, In-vehicle 0.39, and Egress 0.38). Consequently, this study will help identify areas for improvement in Bhopal's multimodal transportation system and provide insights for enhancing its efficiency and user satisfaction. The proposed methodology can be applied to other cities facing similar challenges, aiding in the development of more sustainable and user-friendly transportation systems.