Fuzzy Analytic Hierarchy Process Research Articles

Sustainability-oriented monorail transit construction comprehensive evaluation

ABSTRACT Monorail transit is a relatively small form of urban rail transit, having a considerable influence on city infrastructure; however, its quality evaluation needs to be further developed. This study aims to comprehensively evaluate the construction quality of monorail traffic, considering the newly completed Wuhu Monorail Transit Line 1 as an example. First, a comprehensive evaluation system comprising 5 dimensions, namely sustainability, social, economic, city development and technology, and 23 sub-indexes is constructed. Second, for scientific evaluation, the triangular fuzzy analytic hierarchy process is considered to determine the subjective weight of each index; then, the Critic method is used to determine the objective weight of each index. Finally, the game theory equilibrium weight method is used to determine the total weight of each index. Furthermore, considering randomness and fuzziness, the cloud model is used to evaluate the construction quality of the Wuhu monorail Line 1. The evaluation results show that the Wuhu Monorail Transit Line 1 has a high construction grade, with sustainability dimension A1, sub-index of greenhouse gas emission quality B11, and total energy consumption B12 during the construction period. The proposed evaluation method was verified using sensitivity analysis and has a strong practical value.

Design of a sweeping robot based on fuzzy QFD and ARIZ algorithms

The design of a multifunctional intelligent robotic vacuum cleaner is essential for meeting user needs and replacing human cleaning tasks, thereby improving the effectiveness and convenience of human-robot interaction. Existing robotic vacuum cleaners can address the issues of small dust and hair accumulation; however, a comprehensive and integrated solution for sweeping, mopping, sanitizing, and general waste cleaning remains necessary. This paper employs the Fuzzy Quality Function Deployment (FQFD) tool and the Algorithm for Inventive Problem Solving (ARIZ) algorithm to address these challenges. The research framework integrates FQFD with the Fuzzy Analytic Hierarchy Process (FAHP) methodology to evaluate the weights of user requirements for a robotic vacuum cleaner. Subsequently, these requirements were translated into technical specifications, thus establishing a link between user needs and technical requirements. The ARIZ algorithm is subsequently employed to resolve conflicts and inconsistencies within the FQFD model. Finally, an innovative design for a robotic vacuum cleaner was proposed by utilizing the ARIZ algorithm's problem-solving principles and qualitative and quantitative insights derived from the FQFD. The design is evaluated using the Fuzzy Order Preference Technique (FTOPSIS) method, which resembles the ideal solution. Furthermore, the operational dynamics of the critical components of the design solution that navigate obstacles are analyzed to ensure their effectiveness. The final product can effectively replace humans in complex cleaning tasks while navigating obstacles during sweeping, mopping, and sanitizing for comprehensive space cleaning. The results of this study provide valuable insights for designers to enhance user experience, improve customer emotional satisfaction, and guide future product designs in related product development initiatives.

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.

Research on the Design Method of Camellia oleifera Fruit Picking Machine

Camellia oleifera fruit pickers are essential for improving picking efficiency and promoting the Camellia oleifera industry. However, it is challenging to develop pickers that meet user needs. Current design tools and methods have limitations, such as a single model, poor synergy between integrated models, and subjective bias when analysing user requirements and translating them into product attributes. To solve these problems, this study proposes a new design decision model based on the Fuzzy Analytic Hierarchy Process (FAHP), Function Analysis System Technique (FAST), Theory of Inventive Problem Solving (TRIZ Theory), and extension transformation theory. The model was developed and applied to design an Camellia oleifera fruit picker. In this paper, an empirical investigation of an Camellia oleifera base in Wuhan was carried out, and multi-level demand analysis was used to identify the design demands in the behavioural process; FAHP was used to calculate the demand weights to clarify the design focus; expert knowledge was used to convert the demands into specific product functional features, and FAST was used to decompose these features to find the contradictory conflicts; TRIZ theory was used to determine the principles of resolving the contradictions, and the extension transformation theory were used to generate the creative design solutions for the products. By integrating FAHP, FAST, TRIZ theory and the extension transformation theory, the subjective bias in product design is eliminated, the design decision-making process is improved, and new methods and ideas are provided for the design of oleaginous tea fruit pickers and similar products. Finally, the conceptual design of an Camellia oleifera fruit picking machine was produced. However, the conceptual design has yet to be subjected to exhaustive simulation experiments and prototype testing. Future research will focus on conducting the necessary simulations, prototypes, and field tests to fully assess the feasibility and effectiveness of the design and make the required iterative improvements accordingly to commercialize the product eventually.

Advancing Sustainable Healthcare Technology Management: Developing a Comprehensive Risk Assessment Framework with a Fuzzy Analytical Hierarchy Process, Integrating External and Internal Factors in the Gulf Region

Advancing Sustainable Healthcare Technology Management: Developing a Comprehensive Risk Assessment Framework with a Fuzzy Analytical Hierarchy Process, Integrating External and Internal Factors in the Gulf Region

A Novel Visual System for Conducting Safety Evaluations of Operational Tunnel Linings

Based on the lining structure of an operational tunnel, the AHP and Fuzzy mathematical models were used to determine the weight of the evaluation index and solve the membership matrix. The weighted-average Fuzzy comprehensive function was used to combine the two, and the Fuzzy–AHP evaluation model was built and programmed. According to the self-developed Fuzzy–AHP evaluation-programmed model, a visualized structure safety evaluation system for operational tunnels was developed by using MATLAB. The system’s functional design, program development, and computational visualized interface were implemented, and key codes were provided. The system can be divided into four modules: data management, fuzzy computation, predictive analysis and key disease indexes to focus on. In addition, the system can easily edit and modify the evaluation function, which includes not only the Fuzzy evaluation but also other types of evaluation functions applicable to other practical engineering projects, improving the applicability of the system. After that, the system was applied to the structure safety evaluation of a mountain tunnel, which provided the evaluation results and key indexes to focus on in the tunnel. Finally, the rationality of the system design was verified by constructing the corresponding BP–RBF combined neural network. This study provides a reference for the establishment of an intelligent structure safety warning system for operational tunnels.

Assessment of Potential Aquifer Recharge Zones in the Locumba Basin, Arid Region of the Atacama Desert Using Integration of Two MCDM Methods: Fuzzy AHP and TOPSIS

Natural aquifers used for human consumption are among the most important resources in the world. The Locumba basin faces significant challenges due to its limited water availability for the local population. In this way, the search for possible aquifer recharge zones is crucial work for urban development in areas that have water scarcity. To evaluate this problem, this research proposes the use of the hybrid Fuzzy AHP methodology in conjunction with the TOPSIS algorithm to obtain a potential aquifer recharge map. Ten factors that influence productivity and capacity in an aquifer were implemented, which were subjected to Fuzzy AHP to obtain their weighting. Using the TOPSIS algorithm, the delineation of the most favorable areas with high recharge potential was established. The result shows that the most influential factors for recharge are precipitation, permeability, and slopes, which obtained the highest weights of 0.22, 0.19, and 0.17, respectively. In parallel, the TOPSIS result highlights the potential recharge zones distributed in the Locumba basin, which were classified into five categories: very high (13%), high (28%), moderate (15%), low (28%), and very low (16%). The adapted methodology in this research seeks to be the first step toward effective water resource management in the study area.

An integrated design method for remanufacturing scheme considering carbon emission and customer demands

Design for remanufacturing (DfRem) plays an important role and aims to reduce carbon emissions during the remanufacturing phase at the initial design stage. However, low-carbon requirements and customer demands are not considered integrally when generating schemes, which might to limit the potential to reduce carbon emissions. To address the problem, this paper proposes a DfRem scheme generation method that combines the Quality Function Deployment for Low-Carbon (QFDC) model with the FMAS (Functional-Motion-Action-Structure) model. Firstly, the demands are categorized into customer, remanufacturability and low-carbon demands, and the FAHP (Fuzzy Analytic Hierarchy Process) is used to evaluate the weight of demands. Secondly, the QFDC model is developed to convert demands to engineering characteristics, and to determine the design priorities. Thirdly, the FMAS model is established to map engineering characteristics into related structures, and a low-carbon and customer-satisfied DfRem scheme is generated. Finally, the efficacy of proposed method is demonstrated through a case study on the remanufacturing of a CNC milling machine, displaying its superiority in reducing carbon emissions while meeting customer demands.

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.

A Generalized Method for Deriving Steady-State Behavior of Consistent Fuzzy Priority for Interdependent Criteria

Interdependent criteria play a crucial role in complex decision-making across various domains. Traditional methods often struggle to evaluate and prioritize criteria with intricate dependencies. This paper introduces a generalized method integrating the analytic network process (ANP), the decision-making trial and evaluation laboratory (DEMATEL), and the consistent fuzzy analytic hierarchy process (CFAHP) in a fuzzy environment. The Drazin inverse technique is applied to derive a fuzzy total priority matrix, and we normalize the row sum to achieve the steady-state fuzzy priorities. A numerical example in the information systems (IS) industry demonstrates the approach’s real-world applications. The proposed method derives narrower fuzzy spreads compared to the past fuzzy analytic network process (FANP) approaches, minimizing objective uncertainty. Total priority interdependent maps provide insights into complex technical and usability criteria relationships. Comparative analysis highlights innovations, including non-iterative convergence of the total priority matrix and the ability to understand interdependencies between criteria. The integration of the FANP’s network structure with the fuzzy DEMATEL’s influence analysis transcends the capabilities of either method in isolation, marking a significant methodological advancement. By addressing challenges such as parameter selection and mathematical complexity, this research offers new perspectives for future research and application in multi-attribute decision-making (MADM).

Research on Assessment Method for Cognitive Influence Factors of Nuclear Power Plant Operators Based on Fuzzy Analytic Hierarchy Process and Deep Learning of Electroencephalogram Signals

This study explores the factors influencing the cognitive processes of operators in digital nuclear power plants, with a focus on the correlation between these factors and electroencephalogram (EEG) features. Initially, based on expert consultations, seven factors were considered: stress, time, fatigue, procedural complexity, user interface experience, procedural clarity, and efficiency. From these, four were identified as the most crucial for each stage of the cognitive process, highlighting their significant roles in influencing cognitive performance and potentially correlating with distinct EEG characteristics. These were assessed using the fuzzy analytic hierarchy process (FAHP) to determine the weightings of influences across the cognitive stages of monitoring, decision making, and execution. Employing a simulated scenario of a steam generator tube rupture, subjective questionnaires were utilized to gauge participant perceptions of influencer impacts at each stage, calculating human factors fuzzy synthetic values. Concurrently, EEG signals were segmented by operational steps, extracting around 114 features across the time, frequency, and time-frequency domains, which were then dimensionally reduced to 17 principal components via adaptive principal components analysis (APCA). A correlation analysis was performed between the human factors fuzzy synthetic values and the APCA-reduced EEG features of participants. Subsequently, the EEG feature columns of the eight selected participants were used as inputs to construct a transformer-based self-attention network model to evaluate the participants’ human factors fuzzy comprehensive values. The findings confirm the transformer model’s efficacy in assessing these values, evidencing a significant correlation between the EEG features and human factors fuzzy synthetic values. Integrating FAHP with machine learning methodologies, this model proficiently estimated operators’ cognitive states during various cognitive processes, significantly enhancing human-machine interface design and the operational safety and efficiency at nuclear power plants.

The ranking of factors in selecting the online shopping platform in Malaysia based on fuzzy analytic hierarchy process

The ranking of factors in selecting the online shopping platform in Malaysia based on fuzzy analytic hierarchy process

Fuzzy analytical hierarchy process for analysing the factors that influence the career choices of graduate students at UiTM Perlis

Fuzzy analytical hierarchy process for analysing the factors that influence the career choices of graduate students at UiTM Perlis

Transactional Distance’s Influence on Students’ Social, Cognitive, Teaching Presence, and Academic Achievement

ABSTRACT The study aimed to investigate how different levels of transactional distance impact students’ perceptions of social, cognitive, and teaching presence, as well as their academic achievement. The research involved 72 students from a state university’s education faculty and used a 3 × 2 factorial design, with structured, semi-structured, and unstructured online environments provided, all with a “high” dialog dimension according to transactional distance theory. These environments were established based on the fuzzy analytic hierarchy process. Over 15 weeks, students in the three experimental groups received education through these online environments. The study found no significant difference in academic achievement scores or community of inquiry scale scores among the groups. Students reported positive perceptions of the online environments they were in. The satisfaction they experienced in the online environment was influential in the absence of differences in academic achievement scores and community of inquiry scale scores among the groups. In conclusion, high dialog, provided at different levels of transactional distance, effectively eliminated potential differences arising from the structural dimension among the groups. If dialogue is kept at the maximum level in online environments, transactional distance that may arise from the structure dimension can be prevented.

Analyzing the Digital Infrastructure Enabling Project Management Success: A Hybrid FAHP-FTOPSIS Approach

This research paper examines the digital infrastructure required to achieve project management success by analyzing the enabling elements of this digital infrastructure in terms of three pillars: digital readiness, digital fitness, and digital tools. A comprehensive literature review was conducted to identify these enabling elements and to develop a list of project management success indicators through which the success of project management can be measured. To evaluate and rank the digital infrastructure enabling elements, a Multi-Criteria Analysis (MCA) was implemented using a hybrid approach combining Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS). The study used the digital infrastructure enabling elements as MCA alternatives and the project management success indicators identified in the literature review as MCA criteria. The results indicated that the enabling elements associated with digital tools are the most significant for project management success, with a FTOPSIS closeness coefficient (CCi) of 0.8525, followed by those related to digital fitness (CCi = 0.6481) and digital readiness (CCi = 0.1602). These findings have proven to be robust, as they remained consistent even when weights of the MCA criteria were adjusted in three new scenarios proposed in a scenario analysis. This research highlights the critical role of digital enabling elements in enhancing project management practice and achieving project management success. It also offers a strategic framework for organizations to develop and strengthen their digital infrastructure.

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.

Application of machine learning and fuzzy AHP for identification of suitable groundwater potential zones using field based hydrogeophysical and soil hydraulic factors in a complex hydrogeological terrain

The eastern section of West Bengal grapples with limited surface water availability in its hard rock terrain, compounded by a semi-arid climate, variable rainfall, and a plateau topography, prompting communities to adapt groundwater water-use practices, leading to unsustainable extraction and misuse. Thus, the novel objective of the present research was to produce groundwater potential maps by comparing machine learning techniques with a Fuzzy MCDM model using specific field-based conditioning factors. In the first step, 285 wells were identified, of which 70 percent were used for training and 30 percent for the validation of the models. Secondly, field-based conditioning factors including, longitudinal conductance (SC), longitudinal resistance (ρl), transverse resistance (TR), coefficient of electrical anisotropy (λ), resistivity of formation (ρm), fracture porosity (φf), reflection coefficients (r), hydraulic conductivity (K), transmissivity(Tr), bulk density, porosity, permeability, soil moisture content and water holding capacity were used to analyze the association between these conditioning factors and groundwater occurrences. In the following steps, the XGBoost, Random Forest, and Naïve Bayes models were executed using the training dataset, and factor weights were calculated using Fuzzy Analytical Hierarchy Process of Extent analysis method. To validate and compare the performance of four models, ROC curves, AUCs, MCAs, and correlation plots were used. In general, all four models were successful in evaluating the potential of groundwater occurrences. The predictive capability of the XGBoost techniques with the highest AUC values (0.79) and the highest correlation value (0.78) is superior to those of other machine learning and MCDM models. Geophysical survey revealed that transmissivity and hydraulic conductivity of the aquifer of the river basin range from 1.55 to 440.11 m/day and 10.15–2253 m2/day, indicating a moderate to good hydrodynamic potential. Planners and engineers can use such groundwater potential maps to manage water resources effectively.

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.

Multi-attribute approach for selection of polymeric materials for manufacturing gears: A case study in the automotive industry

Automotive industry is characterized by mass production, a large share in the gross national income and high employment of workers with different knowledge and skills. Improving the production process, as well as product quality, is one of the most important tasks of automotive enterprise as well as government management. This research promotes a new fuzzy hybrid model for determining the priority of polymeric materials for manufacturing gears in an exact manner, which leads to an incremental improvement in the quality of the considered product. The analysis of polymeric materials and their characteristic is based on data from the relevant literature and experiences of the best practice. The relative importance of material characteristics is stated as fuzzy group decision making problem. The weights vector is determined by using the fuzzy Analytic Hierarchical Process and fuzzy geometric mean. The rank of polymeric materials is obtained by employing the proposed Technique for Order Preference by Similarity to Ideal Solution with triangular fuzzy numbers. The fuzzy algebra rules have been used for determining: (i) distances from Fuzzy Positive Ideal Solutions and Fuzzy Negative Ideal Solutions and (ii) closeness coefficient values. The proposed fuzzy hybrid model testing and verification are performed on real data in an automotive enterprise.