Fuzzy Analytic Hierarchy Process Model Research Articles

Selection of waste receiving companies for sustainable industrial symbiosis network: an application a case in Ankara for foundry industry waste

AbstractIn the past twenty years, besides the technological and innovative efforts for production processes, there have been activities for the recovery and quality improvement of waste based on the development of sustainable solutions. These activities bring us closer to zero waste by protecting natural resources and reducing the environmental impact of production facilities. One of the essential approaches in this process is the industrial symbiosis (IS) approach. Efforts to implement the industrial symbiosis approach in an industrial park constitute the motivation of the study. The study aims to identify alternative enterprises that can evaluate the wastes generated in the main production process of the foundry industry with industrial symbiosis and rank them to establish a symbiotic relationship. First, the Pythagorean Fuzzy Analytical Hierarchy Process (PF-AHP) model has been developed to evaluate organizational factors, waste-specific factors, and economic and environmental sustainability criteria for establishing an IS network among alternative businesses. Then, alternative businesses were ranked to establish a symbiosis network with the PF-TOPSIS method. The proposed method contributes to the solution of a real-life problem that may arise when there are multiple alternatives to cooperate in the field of industrial symbiosis. By contributing this framework to the literature, the robustness of the results has been demonstrated through sensitivity analysis and comparative analysis. According to the results obtained, it was seen that the importance levels of economic factors came to the fore. Furthermore, among the alternatives, enterprises with high waste exchange potential, such as cement, took first place in the ranking.

Research on the Key Influencing Goals for Visual Design Sustainability: A Dual Perspective

The United Nations established 17 sustainable development goals (SDGs) in 2015, but research on these goals in the visual design industry remains limited. This study introduces a hybrid approach, combining fuzzy analytical hierarchy process (FAHP) and grey rational analysis (GRA) to assess sustainable factors from the perspectives of both service providers and consumers. In the FAHP model, consumers and visual design professionals had similar views on the ranking of dimensions and indicators. Both reported that the most important dimension for visual design sustainability is the environment. However, the perspective of consumers differed from that of visual design practitioners in the GRA model, as consumers argued that the social aspect has the greatest impact on visual design sustainability, while practitioners believed that the environmental aspect is the most important. The main contribution of the study is to emphasise that the hybrid multi-criteria decision-making (MCDM) mode can help the visual design industry align its services to consumer expectations. A systematic and objective model that presents practical insights relevant to industry is offered by this model. It also serves as a valuable reference for future research in similar areas.

An assessment framework for underground construction methods in the pre-construction stage: a technical perspective

The selection of an appropriate underground construction method plays a crucial role during the pre-construction phase as it must consider various aspects, such as safety, technical feasibility, and cost-effectiveness. However, there is still a lack of technical assessments for selecting suitable underground construction methods in the research field. Therefore, this study aims to build an assessment model to technically assess such methods in the pre-construction stage. Firstly, technical assessment factors were obtained from literature reviews, expert consultations and survey results. Factor analysis was performed to reveal the underlying significance of each factor. The Fuzzy Analytic Hierarchy Process (Fuzzy AHP) model was then constructed to assess the relative importance of each factor. Finally, the assessment model was validated by seeking experts’ opinions on its performance in a practical project. The study resulted in a Fuzzy AHP model established from a list of 8 groups of factors with 35 refined factors, covering many aspects from documents and resources to geology and hydrology. The validation results demonstrated the model’s effectiveness in assisting contractors to make informed decisions. This study has contributed to construction literature by introducing a novel framework for underground construction methods, offering a practical tool for pre-construction planning and decision-making.

Integrating the Coupled Markov Chain and Fuzzy Analytic Hierarchy Process Model for Dynamic Decision Making

This paper introduces a pioneering model that merges coupled Markov chains (CMC) with the fuzzy analytic hierarchy process (FAHP) to enhance multi-criteria decision making (MCDM) amidst the dynamic interplay of criteria. Traditional MCDM frameworks often lack the granularity to manage the intricate and changing relationships among criteria. Our model addresses this gap by integrating fuzzy numbers into AHP, providing a nuanced means to handle the inherent uncertainty of decision criteria. The application of the Kronecker product in CMC enriches our approach, offering a data-driven analysis while mitigating excessive dependence on subjective expert opinion. A comprehensive numerical example underlines the model’s improved decision-making accuracy and efficiency, marking a substantial advancement in MCDM methodologies. This research contributes to the field by presenting a sophisticated yet practical framework for dynamic decision analysis in complex uncertain environments.

Corporate governance for sustainable development in Vietnam: Criteria for SOEs based on MCDM approach.

This research explores the nexus between corporate governance and sustainable development, focusing on State-Owned Enterprises (SOEs) in Vietnam. Recognizing the pivotal role of SOEs in the national economy, this study employs a Multi-Criteria Decision-Making approach (MCDM) to assess and enhance the corporate governance frameworks of these entities. First, the Data Envelopment Analysis (DEA) model is employed to identify the most qualified prospective SOEs firms based on several quantitative criteria. Then, the spherical fuzzy analytic hierarchy process (SF-AHP) model is used to identify priority weights for a given set of qualitative criteria, the Evaluation based on distance from average solution (EDAS) model is implemented to rank enterprises in the SOEs sector. To validate the proposed models, a case study conducted within the Vietnamese electric power industry is utilized. The MCDM methodology integrates diverse factors such as business management, corporate social responsibility, and corporate governance shareholder to construct a comprehensive evaluation framework. By applying this approach, the study aims to identify the key drivers and barriers influencing corporate governance practices within Vietnamese SOEs. The study's findings illustrate the efficacy of the suggested approach in evaluating corporate governance performance, providing valuable insights for policymakers, corporate leaders, and stakeholders involved in shaping the governance landscape of SOEs in Vietnam. By aligning corporate governance with sustainable development principles, the research aims to contribute to the ongoing discourse on responsible business practices, offering practical recommendations to enhance the performance and resilience of SOEs in the pursuit of long-term socio-economic and environmental sustainability.

Modelling on assessment of flood risk susceptibility at the Jia Bharali River basin in Eastern Himalayas by integrating multicollinearity tests and geospatial techniques

AbstractClimate change and anthropogenic factors have exacerbated flood risks in many regions across the globe, including the Himalayan foothill region in India. The Jia Bharali River basin, situated in this vulnerable area, frequently experiences high-magnitude floods, causing significant damage to the environment and local communities. Developing accurate and reliable flood susceptibility models is crucial for effective flood prevention, management, and adaptation strategies. In this study, we aimed to generate a comprehensive flood susceptibility zone model for the Jia Bharali catchment by integrating statistical methods with expert knowledge-based mathematical models. We applied four distinct models, including the Frequency Ratio model, Fuzzy Logic (FL) model, Multi-criteria Decision Making based Analytical Hierarchy Process model, and Fuzzy Analytical Hierarchy Process model, to evaluate the flood susceptibility of the basin. The results revealed that approximately one-third of the Jia Bharali basin area fell within moderate to very high flood-prone zones. In contrast, over 50% of the area was classified as low to very low flood-prone zones. The applied models demonstrated strong performance, with ROC-AUC scores exceeding 70% and MAE, MSE, and RMSE scores below 30%. FL and AHP were recommended for application among the models in areas with similar physiographic characteristics due to their exceptional performance and the training datasets. This study offers crucial insights for policymakers, regional administrative authorities, environmentalists, and engineers working in the Himalayan foothill region. By providing a robust flood susceptibility model, the research enhances flood prevention efforts and management, thereby serving as a vital climate change adaptation strategy for the Jia Bharali River basin and similar regions. The findings also have significant implications for disaster risk reduction and sustainable development in vulnerable areas, contributing to the global efforts towards achieving the United Nations' Sustainable Development Goals.

Evaluating multi-hazard risk associated with tropical cyclones using the fuzzy analytic hierarchy process model

Multi-hazard events have received attention globally due to their increasing frequency and severity in recent years. The coastal region of Bangladesh is particularly vulnerable to multi-hazard events induced by tropical cyclones (TC), including coastal flooding, extreme precipitation, extreme winds, and salinity intrusion. These events inflict substantial damage on human lives and property, yet there has been limited effort to quantitatively assess the associated risks. This study aims to investigate the spatial distribution of multi-hazard risks stemming from TC events, employing a Fuzzy Analytic Hierarchy Process (FAHP) approach. Risk is assessed in relation to hazard, exposure, vulnerability, and mitigation capacities in the study area. Various indicators are selected to define each of these four risk components, with weights determined through expert input for FAHP modeling. The results indicate that more than 50% of the area faces multi-hazard risks, with the hazard component exhibiting the highest degree of risk association, followed by exposure, vulnerability, and adaptive capacity. Storm surge-induced flooding is identified as the most prominent hazard during TC events, followed by intense precipitation, extreme winds, and salinity intrusion. Areas characterized by high population density, a large number of vulnerable populations (e.g., those under 15 years or over 65 years), low elevation, and underdevelopment are found to be the most risk prone. Notably, the presence of hospitals, cyclone centers, and effective warning systems in proximity to an area enhances its potential to withstand multi-hazard impacts. Among the 19 coastal districts, Cox's Bazar and Feni are identified as the most risk prone. The framework and findings presented in this study offer valuable insights for the development and prioritization of multi-hazard risk mitigation policies by identifying the most vulnerable zones and the associated risk factors.

A novel parsimonious spherical fuzzy analytic hierarchy process for sustainable urban transport solutions

Sustainable urban transport is the key factor for surviving the cities and developing the supply quality of the urban transport system has been esteemed in sustainable improvement for the cities. This work attempts to provide a sustainable and efficient solutions for ameliorating public bus transport system in Dublin city, Ireland. The developed system will attract private car users, which interns will achieve detract CO2 emissions, minimize traffic congestions and maximize commuter satisfaction. To evaluate this complex problem, the novel Parsimonious Analytic Hierarchy Process (P-AHP) is structured in a spherical fuzzy environment. The parsimonious spherical fuzzy analytic hierarchy process (P– SF-AHP) model considers as an efficient solution not only for evaluating a large number of alternatives or criteria when using AHP, however, it esteems the hesitant scoring of the decision maker. The results are demonstrated and analyzed in detail and the step-by-step description of the procedure might foment other applications of the model. The unique process for evaluating the supply quality of urban transport system consumes less time and effort during estimating the survey, moreover, it provides more consistent and reliable outcomes through avoiding the uncertainty and ambiguity of decision makers during evaluation process.

Assessment and Mapping of Riverine Flood Susceptibility (RFS) in India through Coupled Multicriteria Decision Making Models and Geospatial Techniques

Progressive environmental and climatic changes have significantly increased hydrometeorological threats all over the globe. Floods have gained global significance owing to their devastating impact and their capacity to cause economic and human loss. Accurate flood forecasting and the identification of high-risk areas are essential for preventing flood impacts and implementing strategic measures to mitigate flood-related damages. In this study, an assessment of the susceptibility to riverine flooding in India was conducted utilizing Multicriteria Decision making (MCDM) and an extensive geospatial database was created through the integration of fourteen geomorphological, meteorological, hydroclimatic, and anthropogenic factors. The coupled methodology incorporates a Fuzzy Analytical Hierarchy Process (FAHP) model, which utilizes Triangular Fuzzy Numbers (TFN) to determine the Importance Weights (IWs) of various parameters and their subclasses based on the Saaty scale. Based on the determined IWs, this study identifies proximity to rivers, drainage density, and mean annual rainfall as the key factors that contribute significantly to the occurrence of riverine floods. Furthermore, as the Geographic Information System (GIS) was employed to create the Riverine Flood Susceptibility (RFS) map of India by overlaying the weighted factors, it was found that high, moderate, and low susceptibility zones across the country span of 15.33%, 26.30%, and 31.35% of the total area of the country, respectively. The regions with the highest susceptibility to flooding are primarily concentrated in the Brahmaputra, Ganga, and Indus River basins, which happen to encompass a significant portion of the country’s agricultural land (334,492 km2) potentially posing a risk to India’s food security. Approximately 28.13% of built-up area in India falls in the highly susceptible zones, including cities such as Bardhaman, Silchar, Kharagpur, Howrah, Kolkata, Patna, Munger, Bareilly, Allahabad, Varanasi, Lucknow, and Muzaffarpur, which are particularly susceptible to flooding. RFS is moderate in the Kutch-Saurashtra-Luni, Western Ghats, and Krishna basins. On the other hand, areas on the outskirts of the Ganga, Indus, and Brahmaputra basins, as well as the middle and outer portions of the peninsular basins, show a relatively low likelihood of riverine flooding. The RFS map created in this research, with an 80.2% validation accuracy assessed through AUROC analysis, will function as a valuable resource for Indian policymakers, urban planners, and emergency management agencies. It will aid them in prioritizing and executing efficient strategies to reduce flood risks effectively.

Hybrid Fuzzy AHP and Frequency Ratio Methods for Assessing Flood Susceptibility in Bayech Basin, Southwestern Tunisia

Flash floods are a significant threat to arid and semi-arid regions, causing considerable loss of life and damage, including roads, bridges, check dams and dikes, reservoir filling, and mudslides in populated areas as well as agricultural fields. Flood risk is a complex process linked to numerous morphological, pedological, geological, anthropic, and climatic factors. In arid environments such as where Bayech basin is located in southwestern Tunisia, the hydrometric data are insufficient due to the absence of measuring points. Using the hybrid fuzzy Analytical Hierarchy Process (F-AHP) and the frequency ratio statistical methods, this study aims to map flooding risks in an ungauged basin that is extremely prone to flooding. Data related to soil texture, slope, land use, altitude, rainfall, drainage density, and distance from the river were used in the risk analysis. The obtained flood risk maps from both F-AHP and FR models were validated on the basis of the Receiver Operating Characteristic (ROC), the Area Under the Curve (AUC), and the inventory map. Results revealed that areas of high and very high susceptibility to flooding are mainly located in the downstream part of the basin, where the town of Gafsa is located. Other upstream sites are also at risk. In this basin, slope is predominantly behind runoff accumulation, whereas soil type plays a major role in amplifying waterproofing and therefore overflow. The results derived from both methods clearly demonstrate a viable and efficient assessment in flood-prone areas. The F-AHP and FR methods have ROC values of 95% and 97%, respectively. Considering these results in the decision-making process, these outputs would enable the implementation of the necessary measures to mitigate flood risk impacts ensure sustainable development along with an effective management in Tunisian arid environments, for the well-being of local communities at risk.

Spherical fuzzy analytic hierarchy process and linear assignment model based MCGDM method with its application in ranking of states for their business climate

Ranking of the states for business climate is often done by survey analysis and hence prejudiced by untrustworthy and instable responses of the participants. In the present study, we integrate spherical fuzzy set (SFS) and linear assignment model (LAM) with analytic hierarchy process (AHP) to develop a multi criteria group decision making (MCGDM) method to rank Indian states for their business climate and potential index. The proposed MCGDM method is developed using state investment potential index (SIPI) published by the National Council of Applied Economic Research (NCAER), New Delhi to analyse the 'ease of doing business' aspects of various states based on six parameters. We take these six parameters, sub parameters as criteria and sub criteria in proposed MCGDM method and find their global weight using AHP method. Advantages of the proposed method are that it uses SFSs which are more efficient than other extensions of fuzzy sets in handling non-determinacy, and applies LAM to avoid subjectivity in the preferences of decision makers. We also do the sensitivity analysis of the ranking by changing the weights of the criteria and sub criteria to confirm the robustness of the proposed MCGDM method.

Multidimensional Evaluation of Consumers’ Shopping Risks under Live-Streaming Commerce

As a new e-commerce model, live-streaming commerce enhances consumers’ shopping experiences by providing deeply involved online interaction. However, in live-streaming commerce, consumers are also faced with many shopping risks, such as fake products, poor after-sales service, etc. Therefore, we propose an analysis framework to evaluate consumers’ shopping risks on live-streaming commerce platforms. In our framework, we first construct a multidimensional consumer shopping risk evaluation index system by considering different stakeholders involved in live-streaming commerce. Then, we assess consumer shopping risks based on an intuitionistic fuzzy analytic hierarchy process and cloud model. Our framework is applied to evaluate consumers’ shopping risks on four typical live streaming commerce platforms in China, i.e., Taobao, Douyin, Kuaishou, and JD.com. Our research results provide decision support for different parties involved in live-streaming commerce and thus promote the sustainable development of the live-streaming commerce industry.

On potential strategic framework for green supply chain management in the energy sector using [formula omitted]-rung picture fuzzy AHP & WASPAS decision-making model

The present communication deals with addressing the problem of handling the uncertainties in the process of green supply chain management system through an integrated approach that takes the information in the form of q-rung picture fuzzy sets and quantitatively blends it into the decision-making techniques of Analytic Hierarchy Process (AHP) and Weighted Aggregated Sum Product Assessment (WASPAS). The proposed model has been implemented in stage-wise form for outlining the potential strategic plans for green supply chain management in the energy sector. At every phase of the study, the detailed analysis has been presented in an interlinked manner for solving the underlying problem of decision-making. A brief sensitivity analysis and comparative study of the potential strategic plans with respect to the deterministic parameters have been performed for better understanding, consistency, and reliability. The findings of the propositions illustrate that while making prioritization for potential strategic plans, concentrating on utilizing customer relationship management to meet the needs of the customers is more prominent than examining the procedure for developing new services and products.

An unsupervised learning based MCDM approach for optimal placement of fault indicators in distribution networks

This paper proposes a novel integrated model based on multi-criteria decision-making (MCDM) method to assess and rank the feeder sections to optimally locate fault indicators in a large-scale distribution network. First, decision makers’ weights are computed based on the subjective weighting information. Then, dataset including data calculated for every alternative under active power flow, failure rate, repair time, number of customers, and customer type criteria are grouped using mixed-type clustering algorithm. This leads to the number of clusters extracted from the dataset, then obtained number of clusters are utilized to set the required linguistic terms. Finally, the ranking order of alternative is assessed by using the fuzzy similarity measure-based technique for order of preference by similarity to ideal solution (TOPSIS). The newly proposed model combines the merits of integrated enhanced particle swarm optimization (EPSO) fuzzy analytic hierarchy process (AHP) model in dealing with fuzziness and vagueness of linguistic assessments, the merits of clustering algorithm focused on representing performance values of each alternative with respect to each decision criterion and the merits of similarity measure-based TOPSIS in solving complex decision-making problems. The proposed model is applied to the RBTS4 and then sensitivity analysis is performed in the optimal solution.

Consuming perception analysis of the yoga wear using fuzzy AHP model

In this paper, we propose to analyse the consumer perception of yoga wear and then improve its design based on the demand-driven design principle. For this purpose, we break down the problem into three levels: Goal Level (development of the new yoga wear), Requirement Level, and Design Solution Level (garment design solutions corresponding to the requirements of the Requirement Level). For the requirement level, we take into account the FEA (functional, expressive and aesthetic) factors for the consumer perception analysis. Due to the hierarchical characteristics of the analysis, we propose a fuzzy AHP (analytic hierarchy process) approach for the related data analysis. By using the AHP model, we can analyse requirements alongside their solutions in design alongside their respective weights. 50 evaluators (yogis) and 20 designers took part in the experiments. Similar design solutions and performance evaluation of the solutions were defined through consumer research and subjective evaluation experiments with standard procedures. We hope that this paper will guide the analysis and development of yoga wear design.

Modelling of Wildfire Susceptibility in Different Climate Zones in Montenegro Using GIS-MCDA

Montenegro has different influences on the weather and climate; in general, according to Köppen’s classification, there are two climate zones: warm temperate (C) and cold temperate (D). The aim of this study is to determine the susceptibility to wildfires in the Montenegrin coastal municipality of Budva and the northern municipality of Rožaje, which are located in different climatic conditions, using multicriteria GIS decision analysis (GIS-MCDA). Nine natural and anthropogenic criteria were used for the analysis. Open geospatial data were used as input data for all criteria. The assignment of weighting coefficients for the criteria in relation to wildfire susceptibility importance was based on the Analytic Hierarchy Process (AHP) and Fuzzy Analytic Hierarchy Process (F-AHP) procedures. The results for the AHP and F-AHP models were obtained using the Weighted Linear Combination (WLC) method. According to the AHP model, the very high and high category covers 80.93% of the total area in Budva and 80.65% in Rožaje. According to the F-AHP model, the very high and high category occupies 80.71% of the total area in Budva and 82.30% in Rožaje. The validation shows that the models of GIS-MCDA perform fair in both climatic zones. The proposed models, especially in the absence of geospatial data, can be a strategic and operational advantage in the development of plans and strategies for protection against wildfires.

Risk assessment of seepage failure in deep excavations based on fuzzy analytic hierarchy process and cloud model

Risk assessment of seepage failure in deep excavations based on fuzzy analytic hierarchy process and cloud model

Management of the construction and demolition waste (CDW) and determination of the best disposal alternative by FAHP (Fuzzy Analytic Hierarchy Process): A case study of Tehran, Iran

ABSTRACT The study focused on the management of the final disposal of Construction and Demolition Waste (CDW) generated in Tehran and the determination of the best disposal method. For this purpose, three of final disposal alternatives such as reusing, recycling and landfilling were selected. Moreover, three effective criteria (environmental, economic and socio-cultural) and 16 sub-criteria were considered. A group of experts participated in the questionnaire in order to create a database. FAHP (Fuzzy Analytic Hierarchy Process) was used to determine the final disposal alternative considering to the sustainable development approach. The results obtained from the FAHP model showed that the weight of the environmental, economic and socio-cultural criteria was 0.330, 0.544 and 0.126, respectively. In the view of the environmental, the weight value of the sub-criteria of recyclable, water pollution, air pollution, soil pollution and natural resources protection were 0.035, 0.127, 0.069, 0.042 and 0.055, respectively. In terms of economic, the weight value of the sub-criteria of raw materials cost, land occupancy rate, profitability, mutual interests, exploitation cost and initial investment were 0.108, 0.045, 0.063, 0.083, 0.094 and 0.149 respectively. In addition, from the point of view of socio-cultural aspect, the weight value of the sub-criteria of community acceptance, government cooperation, people’s awareness level, security in construction and employment were 0.015, 0.050, 0.011, 0.022, and 0.026, respectively. The reuse alternative with a weight of 0.439 was selected as the best disposal method and the recycling (0.312) and landfilling (0.250) were second and third alternatives, respectively. The results also indicated that the generated CDW in Tehran was mostly composed of reusable components such as metals, plastics, wood, glass and gypsum. Therefore, with the selection of this alternative as a final disposal method, the costs of raw materials and the pollution originated from landfilling can be significantly reduced. Implication: The main purpose of this study is to provide a framework in which the priority of criteria and sub-criteria and alternatives in CDW management in Tehran, Iran is determined. The novelty of this method included providing an efficient management method in CDW management, because the production of this type of waste in Iran has become a major problem. The most important part of this method was the decision of local experts to provide the best disposal alternative, since solving problems related to CDW management is achieved by participation and collaboration with experts working in the same system. The obtained results showed that reusing is the first priority in terms of all the studied criteria and sanitary landfilling is the last priority. Sanitary landfilling is currently in place in the study area and respondents are well aware of its disadvantages. The results in terms of each criterion show that economic criteria are the most important criteria. Investment cost in terms of economic criteria, public acceptance in terms of social criteria and water pollution in terms of environmental criteria as the most effective sub-criteria according to the main goal. Various complex factors affect CDW management systems and therefore the use of practical decision-making techniques such as FAHP to deal with the complexity of CDW management will be useful and valuable.

Conceptual Framework for Blockchain Business Success Factors with Fuzzy Analytic Hierarchy Process Model

Blockchain technology is gaining popularity globally, and adoption is on the rise in Malaysia. The success of blockchain business in Malaysia depends on many key factors. By identifying the most influential factors, businesses can make informed strategic decisions that improve their chances of success in the market. Policymakers can also benefit from this research as it can guide them in creating an enabling environment for the growth and advancement of the blockchain industry in Malaysia. As a result, critical success factors are needed to be determined in order to accelerate the implementation of blockchain in organizations. Thus, a conceptual framework to identify the business success factors by using the Fuzzy Analytic Hierarchy Process model is proposed in this study. This research study is significant as the business success factors can be determined by the conceptual framework based on the Fuzzy Analytic Hierarchy Process model. The identification of business success factors for implementing blockchain is very crucial as it can guide policymakers and practitioners in comprehending the significance and importance of business success factors. Hence, viable strategies can be developed to make blockchain technology adoption in various sectors becomes successful.

Identifying Landslides Prone-Areas Using GIS-based Fuzzy Analytical Hierarchy Process Model in Ziz Upper Watershed (Morocco)

Identifying Landslides Prone-Areas Using GIS-based Fuzzy Analytical Hierarchy Process Model in Ziz Upper Watershed (Morocco)