Fuzzy Analytic Network Process Research Articles

EXTRACTING AND PRIORITIZING THE ATTRACTIVENESS PARAMETERS OF SHOPPING CENTERS UNDER INTUITIONISTIC FUZZY NUMBERS

Shopping center plays an important role in distribution system and marketing. These canters provide an appropriate atmosphere for customers; so that, the customers achieve the best service within a short time. However, there is an intense competition among shopping centers to attract more customers for increasing the profit. Therefore, a powerful model assists authorities in identifying the critical competitive aspects and directing their efforts toward performance improvement. However, a number of strategies have been developed to identify the most relevant components. The Delphi technique under intuitionistic fuzzy environment, called intuitionistic fuzzy Delphi method (IFDM) study, is a group-based technique that can simply formulate the uncertainty imposed by decision making circle. On the other hand, multi criteria decision making (MCDM) method such as analytical network process (ANP) is a mathematical tool for taking into account mutual relationships in order to rank a number of criteria. Nonetheless, the ANP is unable to account for the uncertainty involved in the decision-making process. Similarly, the intuitionistic fuzzy set (IFS) can express ambiguity and vagueness by utilizing the given scale. Because the IFS is robust in dealing with complexity and ambiguity, the IFS-GANP (an integrated model of the IFS and ANP methods under group decision) can result in a more specific description of the situation. As a result, the IFS-GANP approach outperforms both conventional ANP and fuzzy ANP. To demonstrate the model’s feasibility, a case study rating the essential aspects impacting the attractiveness of retail centers is shown. The result demonstrates factor C31 (Location) with value of 0.202 plays the greatest role in attracting customers.

Diagnosing the Organizational Climate of Junior High Schools in Taiwan

The study aims to prioritize critical climate components for school improvement. A psychometrically validated instrument named the Chinese “Organizational Climate Diagnostic Instrument for Junior High Schools” (OCDI-JH) was used to diagnose climate factors through expert judgments and fuzzy DEMATEL-based ANP. Results of the study identified the six factors clustered under the two dimensions of ‘Safety' (D1) or ‘Academic emphasis' (D2) should be given particular attention for school effectiveness. The established climate diagnosis appraisal computed by fuzzy DEMATEL-based ANP can be extended to other school regions for systematically developing school climate based on contextually specific needs and concerns. Future research studies are expected to provide incremental and value-added contributions to the competitive advantages of Taiwan's junior high school education.

An IT2FS-ANP- and IT2FS-CM-Based Approach for Conducting Safety Risk Assessments of Nuclear Power Plant Building Projects

This study proposes an approach for simultaneously treating fuzziness and randomness when conducting safety risk assessments of nuclear power plant building projects (NPPBPs). First, the interval type-2 fuzzy set (IT2FS) and analytic network process (ANP) were employed to establish an IT2FS-ANP method for modifying the solution for the weight vector and determining the mutual relationships between the assessment indexes and their weights. Second, the IT2FS and cloud model (CM) were used to establish the IT2FS-CM method to modify the solution for the expectation value. Third, the IT2FS-ANP and IT2FS-CM methods were combined to establish a model for ranking the safety risks and determining the overall safety risk level. Finally, the approach was applied to an NPPBP in northeast China to validate its applicability.

PROPOSING SOLUTIONS TO DEVELOP SUSTAINABLE AGRICULTURE TO ADAPT TO CLIMATE CHANGE USING THE T-FANP MODEL

The aim of this study is to propose priority strategies for agricultural development amidst drought in Ninh Thuan province, Vietnam. In the context of drought, the agricultural sector faces significant challenges due to the impact of both natural and socio-economic factors. In order to conduct this research and propose effective strategies, the study utilized the SWOT analysis model via the TOWS matrix, delineating 15 strategies to pinpoint the most fitting approach for the agricultural sector. The study quantified and ranked factors within the SWOT analysis and prioritized strategies using the Fuzzy Analytic Network Process (FANP). The outcomes identified advantageous strategies for agricultural production, emphasizing the evaluation and identification of drought while focusing on market development for economically valuable agricultural products based on primary crops. The step-by-step application of these prioritized strategies aims to contribute to the sustainable development of agricultural production in Ninh Thuan province and facilitate the utilization of a multi-criteria decision-making model in resource and environmental management.

An empirical analysis of evolutionary computing approaches for IoT security assessment

The Internet of Things (IoT) strategy enables physical objects to easily produce, receive, and exchange data. IoT devices are getting more common in our daily lives, with diverse applications ranging from consumer sector to industrial and commercial systems. The rapid expansion and widespread use of IoT devices highlight the critical significance of solid and effective cybersecurity standards across the device development life cycle. Therefore, if vulnerability is exploited directly affects the IoT device and the applications. In this paper we investigated and assessed the various real-world critical IoT attacks/vulnerabilities that have affected IoT deployed in the commercial, industrial and consumer sectors since 2010. Subsequently, we evoke the vulnerabilities or type of attack, exploitation techniques, compromised security factors, intensity of vulnerability and impacts of the expounded real-world attacks/vulnerabilities. We first categorise how each attack affects information security parameters, and then we provide a taxonomy based on the security factors that are affected. Next, we perform a risk assessment of the security parameters that are encountered, using two well-known multi-criteria decision-making (MCDM) techniques namely Fuzzy-Analytic Hierarchy Process (F-AHP) and Fuzzy-Analytic Network Process (F-ANP) to determine the severity of severely impacted information security measures.

Investigating the Human Reliability in the Healthcare Sector Using the Fuzzy Analytic Network Process and the Success Likelihood Index Method

Background: We aimed to identify the factors contributing to human error in hospital emergency departments using scientific methods.
 Methods: We used the Fuzzy Analytical Network Process (FANP) and Success Likelihood Index Method (SLIM) to investigate human reliability in 54 hospital emergency departments in 15 provinces of Iran from 2021 to 2022.
 Results: The study classified 17 general factors affecting human errors in hospital emergency departments. Organizational (0.349), occupational (0.330), and personal factors (0.320) had the most significant impact on human error. Based on a matrix of paired comparisons for nine emergency tasks using the probability of success index method, "checking test results and diagnosis" had the highest probability of error when referring patients to intensive care or discharge. Although the study prioritized patients, there was still a cumulative probability of human error before disease diagnosis at 0.01332, highlighting the need for further training to minimize these risks.
 Conclusion: The FANP and SLIM were effective in identifying the factors contributing to human error in hospital emergency departments. Doctors and nurses working in these departments require more knowledge, experience, and responsibility to avoid errors. By identifying factors influencing the occurrence of human error and finding solutions to reduce risks, hospitals can improve the quality of their care and prevent errors.

An Integrated Fuzzy MCDM Method for Assessing Crisis Recovery Strategies in the Supply Chain

Making decisions and building strategies to enhance the supply chain are currently essential if companies want to keep up with modern advances and compete in both the local and global markets. However, the coronavirus pandemic heightened the necessity for decision making and the adoption of strategies for supply chain improvement and transformation to effectively deal with crises and ongoing change. This paper introduces a framework for recovery strategies to characterize the correlation at various stages among the key concerns of the supply chain because of COVID-19, recovery decisions, the areas of development, the strategic recovery plan, and the objectives of the recovery strategies to restore the supply chain (SC). This study used a combination of quantitative and qualitative techniques to identify possible areas for supply chain improvement and rank viable solutions. A combined fuzzy ANP–fuzzy TOPSIS method is proposed to prioritize and rank the areas of improvement. The results indicate that digitization and the utilization of emerging technology play critical roles in supply-chain recovery from disruptions and different current and future SC changes.

Spherical fuzzy sets based integrated DEMATEL, ANP, VIKOR approach and its application for renewable energy selection in Turkey

Renewable energy continues occupying the global agenda for the low-carbon transition. Identifying suitable sources for renewable energy involves different criteria and alternatives, which can be defined as a Multi-Criteria Decision-Making (MCDM) problem. MCDM methods can be enriched with Group Decision Making (GDM) to merge individual judgments into group opinions, as well as with fuzzy sets to better simulate human opinions. To handle uncertainties of DMs’ preferences, a recently developed fuzzy set extension, the Spherical Fuzzy Sets (SFSs), is combined in this study with different MCDM tools to establish a decision support tool for the energy source selection problem. This article proposes a novel MCDM approach that integrates SF-DEMATEL (Spherical Fuzzy Decision-Making Trial and Evaluation Laboratory), SF-ANP (Spherical Fuzzy Analytic Network Process), and SF-VIKOR (Spherical Fuzzy Vlse Kriterijumska Optimizacija Kompromisno Resenje) algorithms in a GDM environment and presents its application in a case study for selecting renewable energy sources in Turkey. Comparing wind, geothermal, solar, hydropower, and biogas, the proposed method highlighted both wind and solar energy as a compromise solution as the most suitable options for Turkey. It has been found that compared to similar fuzzy sets, SFS can better reflect the hesitation degree to address the lack of knowledge and errors in the membership function definitions. The robustness and plausibility of these findings are demonstrated with sensitivity and comparative analyses, suggesting this MCDM method can also be applied to similar problems in other settings.

Forest fire risk zoning based on fuzzy logic and analytical network process.

Forest fires have a significant impact on human life, property safety, and ecological environment. Deve-loping high-quality forest fire risk maps is beneficial for preventing forest fires, guiding resource allocation for firefighting, assisting in fire suppression efforts, and supporting decision-making. With a multi-criteria decision analysis (MCDA) method based on geographic information systems (GIS) and literature review, we assessed the main factors influencing the occurrences of forest fires in Youxi County, Fujian Province. We analyzed the importance of each fire risk factor using the analytic network process (ANP) and assigned weights, and evaluated the sub-standard weights using fuzzy logic assessment. Using ArcGIS aggregation functions, we generated a forest fire risk map and validated it with satellite fire points. The results showed that the areas classified as level 4 or higher fire risk accounted for a considerable proportion in Youxi County, and that the central and northern regions were at higher risk. The overall fire risk situation in the county was severe. The fuzzy ANP model demonstrated a high accuracy of 85.8%. The introduction of this novel MCDA method could effectively improve the accuracy of forest fire risk mapping at a small scale, providing a basis for early fire warning and the planning and allocation of firefighting resources.

Sustainable development during economic uncertainty: What drives large construction firms to perform corporate social responsibility?

AbstractWhile the world is grappling with economic uncertainty, corporate social responsibility (CSR) has been extensively studied with a focus on favorable economic conditions. This study aims to investigate and prioritize the drivers for CSR in large‐sized construction organizations during economic uncertainty. Based on the literature review and discussions with experienced professionals, 13 critical drivers for CSR were identified and categorized into four clusters. Using the combination of fuzzy Decision Making Trial and Evaluation Laboratory and fuzzy Analytic Network Process (DANP), drivers for CSR performance during economic uncertainty were ranked regarding their interrelationships. Among 13 drivers, the results illustrated that enhancing company reputation was the most critical driver for CSR, followed by long‐term economic benefit, employee commitment and loyalty, and credibility gain. This study expands the current understanding of CSR practices in the construction industry, shedding light on its unique characteristics during economic uncertainty.

Determination of multi-UAVs formation shape: Using a requirement satisfaction and spherical fuzzy ANP based TOPSIS approach

Multiple unmanned aerial vehicles (multi-UAVs) formation shape refers to the geometric shape when multi-UAVs fly in formation and describes their relative positions. It plays a necessary role in multi-UAVs collaboration to improve performance, avoid collision, and provide reference for control. This study aims to determine the most appropriate multi-UAVs formation shape in a specific mission to meet different and even conflicting requirements. The proposed approach introduces requirement satisfaction and spherical fuzzy analytic network process (SFANP) to improve the technique for order preference by similarity to ideal solution (TOPSIS). First, multi-UAVs capability criteria and their evaluation models are constructed. Next, performance data are transformed into requirement satisfaction of capability and unified into a same scale. Qualitative judgments are made and quantified based on spherical fuzzy sets and nonlinear transformation functions are developed for benefit, cost, and interval metrics. Then, SFANP is used to handle interrelationships among criteria and determine their global weights, which takes decision vagueness and hesitancy into account and extends decision-makers’ preference domain onto a spherical surface. Finally, alternative formation shapes are ranked by their distances to the positive and negative ideal solution according to the TOPSIS. Furthermore, a case study of 9 UAVs performing a search-attack mission is set up to illustrate the proposed approach, and a comparative analysis is conducted to verify the applicability and credibility.

Overhead crane bridge structure design optimization based on safety assessment

In order to achieve safe and economical design results for crane bridge structures, an optimization method with safety assessment constraints is proposed. In this approach, the limit state method is used to test the structure design. Safety assessment is conducted based on the fuzzy analytic network process, allowing the safety score of designs to be quantified. Aiming to minimize the self-weight of the bridge, the dimensions of critical sections are regarded as design variables and constraints related to the process size, strength, stiffness, stability, and safety score are imposed, utilizing the artificial hummingbird algorithm for the structure optimal design. After validating through an engineering example, the results demonstrated that under the settled safety constraints, the proposed optimization method successfully reduced the structural self-weight by 19.709%, while maintaining a safety score proximate to the original design. In comparison to optimization without safety assessment constraints, this method resulted in a 10.441% increase in weight, but its safety was significantly improved by 21.740%. This validates the effectiveness and practicality of incorporating safety assessment into structure optimization, thereby ensuring a balanced trade-off between safety and material economy in the design of crane bridge structures.

Utilizing the Fuzzy Analytic Network Process Technique to Prioritize Safety Challenges in Construction Projects

Utilizing the Fuzzy Analytic Network Process Technique to Prioritize Safety Challenges in Construction Projects

Utilising the fuzzy analytic network process technique to prioritise safety challenges in construction projects

Utilising the fuzzy analytic network process technique to prioritise safety challenges in construction projects

Identification and Prioritization of Factors Affecting the Cultural Model of Good Governance in Bodies Overseeing Government Performance Using Fuzzy ANP Method

Identification and Prioritization of Factors Affecting the Cultural Model of Good Governance in Bodies Overseeing Government Performance Using Fuzzy ANP Method

Evaluating the campus climate factors using an interval type-2 fuzzy ANP

Evaluating the campus climate factors using an interval type-2 fuzzy ANP

Development of a new index for assessing the inherent safety level of chemical processes using a multi-criteria fuzzy decision-making approach

Estimating and evaluating the inherent safety of processes pose a significant challenge in the field of inherent safety. Indicators are commonly employed tools to address this challenge. However, one of the weaknesses of risk-based indicators is their assumption of equal weight and importance for all indicators, despite the fact that the impact of each indicator may vary. This study aimed to investigate whether assigning weights to indicators can enhance the performance of inherent safety indices. To achieve this objective, all indicators that influence inherent safety were identified through systematic literature review and expert interviews. Subsequently, the final weights of these indicators were determined using multicriteria decision-making methods such as BWM, DEMATEL, and fuzzy ANP. By integrating these indicators, a Weighted Inherent Safety Index (WISI) was developed, accompanied by two subsidiary inherent safety indices: process inherent safety (consisting of 5 indicators) and chemical material inherent safety (consisting of 9 indicators). The WISI index serves as a quantitative measure to assess various synthesis routes of a chemical substance and determine the safer option during the conceptual design phase. In a case study, the production process of methyl methacrylate involving 6 different synthesis routes was selected, and the WISI index was applied to validate its effectiveness. Overall, this approach introduces a novel perspective to enhance the performance of inherent safety indices by assigning appropriate weights to indicators, thereby promoting a more comprehensive evaluation of process safety.

APPLICATION OF FUZZY ANALYTICAL NETWORK PROCESS IN DETERMINING THE CHOICE OF AREAS OF INTEREST

The Untan Statistics Study Program offers students a choice of areas of interest to develop competencies, attitudes, and skills. This study aims to analyze the decision to determine the choice of field of interest according to lecturers and students using the Fuzzy Analytical Network Process (FANP) method. A combination of ANP methods and Fuzzy logic, FANP is used to model and analyze complex networks of several factors determining the choice of areas of interest. The step in this study begins with the determination of the criteria and sub-criteria used for tissue formation. Then a comparison was carried out in pairs using the Fuzzy scale, so that the calculation of the global weight value of each criterion and sub-criteria was obtained. The resulting weight can be used for decision making. Data in research affects the opinions of lecturers and students. The decision obtained using the FANP method in this study is in the opinion of lecturers and students that the fields of business and finance are priority alternatives with the highest weight of 44.5%. The second priority with a weight of 37.5%, namely social and industrial interests, and the environmental and disaster sector occupies the last priority with a weight of 18%.

Multi-attribute fire safety evaluation of subway stations based on FANP – FGRA – Cloud model

With the rapid development of subway network, the fire safety of subway stations is becoming outstandingly important in the operation process. But there seem to be few studies developing quantitative models in the existing approaches. To this end, this study aims to develop a multi-attribute hybrid method by combining Fuzzy Analytic Network Process (FANP), and Fuzzy Grey Relational Analysis (FGRA) with Cloud model to evaluate fire safety of them. This research comprehensively sorts out and identifies high-frequency hazard factors associated with fire accidents at stations from personal, equipment, environmental and management aspects, and simultaneously explores dependencies and interactions among them. Seven typical transfer stations of Changsha subway network in China are studied by using the proposed approach, and the results show that: (1) the fire safety order from the best to the worst determined is FBH (A2), SWGY (A7), CSRS (A6), YWZ (A1), HTL (A5), HJT (A4) and WYGC (A3); (2) through comparing the results obtained by the proposed method with that determined fuzzy TOPSIS and fuzzy sum weighted method, it is proved that the method developed in this study is reliable and effective; and (3) sensitivity analysis indicates that the ranking order of stations is influenced by the fluctuation of criterion and sub-criterion weights.

An Integrated Fuzzy Analytic Network Process and Fuzzy Regression Method for Bitcoin Price Prediction

Predicting the prices of cryptocurrencies is more complicated than that of classical financial assets because they do not seem to have reached the maturity stage of their life. In addition, many known and unknown factors may affect Bitcoin prices; these factors and their importance seem to be changing faster than other financial assets. Therefore, the data used to predict the prices of cryptocurrencies can be considered big data challenging to manage due to their volume, variety, and variability. This study presents an integrated approach to managing the data when predicting the Bitcoin price. We first prepare a list of factors affecting the Bitcoin price. We then use the Fuzzy Analytic Network Process (FANP) to screen these factors and select the most important ones based on the experts’ opinions. The selected factors are considered independent variables affecting the Bitcoin price. Next, we extract a fuzzy regression model using the historical data in which the Bitcoin price is considered the dependent variable. Finally, this model is validated with different confidence levels, and the appropriate level is selected to predict the Bitcoin price. The results show that Bitcoin prices fall within the forecasting intervals obtained from the fuzzy regression model for a 99% confidence level. Unlike crisp regression models, the fuzzy regression model used in this study does not predict the Bitcoin price as a crisp value; instead, it predicts the price as an interval value. The contributions of this study are fourfold: (1) identifying the factors affecting the Bitcoin price and investigating their mutual impacts on each other; (2) determining the most influential factors using the FANP method; (3) using fear and greed as essential sentimental independent variables in regression to predict the Bitcoin price; (4) and predicting the Bitcoin price as an interval instead of a crisp value.