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The urgent need to move toward a more sustainable and low-carbon economy has gained increased attention in recent years. There is a global drive to reduce greenhouse gas emissions and address the causes of climate change since its effects are becoming more and more obvious. The study identified several potential green infrastructure projects that could have a significant positive impact on sustainable development in China. The core criteria and sub-criteria for impact investing in green infrastructure were determined by the panel through a Delphi process, and they were then analyzed and weighted using the fuzzy Analytical Hierarchy Process (AHP) method. According to the study's findings, environmental impact was considered the most crucial factor when making decisions to invest in green infrastructure. The economic and social impacts are indicated as the second and third influential factors, respectively. In order to promote impact investing in green infrastructure for sustainable development in China, the study offers several policy recommendations. These recommendations include developing rules and regulations that encourage private sector investment, raising public awareness and participation, forming alliances between the public and private sectors, offering financial support and incentives, and building programs that will help China's green infrastructure industry build its capacity. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The fifth industrial revolution emphasizes human-robot collaboration to enhance transformation processes. While collaborative robots (cobots) are increasingly attracting interest in various industries, integrating them into assembly lines remains challenging. This paper aims to investigate whether cobots are replacing traditional robots or if a synergy between both technologies is essential in the era of Industry 5.0 to maximize benefits. The real case study presented provides a comparison between robots and cobots in an automotive factory using the Fuzzy AHP methodology. The results indicate that cobots are advantageous in production units with low-volume and high-variability tasks. However, they fall short of the reliability, precision, and productivity that traditional robots offer for repetitive tasks. Finally, a framework is proposed to guide decision makers in adopting the suitable solution to their needs while ensuring optimal performance as well as workers wellbeing. [ABSTRACT FROM AUTHOR]

The objective of the study was to investigate different factors that influence the intention to purchase green vehicles and prioritize these factors to identify the most critical factor. The use of green vehicles, such as electric or hybrid vehicles, can significantly reduce greenhouse gas emissions, air pollution, and dependence on fossil fuels. Therefore, purchasing green vehicles can contribute to sustainable development by reducing the negative impact of transportation on the environment. Fuzzy AHP is an extension of the traditional AHP method that incorporates fuzzy logic. AHP breaks down complex problems into hierarchies of criteria and alternatives, enabling evaluation and prioritization. The study used fuzzy AHP to rank factors influencing individuals' intention to purchase green vehicles, based on 302 responses. Fuzzy AHP allows for flexible assessment considering uncertainties in decision-making. Harman's single-factor test was conducted to verify common method bias. The results revealed six factors that affect intentions to purchase green vehicles, with environmental concern (EC) being the most crucial and mass media influence (MMI) the least significant factor. However, as the study was conducted in India only, the generalizability of the findings to other countries may be limited. Additionally, the study is cross-sectional, and several factors that could have a significant impact on customers' purchase intentions are not included in the analysis. The study has implications for managers, practitioners, and government agencies. The use of fuzzy AHP in the proposed conceptual framework adds to the literature by providing a better empirical understanding of the primary determinants of customers' purchasing behaviour concerning green vehicles in India. The research also introduces fuzzy AHP as one of the multi-criteria decision-making (MCDM) techniques in the field of green vehicle purchasing, offering a comprehensive view of the significant motivations behind people's favourable attitude towards green vehicles from a theoretical perspective. [ABSTRACT FROM AUTHOR]

The fifth industrial revolution emphasizes human-robot collaboration to enhance transformation processes. While collaborative robots (cobots) are increasingly attracting interest in various industries, integrating them into assembly lines remains challenging. This paper aims to investigate whether cobots are replacing traditional robots or if a synergy between both technologies is essential in the era of Industry 5.0 to maximize benefits. The real case study presented provides a comparison between robots and cobots in an automotive factory using the Fuzzy AHP methodology. The results indicate that cobots are advantageous in produc- tion units with low-volume and high-variability tasks. However, they fall short of the reli- ability, precision, and productivity that traditional robots offer for repetitive tasks. Finally, a framework is proposed to guide decision makers in adopting the suitable solution to their needs while ensuring optimal performance as well as workers wellbeing.

Abstract 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.

Implementing the right strategies in the agricultural supply chain in the supply of seeds, pesticides, fertilizer, energy, fuel and agricultural mechanization tools and equipment has a great role in increasing agricultural productivity. The main purpose of the study is to rank and evaluate alternatives in choosing a sustainable fertilizer supplier in the agricultural supply chain by using AHP-F and ARAS-F techniques. In an environment of uncertainty and complex supply chain structure, multi-criteria decision making (MCDM) methods are widely used to solve supplier selection problems. In this study, the importance levels and weights of the criteria in the selection of sustainable fertilizer suppliers were measured by the AHP-F method. The criteria that are important for fertilizer supplier selection were evaluated by taking expert opinions, the uncertain and uncertain opinions of the decision makers were modeled with the AHP-F approach and the weights of the criteria were determined. Among the criteria, resource consumption (FSC05) has the highest weight. Then, alternative rankings were obtained with the ARAS-F method. Fertilizer supplier alternatives in the agricultural supply chain were ranked with the ARAS-F method, using the criterion weights found with AHP-F. In the ranking of alternatives, alternative fertilizer supplier FS03 ranked first with the highest value. This study provides a resource for businesses and other stakeholders to make decisions regarding sustainable fertilizer supplier selection.

Abstract Annually, Kogi State in Nigeria experiences significant flooding events, leading to serious fatalities, the destruction of livelihoods, and damage to vital infrastructure. This study presents a multi-faceted approach and methodology to generate a state-wide flood risk map by analyzing both flood vulnerability and flood hazard factors. Seven flood hazard factors (drainage length, distance to river, elevation, slope, rainfall, distance from confluence/dam area, and geomorphology) and vulnerability factors (population density, female population, land cover, road length, distance from hospitals, literacy rate, and employment rate) were ranked and weighted based on their contributions to flooding within the state using the Fuzzy Analytical Hierarchy Process (FAHP). From these, the Flood Hazard Index (FHI), Flood Vulnerability Index (FVI), and Flood Risk Index (FRI) were derived. Results showed that Kabba, Idah, Olamabor, Kotonkar, and the southern part of Ajaokuta LGAs exhibit high flood vulnerability due to dense populations, remoteness from roads and critical infrastructure, and considerable distances from healthcare facilities. Likewise, LGAs exhibiting a very high FHI occur along the geographic zones bounded by the confluence of the Niger and Benue rivers, specifically along the Lokoja area, and the Kotonkar, Kogi, Bassa, and Ibaji LGAs. Five flood risk classes—very low, low, moderate, high, and very high FRI classes—occupy 26.82, 31.12, 22.07, 15.26, and 4.71% of the area, respectively. Out of 295 villages, 65 villages are spread across the high flood risk zone. The safest LGAs include Ankpa, Omala, Dekina, Ijumu, Kabba, and Mopa-Muro LGAs.

Abstract Decision makers consistently face the challenge of simultaneously assessing numerous attributes, determining their respective importance, and selecting an appropriate method for calculating their weights. This article addresses the problem of automatic generation control (AGC) in a two area power system (2-APS) by proposing fuzzy analytic hierarchy process (FAHP), an multi-attribute decision-making (MADM) technique, to determine weights for sub-objective functions. The integral-time-absolute-errors (ITAE) of tie-line power fluctuation, frequency deviations and area control errors, are defined as the sub-objectives. Each of these is given a weight by the FAHP method, which then combines them into an single final objective function. This objective function is then used to design a PID controller. To improve the optimization of the objective function, the Jaya optimization algorithm (JOA) is used in conjunction with other optimization techniques such as sine cosine algorithm (SCA), Luus–Jaakola algorithm (LJA), Nelder–Mead simplex algorithm (NMSA), symbiotic organism search algorithm (SOSA) and elephant herding optimization algorithm (EHOA). Six distinct experimental cases are conducted to evaluate the controller’s performance under various load conditions, with data plotted to show responses corresponding to fluctuations in frequency and tie-line exchange. Furthermore, statistical analysis is performed to gain a better understanding of the effectiveness of the JOA-based PID controller. For non-parametric evaluation, Friedman rank test is also used to validate the performance of the proposed JOA-based controller.

Organic coffee robusta is a leading commodity produced by farmers because it has a significant enough demand. Organic coffee robusta is sold as green beans or processed coffee roasted and ready to brew. Supply chain management is essential considering that agricultural commodities have perishable, seasonal, wide variety, and bulky limitations, thereby increasing the risk of supply chain activities. Complexity in the supply chain creates various risks that must be minimized and mitigated. This study aims to analyze the supply chain risk of organic robusta coffee and determine the risk mitigation strategy of the organic robusta coffee supply chain with a fuzzy approach. Identification and mitigation of upstream to downstream risks are needed to achieve the objectives of the organic coffee supply chain in Pasrujambe District, Lumajang Regency. Supply chain risk identification is carried out using the Fuzzy Failure Mode and Effect Analysis (FFMEA) method and determining risk mitigation strategies using the Fuzzy Analytic Hierarchy Process (FAHP) method. Both methods use expert respondents consisting of 3 farmers, 3 large collectors, and 3 experts in roastery. The results of the identification of the highest supply chain risk at the farmer level are the risk of non-uniform coffee fruit size (5.74), at the large collector level is price fluctuation (4.79), and at the roastery, the level the failure to regulate roasting temperature and time with an fuzzy risk priority number (FRPN) value of 5,26. Therefore, to minimize and mitigate these priority risks at the farmer level, increasing knowledge about organic coffee cultivation is necessary. At the large collector level, it is necessary to build institutional internalization stability, and at the roastery level, to improve the quality of the roastmaster.

Quality monitoring in the welding process of ship construction has been carried out using a rejection ratio. However, this approach is ambiguous and does not provide a clear indication of whether the welding process is under control. Statistical quality control (SQC) is a more effective method of quality monitoring that can provide a more definitive assessment of the welding process. In this paper, we propose a new SQC method for the welding process based on the demerit chart. The demerit chart is a statistical tool that uses a weighted sum of defect sizes to assess the quality of a product. We have developed a new demerit chart that is based on the analytic hierarchy process (AHP), a decision-making method that can be used to assign weights to different factors. We evaluated the performance of our new demerit chart using data from a ship construction project. The results showed that our demerit chart was able to detect welding defects that were not detected by the rejection ratio method. We also found that our demerit chart was more sensitive to changes in the welding process than the rejection ratio method. Our results suggest that the demerit chart is a more effective method of quality control for the welding process than the rejection ratio method. The demerit chart is capable of detecting welding defects that are not detected by the rejection ratio method, and it is more sensitive to changes in the welding process. [ABSTRACT FROM AUTHOR]

Despite presenting several advantages in relation to the mortar coating performed by hand labor traditionally carried out in Brazil, there is little knowledge available about mortar pumping systems. Thus, selecting a mortar pump system is not a trivial procedure, and there are no significative studies published in the specialized literature to fully understand the decision criteria applying to this equipment selection. Therefore, the present study aimed to identify the relevant criteria and quantify their importance in the selection of this equipment, by developing a framework combining both mortar pump specialists expertise and fuzzy analytic hierarchy process method to aid this decision making process. Results showed that acquisition cost and quality are the most relevant criteria, representing togheter around 2/3 of the decision weight, while technical and operational aspects account for aproximatelly 1/10 of relevance in the equipment selection. [ABSTRACT FROM AUTHOR]

Investing in a machinery park is a crucial decision process for businesses, as it requires significant resources and subsequently affects all production activities. Proper utilization of machinery improves production processes, increases efficiency and reliability, and enables flexibility and effectiveness in manufacturing. This process, which requires experience and knowledge, encompasses multiple criteria and involves various departments. This research aims to solve the printing machine selection problem using fuzzy multi-criteria decision-making methods, for an investment in a newspaper printing facility. Fuzzy AHP is employed to determine the importance weights of criteria, while fuzzy EDAS is used to evaluate alternatives based on criteria. The criteria considered in the study, based on expert opinions and literature review, are as follows: cost, efficiency, and flexibility. Four different printing machines that are deemed suitable for the process and feasible for purchase are selected as machine alternatives. The decision-making group consists of the printing manager, business development manager, technical manager, and electrical and mechanical maintenance engineers. Sensitivity analysis results conducted to assess the impact of linguistic expression variations on the analysis outcomes demonstrate a high level of validity in the research. [ABSTRACT FROM AUTHOR]

Advances in technology have converted the traditional paper-based bill of lading into an electronic bill of lading (eBL) format in recent years. In this regard, different eBL systems have been introduced and are increasingly being employed in the maritime transportation industry. In line with this, some major container shipping lines have started to declare their eBL services to their customers recently. However, it has been observed that there have not been come across any Türkiye origin container shipping lines to utilize eBL systems. Therefore, considering container shipping lines of Türkiye, this paper sought to disclose the barriers to the transition of eBL systems in Türkiye. The barriers have been revealed through literature review and confirmed by industrial experts. Following that Fuzzy-AHP method was performed to prioritize their importance quantitatively. It was revealed that the legal barrier among the main barriers appeared as the most important barrier. [ABSTRACT FROM AUTHOR]

Fire frequencies have increased in the natural ecosystems of northeastern Iran because of natural and human factors in the recent years. This study models and maps the danger of fire in natural ecosystems in Northern Khorasan Province in northeastern Iran. First, the hierarchical structure of the main fire-influencing factors (physiographic, climatic, vegetative, and human-made factors) and their sub-factors was determined. The data describing these effective factors were acquired from different sources (satellite images, field survey, etc.) and were converted into the maps in raster format (30 m by 30 m pixel size) in the GIS. A map of past fires in the province was developed from data of the Northern Khorasan Natural Resources Administration and from the fire product of the MODIS (Moderate Resolution Imaging Spectroradiometer) sensor. The weights of each of the factors and sub-factors were determined using FAHP (Fuzzy Analytic Hierarchy Process). A fire-danger map was obtained by overlapping all effective-factor maps in the GIS. The raster values were categorized into five fire-danger classes. The results were validated by comparison to the past fires using OA (Overall Accuracy), and kappa index. Results demonstrated that the human-influence factor, with weight of 0.48, is the most effective variable in fire-danger modeling in the Northern Khorasan Province. The fire-danger map indicated that very high-danger and high-danger areas cover 54.65% of the province, a spatially extensive amount of land having high fire likelihood. In statistical terms, the accuracy of fire danger map was high with OA of 96%, and the kappa index of 0.85, reflects the high accuracy of fire-danger prediction in the study area. The fire danger map of the study area provides a baseline for informed fire management, mitigation, and prevention decisions in the natural ecosystems of Northern Khorasan Province. This map could be very useful for forecasting future fires in northeastern Iran. [ABSTRACT FROM AUTHOR]

This feasibility study explores the viability of solar PV-powered Reverse Osmosis (RO) desalination in five locations in Visakhapatnam, India. The assessment integrates technical, economic, environmental, social, and political considerations using Multi-Criteria Decision Analysis (MCDA) with Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) and Fuzzy Analytic Hierarchy Process (Fuzzy AHP) methods to handle uncertainties in decision-making. The study evaluates the technical feasibility of the integration, conducts economic analysis, examines environmental impacts, investigates social benefits, and challenges, and analyzes the political landscape. The former emphasizes the significance of understanding challenges and potential solutions associated with RO desalination, aiming for sustainable development aligned with local and global goals. Yarada and Bheemili were the most suitable locations selected based on Fuzzy TOPSIS and Fuzzy AHP, respectively. The study also highlighted the need for public awareness and government support for desalination projects. [ABSTRACT FROM AUTHOR]

Using enterprise resource planning provides benefits and added value regarding enterprise processes' efficiency, integration, and effectiveness. However, while selecting the ERP software, parameters in various categories, such as the enterprise's requests, the services of the consultant firm, and the employee's participation, should be considered. When determining between options, the parameters taken into consideration may differ from one another or be imprecise. This study aims to ascertain the criteria that Small and Medium-Sized Enterprises (SMEs) that intend to invest in ERP software should pay attention to when selecting ERP software. The ERP usage process of the enterprise, the success of ERP within the company, the ERP installation process, the ERP company's support service, and the focus on the parameters of meeting the expectations from the ERP system were made. The obtained information was evaluated with the fuzzy AHP technique. It was decided that the most significant criterion that the enterprise should pay attention to in the selection of ERP is the success of ERP within the company and meeting the expectations of the ERP system, and a suggestion was presented in this direction. [ABSTRACT FROM AUTHOR]

Electronic banking grants users access to banking services virtually and offers convenience, ease of use, low cost, time saving, fast delivery and online bill payment. These services are mostly provided through websites; hence evaluation of its quality and services are very important. Website quality evaluation entails numerous dimensions and attributes and the processes which are critical as well as complex requires quality, accuracy and objectivity. This paper surveys the criteria and methodologies that have been used over years in e-banking websites quality as well as e-services quality evaluation. The survey covers articles within a five-year period from 2018 - 2022 which mostly focus on e-banking websites quality evaluation. Twenty-three articles which met the inclusion and exclusion criteria set by the authors are used for the study. Results show that the average number of criteria used in most studies are six with security, privacy and ease-of-use taking the lead as the mostly used criteria across different studies. Also, various types of multi-criteria decision-making model were used by different and there is none that is dominant among them. It was observed that fuzzy type of the various types of multi-criteria decision-making models are mostly used in many studies. In general, studies with focus on e-banking websites quality are not yet given much attention in the literature. [ABSTRACT FROM AUTHOR]

In this study, we address a critical aspect of the routing protocol for low-power and lossy Networks (RPL), namely the selection of the parent node, which plays a pivotal role. As IoT networks expand rapidly, tackling data congestion becomes increasingly crucial. The conventional RPL algorithm, initially designed for smaller networks, lacks mechanisms for balancing loads during parent–child node assignment and does not consider congestion scenarios. To overcome these limitations, we propose a novel objective function (OF) tailored specifically for the RPL algorithm. This OF integrates network load and congestion conditions using an Adaptive fuzzy multi-criteria decision-making approach, combining fuzzy analytic hierarchy process and technique for order of preference by similarity to ideal solution (TOPSIS) techniques. By redefining the process of selecting parent nodes, our approach enhances the efficiency of data transmission, alleviates congestion, and optimizes the performance of IoT networks. Our method introduces a multi-criteria decision-making framework for parent node selection, ensuring that the chosen parent node is both free of congestion and balanced in load, resulting in efficient forwarding of data packets. We prioritize parent node selection based on five crucial criteria, directly addressing load and congestion challenges in IoT networks. Through fuzzy AHP, we determine the relative importance of each criterion, while the TOPSIS method aids in ranking alternatives. This comprehensive approach provides a robust solution to mitigate network congestion, optimize load distribution, and enhance IoT network performance amidst dynamic growth. Implementing the algorithm using Contiki OS and the Cooja simulator, our results demonstrate 0–15% reduction in delay, 20–30% lesser energy consumption, and 10–25% reduction in packet overflow rate while maintaining network throughput by 15% as compare to CQARPL, CAFOR and QHCA and enhancing overall performance. [ABSTRACT FROM AUTHOR]

This study aims to verify the main factors influencing turnover intention in the Iran hospitality industry. The objective of this study is to construct a fuzzy AHP and fuzzy TOPSIS model to evaluate the dimensions of the hotel employee turnover intention model. The performance evaluation for employee turnover intention includes work itself, supervision, coworkers relationship, salary and benefit, career opportunities, job stress, perceived risk, and job insecurity. These dimensions generate a final evaluation for ranking priority among the employee turnover intention of the proposed model. The importance of dimensions is evaluated by 20 experts, and decision-making is processed through the fuzzy concept and fuzzy environment. From the critical fuzzy AHP and fuzzy TOPSIS analysis results, the study shows that the most important dimensions of employee turnover intention in the hotel industry model are salary and benefits. Moreover, the results indicate that the least important dimensions are the Co-workers Relationship, Supervision, and Career Opportunities. The second group dimensions that impact employee turnover in the context of the COVID-19 epidemic are work itself, job stress perceived risk, and job insecurity. In addition, this study's results show that three-star hotels have the highest value of turnover intention; the second is the Four and Five-star hotels, and the third is the below three-star hotels. The results of the study will help businesses in the field of hospitality have a more comprehensive view of human resource management activities. Especially, this study provides implications for hotel managers in understanding employee behavior and their turnover intention during the context of the COVID-19 epidemic based on the eight proposed dimensions. [ABSTRACT FROM AUTHOR]

Failure mode effects and criticality analysis (FMECA) is widely employed across industries to recognize and reduce possible failures. Despite its extensive usage, FMECA encounters challenges in decision‐making. In this paper, a new fuzzy resilience‐based RPN model is created to develop the FMECA method. The fuzzy model transcends the limitations associated with traditional risk priority number calculations by incorporating factors beyond frequency, severity, and detection. This extension includes considerations impacting system cost, sustainability, and safety, providing a more comprehensive risk assessment. In addition, to create trust in decision‐makers, a robust assessment approach is suggested, integrating three methodologies. In the initial phase, the fuzzy analytical hierarchy process and the grey relation analysis method are used to determine the subjective weights of different risk factors and resolve the flaws associated with the deficiency of constructed fuzzy inference rules. In the second phase, an entropy method is applied to handle the uncertainty of individual weightage calculated and capture different conflicting experts' views. The suggested approach is validated through a case study involving a gas turbine. The results demonstrate significant differences in failure mode prioritization between different approaches. The introduction of MTTR addresses critical shortcomings in traditional FMECA, enhancing predictive capabilities. Furthermore, the hybrid approach improved criticality assessment and failure mode ranking, classifying failure modes into fifteen categories, aiding decision‐making, and applying appropriate risk mitigation measures. Overall, the findings validate the efficacy of the proposed approach in addressing uncertainties and divergent expert judgments for risk assessment in complex systems. [ABSTRACT FROM AUTHOR]

Research is about an individual's intellectual acumen and rationality, and inter‐researcher collaboration capability magnifies the outcomes. Despite common belief, there exist fundamental asymmetries in the goals, orientations and expectations among the research collaborators. Seldom studied in‐depth and empirically validated, the challenges and barriers faced by early‐career researchers (ECRs) in social sciences are crucial to understand. In this study, we aim to identify, categorize and rank the order of priority of the research collaboration barriers and their sub‐barriers. This analysis highlights which barrier is likely to impact the research collaboration outcome more as compared to other barriers, as seen through the pairwise comparison. We contribute to the literature on research collaborations by providing a much‐needed assessment of the barriers faced by ECRs in social sciences to develop a nuanced understanding of the dynamics within collaboration research. Key barriers identified and explored in this research relate to research design, interpersonal and interprofessional relationships, team‐level dynamics, spatial and temporal factors, cultural differences and institutional variables. The fuzzy AHP tool was used to prioritize and rank the barriers, check the consistency ratio and then perform the sensitivity analysis. Research design barriers emerged as the most challenging barrier to research collaborations, implying researchers place huge emphasis on the compatibility of research goals, objectives, paradigms and perspectives. The study provides insights for individuals/institutions leading and managing research collaborations into improving the collaborative dynamics in social sciences. [ABSTRACT FROM AUTHOR]

Decision makers consistently face the challenge of simultaneously assessing numerous attributes, determining their respective importance, and selecting an appropriate method for calculating their weights. This article addresses the problem of automatic generation control (AGC) in a two area power system (2-APS) by proposing fuzzy analytic hierarchy process (FAHP), an multi-attribute decision-making (MADM) technique, to determine weights for sub-objective functions. The integral-time-absolute-errors (ITAE) of tie-line power fluctuation, frequency deviations and area control errors, are defined as the sub-objectives. Each of these is given a weight by the FAHP method, which then combines them into an single final objective function. This objective function is then used to design a PID controller. To improve the optimization of the objective function, the Jaya optimization algorithm (JOA) is used in conjunction with other optimization techniques such as sine cosine algorithm (SCA), Luus–Jaakola algorithm (LJA), Nelder–Mead simplex algorithm (NMSA), symbiotic organism search algorithm (SOSA) and elephant herding optimization algorithm (EHOA). Six distinct experimental cases are conducted to evaluate the controller’s performance under various load conditions, with data plotted to show responses corresponding to fluctuations in frequency and tie-line exchange. Furthermore, statistical analysis is performed to gain a better understanding of the effectiveness of the JOA-based PID controller. For non-parametric evaluation, Friedman rank test is also used to validate the performance of the proposed JOA-based controller. [ABSTRACT FROM AUTHOR]

Over the past few decades, flood disasters have emerged as the predominant natural hazard in Cyprus, primarily driven by the escalating influence of climate change in the Mediterranean region. In view of this, the objective of this study is to develop a geospatial flood risk map for the island of Cyprus by considering 14 flood hazard factors and five flood vulnerability factors, utilizing geographic information systems (GIS) and remotely sensed datasets. A comparative assessment was conducted for hazard mapping, employing statistical methods of frequency ratio (FR) and FR Shannon's entropy (FR-SE), and multi-criteria decision analysis method of fuzzy analytic hierarchy process (F-AHP). The main findings indicated that the FR method exhibited the highest predictive capability, establishing it as the most suitable approach for flood hazard mapping. Additionally, vulnerability factors were aggregated using F-AHP to generate the vulnerability map. The resulting flood risk map, which is the product of flood hazard and flood vulnerability, revealed that 9% of the island was located within highly risky regions, while 13.2% was classified as moderate risk zones. Spatial analysis of these high-risk areas indicated their concentration in the primary city districts of the island. Therefore, to mitigate future risks within these cities, an analysis of potential expansion zones was conducted, identifying the best-suited zone exhibiting the lowest risk. The generated flood risk map can serve as a valuable resource for decision-makers on the island, facilitating the integration of flood risk analysis into urban management plans. [ABSTRACT FROM AUTHOR]

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Climate change threatens human survival and development. Cities, as the main gathering places for human production and life, serve as the focal points for the implementation of the policies related to energy efficiency, energy transition, and environmental protection. This study constructs an index system for the evaluation of carbon-neutral cities from the perspectives of carbon sources and carbon sinks. The system includes thirteen indicators under six dimensions. It combines objective and subjective data (i.e., statistical data and expert evaluations) by integrating two approaches: the fuzzy analytic hierarchy process (fuzzy AHP) and vise kriterijumska optimizacija i kompromisno resenje with hesitant fuzzy sets (HFS-VIKOR). We verify the efficacy of the proposed approach through a case study of thirteen low-carbon pilot cities in China. The results indicate that among these cities, Shenzhen performs the best, followed by Guangzhou and Hangzhou, while Kunming, Baoding, and Tianjin show poor performance in terms of carbon neutrality. Kunming and Baoding exhibit shortcomings mainly in carbon sources, while Tianjin faces deficiencies in both carbon sources and carbon sinks. Sensitivity analysis and comparative analysis show the availability and effectiveness of the proposed method. The proposed radar chart further highlights the improvement directions for each city to achieve carbon neutrality. [ABSTRACT FROM AUTHOR]

E-learning approaches have emerged as a prominent method in educational institutions in Malaysia since the Covid-19 outbreak. Moreover, rapid growth in educational technologies with new software and high-end hardware has provided better accessibility for e-learning. To ensure efficient and conducive e-learning environment, the factors and sub-factors that leads towards e-learning success must be recognized and identified. Therefore, this research aims to rank the important factors and sub-factors influencing the success of e-learning in Mathematics from lecturers’ perspectives. Fuzzy Analytic Hierarchy Process (FAHP) is used in analyzing the data collected. The result of this study shows that among the four chosen success factors, Quality of Infrastructure and System is the most important factor (0.3876), followed by Characteristics of Students towards e-learning (0.2428), Quality of Design and Courses (0.1942) and lastly Characteristics of Lecturers Towards e-learning (0.1753). The top four out of 12 e-learning success sub-factors are Design and User Interface System (0.1447), Students’ Attitude Towards e-learning (0.1232), Understanding the Use of Infrastructure (0.1218) and Level of Product Reliability (0.1211). This finding may help to improve the effectiveness of e-learning process to not only for schools and universities but also for corporate and business sectors especially in global training programs. For future studies, students’ perspective towards e-learning success in Mathematics and other subjects should be further studied in order to have a complete view of success factors.

This study addresses the problem of understanding users' preferences for courier services in Malaysia. As online shopping and package delivery become increasingly prevalent, it is essential to identify the key factors that influence users' decisions when choosing a courier service. The Fuzzy Analytical Hierarchy Process (FAHP) method is employed to tackle this problem. The study aims to determine the relative importance of criteria such as responsiveness, reliability, empathy, assurance, and tangibility, along with their corresponding sub-criteria. A group of experts defined these criteria and sub-criteria, and a questionnaire was distributed to gather data on customer preferences. Using the FAHP technique, the study constructed a hierarchy tree, created a fuzzy pairwise comparison matrix, and calculated criterion and sub-criteria weights. The findings highlight the importance of responsiveness, particularly easy contact with courier companies, as the primary criterion. Enhancing services based on these insights can help users select suitable courier services while improving competitiveness in the industry.

The pursuit of success in one's chosen profession is a universal aspiration that requires individuals to engage in competition, not just domestically but also internationally, particularly in today's era of globalization. The rapid advancement of technology presents both opportunities and challenges, necessitating proactive management to achieve professional success. In the field of Informatics Engineering, there is a wide range of professional pathways, each offering unique opportunities and requiring distinct skill sets. For university graduates, it is crucial to have a comprehensive understanding of the specific requirements and demands associated with various job paths to make informed decisions. This knowledge enables them to select a professional route that aligns with their individual aspirations and goals, thereby avoiding potential discontent in their chosen career. Lack of knowledge or awareness during the decision-making process can negatively impact productivity and overall performance. There is a growing demand among university graduates, especially at the undergraduate level, for career-related information. This has led to heightened competition among graduates in terms of skills, knowledge, and availability. To effectively navigate this competitive landscape and engage in meaningful competition with fellow graduates, individuals must possess a comprehensive understanding of their desired career trajectory. To address these challenges, the Decision Support Model (MPK) can be utilized, employing the Fuzzy Analytical Hierarchy Process methodology. This approach considers four primary criteria—Financial Compensation, Non-Financial Compensation, Soft Skills, and Hard Skills—each with several sub-criteria. These criteria are evaluated based on the perspectives of certified graduates of Informatics Engineering and industry specialists. The study successfully identified the primary criteria influencing career decisions, such as job conditions, incentives, and prospects. It also highlighted the most favorable career path, with the role of a Data Analyst being identified as particularly promising. This career path involves roles within the field of Informatics Engineering that focus on processing data according to specific requirements, highlighting the importance of understanding the evolving landscape of technology and its impact on professional opportunities. Doi: 10.28991/ESJ-2024-08-01-011 Full Text: PDF

AbstractThe construction industry has a significant multiplier effect on the economy as a whole. It provides the basis upon which other sectors can grow by constructing the physical facilities required for the production and distribution of goods and services. Building construction projects is one of the major development constraints in developing countries since their development highly depends on the growth of their physical infrastructures. Developing countries allocate a considerable amount of their scarce financial resources towards the development of their infrastructure needs. However, most of these infrastructure projects in developing countries encounter considerable quality management controlling problems, especially in public building construction projects. These results in decreasing customer satisfaction, lower productivity, and decreased service delivery times. Thus, the study focuses on exploring fuzzy AHP approaches for quality management control practices in public building construction projects. The data analysis for this study was carried out by using fuzzy AHP methods. This method is used to determine the preference weights of the input variables and for ranking. To achieve the objective, the data were collected from primary and secondary sources. Microsoft Excel was used as an analysis tool. The study revealed that quality management plans and quality management control tools are identified as mostly practiced quality management control systems in public building construction projects. Finally, it can be recommended that the project participants implement and communicate the organization’s mission, vision, strategy, policies, and processes throughout the organization to enhance the productivity of the projects.

Cage farming in brackishwater resources is an emerging activity in India. Scientific planning and implementation would promote livelihood enhancement and sustainable development for the coastal community. Lagoons and backwaters are often developed for tourism and water sports activities. Identification of potential cage farming locations would improve aqua tourism and generate additional revenue for the fisherman. Hence, this study aims to locate viable cage farming locations in the Muttukadu brackishwater ecosystem, one of the most important lagoon systems on the east coast of Chengalpattu district, Tamil Nadu, for aqua tourism development. Water quality, environmental, and accessibility factors were considered the main factors for identifying suitable cage farming sites. Fourteen designated sampling points covering the lagoon were identified, and water samples were collected and analyzed at monthly intervals from December 2019 to January 2022. Each factor was further sub-classified and ranked by assigning appropriate weightage using the fuzzy analytic hierarchy process (FAHP), one of the multiple-criteria decision-making (MCDM) methods. For sustainable farming, it is important to consider ecologically sensitive areas and other constraining factors. ArcGIS Pro was used to map all the factors for identifying potential cage farming sites. The result shows the potential area available for cage farming development is about 11.52 ha, out of which 0.93 ha is highly suitable, and 10.59 ha is moderately suitable. [ABSTRACT FROM AUTHOR]

The issue of water contamination in reservoirs and watersheds due to the irregular use of fertilizers and uncontrolled occupation by riverside populations has worried managers operating hydroelectric plants. The increase in the levels of certain physical–chemical parameters causes problems of accelerated deterioration of equipment and clogging of water pipes. Within this context, here we estimate a specific water quality index (WQI) for hydroelectric plants. The novelty of this article is to use expert consultation to select the most critical parameters that cause damage to the dam structures and mechanical equipment. We propose a method carried out in three steps: selection of specific parameters for hydropower plants and fuzzy MCDA estimation of their weights; fuzzification of sub-indices; and estimation and classification of the WQI. In this last step, the sub-indices are defuzzified and weighted for classification according to the degree of membership in a fuzzified scale. Fuzzy logic deals with variables that do not allow objective classification, given that the limits between classes are imprecise or non-statistical, such as the parameters analyzed in this study. These thresholds are fuzzy numbers, so the treatment of the sub-indices must follow this logic. We obtained a specific classification for the water quality upstream of the Corumbá IV Hydroelectric Plant, located in the central region of Brazil. Observing the individual ratings of each parameter obtained before aggregation, although producing results for drinking water set by Brazil's National Environmental Council, presents concentrations of some parameters that, if not controlled, could harm the hydroelectric plant. [ABSTRACT FROM AUTHOR]

This paper investigates the Indoor Environmental Quality (IEQ) factors influencing occupant satisfaction in commercial buildings in Iran, contrasting the views of building experts (architects and engineers) with those of building occupants. Employing the fuzzy analytical hierarchy process (FAHP), this study focuses on the four primary IEQ factors: thermal comfort, indoor air quality, visual comfort, and acoustic comfort. The study aims to bridge the gap between expert evaluations and occupant perceptions of IEQ factors in commercial buildings in Iran. By examining the disparities in prioritising IEQ factors between these two groups, the study sheds light on the complexities of IEQ assessment and highlights the importance of considering diverse perspectives in optimising indoor environments. Our methodology includes a survey conducted among 30 building experts (15 architects and 15 building engineers) and 102 occupants, employing FAHP to derive the relative importance weights of each IEQ factor. The results highlight significant disparities between architects, engineers, and occupants in prioritising these factors. Architects emphasise visual comfort (42%), while engineers and occupants view thermal comfort (53% and 41%) as the most crucial factor for occupant satisfaction. The study underscores the complexity of IEQ in commercial buildings and the diverse perspectives influencing its assessment. It contributes to the broader discourse on optimising IEQ, emphasising the need for a comprehensive approach that encompasses both technical expertise and occupant experience. [ABSTRACT FROM AUTHOR]

Women's empowerment programs play a critical role in achieving the United Nations' (UN's) sustainable development goal of "Gender Equality". However, non-profit organizations (NPOs) running women's empowerment (WE) programs face challenges in monitoring, assessing, and evaluating the social impact (SI) and program performance due to the lack of solid guidelines. This study aims to analyze the impact and outcome evaluation indicators of WE programs by providing a quantitative tool. A multi-criteria decision-making (MCDM) model is proposed to identify and prioritize the performance indicators by utilizing Fuzzy TOPSIS (FTOPSIS) and Fuzzy AHP (FAHP) in a combined methodology. Results validated the identification and classification of the indicators by their importance and viability. In a qualitative study with NPOs working on WE in Turkey, social impact and outcome evaluation indicators are defined and ranked by criteria set in the proposed combined MCDM framework. The study aims to contribute to the theoretical frameworks and practices on social impact and outcome evaluation of women's empowerment. [ABSTRACT FROM AUTHOR]