Search Results (10)

The construction industry plays a key role in economic development but continues to face challenges in promoting employee well-being, particularly mental health and social sustainability. While existing decision-making tools emphasize environmental and economic factors, the social dimension remains largely overlooked, creating a significant gap in both research and practice. To address this, the study develops a decision support tool (DST) to help construction organizations prioritize strategic investments that enhance employee social sustainability. The tool is based on a hybrid multi-criteria decision-making framework, combining the Analytical Hierarchy Process (AHP) with Fuzzy MOORA to integrate both quantitative and qualitative assessments. A literature review, along with findings from a previous empirical study, identified 27 validated criteria, grouped into seven core sustainability alternatives. Additionally, five decision criteria (cost, risk, compatibility, return on investment, and difficulty) were refined through expert interviews. The DST was implemented as a modular Excel-based tool allowing users to input data, conduct pairwise comparisons, evaluate alternatives using linguistic scales, and generate a final ranking through defuzzification. A case study in a private construction company showed Training and Development and Work Environment as top priorities. An online expert focus group confirmed the DST’s clarity, usability, and strategic relevance. By addressing the often-neglected social pillar of sustainability, this tool offers a practical and transparent framework to support decision-making, ultimately enhancing employee well-being and organizational performance in the construction sector. Full article

Finding optimal locations for wind farms requires a delicate balance between maximizing energy generation potential and addressing the socio-economic implications for local communities, particularly in regions facing socio-economic challenges. While existing research often focuses on technical and economic aspects of wind farm siting, this study addresses a crucial research gap by integrating sustainable supply-chain-management principles into a comprehensive site-selection framework. We present a novel approach that combines Geographic-Information-System-based spatial analysis, the Fuzzy Analytic Hierarchy Process, and multi-criteria decision-making techniques to identify and prioritize optimal wind farm locations in Tunisia. Our framework considers not only traditional factors, like wind speed, terrain slope, and road and grid infrastructure, but also crucial socio-economic indicators, such as unemployment rates, population density, skilled workforce availability, and land cost. Based on the spatial analysis, it was revealed that 33,138 km² was appropriate for deploying large-scale wind systems, of which 6912 km² (4.39% of the total available area) was categorized as “most suitable”. Considering the SSCM evaluation criteria, despite the minor variations, the ARAS, COPRAS, EDAS, MOORA, VIKOR, and WASPAS techniques showcased that Kasserine, Kebili, and Bizerte stood as ideal locations for hosting large-scale wind systems. These rankings were further validated by the Averaging, Borda, and Copeland methods. By incorporating this framework, the study identifies locations where wind energy development can be a catalyst for economic growth, social upliftment, and improved livelihoods. This holistic approach facilitates informed decision making for policymakers and investors, thus ensuring that wind energy projects contribute to a more sustainable and equitable future for all stakeholders. Full article

The renewable energy transition of oil- and gas-producing countries has specific peculiarities due to the ambivalent position of these countries in the global energy market, both as producers and consumers of energy resources. This task becomes even more challenging when the share of oil and gas in the country’s GDP is very high. These circumstances pose serious challenges for long-term energy policy development and require compromising decisions to better align the existing and newly created energy policies of the country. The scale, scope, and pace of changes in the transition process must be well balanced, considering the increasing pressure of economic and environmental factors. The objective of this paper is to develop models that allow the selection of the most appropriate scenario for renewable energy transition in an oil- and gas-producing country. The distinguishing feature of the proposed model is that alternatives in the decision matrix are presented as scenarios, composed of a set of energy resources and the level of their use. Linguistic descriptions of the alternative scenarios are formalized in the form of fuzzy statements. For the problem solution, four different Multiple-Criteria Decision-Making (MCDM) methods were used: the fuzzy simple additive weighting (F-SAW) method, the distance-based fuzzy TOPSIS method (Technique of Order Preference Similarity to the Ideal Solution), the ratio-analysis-based fuzzy MOORA method (Multi-Objective Optimization Model Based on the Ratio Analysis), and the fuzzy multi-criteria optimization and compromise solution method VIKOR (Serbian: VIekriterijumsko Kompromisno Rangiranje). This approach is illustrated using the example of the energy sector of Azerbaijan. The recommended solution for the country involves increasing natural gas (NG) moderately, maintaining hydro, and increasing solar notably and wind moderately. Full article

People use mHealth applications to help manage and keep track of their health conditions more effectively. With the increase of mHealth applications, it has become more difficult to choose the best applications that are user-friendly and provide user satisfaction. The best techniques for any decision-making challenge are multi-criteria decision-making (MCDM) methodologies. However, traditional MCDM methods cannot provide accurate results in complex situations. Currently, researchers are focusing on the use of hybrid MCDM methods to provide accurate decisions for complex problems. Thus, the authors in this paper proposed two hybrid MCDM methods, CODAS-FAHP and MOORA-FAHP, to assess the usability of the five most familiar mHealth applications that focus on type 2 diabetes mellitus (T2DM), based on ten criteria. The fuzzy Analytic Hierarchy Process (FAHP) is applied for efficient weight estimation by removing the vagueness and ambiguity of expert judgment. The CODAS and MOORA MCDM methods are used to rank the mHealth applications, depending on the usability parameter, and to select the best application. The resulting analysis shows that the ranking from both hybrid models is sufficiently consistent. To assess the proposed framework’s stability and validity, a sensitivity analysis was performed. It showed that the result is consistent with the proposed hybrid model. Full article

India has an ambitious target to promote clean energy penetration, but as of 2021, the electricity mix of India is dominated by coal to about 71%. Therefore, analyzing the clean energy potential and the ability of the individual states to entrench energy transition in the upcoming years will be supportive for policymakers. This study is propounded to assess the clean energy transition potential with a focused analysis on seven major power-producing states of India. These states include Maharashtra, Gujarat, Tamil Nadu, Uttar Pradesh, Karnataka, Madhya Pradesh, and Andhra Pradesh. The clean energy transition potential assessment is performed by utilizing multi-criteria decision analysis methodologies such as the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Multi-Objective Optimization Method by Ratio Analysis (MOORA). Further, the analysis is performed against four major criteria that include high carbon energy resource dependency, low carbon energy resource dependency, clean energy potential, and policy support. Altogether, the assessment criteria include four primary level criteria and fourteen secondary level parameters. In order to reflect the significance of each parameter and criterion to the characteristics of clean energy transition potential, appropriate weightage is provided using the Fuzzy Analytic Hierarchy Process (AHP). The results indicate that Gujarat has the highest clean energy transition potential in both the multi-criteria decision analysis methods. On the other hand, Uttar Pradesh exhibited the least performance, and a complete energy transition to clean energy resources is less likely in this state. The rest of the states obtained intermediate ranking, and a comparative analysis between the two methods was also accomplished. This study suggests that India should focus on the clean energy policy with vigorous efforts on top-performing states which will effectively accelerate the power sector decarbonization. Full article

The purpose of this research article is to develop a hybridization between the Failure Mode and Effect Analysis (FMEA) method and the Combinative Distance-Based Assessment (CODAS) method under Pythagorean Fuzzy environment. The traditional FMEA procedure is based on the multiplication between the parameters of severity, occurrence, and detectability where everyone has equal relative importance; therefore, different combinations of these parameters can generate the same result creating uncertainty in the analysis. In this mode, the hybridization proposed in this research deal with relative importance of each parameter; in the fact to have a more suitable combination which consider the level of knowledge of the experts in the assessment. Finally, a numerical case was carried out concerning the public transportation service to validate our proposal; the results show that 31 failure modes and potential risks can be evaluated using user perceptions, a dominant with high level of knowledge about the public transportation service and an apprentice or common user, as team of experts and exploiting the subjectivity of the information in a mathematical model. Also, we compare the results with a variation of the proposed model with the multi-criteria method multi-objective optimization method by relationship analysis (MOORA); it was observed that the convergence of the failure modes depends on the nature of the mathematical model even under the same conditions at the start. Full article

The siting of Municipal Solid Waste (MSW) landfills is a complex decision process. Existing siting methods utilize expert scores to determine criteria weights, however, they ignore the uncertainty of data and criterion weights and the efficacy of results. In this study, a coupled fuzzy Multi-Criteria Decision-Making (MCDM) approach was employed to site landfills in Lanzhou, a semi-arid valley basin city in China, to enhance the spatial decision-making process. Primarily, 21 criteria were identified in five groups through the Delphi method at 30 m resolution, then criteria weights were obtained by DEMATEL and ANP, and the optimal fuzzy membership function was determined for each evaluation criterion. Combined with GIS spatial analysis and the clustering algorithm, candidate sites that satisfied the landfill conditions were identified, and the spatial distribution characteristics were analyzed. These sites were subsequently ranked utilizing the MOORA, WASPAS, COPRAS, and TOPSIS methods to verify the reliability of the results by conducting sensitivity analysis. This study is different from the previous research that applied the MCDM approach in that fuzzy MCDM for weighting criteria is more reliable compared to the other common methods. Full article

Every country’s influence and livelihood is centered on that country’s water source. Therefore, many studies are being conducted worldwide to improve and sustain water resources. In this research paper, we have selected and researched the water scheme for groundwater recharge and drinking water supply of drought prone areas. The water project is aimed at connecting the drought prone areas of the three districts of Tamil Nadu to filling up the ponds in their respective villages and raising the ground water level and meeting the drinking water requirement. We have chosen a multi-criteria decision method to select the best alternative in a complex situation. When reviewing the implementation of this water project, many experts and people who will benefit from this project may have some hesitation and ambiguity in their suggestion on choosing the best water distribution system.We believe that the benefits of this project can be fully availed of if we choose a water distribution system. Our contribution in this article is to choose the best water distribution system for this project by use of our proposed multi-criteria decision making (MCDM) methods, hesitant fuzzy standard deviation with multi-objective optimization method by ratio analysis (HFSDV-MOORA), hesitant fuzzy standard deviation with technique, for order preference by similarity to an ideal solution (HFSDV-TOPSIS) and hesitant fuzzy standard deviation with VIsekriterijumsko Kompromisno Rangiranje (HFSDV-VIKOR), which will provide the best solution for improving the water resource for the drought-prone areas of three districts. Finally, we have identified and compared the correlation coefficient between proposed methods. As a result of the study, it has been found that the best water supply system is closed concrete pipes laid along agricultural land through the rural areas. Full article

Wearable technology plays a key role in smart healthcare applications. Detection and analysis of the physiological data from wearable devices is an essential process in smart healthcare. Physiological data analysis is performed in fog computing to abridge the excess latency introduced by cloud computing. However, the latency for the emergency health status and overloading in fog environment becomes key challenges for smart healthcare. This paper resolves these problems by presenting a novel tri-fog health architecture for physiological parameter detection. The overall system is built upon three layers as wearable layer, intelligent fog layer, and cloud layer. In the first layer, data from the wearable of patients are subjected to fault detection at personal data assistant (PDA). To eliminate fault data, we present the rapid kernel principal component analysis (RK-PCA) algorithm. Then, the faultless data is validated, whether it is duplicate or not, by the data on-looker node in the second layer. To remove data redundancy, we propose a new fuzzy assisted objective optimization by ratio analysis (FaMOORA) algorithm. To timely predict the user’s health status, we enable the two-level health hidden Markov model (2L-2HMM) that finds the user’s health status from temporal variations in data collected from wearable devices. Finally, the user’s health status is detected in the fog layer with the assist of a hybrid machine learning algorithm, namely SpikQ-Net, based on the three major categories of attributes such as behavioral, biomedical, and environment. Upon the user’s health status, the immediate action is taken by both cloud and fog layers. To ensure lower response time and timely service, we also present an optimal health off procedure with the aid of the multi-objective spotted hyena optimization (MoSHO) algorithm. The health off method allows offloading between overloaded and underloaded fog nodes. The proposed tri-fog health model is validated by a thorough simulation performed in the iFogSim tool. It shows better achievements in latency (reduced up to 3 ms), execution time (reduced up to 1.7 ms), detection accuracy (improved up to 97%), and system stability (improved up to 96%). Full article

The aim of this study is to measure universal health coverage in Emerging 7 (E7) economies. Within this framework, five different dimensions and 14 different criteria are selected by considering the explanations of World Health Organization and United Nations regarding universal health coverage. While weighting the dimensions and criteria, the Decision-making Trial and Evaluation Laboratory (DEMATEL) is considered with the triangular fuzzy numbers. Additionally, Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) approach is used to rank E7 economies regarding Universal Health Coverage (UHC) performance. The novelty of this study is that both service and financial based factors are taken into consideration at the same time. Additionally, fuzzy DEMATEL and MOORA methodologies are firstly used in this study with respect to the evaluation of universal health coverage. The findings show that catastrophic out of pocket health spending, pushed below an international poverty line and annual growth rate of real Gross Domestic Product (GDP) per capita are the most significant criteria for universal health coverage performance. Moreover, it is also concluded that Russia is the country that has the highest universal health coverage performance whereas China, India and Brazil are in the last ranks. It can be understood that macroeconomic conditions play a very significant role on the performance of universal health coverage. Hence, economic conditions should be improved in these countries to have better universal health coverage performance. Furthermore, it is necessary to establish programs that provide exemptions or lower out-of-pocket expenditures which will not prevent the use of health services. This situation can protect people against the financial risks related to health expenditures. In addition to them, it is also obvious that high population has also negative influence on the countries such as, China and India. It indicates that it would be appropriate for these countries to make population planning for this purpose. Full article