10,132 research outputs found

    Sustainability ranking of desalination plants using Mamdani Fuzzy Logic Inference Systems

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    As water desalination continues to expand globally, desalination plants are continually under pressure to meet the requirements of sustainable development. However, the majority of desalination sustainability research has focused on new desalination projects, with limited research on sustainability performance of existing desalination plants. This is particularly important while considering countries with limited resources for freshwater such as the United Arab Emirates (UAE) as it is heavily reliant on existing desalination infrastructure. In this regard, the current research deals with the sustainability analysis of desalination processes using a generic sustainability ranking framework based on Mamdani Fuzzy Logic Inference Systems. The fuzzy-based models were validated using data from two typical desalination plants in the UAE. The promising results obtained from the fuzzy ranking framework suggest this more in-depth sustainability analysis should be beneficial due to its flexibility and adaptability in meeting the requirements of desalination sustainability

    Employing dynamic fuzzy membership functions to assess environmental performance in the supplier selection process

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    The proposed system illustrates that logic fuzzy can be used to aid management in assessing a supplier's environmental performance in the supplier selection process. A user-centred hierarchical system employing scalable fuzzy membership functions implement human priorities in the supplier selection process, with particular focus on a supplier's environmental performance. Traditionally, when evaluating supplier performance, companies have considered criteria such as price, quality, flexibility, etc. These criteria are of varying importance to individual companies pertaining to their own specific objectives. However, with environmental pressures increasing, many companies have begun to give more attention to environmental issues and, in particular, to their suppliers’ environmental performance. The framework presented here was developed to introduce efficiently environmental criteria into the existing supplier selection process and to reflect on its relevant importance to individual companies. The system presented attempts to simulate the human preference given to particular supplier selection criteria with particular focus on environmental issues when considering supplier selection. The system considers environmental data from multiple aspects of a suppliers business, and based on the relevant impact this will have on a Buying Organization, a decision is reached on the suitability of the supplier. This enables a particular supplier's strengths and weaknesses to be considered as well as considering their significance and relevance to the Buying OrganizationPeer reviewe

    Intuitionistic fuzzy edas method: an application to solid waste disposal site selection

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    Evaluation based on Distance from Average Solution (EDAS) is a new multicriteria decision making (MCDM) method, which is based on the distances of alternatives from the average scores of attributes. Classical EDAS has been already extended by using ordinary fuzzy sets in case of vague and incomplete data. In this paper, we propose an interval-valued intuitionistic fuzzy EDAS method, which is based on the data belonging to membership, nonmembership, and hesitance degrees. A sensitivity analysis is also given to show how robust decisions are obtained through the proposed intuitionistic fuzzy EDAS. The proposed intuitionistic fuzzy EDAS method is applied to the evaluation of solid waste disposal site selection alternatives. The comparative and sensitivity analyses are also included

    SUP&R DSS: A sustainability-based decision support system for road pavements

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    Road pavement community members are increasingly becoming aware of the need to incorporating the principles of sustainable development into the sector. Policies are also going in this direction and as a consequence in the recent years researchers and practitioners are coming up with new materials, technologies and practices designed to reduce the negative impacts of their activities in the surroundings. Within this framework the road pavements sector is witnessing a paradigm shift towards the development of pavement technologies incorporating high-content of recycled materials, as well as best practices to decrease the overall carbon footprint. These are all promising solutions that to the most can sound as sustainable practices. However the whole road pavement community is still investigating methodologies and tools to define what actually sustainable means and thereby performing a sustainable decision-making. It is within this context that the need of a sustainability-based decision support system (DSS) that could help road pavement engineers at the design stage was identified and is here presented. The Sustainable Pavements & Railways DSS (SUP&R DSS) relies on a multi-criteria decision analysis (MCDA) method to rank the sustainability of alternatives. It applies life cycle-based approaches to quantify the values of a set of indicators purposely and methodologically selected to capture the cause- effect link between the general concepts of the three wellbeing dimensions of sustainability, i.e., environmental, economic and social, and the infrastructure construction and maintenance practice. Furthermore, the system allows selecting different weighting for the indicators but offers also a default set of values derived from a survey conducted with over 50 stakeholders in Europe and beyond. Together with the development, structure and features of the SUP&R DSS, this paper present its applicability by means of a case study aiming at identifying the most sustainable asphalt mixture for wearing courses. Several promising options for flexible road pavements were selected, ranging from low to hot temperature asphalt. The results show that a foamed warm mix asphalt mixture with a reclaimed asphalt pavement content of 50% is the most sustainable among the competing alternatives. Furthermore, a sensitivity analysis conducted to investigate the influence of the indicators weights, the parameters of the MCDA method and the long-term performance of the alternative asphalt mixtures on the stability of the ranking showed that its first position in the ranking remained unaffected

    A Hybrid Analytic Hierarchy Process and Likert Scale Approach for the Quality Assessment of Medical Education Programs

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    The quality assessment of training courses is of utmost importance in the medical education field to improve the quality of the training. This work proposes a hybrid multicriteria decision-making approach based on two methodologies, a Likert scale (LS) and the analytic hierarchy process (AHP), for the quality assessment of medical education programs. On one hand, the qualitative LS method was adopted to estimate the degree of consensus on specific topics; on the other hand, the quantitative AHP technique was employed to prioritize parameters involved in complex decision-making problems. The approach was validated in a real scenario for evaluating healthcare training activities carried out at the Centre of Biotechnology of the National Hospital A.O.R.N. “A. Cardarelli” of Naples (Italy). The rational combination of the two methodologies proved to be a promising decision-making tool for decision makers to identify those aspects of a medical education program characterized by a lower user satisfaction degree (revealed by the LS) and a higher priority degree (revealed by the AHP), potentially suggesting strategies to increase the quality of the service provided and to reduce the waste of resources. The results show how this hybrid approach can provide decision makers with helpful information to select the most important characteristics of the delivered education program and to possibly improve the weakest ones, thus enhancing the whole quality of the training courses

    An Urban Management Performance Modeling Via Evaluation Using Improved Green Balanced Score Cards And Fuzzy DEMATEL Under Uncertainty Solving By A New Compromised Method Based On TOPSIS And VIKOR

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    Environmental awareness is one of the most important issues that in which general public interest are growing rapidly, especially in the industrialized countries. Some trends that can be clearly seen these days are: the number of members/financial contributors of various environmental preservation societies and associations are increasing dramatically, the amount of legislation related to environmental protection both nationally and at a super-national. The number of recycling and reuse schemes, both in industry and privately is on the rise and most people engage in one or more such programs, Unnatural climate effects suspected to stem from pollution have increased and receive much media attention and so on. This means that it is becoming increasingly more important for an enterprise to be able to manage its operations in a way that minimize the negative environmental impact they might result in, directly or indirectly. At the same time, it is a fact that you can't manage what you can't measure. Thus, performance measurement is a key element in enabling performance management, performance improvement and performance documentation. When combining the pivotal importance of environmental friendliness with the need for performance measurement, we'll face with concept of green performance measurement, an area that has been largely neglected as a pure source of competitive advantages. The balanced scorecard is one of the performance evaluating tools that empower in this research by using of decision making technics and can be used to green performance evaluation. In this thesis, we proposed an urban management performance modeling via evaluation using improved Green Balanced Score Cards and fuzzy DEMATEL under uncertainty solving by a new compromised method based on TOPSIS and VIKOR simultaneously. Keywords: Green performance evaluation, balanced scorecard card, MCDM technics, Fuzzy, new compromised solution method

    Using Pythagorean Fuzzy Sets (PFS) in Multiple Criteria Group Decision Making (MCGDM) Methods for Engineering Materials Selection Applications

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    The process of materials’ selection is very critical during the initial stages of designing manufactured products. Inefficient decision-making outcomes in the material selection process could result in poor quality of products and unnecessary costs. In the last century, numerous materials have been developed for manufacturing mechanical components in different industries. Many of these new materials are similar in their properties and performances, thus creating great challenges for designers and engineers to make accurate selections. Our main objective in this work is to assist decision makers (DMs) within the manufacturing field to evaluate materials alternatives and to select the best alternative for specific manufacturing purposes. In this research, new hybrid fuzzy Multiple Criteria Group Decision Making (MCGDM) methods are proposed for the material selection problem. The proposed methods tackle some challenges that are associated with the material selection decision making process, such as aggregating decision makers’ (DMs) decisions appropriately and modeling uncertainty. In the proposed hybrid models, a novel aggregation approach is developed to convert DMs crisp decisions to Pythagorean fuzzy sets (PFS). This approach gives more flexibility to DMs to express their opinions than the traditional fuzzy and intuitionistic sets (IFS). Then, the proposed aggregation approach is integrated with a ranking method to solve the Pythagorean Fuzzy Multi Criteria Decision Making (PFMCGDM) problem and rank the material alternatives. The ranking methods used in the hybrid models are the Pythagorean Fuzzy TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution) and Pythagorean Fuzzy COPRAS (COmplex PRoportional Assessment). TOPSIS and COPRAS are selected based on their effectiveness and practicality in dealing with the nature of material selection problems. In the aggregation approach, the Sugeno Fuzzy measure and the Shapley value are used to fairly distribute the DMs weight in the Pythagorean Fuzzy numbers. Additionally, new functions to calculate uncertainty from DMs recommendations are developed using the Takagai-Sugeno approach. The literature reveals some work on these methods, but to our knowledge, there are no published works that integrate the proposed aggregation approach with the selected MCDM ranking methods under the Pythagorean Fuzzy environment for the use in materials selection problems. Furthermore, the proposed methods might be applied, due to its novelty, to any MCDM problem in other areas. A practical validation of the proposed hybrid PFMCGDM methods is investigated through conducting a case study of material selection for high pressure turbine blades in jet engines. The main objectives of the case study were: 1) to investigate the new developed aggregation approach in converting real DMs crisp decisions into Pythagorean fuzzy numbers; 2) to test the applicability of both the hybrid PFMCGDM TOPSIS and the hybrid PFMCGDM COPRAS methods in the field of material selection. In this case study, a group of five DMs, faculty members and graduate students, from the Materials Science and Engineering Department at the University of Wisconsin-Milwaukee, were selected to participate as DMs. Their evaluations fulfilled the first objective of the case study. A computer application for material selection was developed to assist designers and engineers in real life problems. A comparative analysis was performed to compare the results of both hybrid MCGDM methods. A sensitivity analysis was conducted to show the robustness and reliability of the outcomes obtained from both methods. It is concluded that using the proposed hybrid PFMCGDM TOPSIS method is more effective and practical in the material selection process than the proposed hybrid PFMCGDM COPRAS method. Additionally, recommendations for further research are suggested

    Evaluation of learning management systems using interval valued intuitionistic fuzzy-z numbers

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    The use of online education tools has increased rapidly with the transition to distance education caused by the pandemic. The obligation to carry out all activities of face-to-face education online made it very important for the tools used in distance education to meet the increasing needs. In line with these needs, radical changes have occurred in the learning management systems used in distance education. Therefore, in this study, it is aimed to determine the features that the systems used in distance education should have and to compare the existing systems according to these features. For this purpose, a novel fuzzy extension, interval valued intuitionistic fuzzy Z-numbers, is defined for modeling uncertainty, and AHP and WASPAS methods using proposed fuzzy numbers are developed to determine the importance of decision criteria and compare alternatives.WOS:0010834495000112-s2.0-85173691458Emerging Sources Citation IndexArticleUluslararası işbirliği ile yapılmayan - HAYIRKasım2023YÖK - 2022-23Eki

    Modeling systems thinking in action among higher education leaders with fuzzy multi-criteria decision making

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    The college and university systems are more complex and required persistent approach towards adoption and transformation. Highly vulnerable environment portrays the need to visualize the regular and strategic issues with the larger perspectives as a whole and develop a model which is more focused towards sustainability and reformation. The current study has attempted to conceptualize systems thinking in action model which consists of four stages of action cycle; diagnosis and analysis, modeling, intervention and review and lessons learned. This is attempting to evaluate the systems thinking among the educational leaders in higher education in Thailand through the fuzzy multi-criteria decision-making method. The study has found that leaders are adopting systems thinking in the moderate level, however, the first three stages are found less in practice and more in perceived importance. The study found that there is higher need of calling for collaborative, cooperative and participation of stakeholders’ involvement. The study has further given managerial implications
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