A fuzzy multicriteria model for comparing energy projects |
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| Affiliation: | 1. Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China;2. Department of Civil Engineering, Tsinghua University, Beijing 100084, China;3. Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, China;4. State Key Lab of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China;1. School of Highway, Chang’an Univ., Xi’an, Shaanxi 710064, China;2. Sichuan Road & Bridge Construction Group Co., Ltd, Chengdu, Sichuan 610071, China;3. Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region Ministry of Education, Chang’an Univ., Xi’an, Shaanxi 710064, China;4. Key Laboratory of Road Structure and Material Ministry of Transport, Chang’an Univ., Xi’an, Shaanxi 710064, China;5. School of Water and Environment, Chang’an Univ., Xi’an, Shaanxi 710064, China;1. Aurel Vlaicu University of Arad, Department of Mathematics and Computer Science, Elena Dragoi 2, RO-310330 Arad, Romania;2. University of Oradea, Department of Energetics, Universitatii 1, RO-485620 Oradea, Romania;3. Agora University of Oradea, Department of Social Sciences, Piata Tineretului 8, RO-410526 Oradea, Romania |
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| Abstract: | A fuzzy set approach to multicriteria modelling is presented for selecting the best alternative solution to a large-scale engineering project. The problems of making expert recommendations, amidst conflicting views of different people and institutions, is handled by this technique which incorporates the viewpoints of different interest groups, both quantitative and qualitative. The efficacy of the methodology is demonstrated by applying it to the real world problem of selecting the best site for the Bay of Fundy Tidal Power Project where emphasis is placed on socio-economic aspects. The direct impacts of developing tidal power energy on good living, transportation, psychological and political factors are studied by using fuzzy binary relations to model preferences. |
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