Inexact fuzzy-stochastic constraint-softened programming – A case study for waste management |
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| Authors: | YP Li GH Huang ZF Yang X Chen |
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| Institution: | 1. College of Urban and Environmental Sciences, Peking University, Beijing 100871, China;2. Environmental Systems Engineering Program, Faculty of Engineering, University of Regina, Regina, Sask S4S 0A2, Canada;3. Chinese Research Academy of Environmental Science, North China Electric Power University, Beijing 100012-102206, China;4. State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China;5. Key Laboratory of Oasis Ecology and Desert Environment, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, Xinjiang 830011, China;1. Centro Nacional Patagónico, Bvd. Brown 2915, U9120ACD, Puerto Madryn, Argentina;2. Universidad Nacional de la Patagonia San Juan Bosco, Bvd. Brown 3051, U9120ACD, Puerto Madryn, Argentina;1. Irstea, UR HBAN, 1, rue Pierre-Gilles de Gennes, CS 10030, 92761 Antony, France;2. CNRM-GAME, Météo-France, CNRS, Toulouse, France;1. Institute of Hydrobiology, Jinan University, Guangzhou, 510632, PR China;2. Key Laboratory of Aquatic Eutrophication and Control of Harmful Algal Blooms, Guangdong Higher Education Institutes, Guangzhou, 510632, PR China;3. Key Laboratory of Marine Bio-resources Sustainable Utilization, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, 510301, PR China;4. Institute of Marine Biology, National Taiwan Ocean University, Keelung, 20224, Taiwan, ROC;1. College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, Fujian Province, China;2. College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, Fujian Province, China;3. School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu Province, China;4. Department of Control System Optimization, Institute of Cybernetics, National Research Tomsk Polytechnic University, Russia;1. State Key Laboratory of Geodesy and Earth''s Dynamics, Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, China;2. Department of Civil Engineering, National Chiao Tung University, 1001 Ta Hsueh Road, Hsinchu 300, Taiwan, ROC;3. National Geodetic Observatory, Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, China;4. Department of Geomatics Engineering, University of Calgary, 2500 University Drive NW, Calgary, AB, Canada T2N 1N4;1. College of Architecture and Art, Heifei University of Technology, Heifei 230009, China;2. Institute of Ecological Simulation and Urban Ecology, Beijing Normal University, Beijing 100875, China;3. Environmental Systems Engineering Program, University of Regina, Regina, Saskatchewan S4S 0A2, Canada;4. Energy and Environmental Research Center, North China Electric Power University, Beijing 102206, China;5. College of Water Conservancy and Civil Engineering, China Agricultural University, Beijing, 100083, China;6. Policy Research Center for Environment and Economy, Ministry of Environmental Protection, Beijing 100029, China |
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| Abstract: | In this study, an inexact fuzzy-stochastic constraint-softened programming method is developed for municipal solid waste (MSW) management under uncertainty. The developed method can deal with multiple uncertainties presented in terms of fuzzy sets, interval values and random variables. Moreover, a number of violation levels for the system constraints are allowed. This is realized through introduction of violation variables to soften system constraints, such that the model’s decision space can be expanded under demanding conditions. This can help generate a range of decision alternatives under various conditions, allowing in-depth analyses of tradeoffs among economic objective, satisfaction degree, and constraint-violation risk. The developed method is applied to a case study of planning a MSW management system. The uncertain and dynamic information can be incorporated within a multi-layer scenario tree; revised decisions are permitted in each time period based on the realized values of uncertain events. Solutions associated with different satisfaction degree levels have been generated, corresponding to different constraint-violation risks. They are useful for supporting decisions of waste flow allocation and system-capacity expansion within a multistage context. |
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